MRI of the placenta - Measurements of volume and intravoxel incoherent motion at weeks 25-27 of gestation

NTNU Norwegian University of Science and Technology Faculty of Medicine and Health Sciences Department of Circulation and Medical Imaging

Master ’s thesis

Heidi Hamill Gorman

MRI of the placenta

Measurements of volume and intravoxel incoherent motion at weeks 25-27 of gestation

Master’s thesis in Medical Magnetic Resonance Imaging May 2020

Heidi Hamill Gorman

MRI of the placenta

Measurements of volume and intravoxel incoherent motion at weeks 25-27 of gestation

Master’s thesis in Medical Magnetic Resonance Imaging Supervisor: Beathe Sitter & Kjell-Inge Gjesdal

May 2020

Norwegian University of Science and Technology Faculty of Medicine and Health Sciences

Department of Circulation and Medical Imaging

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Abstract

Background and objectives: Suitable methods for examining the placenta safely are of interest with respect to both volume and function, in order to discover disorders of the placenta. Magnetic resonance imaging (MRI) can complement ultrasound (US)

examinations, to provide a non-invasive evaluation of volume, diffusion and perfusion without the use of MRI contrast agents. Diffusion and perfusion can be evaluated by obtaining information about intravoxel incoherent motion (IVIM) from diffusion weighted imaging (DWI) by applying several low b-values. From IVIM imaging the diffusion

coefficient (D), pseudodiffusion coefficient (D*), perfusion fraction (f) and diffusion in the extravascular space fraction (1-f) can be found. The objective of the work reported in this thesis was to describe values and distribution of volume and IVIM parameters in

placentas at 25-27 weeks of gestation from a healthy cohort. This was addressed through three research questions: (1) How is placenta volume distributed in weeks 25-27 of gestation?; (2) How are IVIM parameters D, D*, f and (1-f) distributed in placentas in weeks 25-27 of gestation?; and (3) Is there any correlation between volume and IVIM parameters (D, D*, f and (1-f))?

Methods: MRI data for this retrospective descriptive study was collected from 19 participants from an ongoing study, Placenta Volume (PLAVO), at Akershus University Hospital in Norway, using images from gestational weeks 25-27. Balanced fast field echo (bFFE) and DWI images were used to place regions of interest (ROI) that covered the entire placenta to obtain volume and IVIM measurements respectively. A pen tablet was used to place ROI in the software packages IKT-SNAP and NordicICE for volume and IVIM measurements, respectively. Intensity curves of the b-values (0, 5, 10, 25, 50 and 200) were analysed prior to IVIM data collection. IVIM maps were created with a cut-off threshold of 50 s/mm². An ICC test was used to evaluate the reliability of the volume measurements, and Pearson correlation was used to check for correlations.

Results: Mean placental volume was 464.45 ± 92.25 cm³. For different placenta

locations (anterior, posterior or both anterior and posterior) the means were 476.42 cm³, 459.63 cm³ and 434.2 cm³, respectively. IVIM parameters D, D*, f and (1-f) had means of 277.45 ± 36.23 mm²/s, 739.54 ± 211.15 mm²/s, 14.2 ± 2.7%, and 88.4 ± 2.8%, respectively. Correlation analysis showed that volume did not correlate with any of D, D*, f or (1-f).

Discussion: Both volume and IVIM data were deemed to be of good quality. Volume results were in good agreement with the radiologist’s measurements and other studies.

Comparing results based on placental location proved difficult due to lack of data from other studies. IVIM results differed from other studies, likely due to different gestational ages, scanners, cut-off thresholds and choice of b-values. Supine patient positioning may also have affected f. Correlation results were as expected and in agreement with another study.

Conclusion: Volume measurements coincided well with values reported in other studies, while IVIM parameters differed slightly. D and (1-f) were higher, while D* and f were lower than other studies. Furthermore, volume did not correlate with any of the IVIM parameters.

Key words: Placenta, volume, intravoxel incoherent motion (IVIM) imaging, magnetic resonance imaging (MRI), diffusion weighted imaging (DWI)

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Sammendrag

Bakgrunn og formål: Å ha passende metoder for å undersøke placenta trygt er av interesse, både med tanke på volum og funksjon, for å oppdage funksjonsfeil i placenta.

Magnetisk resonans (MR) kan utfylle ultralyd (UL) undersøkelser, som kan gi en

noninvasiv evaluering av volum, diffusjon og perfusjon uten bruk av MR kontrastmidler.

Diffusjon og perfusjon kan evalueres ved å skaffe informasjon om intravoksel

inkoherente bevegelser (IVIM) gjennom diffusjonsvektede (DWI) bilder med flere lave b- verdier. Fra IVIM avbildning kan man finne diffusjonskoeffisienten (D),

pseudodiffusjonskoeffisienten (D*), perfusjonsfraksjonen (f) og diffusjon i det

ekstravaskulære roms fraksjonen (1-f). Formålet med arbeidet presentert i studien var å beskrive verdier og fordeling av volum og IVIM parametere fra placentaer med

gestasjonsalder 25-27 uker fra en sunn kohort. Dette ble undersøkt gjennom tre forskningsspørsmål: (1) Hvordan er placenta volum fordelt ved gestasjonsalder 25-27 uker?; (2) Hvordan er IVIM parameterne D, D*, f and (1-f) fordelt ved gestasjonsalder 25-27 uker?; og (3) Er det korrelasjon mellom volum og IVIM parameterne (D, D*, f og (1-f))?

Metode: MR bildedata for denne retrospektive deskriptive studien ble innhentet via 19 deltagere fra en pågående studie, Placenta Volum (PLAVO), ved Akershus

Universitetssykehus i Norge, ved å bruke bildene fra gestasjonsalder 25-27 uker.

«Balanced fast field echo» (bFFE) og DWI bilder, ble brukt til å plassere «regions of interest» (ROI) som dekte hele placenta for å oppnå henholdsvis volum og IVIM

målinger. Et tegnebrett med penn ble brukt til å plassere ROI i programvarene IKT-SNAP og NordicICE for henholdsvis volum og IVIM målinger. Intensitetskurvene til b-verdiene (0, 5, 10, 25, 50 og 200) ble analysert før IVIM datainnsamling. En grenseverdi på 50 s/mm² ble brukt for å lage IVIM kart. En ICC test ble brukt for å evaluere

påliteligheten til volummålingene, og Pearson korrelasjonstest ble brukt for å undersøke korrelasjoner.

Resultater: Gjennomsnittsvolum for placentaer var 464.45 ± 92.25 cm³. For ulike placentalokalisasjoner (anterior, posterior, og både anterior og posterior) var gjennomsnittsvolumene henholdsvis 476.42 cm³, 459.63 cm³ and 434.2 cm³. IVIM parameterne D, D*, f og (1-f) hadde henholdsvis gjennomsnittverdier på

277.45 ± 36.23 mm²/s, 739.54 ± 211.15 mm²/s, 14.2 ± 2.7%, og 88.4 ± 2.8%.

Korrelasjonsanalysene viste at volum ikke korrelerte med verken D, D*, f eller (1-f).

Diskusjon: Både volum og IVIM data ble ansett for å være av god kvalitet. Volum resultater stemte godt overens med radiologens målinger og andre studier.

Sammenligning av resultater når man tok hensyn til placentalokalisasjon viste seg å være utfordrende på grunn av manglende data i andre studier. IVIM resultatene avvek noe fra andre studier, sannsynligvis på grunn av at det ble brukt forskjellige

gestasjonsaldre, skannere, grenseverdier og b-verdier. Ryggleie kan også ha påvirket f.

Korrelasjonsresultatene var som forventet og i samsvar med en annen studie.

Konklusjon: Volummålingene stemte godt overens med verdier rapportert i andre studier, mens IVIM parameterne avvek noe. D og (1-f) var høyere, mens D* og f var lavere enn andre studier. Videre var det ikke korrelasjon mellom volum og IVIM parameterne.

Nøkkelord: Placenta, volum, intravoksel inkoherente bevegelser (IVIM) avbildning, magnetisk resonans (MR), diffusjonsvektet avbildning (DWI)

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Acknowledgements

This thesis is part of my Master’s degree in Medical Magnetic Resonance Imaging at the Norwegian University of Science and Technology (NTNU). The work was carried out in close cooperation with Akershus University Hospital’s medical imaging department and department of obstetrics and gynaecology. Throughout the process of writing my

Master’s thesis I have received help and support from several people, and I would like to use this opportunity to thank them. I would like to thank my supervisors Beathe Sitter and Kjell-Inge Gjesdal for invaluable help and keeping my hopes up throughout the process. Additionally, I would like to thank the PLAVO project at Akershus University Hospital and Anne Eskild for allowing me to join them in their exciting research, and being so welcoming and open, also Vigdis Hillestad was great during the teaching process of PLAVO. Akershus University Hospital’s medical imaging department has supported my studies, and I would like to thank them for this also. Lastly, I would like to thank my friends, family, loved ones and pets for support, help and keeping my spirits up throughout the process. Without their help and support this thesis might never have been completed – and without their constant interruptions and temptations it might have been finished much sooner.

Lørenskog 25^th May, 2020 Heidi Hamill Gorman

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Table of contents

Abstract ... v

Sammendrag ... vi

Acknowledgements ... vii

List of abbreviations ... ix

1 Introduction ... 1

2 Theory ... 4

2.1 Uterus ... 4

2.2 Placenta ... 4

2.3 Magnetic resonance imaging ... 8

3 Methods ...17

3.1 Population and sampling ...17

3.2 MRI examination ...17

3.3 Data collection ...19

3.4 Statistical analyses ...24

4 Results ...26

4.1 Placenta volume measurements ...26

4.2 Intravoxel incoherent motion measurements ...28

4.3 Correlation between volume and IVIM measurements ...33

5 Discussion ...35

5.1 Study design ...35

5.2 Quality of volume and IVIM data ...35

5.3 Comparing the data to other methods and studies ...39

5.4 Volume, IVIM and MRI ...42

6 Conclusion ...44

References ...45

Appendices: detailed data tables ...49

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List of abbreviations

ASL Arterial Spin Labelling bFFE Balanced Fast Field Echo DWI Diffusion Weighted Imaging FOV Field Of View

Gd Gadolinium

GRE Gradient echo

ICC Intraclass Correlation Coefficient IVIM(s) Intravoxel Incoherent Motion(s) MRI Magnetic Resonance Imaging NMR Nuclear Magnetic Resonance

REC Reginal committees for medical and health research ethics

RF Radiofrequency

ROI Regions of Interest SNR Signal to Noise Ratio

SPSS Statistical Package for the Social Sciences (software product from IBM for performing statistical analyses)

TE Echo Time

TR Repetition Time

US Ultrasound

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The placenta is a vital organ during pregnancy. It provides the fetus with everything it needs such as nutrients, oxygen and a way to dispose waste products (1), and at the same time provides an immunological barrier between the mother and fetus. It also produces peptides and hormones that play an important role in fetal development (2).

Placental size can affect the fetus and research has shown that having a smaller than normal placenta can be associated with smaller infants and fetal growth restriction (3), whereas an enlarged placenta can be associated with several disorders of the fetus and mother (4). It is important to be able to both accurately describe the volume of a placenta, and diagnose placental disorders. In this way placental abnormalities can be discovered, and interventions can be planned (5).

Studying the placenta in vivo is challenging because it is risky for both mother and fetus.

It increases the risk of premature delivery and abortion (6). It is therefore of interest to have suitable methods for examining the placenta safely. Ultrasound (US) is the modality of choice for imaging the placenta and fetus, and has been the primary diagnostic tool for decades. This modality is known for its cost effectiveness, availability and safety.

Additionally, it allows the user to gain information about blood flow and placental volume.

However, US has some limitations, such as problems visualizing posterior placentas due to their depth, a limited field of view (FOV) and limited functional assessment. Further, it is operator dependent and certain pathologies can be hard to assess. Magnetic resonance imaging (MRI) is an alternative to US, and has some advantages over US such as better tissue resolution, larger FOV and less operator dependence. In addition, MRI offers several advanced imaging techniques that can be utilized (3). One can potentially access anatomical and physiological information about the placenta, and use this to assess the wellbeing of the fetus. MRI of the placenta is used as a complementary and problem- solving tool, due to its ability to delineate the placenta-myometrial interface on T2 and diffusion weighted images. When the clinical suspicion is high, or US is negative or

equivocal, MRI should be used (7), particularly if the placenta is placed posteriorly (8, 9).

MRI is important for precise mapping of placental abnormalities and aids in proper planning for the chosen intervention (5). Perhaps the threshold for using MRI should be lowered and used more routinely in challenging cases?

Over the past 20 years MRI has become an increasingly common diagnostic tool in obstetrics. This is because technological development in MRI has led to shorter

acquisition times, and the method has been documented to be safe and well tolerated by both the mother and fetus (3). Even though MRI is considered safe for the fetus, it has been avoided during pregnancy, particularly in the first trimester (10). Undergoing MRI in the first trimester in not associated with increased risk to the fetus or its early childhood life, but it is important to note that MRI contrast agents with Gadolinium (Gd) are

associated with an increased risk for several disorders: stillbirth, neonatal death,

rheumatological, inflammatory or infiltrative skin conditions. This implies that Gd should be avoided if other options are available (11). MRI allows us to study the fetus and placenta in greater detail than US, in particular due to MRI’s ability to give an in-depth assessment of the placental structure, development and function (3), without the use of

1 Introduction

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Gd. Several advanced MRI techniques can be utilized to assess the placenta’s function.

One that has gained special attention is diffusion weighted imaging (DWI) (12).

DWI makes it possible to evaluate the motional properties of water molecules within a tissue. MRI’s sensitivity to motion is enhanced by applying dedicated gradients and radiofrequency (RF) pulses prior to the readout of the nuclear magnetic resonance (NMR) signal. This unique pulse sequence setup is characterized by the b-value, also known as the diffusion sensitivity factor (12). Intravoxel incoherent motion (IVIM) imaging is a more advanced approach that allows us to extract information about the microscopic movement of water molecules due to capillary perfusion in addition to diffusion (13).

It is typical to calculate only the apparent diffusion coefficient (ADC) from DWI (14). But to maximize the full potential of the sequence, we can extract IVIM information from DWI by using several low b-values, allowing us to evaluate additional characteristics of the imaged tissue. We can calculate the diffusion coefficient (D) and the pseudodiffusion coefficient (D*) – the dephasing caused by blood perfusion in capillaries. We can also determine the perfusion fraction (f), which is the percentage of a voxel occupied by capillaries. And from (f) we can calculate (1-f), which is the percentage of a voxel occupied by diffusion in the extravascular space. IVIM imaging has a wide range of applications. It has been used to distinguish malignant childhood abdominal tumours non-invasively (15), and has proven useful in other areas such as neurological tumours and infarctions (16). Previous IVIM-imaging research on test-retest repeatability

discovered that the central part of the placenta was moderately and homogenously perfused with f = 28 ± 9%¹. In addition the research showed that f and D* were moderately repeatable, that D showed the highest repeatability, and that they could be reliably measured (17). It has also been shown that f correlates negatively with

gestational age (18, 19), while D and D* do not (19). Normal f values range from 26- 36% for the whole placenta (12). Additionally, a reduction in f can be used to indicate whether certain abnormalities are present or not. f is reduced with late (20) and early onset fetal growth restriction (21), placental dysfunction (19) and preeclampsia (18, 21).

An increase in f can indicate if patients have abnormal placental adherence or invasion (22). Due to the many application possibilities of IVIM it would be of interest to extract IVIM information from previously acquired DWI MRI images of the placenta, to see if normal values can be obtained.

Signal attenuation is differently influenced by motion at high and low b-values. When using high b-values the signal attenuation is mainly due to diffusion, whereas at lower b- values the attenuation is due to both diffusion and microcapillary perfusion. This makes it possible to separate the two and focus on the perfusion part of the signal, and gain perfusion information from DWI (16). Previous research shows that IVIM-imaging correlates well with Gd-based perfusion measurements of the brain (18). This technique can in theory be used to provide information about placental function and perfusion without the use of Gd, which is advantageous and is an area that could benefit from further research (12). To get a deeper insight into the IVIM concept’s strengths and limitations in a clinical setting, several technical challenges must be overcome before good IVIM-imaging can be achieved. In order to separate diffusion from perfusion it is important to have high signal to noise ratios (SNR). It can also be challenging to

distinguish vascular flow from tubular flow in certain tissues. The range of b-values can also influence which vessel size the sequence is sensitive to. Further, glandular secretion

1 The notation m ± s is used consistently in this thesis to refer to the mean (m) and standard deviation (s) of the data in a sample.

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can also be hard to distinguish from microcapillary perfusion (16). The complex anatomical and biochemical environment, combined with fetal and maternal motion, present challenges in acquisition of high-quality imaging data (17). Fitting of IVIM parameters is sensitive to motion artefacts, field inhomogeneity and magnetic

susceptibility artefacts, which can further complicate matters (22). Despite the many challenges, it would nevertheless be interesting to see how IVIM can be utilized in placental imaging (16). Previous research on IVIM imaging and the placenta suggests that the technique can provide a non-invasive and sensitive measurement of the placenta’s perfusion, but further research is necessary to improve the technique’s predictive value (3, 23). Additionally, the optimal sequences, correction methods and analysis models for placental diffusion need to be determined (24). It would therefore be of interest to take previously acquired MR images of the placenta and use those that have adequate image quality to perform volume and IVIM measurements.

The aim of the study was to describe values and distribution for volume and IVIM

parameters in placentas at 25-27 weeks of gestation from a healthy cohort. The objective of this study was addressed through the following three research questions (RQ):

RQ1 How is placenta volume distributed in weeks 25-27 of gestation?

RQ2 How are IVIM parameters D, D*, f and (1-f) distributed in placentas in weeks 25-27 of gestation?

RQ3 Is there any correlation between volume and IVIM parameters (D, D*, f and (1-f))?

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2.1 Uterus

A non-pregnant uterus can be found between the bladder anteriorly and rectum

posteriorly, and its shape is described as pyriform or pear-shaped. The uterus consists of two major parts, an upper and lower part, the corpus and cervix respectively. During a pregnancy the uterus will undergo several changes in order to sustain the fetus, and is permanently changed afterwards. In a woman who has not born children, the uterus length measures 6-8 cm and weighs 50-70 g, while the uterus of a woman who has born children will vary from 9-10 cm and weighs more than 80 g. During a pregnancy, the uterus will grow due to hypertrophy of the muscle fibres, and the uterine weight will increase to approximately 1100 g at term, and the average volume of the pregnant uterus at term is 5 L. Additionally, the cervix will produce a mucus plug to obstruct the cervical canal, which acts as an immunological barrier to protect the uterine contents (25), which can be seen in Figure 1.

Part of the uterus corpus is the myometrium and within the uterine cavity is the

endometrium. These are essential for implantation of the blastocyst and development of the placenta. The endometrium’s vascular supply comes from the uterine and ovarian arteries, they branch and penetrate the uterine wall and lead to the arcuate arteries.

Radial branches extend at right angles from the arcuate arteries, and enter the endometrium. The radial arteries that enter the endometrium become coiled and are called spiral arteries. The spiral arteries are the placenta’s blood supply, and are therefore an essential part of placental circulation (25).

2.2 Placenta

A full term placenta has a diameter of 15-25 cm, is about 3 cm thick and weighs 500- 600 g (26). For the placenta to start developing, the endometrium must first be receptive for implantation of a blastocyst. Six to seven days after fertilization the embryo implants in the uterine wall, and will be in contact with the endometrium, myometrium and uterine vasculature. The cells necessary for developing a placenta are created shortly after

fertilization, and will continue developing until a full grown placenta is in place (25).

Placentas are developed partly from maternal and partly from fetal tissues, which are respectively called decidua and chorion. Figure 1 shows a section of a pregnant uterus illustrating various anatomical features. Each release of an ovum is accompanied by the formation of decidua. When the ovum is fertilized, the decidua is further developed and becomes the maternal side of a placenta. The decidua is formed from the uterine mucous membrane, which is part of the endometrium. The mucous membrane undergoes

change, and becomes more vascularized and enlarged. The location where the ovum is attached to the decidua is termed decidua serotina, and from it the maternal part of the placenta is developed (27). However the chorion, the fetal side of the placenta, is developed from trophoblasts (28). The trophoblasts ensure development of the villi that form the fetal-maternal interface (25). From the outer surface, several villi of chorion develop and invade the decidua of the uterus. Here nutrients are absorbed to further

2 Theory

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develop the embryo. The villi that invade the decidua serotina increase greatly in size and complexity and constitute the chorion frondosum, which later becomes the fetal part of the placenta (27). It can be said that the decidua and myometrium are the maternal side, and the remaining part the fetal side, which is an important distinction for later in the method chapter. The placenta can implant itself in different locations along the anterior, posterior, fundal (top) or lateral uterus walls (5, 29).

There is a complex circulatory system in place to ensure the wellbeing and development of the fetus, where the placenta receives about 17% of the maternal cardiac output (6).

The placental development starts approximately 6 to 7 days after conception (30).

Around the 17^th day after conception, both fetal and maternal blood vessels are

functioning and placental circulation is established (25). Its circulation differs from other organs in our body, as it is the only organ with two separate blood supplies that come from two separate organisms: the mother and the fetus. It is however important to note that the separate blood supplies never mix, all exchange between the mother and fetus happens via diffusion (6).

Figure 1: Illustration showing a section of a pregnant uterus in the third and fourth month depicting decidua serotina and villi of chorion frondosum, which are important in placental development. The image shows an early stage in the development of a full grown placenta and fetus (27).

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The placenta has a rough side and a smooth side, see Figure 2. On the rough maternal side, of the placenta there are several divisions, these are the cotyledons. Inside a cotyledon, the intervillous space and villi can be found. The villi branch out like trees to maximize transfer of nutrients and waste products. The roughness of the maternal side helps anchor the placenta, along with anchoring villi. However, the fetal side of the placenta is smooth and fetal vessels can be seen radiating from the umbilical cord that is attached in the central part of the placenta, see Figure 2 (6).

Figure 3 illustrates how the placental circulation is organized. The umbilical cord is the fetus’s connection to the placenta, which is in turn anchored to the mother. Each cotyledon is supplied by a branching artery and vein from the main umbilical vessels, which in turn branch out in villi. It is within the placenta’s intervillous space that the diffusional transfer takes place. The maternal blood enters the placenta via arterioles that empty into this space, and is drained by venous sinuses (6). The fetus’s connection to the placenta is via two arteries and one vein within the umbilical cord. The umbilical cord’s blood vessels branch out into villi, finally forming a capillary network, that are bathed in the mother’s blood in the intervillous space of the placenta (25). The

organization of the placenta’s circulation maximizes the transfer of nutrients and waste products (6). To ensure satisfactory placental and fetal growth, metabolism and waste removal, an adequate perfusion of the placental intervillous space is necessary. It is estimated that the uteroplacental blood flow increases during a pregnancy from 500 to 700 ml/min. In perspective, the blood flow of the entire circulation of a non-pregnant woman is approximately 5000 ml/min (25).

The placenta’s unique anatomy and physiology allows it to sustain the fetus throughout a pregnancy. During the pregnancy the placenta will grow continuously in both

circumference and thickness until the end of the 4^th month. The increase in thickness is due to the increasing size and length of the villi and expansion of the intervillous space.

The placenta will not increase in thickness after the 4^th month, but it will continue to grow in circumference throughout the pregnancy (6). The placenta is in use throughout the entire pregnancy, and will grow continuously with the fetus (28). The placenta grows more rapidly than the fetus in the first trimester, and by 17 postmenstrual weeks the fetus and placenta weight approximately the same. By term the placenta weighs about 1/6 of the fetus (25).

Figure 2: Images of a placenta after birth, showing its maternal (left) and fetal (right) side (6).

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Placental volumes vary throughout a pregnancy, and also vary depending on fetal weight. Using 3D US the mean placental volume has been shown to range from 86 cm³ at 12 weeks to 427 cm³ at 40 weeks (29). It has however been shown that US

significantly underestimates placental volume after the first trimester. A study done with MRI, showed that mean placental volume from week 6 to 39 was 10.1 cm³ and

1039 cm³, respectively (31). In Table 1 and 2 placental volumes for week 25-27 are shown for US and MRI, since these weeks are relevant for this study. The tables are from two different studies, and therefore the data is presented differently. Placental volume estimates can be important because smaller or larger placentas can indicate possible disorders of the placenta (3, 4).

Figure 3: Illustration showing how both maternal and fetal circulation are connected to the placenta. The fetus’s connection is via the umbilical vein and arteries, while the mother’s is via the endometrial arteries and veins. The maternal side (decidua and myometrium) and fetal side (chorion) of the placenta are shown, in addition to the intervillous spaces.

Table 1: Shows percentiles of placental volume depending on the gestational age. The study used 3D US (29).

Table 2: Shows placental volume depending on gestational age. The study used MRI.

SD=standard deviation (31).

Gestational age

Percentile of placental volume (cm³)

10^th 50^th 90^th 25 135.9 243.1 361 26 141.9 255.4 379.8 27 147.9 267.7 398.7

Gestational age

Placental volume (cm³) Range Mean SD 25-27 259-637 460.8 89.3

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2.3 Magnetic resonance imaging

2.3.1 MRI safety for the fetus and pregnant patient

The reluctance to using MRI on pregnant patients is linked to the lack of confirming research in the area. It was initially thought that several mechanisms could potentially harm the fetus (10). MRI related heating is the main concern (particularly in the first trimester), and the rapidly switching gradient field’s acoustic noise is a concern during the second and third trimester. Tissue heating is greatest at the body’s surface, and since the fetus is at the body’s centre, the heating is not of great concern when scanning within SAR limits. The gradients can create 80-120 dB, and 90 dB is the acceptable upper limit before permanent fetal ear damage can be caused. However, the mother’s body reduces acoustic noise by at least 30 dB, and therefore acoustic noise is not a concern while performing fetal MRI. During the first trimester theoretical risks include disruption of cells undergoing implanting, and rapid dividing and organogenesis due to MRI related heating. Heating can be reduced by limiting specific absorption rate (SAR), which can be done by using low flip angles, large RF spacing, long repetition time (TR) and low B0

(32). Also, recent research shows that exposure to MRI compared to no exposure, during the first trimester is not associated with increased risk to the fetus or early childhood (11).

Today it is still recommended that each examination should be reviewed on a case-by- case basis, to assess the relative risk versus the benefit of the examination, or if the examination can be delayed until after or later in the pregnancy (10). Additionally, the risk must be weighed against alternative diagnostic tests that may involve ionization (33). Furthermore, the use of Gd should be avoided if possible due to the risks mentioned in the introduction (11).

2.3.2 Balanced Fast Field Echo

The balanced fast field echo (bFFE) sequence is well suited for placental imaging, because it results in fewer motion–related artefacts than other sequences, due to its relative resistance to maternal and fetal motion. It also provides reasonable

differentiation between the placenta and underlying myometrium. The sequence is often combined with parallel imaging that reduces acquisition time, increases sharpness and reduces SAR (5). The bFFE is a gradient echo (GRE) MRI sequence with balanced rewinding gradients in three directions (33). The sequence is termed balanced because the net gradient induced dephasing during one TR is zero. This means that both the free induction decay (FID) and echo components are refocused at the centre of the TR

interval resulting in a signal (Figure 4). The figure shows that the gradients are balanced on both sides of the signal (34). Because the balanced gradients maintain both

transverse and longitudinal magnetization, the resulting image contains both T1 and T2 contrast. Therefore the images have increased signal from fluid, but at the same time contain T1 weighted tissue contrast (35).

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The bFFE sequence requires high-performance gradient systems to obtain the needed short TE and good shimming (33). The bFFE sequence goes by several names, depending on the manufacturer: True FISP (Siemens), FIESTA (GE), BASG (Hitachi), True SSFP (Toshiba) and bFFE (Philips). The generic name of the sequence is coherent GRE with balanced “FID/echo” refocusing (36).

2.3.3 Diffusion, perfusion and MRI

Molecules in gasses and fluids have random translational motion. They will randomly collide and therefore continually change direction. This phenomenon is called diffusion (1), also known as Brownian motion (37). The continuous collisions and change of direction are described as a random walk (38).

Diffusion is an essential transportation method in the human body, allowing substances such as gases, nutrients and waste products to be effectively transported from one area to another. The net effect of diffusion is transport of substances from an area with high concentration to an area with low concentration, in order to even out the concentration differences, which happens via capillaries. How quickly a substance can be transported from the capillary to the surrounding area depends on the difference in concentration and the capillary wall’s permeability for a certain substance. Additionally, gases diffuse from areas with higher partial pressure to areas with lower partial pressure. An example of this can be seen in tissue fluid, where the partial pressure of oxygen is lower than within the capillaries. The cells within the tissue fluid have an even lower partial pressure. Therefore oxygen will diffuse from the blood within the capillaries, via the tissue fluid, to the cells (1).

Figure 4: bFFE sequence diagram showing that all gradients are balanced over one TR period, and the echo is formed in the middle of the sequence. The various parts of the diagram are:

=flip angle, RF is the radiofrequency pulse, G_ss is the slice-selective gradient, G_PE is the phase encoding gradient, and G_FE is the frequency-encoding gradient (33).

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Several substances are transported via diffusion, but when using MRI to detect the diffusion within the body it is the diffusion of water molecules that is studied (39). Since water accounts for 70% of body weight (1), there is a lot of hydrogen available for diffusion MRI. One typically investigates the self-diffusion of water molecules in tissue fluid. When water molecules interact with cell membranes through diffusion, MRI can be used to provide information about the functional architecture of tissues (39).

DWI is an MRI technique that allows us to evaluate several aspects related to the motional properties within a tissue. In the 1990s the technique was primarily used for brain imaging, but it has in later years been applied to the rest of the body, since technological innovations now enable it (14).

To obtain DWI, a spin-echo (SE) echo-planar imaging (EPI) sequence was used as an MR-signal readout sequence, due to its rapid acquisition time that minimizes the chances of motion-induced artefacts (33). When using SE over GRE, phase shifts resulting from magnetic field inhomogeneities, static tissue susceptibility gradients and chemical shifts are cancelled by SE’s refocusing pulse. This is not the case for the refocusing gradient.

Furthermore, image contrast in SE allows for true T2 relaxation rather than T2*. All of these factors lead to SE being less frequently troubled by susceptibility and chemical shift artefacts, but they do however take longer than GRE (40). EPI’s short acquisition time is a result of several k-space lines being filled during a single TR. In blipped EPI, the

frequency-encoding gradients oscillate rapidly from positive to negative to form a train of gradient echoes, and each echo is phase encoded differently by phase encoding blips (41). The EPI sequence is suitable for fetal imaging because it reduces motional blurring as a result of fetal movement (42).

The pulsed gradient spin echo (PGSE) method is one of the most commonly used

methods for obtaining diffusion-weighted contrast. The sequence consists of a 90°-180°

spin echo pair of RF pulses, where two equal gradients are placed on either side of the 180° pulse (Figure 5). This will precede the EPI acquisition. To control the degree of motion sensitivity in the image we can manipulate the diffusion-weighting gradient strength (G), and the timing elements pulse width (δ) and center-to-center spacing (Δ) (33).

In terms of these parameters we define the b-value, the diffusion sensitivity factor, as:

𝑏 = 𝛾²𝐺²𝛿²(∆ −𝛿 3)

γ is the gyromagnetic ratio (43), G is the gradient amplitude, δ is the duration of the gradient. The trailing-to-leading edge separation can be described with (Δ-δ) (33). The higher the chosen b-value, the stronger the diffusional effect will be. To increase the b- value, the gradient (G) amplitude and duration (δ) must be increased in addition to widening the interval between gradient pulses (the center-to-center spacing (Δ)) (44).

The b-value reflects the strength and duration of the pulsed diffusion gradients, is

expressed in units of s/mm² (33), and is typically between 0 and 4000 s/mm² on modern MRI scanners (44). The last part of the equation, the quantity (Δ-δ/3), is the diffusion time (τ) which is related to molecular motion (33). It is related to the time diffusion gradients are active before the refocusing gradient is switched on again (43).

Equation 1

11

The resulting image contrast is affected by the diffusional properties in the tissue.

Tissues with very mobile molecules result in a lower MR signal, while more static

molecules give a stronger signal (33). The mobility of molecules is characterized by the diffusion coefficient D, which describes their mean square displacement (R²) during a given time interval (T), see Figure 6 (45). For example, the diffusion constant of pure water at body temperature is 3.0×10^-3 mm²/s (37), which gives a displacement of 17 µm in 50 msec (45). The diffusion signal intensity is described by Equation 2 (33):

𝑆𝑏= 𝑆0𝑒^−𝑏𝐷

S_b is the signal for a particular b-value (33), S₀ is the signal at baseline, D is the diffusion coefficient and b is the chosen b-value (44). From Equation 2 we can see that the b- value is a decisive factor in the outcome of the signal strength, and that higher b-values result in lower signal and vice versa.

Diffusion MRI is based on Einstein’s diffusion equation, which assumes free diffusion of water and Gaussian distribution (39). The Stokes-Einstein equation, see Equation 3, shows how the diffusion coefficient D is estimated:

𝐷 = 𝑘𝐵𝑇 6𝜋𝜇𝑅0

Figure 5: PGSE sequence for DWI, which would precede an EPI acquisition. G is the magnitude of the diffusion-weighting gradients, δ is the gradients duration, and Δ is the gradient center-to- center spacing in time (33).

Equation 2

Equation 3

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k_B is Boltzmann’s constant, T is temperature, µ is the solvent viscosity and R₀ is the solute radius (46). Since the human body has a temperature of 37ºC, T can be

considered constant (47). However, in biological tissues the diffusion is hindered due to obstacles such as cell membranes, fibres and macromolecules, and is considered non- Gaussian diffusion. Therefore, the diffusion coefficient derived from DWI is not the free diffusion coefficient of water. To emphasize that the resulting information differs from the true free diffusion coefficient, the apparent diffusion coefficient (ADC) was introduced (39).

From DWI it is possible to calculate the tissue’s ADC which is a quantitative measurement of tissue diffusivity (14). To calculate ADC, two images with different b-values are

needed. The calculation uses Equation 4 (48):

𝐴𝐷𝐶 = 𝑙𝑛 (𝑆0

𝑆₁) (𝑏₁− 𝑏0)

In this case S₀ and S₁ are the signal intensities achieved with b-values b₀ and b₁, respectively. The ADC is displayed as a parametric map that reflects the degree of diffusion of water molecules. ADC measurements can be taken by placing regions of interest (ROI) on a map, and are measured in units of mm²/s. Typical values vary depending on the imaged organ or pathology (49). Additionally, the calculated ADC values can vary depending on the choice of b-values used (b₀ and b₁). Due to this it is important to make the appropriate choice of b-values for the chosen tissue. In brain imaging for instance, the appropriate b-values are b₀=0 and b₁=1000 (48), with typical ADC values of 2.94×10^-3 mm²s^-1 for CSF, 0.76×10^-3 mm²s^-1 for grey matter and

0.45×10^-3 mm²s^-1 for white matter (33). The average ADC value for placentas has been shown to be 1.827 ± 0.19×10^-3 mm²s^-1 (50) and 1.77 ± 0.19×10^-3 mm²s^-1 in another article, but it varies depending on gestational age (7).

Since the ADC value is affected by all motions within imaged voxels (perfusion and

diffusion), the term IVIM imaging emerged in addition to diffusion imaging (51). The ADC Figure 6: Illustration showing how diffusion (A) and perfusion (B) both resemble intravoxel incoherent motions. The illustration on the right shows the square displacement (R²) during a given time interval T, of the diffusion coefficient D. The illustration on the left shows how the pseudorandom orientation of the capillaries can resemble incoherent motion at voxel level (45).

Equation 4

13

integrates the effects of both diffusion and perfusion, and quantifies the IVIM images, which can be used to obtain separate images of diffusion and perfusion (45).

Perfusion is the delivery of blood via capillaries (microcirculation), and is closely related to the delivery of nutrients and oxygens to tissues. It is however important to distinguish perfusion from bulk blood flow, which is the flow that occurs in major arteries and veins.

Perfusion is measured in units of millilitres per 100 gram per minute (ml/100g/min) (52).

As an example, perfusion is 50 ± 15 ml/100g/min in grey brain matter, and

22 ± 5 ml/100g/min in white brain matter (53). In comparison, the average maternal blood perfusion in the human placenta has been shown to be 176 ± 24 ml/100g/min using EPI sequences (54), and ASL in healthy pregnancies has shown placental perfusion of 188 ± 40 ml/100g/min. With US the perfusion was 177 ml/100g/min and

145 ml/100g/min in two separate studies (55). Perfusion can be measured by MRI, with or without the use of contrast agent. Dynamic susceptibility contrast MRI and dynamic contrast enhanced MRI depend on contrast agent enhancement, while arterial spin labelling (ASL) (52) and IVIM (38) provide measures of perfusion without the use of contrast agents.

2.3.4 Intravoxel incoherent motion (IVIM) imaging

The diffusional signal is influenced by both the molecular diffusion of water and perfusion within the voxels, see Figure 7. At voxel level they can both mimic incoherent

movements, see Figure 6. These movements within the voxels are defined as IVIMs (51), and during the imaging process, the IVIMs cause spin dephasing and signal attenuation (13). To sensitize the DWI to the IVIMs, and separate the diffusion and perfusion, several low b-values, also called “motion” probing gradients pulses, must be used (51). With high b-values the signal loss is mainly due to diffusion. With lower b-values however, both diffusion and perfusion contribute to signal attenuation (13). Exploiting this makes it possible to distinguish perfusion and diffusion from each other (45). The IVIM-technique was originally proposed by Denis Le Bihan (13).

Another approach to describe D is based on molecular mobility, as shown in Equation 5 (45):

𝐷 = 𝒍𝒗/6

𝒍 is the mean vector length of “molecular jumps” due to Brownian motion (or capillary segments in IVIM imaging), while 𝒗 is the mean molecular velocity vector. Both play an important role in distinguishing perfusion from diffusion using the IVIM technique.

Previously, Equation 2 was used to describe the diffusion signal strength, but it does not take into consideration the perfusion part of the signal attenuation. The attenuation factor F must also be taken into account, as shown in Equation 6 (45).

𝑆_𝑏= 𝑆₀𝑒^{−𝑏𝐷∗𝐹}

F is 1 or less, and depends on the mean length of 𝒍, and the mean velocity 𝒗 of blood in capillaries, in addition to the mean measurement of time T, which is approximately TE in MRI. When using the assumption that the capillary network can be modelled with a network made of a succession of straight segments, the perfusion in the capillaries can affect the MRI signal in two ways, depending on how fast (𝒗) the blood travels and how many capillary segments (𝒍) are traversed during a set T. Therefore, there are two scenarios for how F can be expressed. In the first scenario, when several capillary segments are traversed during one T, making the water molecules within the capillary mimic diffusion, F will be as shown in Equation 7:

Equation 5

Equation 6

14 𝐹 = 𝑒^{−𝑏𝐷∗}

D* is the pseudodiffusion coefficient, which reflects dephasing caused by perfusion in semi-randomly organized capillaries, which can be approximated by Equation 5. In the first scenario, this results in the value of D* being about ten times higher than D measured in biological tissues. Hence, the perfusion part of the signal attenuation will always be larger than the diffusional attenuation. In the second scenario, when capillary segments are not changed due to slower blood flow, longer segments or shorter T, the expression for F will change. There are several equations available for calculating F in this scenario, but they also result in the same: the signal attenuation due to perfusion being greater than diffusion. In conclusion, both scenarios end up with the same result,

regardless of capillary geometry or blood velocity. This differential effect of diffusion and perfusion on the signal attenuation makes it possible to separate them on a quantitative basis, and makes IVIM imaging possible (45).

When separating diffusion from perfusion, several terms are used to describe IVIMs within the voxels. The measured water flowing through perfused capillaries is called the perfusion fraction f, which describes the fraction of a voxel occupied by capillaries, and its values range from 0 to 1 (13). (1-f) characterizes the static, diffusing only, intra- and extracellular water (45), which is where diffusion effects D take place (13). From this we can see that (1-f) represents the opposite of f, i.e. the fraction of the voxel not occupied by capillaries, and also has a maximum value of 1, see Figure 7. Furthermore, f can give

an indication of the amount of blood flowing, rather than the flow velocity (17). The pseudodiffusion coefficient D^* is also used. D* is normally 5-10 times greater than D, but how much depends on the steepness of the initial part of the curve (see Figure 8). The steepness is determined by the capillary density and perfusion (13). It is also associated with larger scale movements and characterizes incoherent blood flow (19), such as blood within the intervillous spaces and in fetal capillaries within villi (12). D, D*, f and (1-f) are known under other names also, depending in which programs and

literature are used² (13).

When only diffusion is taken into account, the semi-logarithmic plot of signal

attenuation versus b-value results in a straight line with slope D (Equation 2), the straight black line in Figure 8 (13).

But at low b-values the signal can also be affected by perfusion, and the IVIM effect causes deviation from the straight line whereby the signal drops faster (as shown

2 D is also known as ADC_slow or ADC_low, D* can be referred to as ADC_fast or ADC_high, whereas f is also known as perfusion fraction or volume fraction_fast, and (1-f) is also known as the diffusion in the extravascular space fraction or volume fractionslow (13).

Equation 7

Figure 7: Illustrates IVIMs within a voxel.

Perfusion fraction f can be seen as the perfusion through the blood vessel (burgundy arrows), while the diffusion in the extravascular space (1-f) can be seen as the green arrows in the image (45).

15

in the red-shaded area at the top-left of Figure 8). At the other end of the scale, for high b-values, the kurtosis effect causes a deviation whereby the signal drops more slowly (as shown in the green-shaded area at the bottom-right of Figure 8). The non-Gaussian behaviour of water molecules in complex biological tissues leads to the kurtosis effect.

The effect is most prominent with b-values above 1500 s/mm² (56). These variations in signal attenuation affect the calculated ADC value, with greater signal attenuation leading to a higher ADC values and vice versa (13).

A more advanced bi-exponential model, made by Le Bihan et al (45), also includes perfusion (13), and can be used to describe IVIM (17). The signal can be described by the bi-exponential model of IVIM in Equation 8:

𝑆_𝑏/𝑆₀= 𝑓𝑒^{−𝑏(𝐷+𝐷∗)}+ (1 − 𝑓)𝑒^𝑏𝐷

The tissue diffusion and blood flow component separately affect the signal, which results in a bi-exponential shape. The first part of Equation 8, 𝑓𝑒^{−𝑏(𝐷+𝐷∗)}, is related to the

pseudodiffusion (perfusion) signal attenuation, while the second part of the equation, (1 − 𝑓)𝑒^𝑏𝐷, is related to the diffusion attenuation (38). To separate the

perfusion from the diffusion, DWI images with several b-values must be acquired. From them it is possible to both estimate and create maps of D, D*, f and (1-f), by fitting the Figure 8: Relationship between signal attenuation ln(S/S₀) and the b-value. The figure shows the deviation of the expected MRI signal in DWI from a mono-exponential model. The straight black line shows the theoretical relationship, with the slope of the line indicating the ADC (apparent diffusion coefficient). The dotted blue line shows the actual relationship, when IVIM effects (red area) at low b-values and kurtosis (green area) effects at high values are taken into account (13).

Equation 8

16

data to Equation 8 (13). Only at low b-values can the IVIM effects be considered

significant. If b-values exceed a threshold, the perfusion fraction is considered negligible.

The cut off threshold, typically in the range 200-400 s/mm² (see Figure 8), is expected to differ depending on which organ or pathology is being imaged (16). At least half of the obtained b-values should be less than 250 s/mm² (13). More than four b-values are needed to calculate IVIM-parameters (20).

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3.1 Population and sampling

Participants’ image data for this retrospective descriptive study was collected from an ongoing study, Placenta Volume (PLAVO), at Akershus University Hospital, in Lørenskog in Norway. PLAVO’s research was approved by the local Regional Committees for Medical and Health Research Ethics (REC) (2016/1185/REK sør-øst A) and an extension of the REC application was added to include me as an associate of the project. This project was additionally approved by Akershus University Hospital’s data protection official

(19/07791).

The PLAVO cohort consisted of pregnant women recruited through Akershus University Hospital from 2017 to 2018, and performed MRI at 25-27 and 34-36 weeks of gestation.

Data for this study was collected using MRI images from the first scan, weeks 25-27 (N=106), while the second scan was used purely for training purposes. This study aimed to sample 26 participants from the PLAVO group’s participants.

The sample was taken by systematic sampling from an anonymized list, starting at participant number 33 (random number generated in SPSS (57)), and sampling every 4^th participant (106/26=4.08≈4).

3.2 MRI examination

PLAVO’s MRI examination had been performed by a selected group of MRI radiographers on a 1,5 T Phillips Ingenia (software version 5.3.0.3, Philips Healthcare, Best, The

Netherlands) with a 70 cm diameter bore. There was a routine protocol in place to be used by all radiographers performing the examination. They placed the women supine and feet first with an anterior coil over the abdomen. To support the weight of the anterior coil and to minimize pressure and discomfort on the abdomen, support cushions were used on the legs and chest. Additionally, a cushion under the knees and a pillow under the head was provided for comfort. All women received hearing protection and were screened with regards to MRI safety. The women received instructions on how to hold their breath, and were instructed to lie still. The number of slices in both

measurements varied with placental size, and the length and number of breath holds was changed if more slices were added to cover the entire placenta, or if the length of the breath hold had to be shortened for the patient to manage. The SAR of the examination was limited to 2 W/kg.

The total scan time was approximately 20 minutes. If motion artefacts were discovered, the sequence was repeated in an attempt to get images of optimal quality, however they could not be repeated indefinitely, and therefore some images remained suboptimal.

3 Methods

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PLAVO performed several sequences, but only the bFFE and DWI sequences were of interest to this study (see Table 3). The bFFE was used to take volume measurements and the DWI for IVIM measurements. Before including PLAVO’s images, they had to meet the inclusion criteria of this study:

 Every participant included in the study must have completed both MRI sequences:

bFFE and DWI at gestational age 25-27 weeks.

 The entire placenta must be present in the images.

 The placentas should be healthy without obvious pathology, to avoid the pathology leading to false volume and IVIM measurements.

 It should be a singleton pregnancy.

 To perform the needed measurements accurately, the included participants’

images should be of adequate quality. There should be no artefacts present on the placenta, and breaths should not lead to placental jumps between slices.

Patients that did not meet the criteria were discarded from the study. Of the 26 women selected through systematic sampling, seven (26.9%) of them were excluded from the study due to missing images (1 participant) and various artefacts: motion (1 participant), distortion (3 participants), susceptibility (7 participants), and zebra stripes (1

participant). Most of the artefacts that led to exclusion were related to the DW-images.

In some cases there were multiple reasons to exclude participants, due to the presence of several artefacts in either one or both sequences, or missing images altogether. The youngest included participant was 21 years old and the oldest 37, and the mean age was 29.9 years. The gestational age varied from 180 to 195 days, and the mean was

188.3 days. Of the 19 placentas measured ten were anteriorly located, six were posteriorly located, and three were both anteriorly and posteriorly located.

Table 3: MRI sequence parameters used for volume (GRE bFFE BH) and IVIM (SE EPI DWI BH) measurements. BH – breath hold sequence.

Sequence GRE bFFE BH SE EPI DWI BH

Orientation Sagittal Sagittal

Coverage Entire placenta Entire placenta

Slices 42-54 19-25

Acquisition type 2D 2D

Slice thickness 5 10

Gap 0 1

Repetition time 3.3 779

Echo time 1.6 51

Number of signal averages 2 1

Echo train length 1 75

Flip angle 60 90

FOV 300×300×225 300×300×230

Voxel size 1.5×1.5×5 2×2×10

Number of phase encoding steps 200 150

Standard breath hold duration 3×12 seconds 3×14 seconds

b-values - 0-5-10-25-50-200

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3.3 Data collection

To obtain the volume and IVIM data, two programs were used: IKT-SNAP (58, 59) and NordicICE (60). Prior to data collection I received training in both programs from a physicist and radiologist from the PLAVO group. The training was done with the physicist and radiologist separately, and took 1-2 hours with each of them. The instructions and training provided by PLAVO associates was based on images from both the first and second MRI scans. Additionally, images of placentas with pathology were shown, so that they could be recognised and avoided during data collection. The self-training was done on 10 random exams from the MRI scans performed in week 34-36, and performing the various measurements. Data collection was done on MRI scans performed during 25-27 weeks of gestation.

In both programs it was necessary to place regions of interest (ROI). To simplify ROI placements in both IKT-SNAP and NordicICE, a Wacom Cintiq 13HD Creative Pen Display (model: DTK-1301) pen tablet connected to a laptop was used. This made it possible to draw ROI with a pen rather than a mouse – simplifying the task in terms of convenience, speed and precision. The pen tablet was calibrated before each use. The number of slices with a ROI placed on them can be seen in Table 4.

3.3.1 Volume measurements

The sagittal bFFE images were used to obtain volume data using the Waqom Cintiq 13HD pen tablet. The bFFE images were loaded in IKT-SNAP, and then a ROI was placed on each individual slice of the placenta until the entire placenta was traced (see Figure 9 for an example of ROI placement in IKT-SNAP).

During ROI placement the decidua, maternal side of the placenta, was avoided. The DW- images (b=5) were used to help differentiate the decidua from the rest of the placenta (see Figure 10). The DWI and bFFE images were kept side by side during ROI placement, to aid in decidua differentiation. In cases where it proved difficult to distinguish the transition from placenta to uterus on the sagittal images, reconstructed transverse and coronal images were made for guidance. When the entire placenta was marked, IKT- SNAP was used to compute the volume of the marked area.

Table 4: The left side of the table shows how many slices were used to cover the entire area during imaging, while the right side shows how many slices contained placenta and had a ROI placed. The numbers depict range (mean).

Number of slices per sequence Number of slices containing placenta

bFFE DWI bFFE DWI

42-54 (47.0) 19-25 (21.8) 24-38 (31.0) 11-17 (13.7)

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Figure 9: Image from IKT-snap (59) showing a placed ROI (coloured area) in both an anterior (image A) and anterior + posterior (image B) placed placenta. The transition between fetal and maternal side of placenta can also be seen (grey vs coloured area).

Figure 10: DW-image of placenta from the same slice where b=0 (A) and b=5 (B, C). In image B the green arrow indicates where the decidua begins (the darker area), which is easily seen and makes it easier to avoid while placing the ROI (Image C; red arrow pointing towards red ROI line).

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3.3.2 IVIM measurements

The sagittal DW-images were loaded into the NordicICE software. A similar approach was used for placing ROI on the entire placenta on the DW-images with the Waqom Cintiq 13HD pen tablet. Again, the decidua was avoided, and the ROI were drawn on b=5 to easily distinguish it (see Figure 10 for an example of avoiding decidua during ROI

placement in NordicICE). While placing ROI on the DWI, the bFFE was also here kept side by side to help differentiate the decidua in tough cases.

After the ROI were placed, NordicICE was used to generate maps of D, D*, f and (1-f), see Figure 11 for an example of maps. From the maps of D, D*, f and (1-f) the

corresponding value of each voxel was extracted. The number of voxels extracted from each placenta for each map can be seen in Table 7 (in appendix). The number of extracted voxels collected per placenta for D ranged from 31889 to 94320, D* ranged from 31913 to 94322, f ranged from 23231 to 82944, and (1-f) ranged from 31920 to 94330. Zero value voxels were excluded in NordicICE and therefore the number of voxels varies slightly between maps, even though the same ROI was used.

Figure 11: Maps from the same slice of the same placenta of D*, D, f and (1-f) from NordicICE (60). Colouring has been added to more easily visualize differences in them. The voxel value is reflected in the assigned colour: higher values have colours from the top end of the colour scale to the right, while lower values have darker colours at the bottom end of the scale.

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When extracting the various IVIM distributions, a bi-exponential approach with a cut-off threshold of 50 s/mm² was used for creating the maps. To help evaluate the quality of the IVIM-data collected from the maps, logarithmic intensity curves of the b-values signal decay for every placental slice was generated in NordicICE. The data from the intensity curves was processed in SPSS, and line charts for all included participants were generated: examples of these can be seen in Figure 12. Each chart line was based on the mean voxel values of that slice for a specific b-value. The charts could be used to express whether the intensity curves followed the expected development for a bi-exponential function, or were characterized by disturbances. The line charts were also used to decide the optimal cut-off threshold, which was determined to be 50. Each logarithmic intensity curve in the line charts was based on the mean voxel values collected for each b-value for each slice. These values from each slice were in turn used to create a mean for the entire placenta, which can be seen in Figure 13. This was done for all placentas, and the data is summarized in Table 5.

After evaluating the intensity curves of each slice, the placentas were deemed to be good, or have minor or major disturbances on some of their slices. This was done by checking the line charts visually and scoring them. Three (≈16%) placentas had no disturbances, nine (≈47%) had minor disturbances and seven had “major” disturbances (≈37%). This does not mean that all slices of the placenta were classified as having, for example major disturbances, but that certain slices did. In Figure 12, the top line chart shows an example of a slice (bottom blue line) with a “major” disturbance, while the bottom line chart shows a line chart without disturbances. Despite there being some disturbances of some slices, we can see in Figure 13 that the mean sum of slices has the expected appearance. After evaluating the intensity curves of each slice and placenta as a whole were deemed to be of adequate quality with few disturbances in total.

Table 5: Shows how the voxel intensity values vary across all 19 placentas depending on the b- value. The mean (± standard deviation), minimum and maximum values are shown. The values are based on the intensity curves created in NordicICE.

b-value 0 5 10 25 50 200

Mean

(±standard deviation)

0.830 (±1.374)

0.795 (±1.369)

0.767 (±1.365)

0.708 (±1.372)

0.641 (±1.374)

0.432 (±1.406) Minimum 0.397 0.392 0.388 0.357 0.306 0.197 Maximum 1.411 1.373 1.370 1.251 1.134 0.849

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Figure 12: Line charts from SPSS showing the b-value curve of all slices of a placenta, based on the voxel value intensity. Each line represents a single slice and shows how the voxel value intensity changes depending on the b-value. Each line is based on the mean voxel values of that slice for a specific b-value. The top line chart shows an example where all slices do not follow the expected bi- exponential curve, while in the bottom line chart it is possible to see that they follow an

approximate bi-exponential development. Additionally, they show if the slices were affected by disturbances: the top chart shows an example of a slice (bottom blue line) with a “major”

disturbance, while the bottom line chart shows one without disturbances.

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3.4 Statistical analyses

All statistical analyses and figures were produced using data obtained from investigations using the 19 included volunteers and processing it using the SPSS statistical software package, version 25 (57).

3.4.1 Volume

To evaluate the reliability of the performed volume measurements in this study, they were compared to the PLAVO radiologist’s measurements, which were deemed the “gold standard”. The author, a radiographer, was blinded to the radiologist’s measurements during collection. An intraclass correlation coefficient (ICC) test was used to evaluate the reliability of the volume measurements. ICC estimates and their 95% confidence interval were calculated using SPSS. The ICC estimates were performed based on an absolute agreement 2-way random effects model.

3.4.2 Correlation between IVIM and volume

A Pearson correlation analysis was performed to see if there was any correlation between volume and each of the IVIM parameters: volume and D, volume and D*, volume and f, and volume and (1-f). The correlation was deemed statistically significant if the 2-tailed Figure 13: Line charts showing the b-value curve of all included placentas, based on the voxel value intensity. Each line represents one placenta, and is the mean of all slices of that placenta.

When all the slices are added together, the curve follows the expected bi-exponential development.

25

significance was below 0.05. The Pearson test was chosen because the data was at a scale level and approximately normally distributed, in addition to appearing to have a linear relationship, and outliers were kept to a minimum. Spearman is an option for when the assumptions for using the Pearson test are markedly violated, which is not the case.

Furthermore, it is preferable to use Pearson if possible, rather than the non-parametric Spearman test, especially when the variables are at scale level (61).

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The study was based on a group of 19 pregnant women in gestational age 25-27 weeks and measured the following:

 Placenta volume

 IVIM parameters diffusion coefficient (D), pseudodiffusion coefficient (D*), perfusion fraction (f), and diffusion in the extravascular space fraction (1-f)

 Correlation between volume and IVIMs.

4.1 Placenta volume measurements

Of the 19 volume measurements performed, the largest placenta volume measurement was 728.17 cm³ and the smallest 333.06 cm³. The mean placenta volume was

464.45 cm³ with a standard deviation of 92.25 cm³. The median was 445.36 cm³, and the skewness and kurtosis were respectively 1.59 and 2.94. The distribution of placenta volume measurements is shown in the histogram in Figure 14. The data in general appears relatively evenly distributed, but higher volumes appear to be more spread out.

The 90^th, 75^th, 50^th, 25^th and 5^th percentiles were respectively 630.86 cm³, 489.77 cm³, 445.36 cm³, 406.48 cm³ and 333.06 cm³. Additional details at placental level can be seen in the appendix in Table 8.

4 Results

Figure 14: Distribution of placenta volumes (cm³).