Spatio-temporal motion correction and iterative reconstruction of in-utero fetal fMRI

Resting-state functional Magnetic Resonance Imaging (fMRI) is a powerful imaging technique for studying functional development of the brain in utero. However, unpredictable and excessive movement of fetuses have limited its clinical applicability. Previous studies have focused primarily on the accurate estimation of the motion parameters employing a single step 3D interpolation at each individual time frame to recover a motion-free 4D fMRI image. Using only information from a 3D spatial neighborhood neglects the temporal structure of fMRI and useful information from neighboring timepoints. Here, we propose a novel technique based on four dimensional iterative reconstruction of the motion scattered fMRI slices. Quantitative evaluation of the proposed method on a cohort of real clinical fetal fMRI data indicates improvement of reconstruction quality compared to the conventional 3D interpolation approaches.Comment: Accepted by MICCAI 202

Post-mortem correlates of Virchow-Robin spaces detected on in vivo MRI

The purpose of our study is to quantify the extent to which Virchow-Robin spaces (VRS) detected on in vivo MRI are reproducible by post-mortem MRI.Double Echo Steady State 3T MRIs were acquired post-mortem in 49 double- and 32 single-hemispheric formalin-fixed brain sections from 12 patients, who underwent conventional diagnostic 1.5 or 3T MRI in median 22 days prior to death (25% to 75%: 12 to 134 days). The overlap of in vivo and post-mortem VRS segmentations was determined accounting for potential confounding factors.The reproducibility of VRS found on in vivo MRI by post-mortem MRI, in the supratentorial white matter was in median 80% (25% to 75%: 60 to 100). A lower reproducibility was present in the basal ganglia, with a median of 47% (25% to 75%: 30 to 50).VRS segmentations were histologically confirmed in one double hemispheric section.Overall, the majority of VRS found on in vivo MRI was stable throughout death and formalin fixation, emphasizing the translational potential of post-mortem VRS studies

Fetal eye movements on magnetic resonance imaging.

OBJECTIVES: Eye movements are the physical expression of upper fetal brainstem function. Our aim was to identify and differentiate specific types of fetal eye movement patterns using dynamic MRI sequences. Their occurrence as well as the presence of conjugated eyeball motion and consistently parallel eyeball position was systematically analyzed. METHODS: Dynamic SSFP sequences were acquired in 72 singleton fetuses (17-40 GW, three age groups [17-23 GW, 24-32 GW, 33-40 GW]). Fetal eye movements were evaluated according to a modified classification originally published by Birnholz (1981): Type 0: no eye movements; Type I: single transient deviations; Type Ia: fast deviation, slower reposition; Type Ib: fast deviation, fast reposition; Type II: single prolonged eye movements; Type III: complex sequences; and Type IV: nystagmoid. RESULTS: In 95.8% of fetuses, the evaluation of eye movements was possible using MRI, with a mean acquisition time of 70 seconds. Due to head motion, 4.2% of the fetuses and 20.1% of all dynamic SSFP sequences were excluded. Eye movements were observed in 45 fetuses (65.2%). Significant differences between the age groups were found for Type I (p = 0.03), Type Ia (p = 0.031), and Type IV eye movements (p = 0.033). Consistently parallel bulbs were found in 27.3-45%. CONCLUSIONS: In human fetuses, different eye movement patterns can be identified and described by MRI in utero. In addition to the originally classified eye movement patterns, a novel subtype has been observed, which apparently characterizes an important step in fetal brainstem development. We evaluated, for the first time, eyeball position in fetuses. Ultimately, the assessment of fetal eye movements by MRI yields the potential to identify early signs of brainstem dysfunction, as encountered in brain malformations such as Chiari II or molar tooth malformations

MR-based morphometry of the posterior fossa in fetuses with neural tube defects of the spine.

OBJECTIVES: In cases of "spina bifida," a detailed prenatal imaging assessment of the exact morphology of neural tube defects (NTD) is often limited. Due to the diverse clinical prognosis and prenatal treatment options, imaging parameters that support the prenatal differentiation between open and closed neural tube defects (ONTDs and CNTDs) are required. This fetal MR study aims to evaluate the clivus-supraocciput angle (CSA) and the maximum transverse diameter of the posterior fossa (TDPF) as morphometric parameters to aid in the reliable diagnosis of either ONTDs or CNTDs. METHODS: The TDPF and the CSA of 238 fetuses (20-37 GW, mean: 28.36 GW) with a normal central nervous system, 44 with ONTDS, and 13 with CNTDs (18-37 GW, mean: 24.3 GW) were retrospectively measured using T2-weighted 1.5 Tesla MR -sequences. RESULTS: Normal fetuses showed a significant increase in the TDPF (r = .956; p<.001) and CSA (r = .714; p<.001) with gestational age. In ONTDs the CSA was significantly smaller (p<.001) than in normal controls and CNTDs, whereas in CNTDs the CSA was not significantly smaller than in controls (p = .160). In both ONTDs and in CNTDs the TDPF was significantly different from controls (p<.001). CONCLUSIONS: The skull base morphology in fetuses with ONTDs differs significantly from cases with CNTDs and normal controls. This is the first study to show that the CSA changes during gestation and that it is a reliable imaging biomarker to distinguish between ONTDs and CNTDs, independent of the morphology of the spinal defect

Motion corrected 3D reconstruction of the fetal thorax from prenatal MRI

In this paper we present a semi-automatic method for analysis of the fetal thorax in genuine three-dimensional volumes. After one initial click we localize the spine and accurately determine the volume of the fetal lung from high resolution volumetric images reconstructed from motion corrupted prenatal Magnetic Resonance Imaging (MRI). We compare the current state-of-the-art method of segmenting the lung in a slice-by-slice manner with the most recent multi-scan reconstruction methods. We use fast rotation invariant spherical harmonics image descriptors with Classification Forest ensemble learning methods to extract the spinal cord and show an efficient way to generate a segmentation prior for the fetal lung from this information for two different MRI field strengths. The spinal cord can be segmented with a DICE coefficient of 0.89 and the automatic lung segmentation has been evaluated with a DICE coefficient of 0.87. We evaluate our method on 29 fetuses with a gestational age (GA) between 20 and 38 weeks and show that our computed segmentations and the manual ground truth correlate well with the recorded values in literature

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

Distributed changes of the functional connectome in patients with glioblastoma

Glioblastoma might have widespread effects on the neural organization and cognitive function, and even focal lesions may be associated with distributed functional alterations. However, functional changes do not necessarily follow obvious anatomical patterns and the current understanding of this interrelation is limited. In this study, we used resting-state functional magnetic resonance imaging to evaluate changes in global functional connectivity patterns in 15 patients with glioblastoma. For six patients we followed longitudinal trajectories of their functional connectome and structural tumour evolution using bi-monthly follow-up scans throughout treatment and disease progression. In all patients, unilateral tumour lesions were associated with inter-hemispherically symmetric network alterations, and functional proximity of tumour location was stronger linked to distributed network deterioration than anatomical distance. In the longitudinal subcohort of six patients, we observed patterns of network alterations with initial transient deterioration followed by recovery at first follow-up, and local network deterioration to precede structural tumour recurrence by two months. In summary, the impact of focal glioblastoma lesions on the functional connectome is global and linked to functional proximity rather than anatomical distance to tumour regions. Our findings further suggest a relevance for functional network trajectories as a possible means supporting early detection of tumour recurrence

A Dempster-Shafer approach to trustworthy AI with application to fetal brain MRI segmentation

Deep learning models for medical image segmentation can fail unexpectedly and spectacularly for pathological cases and images acquired at different centers than training images, with labeling errors that violate expert knowledge. Such errors undermine the trustworthiness of deep learning models for medical image segmentation. Mechanisms for detecting and correcting such failures are essential for safely translating this technology into clinics and are likely to be a requirement of future regulations on artificial intelligence (AI). In this work, we propose a trustworthy AI theoretical framework and a practical system that can augment any backbone AI system using a fallback method and a fail-safe mechanism based on Dempster-Shafer theory. Our approach relies on an actionable definition of trustworthy AI. Our method automatically discards the voxel-level labeling predicted by the backbone AI that violate expert knowledge and relies on a fallback for those voxels. We demonstrate the effectiveness of the proposed trustworthy AI approach on the largest reported annotated dataset of fetal MRI consisting of 540 manually annotated fetal brain 3D T2w MRIs from 13 centers. Our trustworthy AI method improves the robustness of a state-of-the-art backbone AI for fetal brain MRIs acquired across various centers and for fetuses with various brain abnormalities