Personalized local SAR prediction for parallel transmit neuroimaging at 7T from a single T1-weighted dataset

Purpose: Parallel RF transmission (PTx) is one of the key technologies enabling high quality imaging at ultra-high fields (≥7T). Compliance with regulatory limits on the local specific absorption rate (SAR) typically involves over-conservative safety margins to account for intersubject variability, which negatively affect the utilization of...

Esophageal Tumor Segmentation in CT Images using a Dilated Dense Attention Unet (DDAUnet)

Manual or automatic delineation of the esophageal tumor in CT images is known to be very challenging. This is due to the low contrast between the tumor and adjacent tissues, the anatomical variation of the esophagus, as well as the occasional presence of foreign bodies (e.g. feeding tubes). Physicians therefore usually exploit additional...

Hierarchical Prediction of Registration Misalignment using a Convolutional LSTM: Application to Chest CT Scans

In this paper we propose a supervised method to predict registration misalignment using convolutional neural networks (CNNs). This task is casted to a classification problem with multiple classes of misalignment: 'correct' 0-3 mm, 'poor' 3-6 mm and 'wrong' over 6 mm. Rather than a direct prediction, we propose a hierarchical approach, where...

An adaptive intelligence algorithm for undersampled knee MRI reconstruction

Adaptive intelligence aims at empowering machine learning techniques with the additional use of domain knowledge. In this work, we present the application of adaptive intelligence to accelerate MR acquisition. Starting from undersampled k-space data, an iterative learning-based reconstruction scheme inspired by compressed sensing theory is...

A Novel Motion Detection Method Using 3D Discrete Wavelet Transform

The problem of motion detection has received considerable attention due to the explosive growth of its applications in video analysis and surveillance systems. While the previous approaches can produce good results, the accurate detection of motion remains a challenging task due to the difficulties raised by illumination variations, occlusion...

Robust contour propagation using deep learning and image registration for online adaptive proton therapy of prostate cancer

Purpose: To develop and validate a robust and accurate registration pipeline for automatic contour propagation for online adaptive Intensity-Modulated Proton Therapy (IMPT) of prostate cancer using elastix software and deep learning. Methods: A three-dimensional (3D) Convolutional Neural Network was trained for automatic bladder segmentation...