Medical computer vision presents unique challenges compared to natural images, largely due to the high dimensionality of the data (e.g., 3D and 4D imaging) and limited training samples. Certain medical imaging modalities, such as Diffusion-weighted Magnetic Resonance Imaging (dMRI) — a critical tool for examining brain microstructure and neural connectivity — poses even greater complexity with its data dimensionality, encompassing three spatial dimensions and at least three degrees of freedom for the diffusion weighting. Therefore, current deep learning methods for dMRI are often limited by a “case-by-case” design. In this work, we introduce a Unified Polyhedral Representation (UPER) to model multi-dimensional dMRI data across over 10 unique shells, sourced from 2800 scans across 17 datasets. The key innovation involves revisiting the dMRI data representation by transforming traditional model-based or signal-based methods into a polyhedral representation. We utilized a masked auto encoder with both self-supervised and supervised learning within a single unified, end-to-end deep learning framework. This approach effectively encodes complex spatial, radial, and angular information within a simple polyhedron. Furthermore, the transformer-based UPER framework models heterogeneous shell configurations through a unified model. To our knowledge, UPER is the first deep learning model capable of (1) modeling varying radial and angular spaces, and (2) accommodating diverse shell configurations. Note that signal-processing approaches such as SHORE and SHARD can also handle multi-shell configurations through analytical formulations; UPER’s novelty lies in its unified, end-to-end deep learning framework. Extensive experiments demonstrate that our pretrained unified model achieves superior performance in various downstream tasks as compared with traditional deep learning framework, offering a holistic model for analyzing diverse dMRI datasets. The code for this paper is publicly available at https://github.com/hrlblab/UPER
Introduction Risk of recurrent opioid use during buprenorphine-naloxone (bup-nx) treatment is dynamic and remains elevated after initiation, with vulnerability shaped in part by treatment intensity and gaps between visits, yet routine outpatient care relies on episodic encounters and retrospective data. This mismatch can delay recognition of emerging instability and limit timely treatment adjustments. This paper reports the development and specification of an intervention strategy to address this mismatch. Methods We used a structured, multi-phase design process to specify and configure a measurement-based care (MBC) strategy for bup-nx treatment (Bup-MBC) in outpatient addiction clinics through three phases: (1) a systematic review of patient-reported outcome measures (PROMs) for substance use treatment; (2) a qualitative needs assessment using the Theoretical Domains Framework and COM-B (Capability, Opportunity, Motivation–Behavior) model to identify gaps in risk monitoring, agency, and trust; and (3) iterative co-design with multidisciplinary clinicians to refine workflow fit and trust-preserving use of data. Patients informed item and feedback content during the needs assessment but did not participate in the co-design cycles. Results Bup-MBC integrates (1) brief between-visit PROMs (e.g., withdrawal, craving, adherence); (2) immediate non-punitive patient feedback; (3) clinician-facing summaries and non-directive prompts in the electronic health record (EHR); and (4) an opt-in between-visit outreach pathway with predefined safety triggers, all configured within existing EHR and patient portal infrastructure. It targets patient and clinician capability to recognize changes in risk, opportunity for action through structured monitoring and visit preparation, and trust and agency through non-punitive communication, without adding substantial burden. The full measure set, severity bands, and question-to-action map are provided as supplementary material. Key trade-offs included prioritizing single-item measures for feasibility, balancing opt-in outreach with safety overrides, and assuming routine clinician use of summaries. Conclusion This development study specifies an EHR-integrated MBC strategy for outpatient bup-nx treatment. As single-center design work with co-design limited to clinicians and delivery contingent on portal or text-message access, its outputs are hypotheses about mechanism and fit rather than demonstrated effects. Feasibility studies are needed to evaluate uptake, acceptability, workflow fit, and effects on treatment.
Inferior vena cava obstruction following liver transplantation is rare but life-threatening. Although endovascular management has been described in delayed presentations, evidence supporting intervention in the immediate postoperative period remains limited. We report the case of a 43-year-old man with acute caval thrombosis and venous outflow obstruction following bicaval liver transplantation. Impending graft failure and hemodynamic instability prompted urgent endovascular intervention. Percutaneous mechanical thrombectomy was performed. Two torsion-related stenotic segments persisted, and a self-expanding bare-metal venous stent was deployed, restoring caval flow. Early thrombectomy and stenting may offer effective salvage in select high-risk patients with acute caval obstruction.
This study evaluated whether diffusion MRI measures from diffusion tensor imaging (DTI) and the spherical mean technique (SMT) can detect region-specific, fine-grained tissue damage and white matter (WM) tract disruptions after spinal cord injury. Diffusion MRI data were acquired from the cervical spinal cord of monkeys before and after a controlled unilateral dorsal column lesion at C5 using a 9.4 T scanner and assessed similar to 16 weeks post-injury. Post-mortem silver staining served as the reference standard for regional fiber damage. Both DTI-and SMT-derived parametric maps identified the location and severity of WM damage and showed good agreement with histological results. The MRI measures captured consistent severity patterns at the lesioned segment: damage was greatest in the dorsal WM, followed by ventral and then lateral regions. Along the dorsal column tract, damage was most severe at the lesion epicenter and decreased rostrally and caudally. Among diffusion metrics, fractional anisotropy (FA) demonstrated the most significant changes across regions at and around the lesion site, including changes in some regions of caudal segments where neither SMT-derived axonal volume fraction (V-ax) nor the histology-based fiber-density indices showed significant differences. In contrast, V-ax showed the strongest regional agreement with histology; its reduction reflected histology-detected regional fiber damage more closely than FA, yielding an increase in regional correlation by more than 24%. Overall, SMT provides V-ax as a promising biomarker that is more strongly associated with fiber density and may offer a more robust assessment of regional axonal loss across different injury severities.
