Supervised machine learning quality control for magnetic resonance artifacts in neonatal data sets

Abstract

Quality control (QC) of brain magnetic resonance images (MRI) is an important process requiring a significant amount of manual inspection. Major artifacts, such as severe subject motion, are easy to identify to naïve observers but lack automated identification tools. Clinical trials involving motion‐prone neonates typically pool data to obtain sufficient power, and automated quality control protocols are especially important to safeguard data quality. Current study tested an open source method to detect major artifacts among 2D neonatal MRI via supervised machine learning. A total of 1,020 two‐dimensional transverse T2‐weighted MRI images of preterm newborns were examined and classified as either QC Pass or QC Fail. Then 70 features across focus, texture, noise, and natural scene statistics categories were extracted from each image. Several different classifiers were trained and their performance was compared with subjective rating as the gold standard. We repeated the rating process again to examine the stability of the rating and classification. When tested via 10‐fold cross validation, the random undersampling and adaboost ensemble (RUSBoost) method achieved the best overall performance for QC Fail images with 85% positive predictive value along with 75% sensitivity. Similar classification performance was observed in the analyses of the repeated subjective rating. Current results served as a proof of concept for predicting images that fail quality control using no‐reference objective image features. We also highlighted the importance of evaluating results beyond mere accuracy as a performance measure for machine learning in imbalanced group settings due to larger proportion of QC Pass quality images.

A single institution retrospective analysis on survival based on treatment paradigms for patients with anaplastic oligodendroglioma

“…Several studies have suggested that chemotherapy without upfront radiation may be a viable option for patients with grade II oligodendrogliomas [16][17][18]; this also may hold true for patients diagnosed with anaplastic oligodendroglioma [19]. One major benefit of prolonging the use of radiation in patients would be limiting the potential negative long term effects of radiation, including potential cognitive [7,9] and some emerging evidence suggests it may alter wide scale functional connectivity [20]. In our study, the median age of diagnosis was 44, and the median overall survival was not reached at greater than 10 years of follow-up.…”

Technical Note: MRQy — An open‐source tool for quality control of MR imaging data

“…One of our planned functionalities is to enable real‐time generation of the embedding scatter plots directly within the MRQy HTML front‐end rather than via the backend. Similarly, the Python backend can be easily modified via user‐developed plugins to use a different foreground detection algorithm, compute additional quality measures, or even to integrate quality prediction algorithms 25 in the future. One of the current limitations of MRQy is that it can only be used for quality control of structural MRI data, but we are working on developing new measures that could allow MRQy to be used to interrogate non‐structural MRIs (e.g., diffusion or dynamic contrast‐enhanced MRI).…”

“…Quality control of MR imaging has long been a topic of active research, 9,25 but a majority of the resulting tools have been specifically developed for brain MRIs 6,11,12,13,14 in order to identify acceptable scans via supervised learning (based on subjective expert quality ratings). By contrast, MRQy represents a unique solution for quality control of MRI datasets that has been designed to work with scans of any body region while running in an efficient and unsupervised fashion.…”

Image-based Motion Artifact Reduction on Liver Dynamic Contrast Enhanced MRI

“…Recently, deep learning (DL) approaches have been developed in medical imaging use cases including image reconstruction and artifact reduction (11,12), motion detection and correction (13), and image quality control (14,15). DL methods utilized convolutional neural networks (CNNs) to extract features of different types of artifacts and correct them in brain (16,17), abdominal (18)(19)(20) and cardiac imaging (13).…”