Measuring repeatability of dynamic contrast-enhanced MRI biomarkers improves evaluation of biological response to radiotherapy in lung cancer.

OBJECTIVES: To measure dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) biomarker repeatability in patients with non-small cell lung cancer (NSCLC). To use these statistics to identify which individual target lesions show early biological response. MATERIALS AND METHODS: A single-centre, prospective DCE-MRI study was performed between September 2015 and April 2017. Patients with NSCLC were scanned before standard-of-care radiotherapy to evaluate biomarker repeatability and two weeks into therapy to evaluate biological response. Volume transfer constant (Ktrans), extravascular extracellular space volume fraction (ve) and plasma volume fraction (vp) were measured at each timepoint along with tumour volume. Repeatability was assessed using a within-subject coefficient of variation (wCV) and repeatability coefficient (RC). Cohort treatment effects on biomarkers were estimated using mixed-effects models. RC limits of agreement revealed which individual target lesions changed beyond that expected with biomarker daily variation. RESULTS: Fourteen patients (mean age, 67 years +/- 12, 8 men) had 22 evaluable lesions (12 primary tumours, 8 nodal metastases, 2 distant metastases). The wCV (in 8/14 patients) was between 9.16% to 17.02% for all biomarkers except for vp, which was 42.44%. Cohort-level changes were significant for Ktrans and ve (p < 0.001) and tumour volume (p = 0.002). Ktrans and tumour volume consistently showed the greatest number of individual lesions showing biological response. In distinction, no individual lesions had a real change in ve despite the cohort-level change. CONCLUSION: Identifying individual early biological responders provided additional information to that derived from conventional cohort cohort-level statistics, helping to prioritise which parameters would be best taken forward into future studies. CLINICAL RELEVANCE STATEMENT: Dynamic contrast-enhanced magnetic resonance imaging biomarkers Ktrans and tumour volume are repeatable and detect early treatment-induced changes at both cohort and individual lesion levels, supporting their use in further evaluation of radiotherapy and targeted therapeutics. KEY POINTS: Few literature studies report quantitative imaging biomarker precision, by measuring repeatability or reproducibility. Several DCE-MRI biomarkers of lung cancer tumour microenvironment were highly repeatable. Repeatability coefficient measurements enabled lesion-specific evaluation of early biological response to therapy, improving conventional assessment.

Pore size estimation in axon-mimicking microfibers with diffusion-relaxation MRI.

PURPOSE: This study aims to evaluate two distinct approaches for fiber radius estimation using diffusion-relaxation MRI data acquired in biomimetic microfiber phantoms that mimic hollow axons. The methods considered are the spherical mean power-law approach and a T2-based pore size estimation technique. THEORY AND METHODS: A general diffusion-relaxation theoretical model for the spherical mean signal from water molecules within a distribution of cylinders with varying radii was introduced, encompassing the evaluated models as particular cases. Additionally, a new numerical approach was presented for estimating effective radii (i.e., MRI-visible mean radii) from the ground truth radii distributions, not reliant on previous theoretical approximations and adaptable to various acquisition sequences. The ground truth radii were obtained from scanning electron microscope images. RESULTS: Both methods show a linear relationship between effective radii estimated from MRI data and ground-truth radii distributions, although some discrepancies were observed. The spherical mean power-law method overestimated fiber radii. Conversely, the T2-based method exhibited higher sensitivity to smaller fiber radii, but faced limitations in accurately estimating the radius in one particular phantom, possibly because of material-specific relaxation changes. CONCLUSION: The study demonstrates the feasibility of both techniques to predict pore sizes of hollow microfibers. The T2-based technique, unlike the spherical mean power-law method, does not demand ultra-high diffusion gradients, but requires calibration with known radius distributions. This research contributes to the ongoing development and evaluation of neuroimaging techniques for fiber radius estimation, highlights the advantages and limitations of both methods, and provides datasets for reproducible research.

An efficient semi-supervised quality control system trained using physics-based MRI-artefact generators and adversarial training.

Large medical imaging data sets are becoming increasingly available. A common challenge in these data sets is to ensure that each sample meets minimum quality requirements devoid of significant artefacts. Despite a wide range of existing automatic methods having been developed to identify imperfections and artefacts in medical imaging, they mostly rely on data-hungry methods. In particular, the scarcity of artefact-containing scans available for training has been a major obstacle in the development and implementation of machine learning in clinical research. To tackle this problem, we propose a novel framework having four main components: (1) a set of artefact generators inspired by magnetic resonance physics to corrupt brain MRI scans and augment a training dataset, (2) a set of abstract and engineered features to represent images compactly, (3) a feature selection process that depends on the class of artefact to improve classification performance, and (4) a set of Support Vector Machine (SVM) classifiers trained to identify artefacts. Our novel contributions are threefold: first, we use the novel physics-based artefact generators to generate synthetic brain MRI scans with controlled artefacts as a data augmentation technique. This will avoid the labour-intensive collection and labelling process of scans with rare artefacts. Second, we propose a large pool of abstract and engineered image features developed to identify 9 different artefacts for structural MRI. Finally, we use an artefact-based feature selection block that, for each class of artefacts, finds the set of features that provide the best classification performance. We performed validation experiments on a large data set of scans with artificially-generated artefacts, and in a multiple sclerosis clinical trial where real artefacts were identified by experts, showing that the proposed pipeline outperforms traditional methods. In particular, our data augmentation increases performance by up to 12.5 percentage points on the accuracy, F1, F2, precision and recall. At the same time, the computation cost of our pipeline remains low - less than a second to process a single scan - with the potential for real-time deployment. Our artefact simulators obtained using adversarial learning enable the training of a quality control system for brain MRI that otherwise would have required a much larger number of scans in both supervised and unsupervised settings. We believe that systems for quality control will enable a wide range of high-throughput clinical applications based on the use of automatic image-processing pipelines.