Richard Kijowski

TL;DR: An automated approach that allows generation of discrete-valued pseudo CT scans from a single high-spatial-resolution diagnostic-quality three-dimensional MR image and evaluated it in brain PET/MR imaging provided reduced PET reconstruction error relative to a CT-based standard within the brain compared with current MR imaging-based AC approaches.

TL;DR: A method to modulate refocusing FAs that produces sharp point spread functions (PSFs) from very long echo trains while exercising direct control over minimum, center‐k‐space, and maximum FAs in order to accommodate the presence of flow and motion, SNR requirements, and RF power limits is described.

TL;DR: A new fully automated musculoskeletal tissue segmentation method using deep convolutional neural network (CNN) and three‐dimensional (3D) simplex deformable modeling to improve the accuracy and efficiency of cartilage and bone segmentation within the knee joint.

TL;DR: Three-dimensional isotropic resolution fast spin-echo sequence FSE-Cube has similar diagnostic performance as a routine MR imaging protocol for detecting cartilage lesions, cruciate ligament tears, collateral ligament Tears, meniscal tears, and bone marrow edema lesions within the knee joint at 3.0 T.

TL;DR: This study demonstrated the feasibility of using a fully automated deep learning-based cartilage lesion detection system to evaluate the articular cartilage of the knee joint with high diagnostic performance and good intraobserver agreement for detecting cartilage degeneration and acute cartilage injury.