Automated MRI Field of View Prescription from Region of Interest Prediction by Intra-stack Attention Neural Network

TL;DR

This paper introduces a deep learning framework that automates MRI field of view prescription by predicting regions of interest using intra-stack attention mechanisms, achieving accuracy comparable to radiologists and reducing variability.

Contribution

The novel intra-stack attention neural network effectively automates MRI FOV prescription, improving accuracy and consistency over baseline models and aligning closely with expert radiologist performance.

Findings

Achieved an average IoU of 0.867 in ROI prediction.

Significantly better performance than baseline models (P<0.05).

92% acceptance rate of FOVs by radiologists.

Abstract

Manual prescription of the field of view (FOV) by MRI technologists is variable and prolongs the scanning process. Often, the FOV is too large or crops critical anatomy. We propose a deep-learning framework, trained by radiologists' supervision, for automating FOV prescription. An intra-stack shared feature extraction network and an attention network are used to process a stack of 2D image inputs to generate output scalars defining the location of a rectangular region of interest (ROI). The attention mechanism is used to make the model focus on the small number of informative slices in a stack. Then the smallest FOV that makes the neural network predicted ROI free of aliasing is calculated by an algebraic operation derived from MR sampling theory. We retrospectively collected 595 cases between February 2018 and February 2022. The framework's performance is examined quantitatively with intersection over union (IoU) and pixel error on position, and qualitatively with a reader study. We use the t-test for comparing quantitative results from all models and a radiologist. The proposed model achieves an average IoU of 0.867 and average ROI position error of 9.06 out of 512 pixels on 80 test cases, significantly better (P<0.05) than two baseline models and not significantly different from a radiologist (P>0.12). Finally, the FOV given by the proposed framework achieves an acceptance rate of 92% from an experienced radiologist.