DEEP AUTOMATIC ALIGNMENT OF MPOX DERMATOLOGICAL HAND PHOTOGRAPHY.

Mpox is a viral illness with heavy cutaneous involvement. Automatic tracking of mpox lesion progression is critical in determining the resolution of evolving lesions. This work introduces a novel application of deep learning for lesion monitoring through alignment of dermatological hand photographs.

TRANSFORMER-BASED T1-TRACTOGRAPHY.

Diffusion MRI (dMRI) streamline tractography has been the gold standard for non-invasive estimation of white matter (WM) pathways in the human brain. Recent advancements in deep learning have enabled the generation of streamlines from T1-weighted (T1w) MRI, a more common imaging method. The accuracy of current T1w tracking methods is limited by their recurrent architecture.

Brain age identification from diffusion MRI synergistically predicts neurodegenerative disease.

Estimated brain age from magnetic resonance image (MRI) and its deviation from chronological age can provide early insights into potential neurodegenerative diseases, supporting early detection and implementation of prevention strategies to slow disease progression and onset. Diffusion MRI (dMRI), a widely used modality for brain age estimation, presents an opportunity to build an earlier biomarker for neurodegenerative disease prediction because it captures subtle microstructural changes that precede more perceptible macrostructural changes. However, the coexistence of macro- and micro-structural information in dMRI raises the question of whether current dMRI-based brain age estimation models are leveraging the intended microstructural information or if they inadvertently rely on the macrostructural information.

Tractography from T1-weighted MRI: Empirically exploring the clinical viability of streamline propagation without diffusion MRI.

Over the last few decades, diffusion MRI (dMRI) streamline tractography has emerged as the dominant method forestimation of white matter (WM) pathways in the brain. One key limitation to this technique is that modern tractography implementations require high angular resolution diffusion imaging (HARDI). However, HARDI can be difficult to collect clinically, limiting the reach of tractography analyses to research cohorts and thus limiting many WM investigations to certain populations and pathologies.

Development of a Machine Learning Model for Determining Alignment in Knees Following Total Knee Arthroplasty.

Background: Malalignment remains a major reason for implant failure following total knee arthroplasty (TKA). Manual analysis of images at a large scale is untenable, and machine learning (ML) models may be a useful tool for determining alignment following TKA and can help identify patients who are at risk for failure. We aimed to develop an ML model that can accurately determine TKA alignment from full-length hip-to-ankle films.

Microstructural Characterization of Short Association Fibers Related to Long-Range White Matter Tracts in Normative Development.

Short association fibers (SAFs) in the superficial white matter play a key role in mediating local cortical connections but have not been well-studied as innovations in whole-brain diffusion tractography have only recently been developed to study superficial white matter. Characterizing SAFs and their relationship to long-range white matter tracts is crucial to advance our understanding of neurodevelopment during the period from childhood to young adulthood. This study aims to (1) map SAFs in relation to long-range white matter tracts, (2) characterize typical neurodevelopmental changes across these white matter pathways, and (3) investigate the relationship between microstructural changes in SAFs and long-range white matter tracts.

Learning disentangled representations to harmonize connectome network measures.

Purpose: Connectome network metrics are commonly regarded as fundamental properties of the brain, and their alterations have been implicated in the development of Alzheimer's disease, multiple sclerosis, and traumatic brain injury. However, these metrics are actually estimated properties through a multistage propagation from local voxel diffusion estimations, regional tractography, and region of interest mapping. These estimation processes are significantly influenced by choices specific to imaging protocols and software, producing site-wise effects.

Head Motion in Diffusion Magnetic Resonance Imaging: Quantification, Mitigation, and Structural Associations in Large, Cross-Sectional Datasets Across the Lifespan.

Head motion during diffusion magnetic resonance imaging (MRI) scans can cause numerous artifacts and biases subsequent quantification. However, a thorough characterization of motion across multiple scans, cohorts, and consortiums has not been performed. To address this, we designed a study with three aims.

Development of a Deep Learning Model for Automating Implant Position in Total Hip Arthroplasty.

Background: Novel methods for annotating antero-posterior pelvis radiographs and fluoroscopic images with deep-learning models have recently been developed. However, their clinical use has been limited. Therefore, the purpose of this study was to develop a deep learning model that could annotate clinically relevant pelvic landmarks on both radiographic and fluoroscopic images and automate total hip arthroplasty (THA)-relevant measurements.

Deep learning-based free-water correction for single-shell diffusion MRI.

Free-water elimination (FWE) modeling in diffusion magnetic resonance imaging (dMRI) is crucial for accurate estimation of diffusion properties by mitigating the partial volume effects caused by free water, particularly at the interface between white matter and cerebrospinal fluid. The presence of free water partial volume effects leads to biases in estimating diffusion properties. Additionally, the existing mathematical FWE model is a two-compartment model, which can be well posed for multi-shell data.

Novel dilation-erosion labeling technique allows for rapid, accurate and adjustable alignment measurements in primary TKA.

Background: Optimal implant position and alignment remains a controversial, yet critical topic in primary total knee arthroplasty (TKA). Future study of ideal implant position will require the ability to efficiently measure component positions at scale. Previous algorithms have limited accuracy, do not allow for human oversight and correction in deployment, and require extensive training time and dataset.

MICCAI-CDMRI 2023 QuantConn Challenge Findings on Achieving Robust Quantitative Connectivity through Harmonized Preprocessing of Diffusion MRI.

White matter alterations are increasingly implicated in neurological diseases and their progression. International-scale studies use diffusion-weighted magnetic resonance imaging (DW-MRI) to qualitatively identify changes in white matter microstructure and connectivity. Yet, quantitative analysis of DW-MRI data is hindered by inconsistencies stemming from varying acquisition protocols.

FastAd: A versatile toolkit for rapid generation of single adenoviruses or diverse adenoviral vector libraries.

Adenoviruses (Ads) are potent gene delivery vectors for and applications. However, current methods for their construction are time-consuming and inefficient, limiting their rapid production and utility in generating complex genetic libraries. Here, we introduce FastAd, a rapid and easy-to-use technology for inserting recombinant "donor" DNA directly into infectious "receiver" Ads in mammalian cells by the concerted action of two efficient recombinases: Cre and Bxb1.

Evaluation of Mean Shift, ComBat, and CycleGAN for Harmonizing Brain Connectivity Matrices Across Sites.

Connectivity matrices derived from diffusion MRI (dMRI) provide an interpretable and generalizable way of understanding the human brain connectome. However, dMRI suffers from inter-site and between-scanner variation, which impedes analysis across datasets to improve robustness and reproducibility of results. To evaluate different harmonization approaches on connectivity matrices, we compared graph measures derived from these matrices before and after applying three harmonization techniques: mean shift, ComBat, and CycleGAN.

Predicting Age from White Matter Diffusivity with Residual Learning.

Imaging findings inconsistent with those expected at specific chronological age ranges may serve as early indicators of neurological disorders and increased mortality risk. Estimation of chronological age, and deviations from expected results, from structural magnetic resonance imaging (MRI) data has become an important proxy task for developing biomarkers that are sensitive to such deviations. Complementary to structural analysis, diffusion tensor imaging (DTI) has proven effective in identifying age-related microstructural changes within the brain white matter, thereby presenting itself as a promising additional modality for brain age prediction.

Zero-shot prompt-based video encoder for surgical gesture recognition.

Purpose: In order to produce a surgical gesture recognition system that can support a wide variety of procedures, either a very large annotated dataset must be acquired, or fitted models must generalize to new labels (so-called zero-shot capability). In this paper we investigate the feasibility of latter option.

Methods: Leveraging the bridge-prompt framework, we prompt-tune a pre-trained vision-text model (CLIP) for gesture recognition in surgical videos.

Learning-based Free-Water Correction using Single-shell Diffusion MRI.

Diffusion magnetic resonance imaging (dMRI) offers the ability to assess subvoxel brain microstructure through the extraction of biomarkers like fractional anisotropy, as well as to unveil brain connectivity by reconstructing white matter fiber trajectories. However, accurate analysis becomes challenging at the interface between cerebrospinal fluid and white matter, where the MRI signal originates from both the cerebrospinal fluid and the white matter partial volume. The presence of free water partial volume effects introduces a substantial bias in estimating diffusion properties, thereby limiting the clinical utility of DWI.

Learning site-invariant features of connectomes to harmonize complex network measures.

Multi-site diffusion MRI data is often acquired on different scanners and with distinct protocols. Differences in hardware and acquisition result in data that contains site dependent information, which confounds connectome analyses aiming to combine such multi-site data. We propose a data-driven solution that isolates site-invariant information whilst maintaining relevant features of the connectome.

Characterizing Low-cost Registration for Photographic Images to Computed Tomography.

Mapping information from photographic images to volumetric medical imaging scans is essential for linking spaces with physical environments, such as in image-guided surgery. Current methods of accurate photographic image to computed tomography (CT) image mapping can be computationally intensive and/or require specialized hardware. For general purpose 3-D mapping of bulk specimens in histological processing, a cost-effective solution is necessary.

Tractography with T1-weighted MRI and associated anatomical constraints on clinical quality diffusion MRI.

Diffusion MRI (dMRI) streamline tractography, the gold-standard for in vivo estimation of white matter (WM) pathways in the brain, has long been considered as a product of WM microstructure. However, recent advances in tractography demonstrated that convolutional recurrent neural networks (CoRNN) trained with a teacher-student framework have the ability to learn to propagate streamlines directly from T1 and anatomical context. Training for this network has previously relied on high resolution dMRI.

Characterizing patterns of diffusion tensor imaging variance in aging brains.

Purpose: As large analyses merge data across sites, a deeper understanding of variance in statistical assessment across the sources of data becomes critical for valid analyses. Diffusion tensor imaging (DTI) exhibits spatially varying and correlated noise, so care must be taken with distributional assumptions. Here, we characterize the role of physiology, subject compliance, and the interaction of the subject with the scanner in the understanding of DTI variability, as modeled in the spatial variance of derived metrics in homogeneous regions.

SE(3)-Equivariant and Noise-Invariant 3D Rigid Motion Tracking in Brain MRI.

Rigid motion tracking is paramount in many medical imaging applications where movements need to be detected, corrected, or accounted for. Modern strategies rely on convolutional neural networks (CNN) and pose this problem as rigid registration. Yet, CNNs do not exploit natural symmetries in this task, as they are equivariant to translations (their outputs shift with their inputs) but not to rotations.

Correction to: White matter tract microstructure, macrostructure, and associated cortical gray matter morphology across the lifespan.

[This corrects the article DOI: 10.1162/imag_a_00050.].