FastMRI Prostate: A Publicly Available, Biparametric MRI Dataset to Advance Machine Learning for Prostate Cancer Imaging

The fastMRI brain and knee dataset has enabled significant advances in exploring reconstruction methods for improving speed and image quality for Magnetic Resonance Imaging (MRI) via novel, clinically relevant reconstruction approaches. In this study, we describe the April 2023 expansion of the fastMRI dataset to include biparametric prostate MRI data acquired on a clinical population. The dataset consists of raw k-space and reconstructed images for T2-weighted and diffusion-weighted sequences along with slice-level labels that indicate the presence and grade of prostate cancer. As has been the case with fastMRI, increasing accessibility to raw prostate MRI data will further facilitate research in MR image reconstruction and evaluation with the larger goal of improving the utility of MRI for prostate cancer detection and evaluation. The dataset is available at https://fastmri.med.nyu.edu.

Robust Automated Thalamic Nuclei Segmentation using a Multi-planar Cascaded Convolutional Neural Network

Purpose: To develop a fast, accurate, and robust convolutional neural network (CNN) based method for segmentation of thalamic nuclei. Methods: A cascaded multi-planar scheme with a modified residual U-Net architecture was used to segment thalamic nuclei on clinical datasets acquired using the white-matter-nulled Magnetization Prepared Rapid Gradient Echo (MPRAGE) sequence. A single network was optimized for healthy controls and disease types (multiple sclerosis, essential tremor) and magnetic field strengths (3T and 7T). Another network was developed to use conventional MPRAGE data. Clinical utility was assessed by comparing a cohort of MS patients to healthy subjects. Results: Segmentation of each thalamus into 12 nuclei was achieved in under 4 minutes. For 7T WMn-MPRAGE, the proposed method outperformed current state-of-the-art with statistically significant improvements in Dice ranging from 1.2% to 5.3% for MS and from 2.6% to 38.8% for ET patients. Comparable accuracy (Dice/VSI) was achieved between 7T and 3T data, attesting to the robustness of the method. For conventional MPRAGE, Dice of > 0.7 was achieved for larger nuclei and > 0.6 for the smaller nuclei. Atrophy of five thalamic nuclei and the whole thalamus was observed for MS patients compared to healthy control subjects, after controlling for intracranial volume and age (p<0.004). Conclusion: The proposed segmentation method is fast, accurate, and generalizes across disease types and field strengths and shows great potential for improving our understanding of thalamic nuclei involvement in neurological diseases and healthy aging. KEYWORDS Deep learning, convolutional neural network, transfer learning, thalamic nuclei segmentation