NODEO: A Neural Ordinary Differential Equation Based Optimization Framework for Deformable Image Registration

Deformable image registration (DIR), aiming to find spatial correspondence between images, is one of the most critical problems in the domain of medical image analysis. In this paper, we present a novel, generic, and accurate diffeomorphic image registration framework that utilizes neural ordinary differential equations (NODEs). We model each voxel as a moving particle and consider the set of all voxels in a 3D image as a high-dimensional dynamical system whose trajectory determines the targeted deformation field. Our method leverages deep neural networks for their expressive power in modeling dynamical systems, and simultaneously optimizes for a dynamical system between the image pairs and the corresponding transformation. Our formulation allows various constraints to be imposed along the transformation to maintain desired regularities. Our experiment results show that our method outperforms the benchmarks under various metrics. Additionally, we demonstrate the feasibility to expand our framework to register multiple image sets using a unified form of transformation,which could possibly serve a wider range of applications

Regional Deep Atrophy: a Self-Supervised Learning Method to Automatically Identify Regions Associated With Alzheimer's Disease Progression From Longitudinal MRI

Longitudinal assessment of brain atrophy, particularly in the hippocampus, is a well-studied biomarker for neurodegenerative diseases, such as Alzheimer's disease (AD). In clinical trials, estimation of brain progressive rates can be applied to track therapeutic efficacy of disease modifying treatments. However, most state-of-the-art measurements calculate changes directly by segmentation and/or deformable registration of MRI images, and may misreport head motion or MRI artifacts as neurodegeneration, impacting their accuracy. In our previous study, we developed a deep learning method DeepAtrophy that uses a convolutional neural network to quantify differences between longitudinal MRI scan pairs that are associated with time. DeepAtrophy has high accuracy in inferring temporal information from longitudinal MRI scans, such as temporal order or relative inter-scan interval. DeepAtrophy also provides an overall atrophy score that was shown to perform well as a potential biomarker of disease progression and treatment efficacy. However, DeepAtrophy is not interpretable, and it is unclear what changes in the MRI contribute to progression measurements. In this paper, we propose Regional Deep Atrophy (RDA), which combines the temporal inference approach from DeepAtrophy with a deformable registration neural network and attention mechanism that highlights regions in the MRI image where longitudinal changes are contributing to temporal inference. RDA has similar prediction accuracy as DeepAtrophy, but its additional interpretability makes it more acceptable for use in clinical settings, and may lead to more sensitive biomarkers for disease monitoring in clinical trials of early AD.Comment: Submitted to NeuroImage for revie

In-vivo heterogeneous functional and residual strains in human aortic valve leaflets

Residual and physiological functional strains in soft tissues are known to play an important role in modulating organ stress distributions. Yet, no known comprehensive information on residual strains exist, or non-invasive techniques to quantify in-vivo deformations for the aortic valve (AV) leaflets. Herein we present a completely non-invasive approach for determining heterogeneous strains – both functional and residual – in semilunar valves and apply it to normal human AV leaflets. Transesophageal 3D echocardiographic (3DE) images of the AV were acquired from open-heart transplant patients, with each AV leaflet excised after heart explant and then imaged in a flattened configuration ex-vivo. Using an established spline parameterization of both 3DE segmentations and digitized ex-vivo images (Aggarwal et al., 2014), surface strains were calculated for deformation between the ex-vivo and three in-vivo configurations: fully open, just-coapted, and fully-loaded. Results indicated that leaflet area increased by an average of 20% from the ex-vivo to in-vivo open states, with a highly heterogeneous strain field. The increase in area from open to just-coapted state was the highest at an average of 25%, while that from just-coapted to fully-loaded remained almost unaltered. Going from the ex-vivo to in-vivo mid-systole configurations, the leaflet area near the basal attachment shrank slightly, whereas the free edge expanded by ~10%. This was accompanied by a 10° −20° shear along the circumferential-radial direction. Moreover, the principal stretches aligned approximately with the circumferential and radial directions for all cases, with the highest stretch being along the radial direction. Collectively, these results indicated that even though the AV did not support any measurable pressure gradient in the just-coapted state, the leaflets were significantly pre-strained with respect to the excised state. Furthermore, the collagen fibers of the leaflet were almost fully recruited in the just-coapted state, making the leaflet very stiff with marginal deformation under full pressure. Lastly, the deformation was always higher in the radial direction and lower along the circumferential one, the latter direction made stiffer by the preferential alignment of collagen fibers. These results provide significant insight into the distribution of residual strains and the in-vivo strains encountered during valve opening and closing in AV leaflets, and will form an important component of the tool that can evaluate valve׳s functional properties in a non-invasive manner

Streamlined structure determination by cryo-electron tomography and subtomogram averaging using TomoBEAR

Structures of macromolecules in their native state provide unique unambiguous insights into their functions. Cryo-electron tomography combined with subtomogram averaging demonstrated the power to solve such structures in situ at resolutions in the range of 3 Angstrom for some macromolecules. In order to be applicable to the structural determination of the majority of macromolecules observable in cells in limited amounts, processing of tomographic data has to be performed in a high-throughput manner. Here we present TomoBEAR—a modular configurable workflow engine for streamlined processing of cryo-electron tomographic data for subtomogram averaging. TomoBEAR combines commonly used cryo-EM packages with reasonable presets to provide a transparent (“white box”) approach for data management and processing. We demonstrate applications of TomoBEAR to two data sets of purified macromolecular targets, to an ion channel RyR1 in a membrane, and the tomograms of plasma FIB-milled lamellae and demonstrate the ability to produce high-resolution structures. TomoBEAR speeds up data processing, minimizes human interventions, and will help accelerate the adoption of in situ structural biology by cryo-ET. The source code and the documentation are freely available

Quantitative three-dimensional echocardiographic analysis of the bicuspid aortic valve and aortic root:A single modality approach

Background Patients with bicuspid aortic valves (BAV) are heterogeneous with regard to patterns of root remodeling and valvular dysfunction. Two-dimensional echocardiography is the standard surveillance modality for patients with aortic valve dysfunction. However, ancillary computed tomography or magnetic resonance imaging is often necessary to characterize associated patterns of aortic root pathology. Conversely, the pairing of three-dimensional (3D) echocardiography with novel quantitative modeling techniques allows for a single modality description of the entire root complex. We sought to determine 3D aortic valve and root geometry with this quantitative approach. Methods Transesophageal real-time 3D echocardiography was performed in five patients with tricuspid aortic valves (TAV) and in five patients with BAV. No patient had evidence of valvular dysfunction or aortic root pathology. A customized image analysis protocol was used to assess 3D aortic annular, valvular, and root geometry. Results Annular, sinus and sinotubular junction diameters and areas were similar in both groups. Coaptation length and area were higher in the TAV group (7.25 +/- 0.98 mm and 298 +/- 118 mm(2), respectively) compared to the BAV group (5.67 +/- 1.33 mm and 177 +/- 43 mm(2); P = .07 and P = .01). Cusp surface area to annular area, coaptation height, and the sub- and supravalvular tenting indices did not differ significantly between groups. Conclusions Single modality 3D echocardiography-based modeling allows for a quantitative description of the aortic valve and root geometry. This technique together with novel indices will improve our understanding of normal and pathologic geometry in the BAV population and may help to identify geometric predictors of adverse remodeling and guide tailored surgical therapy

Multimodal image analysis and subvalvular dynamics in ischemic mitral regurgitation

Background: The exact geometric pathogenesis of leaflet tethering in ischemic mitral regurgitation (IMR) and the relative contribution of each component of the mitral valve complex (MVC) remain largely unknown. In this study, we sought to further elucidate mitral valve (MV) leaflet remodeling and papillary muscle dynamics in an ovine model of IMR with magnetic resonance imaging (MRI) and 3-dimensional echocardiography (3DE). Methods: Multimodal imaging combining 3DE and MRI was used to analyze the MVC at baseline, 30 minutes post–myocardial infarction (MI), and 12 weeks post-MI in ovine IMR models. Advanced 3D imaging software was used to trace the MVC from each modality, and the tracings were verified against resected specimens. Results: 3DE MV remodeling was regionally heterogenous and observed primarily in the anterior leaflet, with significant increases in surface area, especially in A2 and A3. The posterior leaflet was significantly shortened in P2 and P3. Mean posteromedial papillary muscle (PMPM) volume was decreased from 1.9 ± 0.2 cm3 at baseline to 0.9 ± 0.3 cm3 at 12 weeks post-MI (P <.05). At 12 weeks post-MI, the PMPM was predominately displaced horizontally and outward along the intercommissural axis with minor apical displacement. The subvalvular contribution to tethering is a combination of unilateral movement, outward displacement, and degeneration of the PMPM. These findings have led to a proposed new framework for characterizing PMPM dynamics in IMR. Conclusions: This study provides new insights into the complex interrelated and regionally heterogenous valvular and subvalvular mechanisms involved in the geometric pathogenesis of IMR tethering