Mahesh Keerthivasan

Radhika Tibrewala, Tarun Dutt, Angela Tong et al.

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.

Lavanya Umapathy, Mahesh Bharath Keerthivasan, Natalie M. Zahr et al.

Thalamic nuclei have been implicated in several neurological diseases. WMn-MPRAGE images have been shown to provide better intra-thalamic nuclear contrast compared to conventional MPRAGE images but the additional acquisition results in increased examination times. In this work, we investigated 3D Convolutional Neural Network (CNN) based techniques for thalamic nuclei parcellation from conventional MPRAGE images. Two 3D CNNs were developed and compared for thalamic nuclei parcellation using MPRAGE images: a) a native contrast segmentation (NCS) and b) a synthesized contrast segmentation (SCS) using WMn-MPRAGE images synthesized from MPRAGE images. We trained the two segmentation frameworks using MPRAGE images (n=35) and thalamic nuclei labels generated on WMn-MPRAGE images using a multi-atlas based parcellation technique. The segmentation accuracy and clinical utility were evaluated on a cohort comprising of healthy subjects and patients with alcohol use disorder (AUD) (n=45). The SCS network yielded higher Dice scores in the Medial geniculate nucleus (P=.003) and Centromedian nucleus (P=.01) with lower volume differences for Ventral anterior (P=.001) and Ventral posterior lateral (P=.01) nuclei when compared to the NCS network. A Bland-Altman analysis revealed tighter limits of agreement with lower coefficient of variation between true volumes and those predicted by the SCS network. The SCS network demonstrated a significant atrophy in Ventral lateral posterior nucleus in AUD patients compared to healthy age-matched controls (P=0.01), agreeing with previous studies on thalamic atrophy in alcoholism, whereas the NCS network showed spurious atrophy of the Ventral posterior lateral nucleus. CNN-based contrast synthesis prior to segmentation can provide fast and accurate thalamic nuclei segmentation from conventional MPRAGE images.

Lavanya Umapathy, Mahesh Bharath Keerthivasan, Jean-Phillipe Galons et al.

Motion-robust 2D Radial Turbo Spin Echo (RADTSE) pulse sequence can provide a high-resolution composite image, T2-weighted images at multiple echo times (TEs), and a quantitative T2 map, all from a single k-space acquisition. In this work, we use a deep-learning convolutional neural network (CNN) for the segmentation of liver in abdominal RADTSE images. A modified UNET architecture with generalized dice loss objective function was implemented. Three 2D CNNs were trained, one for each image type obtained from the RADTSE sequence. On evaluating the performance of the CNNs on the validation set, we found that CNNs trained on TE images or the T2 maps had higher average dice scores than the composite images. This, in turn, implies that the information regarding T2 variation in tissues aids in improving the segmentation performance.

Mohammad S Majdi, Mahesh B Keerthivasan, Brian K Rutt et al.

A cascaded multi-planar scheme with a modified residual U-Net architecture was used to segment thalamic nuclei on conventional and white-matter-nulled (WMn) magnetization prepared rapid gradient echo (MPRAGE) data. A single network was optimized to work with images from healthy controls and patients with multiple sclerosis (MS) and essential tremor (ET), acquired at both 3T and 7T field strengths. Dice similarity coefficient and volume similarity index (VSI) were used to evaluate performance. Clinical utility was demonstrated by applying this method to study the effect of MS on thalamic nuclei atrophy. Segmentation of each thalamus into twelve nuclei was achieved in under a minute. For 7T WMn-MPRAGE, the proposed method outperforms current state-of-the-art on patients with ET with statistically significant improvements in Dice for five nuclei (increase in the range of 0.05-0.18) and VSI for four nuclei (increase in the range of 0.05-0.19), while performing comparably for healthy and MS subjects. Dice and VSI achieved using 7T WMn-MPRAGE data are comparable to those using 3T WMn-MPRAGE data. For conventional MPRAGE, the proposed method shows a statistically significant Dice improvement in the range of 0.14-0.63 over FreeSurfer for all nuclei and disease types. Effect of noise on network performance shows robustness to images with SNR as low as half the baseline SNR. Atrophy of four thalamic nuclei and whole thalamus was observed for MS patients compared to healthy control subjects, after controlling for the effect of parallel imaging, intracranial volume, gender, and age (p<0.004). The proposed segmentation method is fast, accurate, performs well across disease types and field strengths, and shows great potential for improving our understanding of thalamic nuclei involvement in neurological diseases.