Application of Advanced MRI to Fetal Medicine and Surgery

Robust imaging is essential for comprehensive preoperative evaluation, prognostication, and surgical planning in the field of fetal medicine and surgery. This is a challenging task given the small fetal size and increased fetal and maternal motion which affect MRI spatial resolution. This thesis explores the clinical applicability of post-acquisition processing using MRI advances such as super-resolution reconstruction (SRR) to generate optimal 3D isotropic volumes of anatomical structures by mitigating unpredictable fetal and maternal motion artefact. It paves the way for automated robust and accurate rapid segmentation of the fetal brain. This enables a hierarchical analysis of volume, followed by a local surface-based shape analysis (joint spectral matching) using mathematical markers (curvedness, shape index) that infer gyrification. This allows for more precise, quantitative measurements, and calculation of longitudinal correspondences of cortical brain development. I explore the potential of these MRI advances in three clinical settings: fetal brain development in the context of fetal surgery for spina bifida, airway assessment in fetal tracheolaryngeal obstruction, and the placental-myometrial-bladder interface in placenta accreta spectrum (PAS). For the fetal brain, MRI advances demonstrated an understanding of the impact of intervention on cortical development which may improve fetal candidate selection, neurocognitive prognostication, and parental counselling. This is of critical importance given that spina bifida fetal surgery is now a clinical reality and is routinely being performed globally. For the fetal trachea, SRR can provide improved anatomical information to better select those pregnancies where an EXIT procedure is required to enable the fetal airway to be secured in a timely manner. This would improve maternal and fetal morbidity outcomes associated with haemorrhage and hypoxic brain injury. Similarly, in PAS, SRR may assist surgical planning by providing enhanced anatomical assessment and prediction for adverse peri-operative maternal outcome such as bladder injury, catastrophic obstetric haemorrhage and maternal death

STUDI LITERATUR: TANGRAM SEBAGAI MEDIA PEMBELAJARAN GEOMETRI

Tangram adalah suatu permainan dari China berbentuk puzzle yang terdiri dari tujuh keping bangun datar yang diantaranya terdapat lima buah segitiga, satu buah persegi, dan satu buah jajar genjang. Ketujuh kepingan tersebut disusun dan ditempel sehingga dapat membentuk berbagai pola seperti gambar kucing, ikan, rumah, dan sebagainya. Tujuan dari penelitian ini adalah untuk mengetahui manfaat dari permainan Tangram ketika digunakan sebagai media pembelajaran geometri. Metode dari penelitian ini adalah melalui studi literatur. Data yang diperoleh dikompulasi, dianalisis, dan disimpulkan sehingga mendapatkan kesimpulan dari beberapa penelitian terdahulu untuk menjawab bagaimana efek atau manfaat dari permainan Tangram ketika digunakan sebagai media pembelajaran. Hasil dari studi ini menunjukkan bahwa permainan Tangram memiliki beberapa manfaat ketika digunakan dalam pembelajaran geometri, yakni: 1) Meningkatkan kreativitas siswa; 2) Meningkatkan pemahaman konsep geometri pada siswa; 3) Menjadi media visualisasi bangun datar yang konkret untuk siswa; 4) Meningkatkan minat belajar siswa pada proses pembelajaran bangun datar; 5) Meningkatkan hasil belajar siswa pada materi bangun datar. Dengan demikian, dapat disimpulkan bahwa dengan berbagai manfaat tersebut, permainan Tangram dapat digunakan sebagai media pembelajaran geometri khususnya materi bangun datar. Oleh karena itu, disarankan pada penelitian selanjutnya untuk dapat mengembangkan permainan Tangram untuk pembelajaran geometri di sekolah

A spatio-temporal atlas of the developing fetal brain with spina bifida aperta

Background: Spina bifida aperta (SBA) is a birth defect associated with severe anatomical changes in the developing fetal brain. Brain magnetic resonance imaging (MRI) atlases are popular tools for studying neuropathology in the brain anatomy, but previous fetal brain MRI atlases have focused on the normal fetal brain. We aimed to develop a spatio-temporal fetal brain MRI atlas for SBA. Methods: We developed a semi-automatic computational method to compute the first spatio-temporal fetal brain MRI atlas for SBA. We used 90 MRIs of fetuses with SBA with gestational ages ranging from 21 to 35 weeks. Isotropic and motion-free 3D reconstructed MRIs were obtained for all the examinations. We propose a protocol for the annotation of anatomical landmarks in brain 3D MRI of fetuses with SBA with the aim of making spatial alignment of abnormal fetal brain MRIs more robust. In addition, we propose a weighted generalized Procrustes method based on the anatomical landmarks for the initialization of the atlas. The proposed weighted generalized Procrustes can handle temporal regularization and missing annotations. After initialization, the atlas is refined iteratively using non-linear image registration based on the image intensity and the anatomical land-marks. A semi-automatic method is used to obtain a parcellation of our fetal brain atlas into eight tissue types: white matter, ventricular system, cerebellum, extra-axial cerebrospinal fluid, cortical gray matter, deep gray matter, brainstem, and corpus callosum. Results: An intra-rater variability analysis suggests that the seven anatomical land-marks are sufficiently reliable. We find that the proposed atlas outperforms a normal fetal brain atlas for the automatic segmentation of brain 3D MRI of fetuses with SBA. Conclusions: We make publicly available a spatio-temporal fetal brain MRI atlas for SBA, available here: https://doi.org/10.7303/syn25887675. This atlas can support future research on automatic segmentation methods for brain 3D MRI of fetuses with SBA

A spatio-temporal atlas of the developing fetal brain with spina bifida aperta [version 2; peer review: 2 approved]

Background: Spina bifida aperta (SBA) is a birth defect associated with severe anatomical changes in the developing fetal brain. Brain magnetic resonance imaging (MRI) atlases are popular tools for studying neuropathology in the brain anatomy, but previous fetal brain MRI atlases have focused on the normal fetal brain. We aimed to develop a spatio-temporal fetal brain MRI atlas for SBA. Methods: We developed a semi-automatic computational method to compute the first spatio-temporal fetal brain MRI atlas for SBA. We used 90 MRIs of fetuses with SBA with gestational ages ranging from 21 to 35 weeks. Isotropic and motion-free 3D reconstructed MRIs were obtained for all the examinations. We propose a protocol for the annotation of anatomical landmarks in brain 3D MRI of fetuses with SBA with the aim of making spatial alignment of abnormal fetal brain MRIs more robust. In addition, we propose a weighted generalized Procrustes method based on the anatomical landmarks for the initialization of the atlas. The proposed weighted generalized Procrustes can handle temporal regularization and missing annotations. After initialization, the atlas is refined iteratively using non-linear image registration based on the image intensity and the anatomical land-marks. A semi-automatic method is used to obtain a parcellation of our fetal brain atlas into eight tissue types: white matter, ventricular system, cerebellum, extra-axial cerebrospinal fluid, cortical gray matter, deep gray matter, brainstem, and corpus callosum. Results: An intra-rater variability analysis suggests that the seven anatomical land-marks are sufficiently reliable. We find that the proposed atlas outperforms a normal fetal brain atlas for the automatic segmentation of brain 3D MRI of fetuses with SBA. Conclusions: We make publicly available a spatio-temporal fetal brain MRI atlas for SBA, available here: https://doi.org/10.7303/syn25887675. This atlas can support future research on automatic segmentation methods for brain 3D MRI of fetuses with SBA

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

Cortical Surface Matching of the Fetal Brain Pre and Post Fetal Surgery for Open Spina Bifida

Introduction: Fetal surgery has become a clinical reality, even for non-lethal conditions such as open spina bifidayelomeningocele, the spinal cord extrudes into a cereberospinal fluid (CSF) filled sac 1, 2. It is associated with brain anomalies such as hindbrain herniation and variable degrees of ventriculomegaly. Prenatalrepair yields better outcomes compared to postnatal surgery3. Nonetheless, mechanical tissue damage of brain parenchyma due to ventriculomegaly and damage to the neural tracts lead to abnormal white matter development, as demonstrated by diffusion weighted imaging studies4-9. This may lead to altered gyrification patterns in MMC patients. Gyrification, measured by magnetic resonance imaging (MRI), correlates with motor and cognitive function in infants, children and adolescents with who have undergone postnatal closure 6. Evaluation of cognitiveand motor function in fetuses who have had prenatal surgery, is performed only after birth, with deficits becoming more evident with increasing age. Clinicians therefore urgently need early fetal brain imaging methods that can predict the cognitive and motor challenges that fetuses may encounter after birth. We aim to demonstrate that longitudinal quantitative MRI measurement of cortical gyrification is possible, before and after repair. We will also demonstrate the curvature (curvedness and shape index) of cerebellum and ventricles before and after surgery. Methods: T2-weighted single-shot fast spin-echo (SSFSE) was performed of the fetal brain in multiple containing an axial, coronal and sagittal planewith 3mm slice thicknesson women with , both before surgery (n=12, 23+6 1+7 weeks, (22+1–25+6)) and after surgery (n=12, 26+1 1+3 weeks, (24+1– 29+4))acquisition time thirty-forty minutes. Initial diagnosis of open spina bifida was made on mid-trimester ultrasound. Fetuses affected by aneuploidy or with structural anomalies outside the CNS were excluded. A novel automated super resolution reconstruction (SRR) algorithm10, 11 was used to build 3D volumes of the fetal brain based on the 2D stacks that were acquired in different directions. Rigid slice-to-volume registration correcting for fetal motion was used to generate an SRR image in standard anatomical orientation, from which we automatically segmented white matter, ventricles, and cerebellum using template brain segmentations12, 13. 14make us of 12Brain masks for pre-operative SRR volumes were resampled from their corresponding post-operative MMC masks after affine and non-rigid alignment. All masks where manually corrected and meshes were generated using ITK-Snap14. A rigid coherent point drift algorithm was applied to find an initial correspondence for the intrasubject cortical, cerebellar and ventricle regions before and after surgery. Joint spectral matching (JSM) was then used to find the correspondence for the intrasubject at those two different time points. In JSM a dual layered graph was produced whereby layers correspond to the surface of the white matter, cerebellum or ventricles of each subject. The correspondence links from the initial intrasubject matching, connecting both layers to produce a set of shared eigenmodes of the surfaces. After mapping the post-operative surface to the pre-operative surface using JSM, we computed the change in parameters at the vertex of each mesh to explore longitudinal cortical gyrification, and curvature (curvedness and shape index) of the 12. Results: Figure 1 illustrates five spectral modes for the white matter, ventricles and cerebellum of a fetus before surgery (24 weeks), and therafter (26 weeks). Although the meshes are quite different in the three-dimensional space, with respect to different levels of folding, variation in shape, surface area and volume, they have similar representations in the spectral domain. Figure 2 shows the curvedness and shape index in the white matter, cerebellum and ventricles before and after fetal surgery. JSM allows us to map the mean curvatures of each mesh to compute changes in the mean and to generate the shape index.Figure 3 shows maps of mean curvature of a fetus pre and post-surgery. Positive values are depicted in red/yellow and represent gyri (convex structures), and negative values in blue represent sulci (concave) structures. JSM allows mapping of mean curvatures from the post-op to the pre-op space, computing the changes in mean curvature between these two time points in the pre-op space.Figure 4 illustrates the shape index histogram for white matter, showing the differences in gyri and sulci between the pre and post-operative MMC brain.Discussion: Surface-based matching provides additional information about changes in growth and gyrification of the fetal brain compared to measurement of total volume and shape change. This may be useful in evaluating changes in cerebral growth of MMC fetuses before and after fetal surgery. Spectral graph matching is a promising tool for matching shapes with significant differences in cortical folding, surface area, and volume, but with similar representations in the spectral domain such as depicted with fetuses before and after surgery12, 15. Future work may be able to better explore the physiological and mechanical properties contributing to the differences observed in brain growth and development in the context of fetal surgery. Conclusion: Novel analysis of fetal longitudinal correspondence of white matter, and development of specific regions of the brain as secondary gyri emerges, in the context of fetal surgery is demonstrated. This tool allows the measurement of the shape and growth of the white matter surface may help establish longitudinal growth trajectories. References: 1. Rethmann, C., et al., Evolution of posterior fossa and brain morphology after in utero repair of open neural tube defects assessed by MRI. Eur Radiol, 2017. 27(11): p. 4571-4580. 2. Zarutskie, A., et al., Prenatal brain imaging for predicting need for postnatal hydrocephalus treatment in fetuses that had neural tube defect repair in utero. Ultrasound Obstet Gynecol, 2019. 53(3): p. 324-334. 3. Adzick, N.S.T., E.A.; Spong, C. Y.; Brock III, J. W.; Burrows, P. K.; Johnson, M. P.; Howell, R. N.; Farrell, J. N.; Dabrowiak, M.E.; Sutton, L.N.; Gupta, N.; Tulipan, N.B.; D’Alton, M.E.; Farmer, D.L., A Randomised Trial of Prenatal versus Postnatal Repair of Myelomeningocele. N Engl J Med, 2011. 364: p. 993-1004. 4. Juranek, J., et al., Neocortical reorganization in spina bifida. Neuroimage, 2008. 40(4): p. 1516-22. 5. Juranek, J. and M.S. Salman, Anomalous development of brain structure and function in spina bifida myelomeningocele. Developmental Disabilities Research Reviews, 2010. 16(1): p. 23-30. 6. Treble, A., et al., Functional significance of atypical cortical organization in spina bifida myelomeningocele: relations of cortical thickness and gyrification with IQ and fine motor dexterity. Cereb Cortex, 2013. 23(10): p. 2357-69. 7. Hasan, K.M., et al., White matter microstructural abnormalities in children with spina bifida myelomeningocele and hydrocephalus: a diffusion tensor tractography study of the association pathways. J Magn Reson Imaging, 2008. 27(4): p. 700-9. 8. Mignone Philpott, C., et al., Diffusion-weighted imaging of the cerebellum in the fetus with Chiari II malformation. AJNR Am J Neuroradiol, 2013. 34(8): p. 1656-60. 9. Woitek, R., et al., Fetal diffusion tensor quantification of brainstem pathology in Chiari II malformation. Eur Radiol, 2016. 26(5): p. 1274-83. 10. Ebner, M., et al., An Automated Localization, Segmentation and Reconstruction Framework for Fetal Brain MRI, in Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. 2018. p. 313-320. 11. Ebner, M., et al., An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI. NeuroImage, 2019. 12. Orasanu, E., et al., Cortical folding of the preterm brain: a longitudinal analysis of extremely preterm born neonates using spectral matching. Brain Behav, 2016. 6(8): p. e00488. 13. Kuklisova-Murgasova, M., et al., A dynamic 4D probabilistic atlas of the developing brain. Neuroimage, 2011. 54(4): p. 2750-63. 14. Yushkevich, P.A., et al., User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage, 2006. 31(3): p. 1116-28. 15. Lomabert, H., Spopring J., and Siddiqi K., Diffeomorphic spectral matching of cortical surafaces. Inf Process Med Imaging, 2013. 7917: p. 376-289

Distributionally Robust Deep Learning using Hardness Weighted Sampling

Limiting failures of machine learning systems is vital for safety-critical applications. In order to improve the robustness of machine learning systems, Distributionally Robust Optimization (DRO) has been proposed as a generalization of Empirical Risk Minimization (ERM)aiming at addressing this need. However, its use in deep learning has been severely restricted due to the relative inefficiency of the optimizers available for DRO in comparison to the wide-spread variants of Stochastic Gradient Descent (SGD) optimizers for ERM. We propose SGD with hardness weighted sampling, a principled and efficient optimization method for DRO in machine learning that is particularly suited in the context of deep learning. Similar to a hard example mining strategy in essence and in practice, the proposed algorithm is straightforward to implement and computationally as efficient as SGD-based optimizers used for deep learning, requiring minimal overhead computation. In contrast to typical ad hoc hard mining approaches, and exploiting recent theoretical results in deep learning optimization, we prove the convergence of our DRO algorithm for over-parameterized deep learning networks with ReLU activation and finite number of layers and parameters. Our experiments on brain tumor segmentation in MRI demonstrate the feasibility and the usefulness of our approach. Using our hardness weighted sampling leads to a decrease of 2% of the interquartile range of the Dice scores for the enhanced tumor and the tumor core regions. The code for the proposed hard weighted sampler will be made publicly available

Feature Selection To Facilitate Surgical Planning From MRI Of Placenta Accreta Spectrum Disorder

Feature Selection Models provide a ranking of pathological MRI markers able to predict the outcome of Placenta Accreta Spectrum Disorder, which could be used to aid in clinical decision-making and improve maternal outcome. The potential being to reduce the workload of radiologists by establishing the most clinically relevant pathological MRI markers that predict outcome. Our results found three pathological markers to have the highest ranking to the outcomes with an average accuracy of 75% using a Random Forest Selection Model and Boruta algorithm

Use of Super Resolution Reconstruction MRI for surgical planning in Placenta Accreta Spectrum Disorder: Case Series

INTRODUCTION: Comprehensive imaging using ultrasound and MRI of placenta accreta spectrum (PAS) aims to prevent catastrophic haemorrhage and maternal death. Standard MRI of the placenta is limited by between-slice motion which can be mitigated by super-resolution reconstruction (SRR) MRI. We applied SRR in suspected PAS cases to determine its ability to enhance anatomical placental assessment and predict adverse maternal outcome. METHODS: Suspected PAS patients (n = 22) underwent MRI at a gestational age (weeks + days) of (32+3±3+2, range (27+1-38+6)). SRR of the placental-myometrial-bladder interface involving rigid motion correction of acquired MRI slices combined with robust outlier detection to reconstruct an isotropic high-resolution volume, was achieved in twelve. 2D MRI or SRR images alone, and paired data were assessed by four radiologists in three review rounds. All radiologists were blinded to results of the ultrasound, original MR image reports, case outcomes, and PAS diagnosis. A Random Forest Classification model was used to highlight the most predictive pathological MRI markers for major obstetric haemorrhage (MOH), bladder adherence (BA), and placental attachment depth (PAD). RESULTS: At delivery, four patients had placenta praevia with no abnormal attachment, two were clinically diagnosed with PAS, and six had histopathological PAS confirmation. Pathological MRI markers (T2-dark intraplacental bands, and loss of retroplacental T2-hypointense line) predicting MOH were more visible using SRR imaging (accuracy 0.73), in comparison to 2D MRI or paired imaging. Bladder wall interruption, predicting BA, was only easily detected by paired imaging (accuracy 0.72). Better detection of certain pathological markers predicting PAD was found using 2D MRI (placental bulge and myometrial thinning (accuracy 0.81)), and SRR (loss of retroplacental T2-hypointense line (accuracy 0.82)). DISCUSSION: The addition of SRR to 2D MRI potentially improved anatomical assessment of certain pathological MRI markers of abnormal placentation that predict maternal morbidity which may benefit surgical planning