MRI Field-transfer Reconstruction with Limited Data: Regularization by Neural Style Transfer

Recent works have demonstrated success in MRI reconstruction using deep learning-based models. However, most reported approaches require training on a task-specific, large-scale dataset. Regularization by denoising (RED) is a general pipeline which embeds a denoiser as a prior for image reconstruction. The potential of RED has been demonstrated for multiple image-related tasks such as denoising, deblurring and super-resolution. In this work, we propose a regularization by neural style transfer (RNST) method to further leverage the priors from the neural transfer and denoising engine. This enables RNST to reconstruct a high-quality image from a noisy low-quality image with different image styles and limited data. We validate RNST with clinical MRI scans from 1.5T and 3T and show that RNST can significantly boost image quality. Our results highlight the capability of the RNST framework for MRI reconstruction and the potential for reconstruction tasks with limited data.Comment: 30 pages, 8 figures, 2 tables, 1 algorithm char

Palatal perforation caused by Alternaria alternata infection in an immunocompetent adolescent

ABSTRACT: Opportunistic oral mucosal fungal infection caused by Alternaria alternata is extremely rare. Herein, we present a rare palatal perforation as a result of oral infection caused by A. alternata in an immunocompetent adolescent. An 18-year-old boy, who had previously been healthy, was admitted to our institution with persistent pain in the palate for the past 12 months. Upon impression of palatal bone resorption based on computed tomography imaging and chronic granulomatous inflammation based on biopsy (hematoxylin-eosin staining), the patient was examined for commonly relevant causes such as potential tumor and Mycobacterium tuberculosis infection. All test results were inconclusive. After a thorough diagnostic investigation, an unusual fungal infection, A. alternata infection, was confirmed by next-generation sequencing and biopsy (periodic acid-Schiff staining and immunofluorescence staining). The patient underwent surgical debridement and was subjected to voriconazole treatment postoperatively for over a period of 5 months. Thus, these findings highlight the importance of considering A. alternata as a potential pathogenic factor in an etiological palatal perforation

Attention hybrid variational net for accelerated MRI reconstruction

The application of compressed sensing (CS)-enabled data reconstruction for accelerating magnetic resonance imaging (MRI) remains a challenging problem. This is due to the fact that the information lost in k-space from the acceleration mask makes it difficult to reconstruct an image similar to the quality of a fully sampled image. Multiple deep learning-based structures have been proposed for MRI reconstruction using CS, in both the k-space and image domains, and using unrolled optimization methods. However, the drawback of these structures is that they are not fully utilizing the information from both domains (k-space and image). Herein, we propose a deep learning-based attention hybrid variational network that performs learning in both the k-space and image domains. We evaluate our method on a well-known open-source MRI dataset (652 brain cases and 1172 knee cases) and a clinical MRI dataset of 243 patients diagnosed with strokes from our institution to demonstrate the performance of our network. Our model achieves an overall peak signal-to-noise ratio/structural similarity of 40.92 ± 0.29/0.9577 ± 0.0025 (fourfold) and 37.03 ± 0.25/0.9365 ± 0.0029 (eightfold) for the brain dataset, 31.09 ± 0.25/0.6901 ± 0.0094 (fourfold) and 29.49 ± 0.22/0.6197 ± 0.0106 (eightfold) for the knee dataset, and 36.32 ± 0.16/0.9199 ± 0.0029 (20-fold) and 33.70 ± 0.15/0.8882 ± 0.0035 (30-fold) for the stroke dataset. In addition to quantitative evaluation, we undertook a blinded comparison of image quality across networks performed by a subspecialty trained radiologist. Overall, we demonstrate that our network achieves a superior performance among others under multiple reconstruction tasks