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TBrecon: Towards Integration of DL Image Reconstruction and Post-Processing Tasks
We are currently working on collecting, annotating, and distributing 3D high-resolution knee MRI data, specifically related to knee osteoarthritis. This dataset includes multi-tissue segmentation and anomaly grading. In our project, we aim to leverage the power of multi-task learning to simultaneously address reconstruction and post-processing tasks within an integrated framework, which encompasses segmentation and object detection. To achieve this, we are developing a strategy based on adversarial robust training and confounding factors learning to ensure the reconstruction of small, rare features, even in under-sampled acquisitions.
Role: Supporting Researcher, the Main lead is a Grad student
Generating Synthetic T1ρ Maps from T2 Maps for Knee MRI with Deep Learning
A 2D U-Net was trained to generate synthetic T1rho maps from T2 maps for knee MRI to explore the feasibility of domain adaptation to enrich existing datasets or for faster image reconstruction. The network was developed using images from two prior research studies across three institutions. Network generalizability was evaluated on two new datasets acquired as part of the standard-of-care acquired in a clinical setting and from simultaneous bilateral acquisition in a research setting. This study found the network performed excellent reconstruction of T1rho maps preserving textures and local T1rho elevation patterns in cartilage with NMSE of 2.4% and Pearson’s correlation coefficient of 0.93. Decreased performance for external datasets may be attributed to slight variation in acquisition from different MR scanners and knee coils, suggesting deep learning networks would benefit from volume-wise consideration of scanner properties for performance agnostic to MR scanner equipment.
Role: Supporting Researcher, the Main lead is a Grad student
Towards a Generalizable Foundation Model for Multi-Tissue Musculoskeletal MRI Segmentation
In medical image processing, robust segmentation models are crucial as a typical first step in image analysis, driving imaging biomarker discovery, and prognostic tool development. While ad-hoc solutions for specific problems were proposed revolutionizing the field, lack of out of distribution generalizability make the translation of such techniques to clinical practice infeasible. Typically, segmentation models trained on a single anatomy and imaging modality perform poorly for data of additional anatomies, MRI sequences, and vendors, or derived from a suboptimal or accelerated imaging acquisition regime. In the last year, the foundation model, Segment Anything Model (SAM), was proposed and showed promise in providing a general-purpose solution in natural image segmentation; however, the application of such models to medical images, particularly musculoskeletal MRI, poses unique challenges.Recent studies have investigated a range of imaging modalities such as CT and Ultrasound, as well as anatomical regions [2] but overlook the specific challenges inherent to Musculoskeletal (MSK) Magnetic Resonance Imaging and analysis. In contrast, our study stands out by offering a comprehensive evaluation of a diverse collection of segmented musculoskeletal MRI data. This dataset spans various anatomy (knee, spine, hip, thigh), MRI sequences, and quantitative maps, and encompasses data acquired with various fast-acquisition parameters. We examine the Segment Anything Model in both zero-shot and fine-tuning scenarios, aiming to evaluate its potential to increase the efficiency of research-based and clinical segmentation workflows. Our research provides valuable insights into the capabilities of a foundation model when applied to the complexity of musculoskeletal MRI.
Role: Supporting Researcher, the Main lead are two Grad students
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