Machine learning based prediction of image quality in prostate MRI using rapid localizer images

Abstract

Purpose: Diagnostic performance of prostate MRI depends on high-quality imaging. Prostate MRI quality is inversely proportional to the amount of rectal gas and distention. Early detection of poor-quality MRI may enable intervention to remove gas or exam rescheduling, saving time. We developed a machine learning based quality prediction of yet-to-be acquired MRI images solely based on MRI rapid localizer sequence, which can be acquired in a few seconds.

Approach: The dataset consists of 213 (147 for training and 64 for testing) prostate sagittal T2-weighted (T2W) MRI localizer images and rectal content, manually labeled by an expert radiologist. Each MRI localizer contains seven two-dimensional (2D) slices of the patient, accompanied by manual segmentations of rectum for each slice. Cascaded and end-to-end deep learning models were used to predict the quality of yet-to-be T2W, DWI, and apparent diffusion coefficient (ADC) MRI images. Predictions were compared to quality scores determined by the experts using area under the receiver operator characteristic curve and intra-class correlation coefficient.

Results: In the test set of 64 patients, optimal versus suboptimal exams occurred in 95.3% (61/64) versus 4.7% (3/64) for T2W, 90.6% (58/64) versus 9.4% (6/64) for DWI, and 89.1% (57/64) versus 10.9% (7/64) for ADC. The best performing segmentation model was 2D U-Net with ResNet-34 encoder and ImageNet weights. The best performing classifier was the radiomics based classifier.

Conclusions: A radiomics based classifier applied to localizer images achieves accurate diagnosis of subsequent image quality for T2W, DWI, and ADC prostate MRI sequences.

Keywords: artifact; gas; magnetic resonance imaging MRI; prostate; quality.

Fig. 1

Sagittal T2W large field of view ssFSE MRI-localizer images in two different patients (a) and (b). Segmentation in (c) and (d) corresponds to manual segmentation. In (c), the rectum is decompressed and empty, image quality of subsequent diagnostic T2W, DWI, and ADC map images were rated as excellent. In (d), the rectum is distended with gas, image quality of the subsequent diagnostic T2W, DWI, and ADC map images were rated as suboptimal or non-diagnostic. In (e) and (f), the predicted segmentations for patients in (a) and (b), respectively, are shown.

Fig. 2

Automated quality prediction pipeline. An image is an input into the segmentation model, where a deep learning model generates rectum segmentation masks. The image along with the predicted segmentation mask is passed into the classification network to generate a quality output as either optimal or suboptimal. The classification can be based on rectal content, deep learning models, or using radiologist-assigned quality scores as features. The output is a final prediction for the yet to be acquired T2W, DWI, and ADC images.