Spinet-QSM: model-based deep learning with schatten p-norm regularization for improved quantitative susceptibility mapping.

OBJECTIVE: Quantitative susceptibility mapping (QSM) provides an estimate of the magnetic susceptibility of tissue using magnetic resonance (MR) phase measurements. The tissue magnetic susceptibility (source) from the measured magnetic field distribution/local tissue field (effect) inherent in the MR phase images is estimated by numerically solving the inverse source-effect problem. This study aims to develop an effective model-based deep-learning framework to solve the inverse problem of QSM. MATERIALS AND METHODS: This work proposes a Schatten p -norm-driven model-based deep learning framework for QSM with a learnable norm parameter p to adapt to the data. In contrast to other model-based architectures that enforce the l 2 -norm or l 1 -norm for the denoiser, the proposed approach can enforce any p -norm ( 0 < p ≤ 2 ) on a trainable regulariser. RESULTS: The proposed method was compared with deep learning-based approaches, such as QSMnet, and model-based deep learning approaches, such as learned proximal convolutional neural network (LPCNN). Reconstructions performed using 77 imaging volumes with different acquisition protocols and clinical conditions, such as hemorrhage and multiple sclerosis, showed that the proposed approach outperformed existing state-of-the-art methods by a significant margin in terms of quantitative merits. CONCLUSION: The proposed SpiNet-QSM showed a consistent improvement of at least 5% in terms of the high-frequency error norm (HFEN) and normalized root mean squared error (NRMSE) over other QSM reconstruction methods with limited training data.

Transformer-Based Automated Segmentation of the Median Nerve in Ultrasound Videos of Wrist-to-Elbow Region.

Segmenting the median nerve is essential for identifying nerve entrapment syndromes, guiding surgical planning and interventions, and furthering understanding of nerve anatomy. This study aims to develop an automated tool that can assist clinicians in localizing and segmenting the median nerve from the wrist, mid-forearm, and elbow in ultrasound videos. This is the first fully automated single deep learning model for accurate segmentation of the median nerve from the wrist to the elbow in ultrasound videos, along with the computation of the cross-sectional area (CSA) of the nerve. The visual transformer architecture, which was originally proposed to detect and classify 41 classes in YouTube videos, was modified to predict the median nerve in every frame of ultrasound videos. This is achieved by modifying the bounding box sequence matching block of the visual transformer. The median nerve segmentation is a binary class prediction, and the entire bipartite matching sequence is eliminated, enabling a direct comparison of the prediction with expert annotation in a frame-by-frame fashion. Model training, validation, and testing were performed on a dataset comprising ultrasound videos collected from 100 subjects, which were partitioned into 80, ten, and ten subjects, respectively. The proposed model was compared with U-Net, U-Net++, Siam U-Net, Attention U-Net, LSTM U-Net, and Trans U-Net. The proposed transformer-based model effectively leveraged the temporal and spatial information present in ultrasound video frames and efficiently segmented the median nerve with an average dice similarity coefficient (DSC) of approximately 94% at the wrist and 84% in the entire forearm region.

Model resolution-based deconvolution for improved quantitative susceptibility mapping.

Quantitative susceptibility mapping (QSM) utilizes the relationship between the measured local field and the unknown susceptibility map to perform dipole deconvolution. The aim of this work is to introduce and systematically evaluate the model resolution-based deconvolution for improved estimation of the susceptibility map obtained using the thresholded k-space division (TKD). A two-step approach has been proposed, wherein the first step involves the TKD susceptibility map computation and the second step involves the correction of this susceptibility map using the model-resolution matrix. The TKD-estimated susceptibility map can be expressed as the weighted average of the true susceptibility map, where the weights are determined by the rows of the model-resolution matrix, and hence a deconvolution of the TKD susceptibility map using the model-resolution matrix yields a better approximation to the true susceptibility map. The model resolution-based deconvolution is realized using closed-form, iterative, and sparsity-regularized implementations. The proposed approach was compared with L2 regularization, TKD, rescaled TKD in superfast dipole inversion, the modulated closed-form method, and iterative dipole inversion, as well as sparsity-regularized dipole inversion. It was observed that the proposed approach showed a substantial reduction in the streaking artifacts across 94 test volumes considered in this study. The proposed approach also showed better error reduction and edge preservation compared with other approaches. The proposed model resolution-based deconvolution compensates for the truncation of zero coefficients in the dipole kernel at the magic angle and hence provides a closer approximation to the true susceptibility map compared with other direct methods.