Supervised tractogram filtering using Geometric Deep Learning

Fast and ultra-high shot diffusion MRI image reconstruction with self-adaptive Hankel subspace

Multi-shot interleaved echo planar imaging is widely employed for acquiring high-resolution and low-distortion diffusion weighted images (DWI). These DWI images, however, are easily affected by motion artifacts induced by inter-shot phase variations which could be removed by enforcing the low-rankness of a huge 2D block Hankel matrix of the k-space. Successful applications have been evidenced on 4∼8 shots DWI but failure was observed on ultra-high shots, e.g. 10∼12 shots, limiting the extension to higher-resolution DWI. Moreover, the 2D Hankel matrix reconstruction is very time-consuming. Here, we propose to accelerate the reconstruction through decomposing this huge 2D matrix into small 1D lOw-raNk HAnkel (DONA) matrices from every k-space readout line. This extension encounters another problem of variant low-rankness across the k-space. To address this issue, we propose to separate signal subspaces of 1D Hankel matrices into the strong and uncertain ones. The former is pre-estimated from an initial image to reduce the degree of freedom in reconstruction. The latter protects image details in reconstruction by avoiding the overshadowing on small singular values. This method is called DONA with self-adapTive subspacE estimation (DONATE). In vivo results show that DONATE can not only accomplish 4-shot reconstruction in 10 s but also the reconstruction of 12 shots with 10 times faster computation. Besides, DONATE shows superiority on low-distortion spine DWI reconstruction and subjective image quality evaluation in terms of blind scoring by 4 radiologists.

Classification, registration and segmentation of ear canal impressions using convolutional neural networks

Today, fitting bespoke hearing aids involves injecting silicone into patients' ears to produce ear canal molds. These are subsequently 3D scanned to create digital ear canal impressions. However, before digital impressions can be used they require a substantial amount of effort in manual 3D editing. In this article, we present computational methods to pre-process ear canal impressions. The aim is to create automation tools to assist the hearing aid design, manufacturing and fitting processes as well as normalizing anatomical data to assist the study of the outer ear canal's morphology. The methods include classifying the handedness of the impression into left and right ear types, orienting the geometries onto the same coordinate system sense, and removing extraneous artifacts introduced by the silicone mold. We investigate the use of convolutional neural networks for performing these semantic tasks and evaluate their accuracy using a dataset of 3000 ear canal impressions. The neural networks proved highly effective at performing these tasks with 95.8% adjusted accuracy in classification, 92.3% within 20° angular error in registration and 93.4% intersection over union in segmentation.