Tracer-Kinetic Model-Driven Motion Correction with Application to Renal DCE-MRI

A major challenge of the image registration in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is related to the image contrast variations caused by the contrast agent passage. Tracer-kinetic model-driven motion correction is an attractive solution for DCE-MRI, but previous studies only use the 3-parameter modified Tofts model. Firstly, a generalisation based on a 4-parameter 2-compartment tracer-kinetic model is presented. A practical limitation of these models is the need for non-linear least-squares (NLLS) fitting. This is prohibitively slow for image-wide parameter estimations, and is biased by the choice of initial values. To overcome this limitation, a fast linear least-squares (LLS) method to fit the two-compartment exchange and -filtration models (2CFM) to the data is introduced. Simulations of normal and pathological data were used to evaluate calculation time, accuracy and precision of the LLS against the NLLS method. Results show that the LLS method leads to a significant reduction in the calculation times. Secondly, a novel tracer-kinetic model-driven motion correction algorithm is introduced which uses a 4-parameter 2-compartment model to tackle the problem of image registration in 2D renal DCE-MRI. The core architecture of the algorithm can briefly described as follows: the 2CFM is linearly fitted pixel-by-pixel and the model fit is used as target for registration; then a free-form deformation model is used for pairwise co-registration of source and target images at the same time point. Another challenge that has been addressed is the computational complexity of non-rigid registration algorithms by precomputing steps to remove redundant calculations. Results in 5 subjects and simulated phantoms show that the algorithm is computationally efficient and improves alignment of the data. The proposed registration algorithm is then translated to 3D renal dynamic MR data. Translation to 3D is however challenging due to ghosting artefacts caused by within-frame breathing motion. Results in 8 patients show that the algorithm effectively removes between-frame breathing motion despite significant within-frame artefacts. Finally, the effect of motion correction on the clinical utility has been examined. Quantitative evaluation of single-kidney glomerular filtration rate derived from DCE-MRI against reference measurements shows a reduction of the bias, but precision is limited by within-frame artefacts. The suggested registration algorithm with a 4-parameter model is shown to be a computational efficient approach which effectively removes between-frame motion in a series of 2D and 3D renal DCE-MRI data

PIPPI2021: An Approach to Automated Diagnosis and Texture Analysis of the Fetal Liver & Placenta in Fetal Growth Restriction

Fetal growth restriction (FGR) is a prevalent pregnancy condition characterised by failure of the fetus to reach its genetically predetermined growth potential. We explore the application of model fitting techniques, linear regression machine learning models, deep learning regression, and Haralick textured features from multi-contrast MRI for multi-fetal organ analysis of FGR. We employed T2 relaxometry and diffusion-weighted MRI datasets (using a combined T2-diffusion scan) for 12 normally grown and 12 FGR gestational age (GA) matched pregnancies. We applied the Intravoxel Incoherent Motion Model and novel multi-compartment models for MRI fetal analysis, which exhibit potential to provide a multi-organ FGR assessment, overcoming the limitations of empirical indicators - such as abnormal artery Doppler findings - to evaluate placental dysfunction. The placenta and fetal liver presented key differentiators between FGR and normal controls (decreased perfusion, abnormal fetal blood motion and reduced fetal blood oxygenation. This may be associated with the preferential shunting of the fetal blood towards the fetal brain. These features were further explored to determine their role in assessing FGR severity, by employing simple machine learning models to predict FGR diagnosis (100\% accuracy in test data, n=5), GA at delivery, time from MRI scan to delivery, and baby weight. Moreover, we explored the use of deep learning to regress the latter three variables. Image texture analysis of the fetal organs demonstrated prominent textural variations in the placental perfusion fractions maps between the groups (p$<$0.0009), and spatial differences in the incoherent fetal capillary blood motion in the liver (p$<$0.009). This research serves as a proof-of-concept, investigating the effect of FGR on fetal organs

Placenta Imaging Workshop 2018 report:Multiscale and multimodal approaches

The Centre for Medical Image Computing (CMIC) at University College London (UCL) hosted a two-day workshop on placenta imaging on April 12th and 13th 2018. The workshop consisted of 10 invited talks, 3 contributed talks, a poster session, a public interaction session and a panel discussion about the future direction of placental imaging. With approximately 50 placental researchers in attendance, the workshop was a platform for engineers, clinicians and medical experts in the field to network and exchange ideas. Attendees had the chance to explore over 20 posters with subjects ranging from the movement of blood within the placenta to the efficient segmentation of fetal MRI using deep learning tools. UCL public engagement specialists also presented a poster, encouraging attendees to learn more about how to engage patients and the public with their research, creating spaces for mutual learning and dialogue

Developments in functional imaging of the placenta

The placenta is both the literal and metaphorical black box of pregnancy. Measurement of the function of the placenta has the potential to enhance our understanding of this enigmatic organ and serve to support obstetric decision making. Advanced imaging techniques are key to supporting these measurements. This review summarises emerging imaging technology being used to measure the function of the placenta and new developments in the computational analysis of this data. We address three important examples where functional imaging is supporting our understanding of these conditions: fetal growth restriction, placenta accreta, and twin-twin transfusion syndrome

Quantifying the intra-operative hemodynamic effects of glue embolization in vein of Galen malformations

Vein of Galen malformation is a rare congenital pathological intracranial arteriovenous shunt which carries 30% risk of death before 28 days-of-age. Treatment is by high risk minimally invasive endovascular glue embolization of shunt feeding arteries under angiographic control. A tool to support intra-operative decision making would be useful. We present a novel method for visualizing angiography data to demonstrate the effect of the intervention based upon change the after embolization in the delay in time of peak contrast density relative to the injected artery and a novel method for quantifying the immediate effect of embolization on the hemodynamics of the shunt. The method is demonstrated on the angiograms of five neonates who underwent embolization. We show consistent results including a post-embolization increase in the delay in time of peak contrast density relative to the injected artery at the venous outflow in keeping with reduced shunting and redistribution of blood following embolization

Separating fetal and maternal placenta circulations using multiparametric MRI

PURPOSE: The placenta is a vital organ for the exchange of oxygen, nutrients, and waste products between fetus and mother. The placenta may suffer from several pathologies, which affect this fetal-maternal exchange, thus the flow properties of the placenta are of interest in determining the course of pregnancy. In this work, we propose a new multiparametric model for placental tissue signal in MRI. METHODS: We describe a method that separates fetal and maternal flow characteristics of the placenta using a 3-compartment model comprising fast and slowly circulating fluid pools, and a tissue pool is fitted to overlapping multiecho T2 relaxometry and diffusion MRI with low b-values. We implemented the combined model and acquisition on a standard 1.5 Tesla clinical system with acquisition taking less than 20 minutes. RESULTS: We apply this combined acquisition in 6 control singleton placentas. Mean myometrial T2 relaxation time was 123.63 (±6.71) ms. Mean T2 relaxation time of maternal blood was 202.17 (±92.98) ms. In the placenta, mean T2 relaxation time of the fetal blood component was 144.89 (±54.42) ms. Mean ratio of maternal to fetal blood volume was 1.16 (±0.6), and mean fetal blood saturation was 72.93 (±20.11)% across all 6 cases. CONCLUSION: The novel acquisition in this work allows the measurement of histologically relevant physical parameters, such as the relative proportions of vascular spaces. In the placenta, this may help us to better understand the physiological properties of the tissue in disease.status: publishe

Improved fetal blood oxygenation and placental estimated measurements of diffusion-weighted MRI using data-driven Bayesian modeling

PURPOSE: Motion correction in placental DW-MRI is challenging due to maternal breathing motion, maternal movements, and rapid intensity changes. Parameter estimates are usually obtained using least-squares methods for voxel-wise fitting; however, they typically give noisy estimates due to low signal-to-noise ratio. We introduce a model-driven registration (MDR) technique which incorporates a placenta-specific signal model into the registration process, and we present a Bayesian approach for Diffusion-rElaxation Combined Imaging for Detailed placental Evaluation model to obtain individual and population trends in estimated parameters. METHODS: MDR exploits the fact that a placenta signal model is available and thus we incorporate it into the registration to generate a series of target images. The proposed registration method is compared to a pre-existing method used for DCE-MRI data making use of principal components analysis. The Bayesian shrinkage prior (BSP) method has no user-defined parameters and therefore measures of parameter variation in a region of interest are determined by the data alone. The MDR method and the Bayesian approach were evaluated on 10 control 4D DW-MRI singleton placental data. RESULTS: MDR method improves the alignment of placenta data compared to the pre-existing method. It also shows a further reduction of the residual error between the data and the fit. BSP approach showed higher precision leading to more clearly apparent spatial features in the parameter maps. Placental fetal oxygen saturation (FO2 ) showed a negative linear correlation with gestational age. CONCLUSIONS: The proposed pipeline provides a robust framework for registering DW-MRI data and analyzing longitudinal changes of placental function

Maternal tadalafil treatment does not increase uterine artery blood flow or oxygen delivery in the pregnant ewe

Increasing placental perfusion (PP) could improve outcomes of growth-restricted fetuses. One way of increasing PP may be by using phosphodiesterase (PDE)-5 inhibitors, which induce vasodilatation of vascular beds. We used a combination of clinically relevant magnetic resonance imaging (MRI) techniques to characterize the impact that tadalafil infusion has on maternal, placental and fetal circulations. At 116–117 days’ gestational age (dGA; term, 150 days), pregnant ewes (n = 6) underwent fetal catheterization surgery. At 120–123 dGA ewes were anaesthetized and MRI scans were performed during three acquisition windows: a basal state and then ∼15–75 min (TAD 1) and ∼75–135 min (TAD 2) post maternal administration (24 mg; intravenous bolus) of tadalafil. Phase contrast MRI and T2 oximetry were used to measure blood flow and oxygen delivery. Placental diffusion and PP were assessed using the Diffusion-Relaxation Combined Imaging for Detailed Placental Evaluation—‘DECIDE’ technique. Uterine artery (UtA) blood flow when normalized to maternal left ventricular cardiac output (LVCO) was reduced in both TAD periods. DECIDE imaging found no impact of tadalafil on placental diffusivity or fetoplacental blood volume fraction. Maternal-placental blood volume fraction was increased in the TAD 2 period. Fetal (Formula presented.) and (Formula presented.) were not affected by maternal tadalafil administration. Maternal tadalafil administration did not increase UtA blood flow and thus may not be an effective vasodilator at the level of the UtAs. The increased maternal–placental blood volume fraction may indicate local vasodilatation of the maternal intervillous space, which may have compensated for the reduced proportion of UtA (Formula presented.).</p

Placental MRI Predicts Fetal Oxygenation and Growth Rates in Sheep and Human Pregnancy

Magnetic resonance imaging (MRI) assessment of fetal blood oxygen saturation (SO(2)) can transform the clinical management of high‐risk pregnancies affected by fetal growth restriction (FGR). Here, a novel MRI method assesses the feasibility of identifying normally grown and FGR fetuses in sheep and is then applied to humans. MRI scans are performed in pregnant ewes at 110 and 140 days (term = 150d) gestation and in pregnant women at 28(+3) ± 2(+5) weeks to measure feto‐placental SO(2). Birth weight is collected and, in sheep, fetal blood SO(2) is measured with a blood gas analyzer (BGA). Fetal arterial SO(2) measured by BGA predicts fetal birth weight in sheep and distinguishes between fetuses that are normally grown, small for gestational age, and FGR. MRI feto‐placental SO(2) in late gestation is related to fetal blood SO(2) measured by BGA and body weight. In sheep, MRI feto‐placental SO(2) in mid‐gestation is related to fetal SO(2) later in gestation. MRI feto‐placental SO(2) distinguishes between normally grown and FGR fetuses, as well as distinguishing FGR fetuses with and without normal Doppler in humans. Thus, a multi‐compartment placental MRI model detects low placental SO(2) and distinguishes between small hypoxemic fetuses and normally grown fetuses