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Objective: Investigate the performance of qualitative review (QR) for assessing dynamic susceptibility contrast (DSC-) MRI data quality in paediatric normal brain and develop an automated alternative to QR. Methods: 1027 signal–time courses were assessed by Reviewer 1 using QR. 243 were additionally assessed by Reviewer 2 and % disagreements and Cohen’s κ (κ) were calculated. The signal drop-to-noise ratio (SDNR), root mean square error (RMSE), full width half maximum (FWHM) and percentage signal recovery (PSR) were calculated for the 1027 signal–time courses. Data quality thresholds for each measure were determined using QR results. The measures and QR results trained machine learning classifiers. Sensitivity, specificity, precision, classification error and area under the curve from a receiver operating characteristic curve were calculated for each threshold and classifier. Results: Comparing reviewers gave 7% disagreements and κ = 0.83. Data quality thresholds of: 7.6 for SDNR; 0.019 for RMSE; 3 s and 19 s for FWHM; and 42.9 and 130.4% for PSR were produced. SDNR gave the best sensitivity, specificity, precision, classification error and area under the curve values of 0.86, 0.86, 0.93, 14.2% and 0.83. Random forest was the best machine learning classifier, giving sensitivity, specificity, precision, classification error and area under the curve of 0.94, 0.83, 0.93, 9.3% and 0.89. Conclusion: The reviewers showed good agreement. Machine learning classifiers trained on signal–time course measures and QR can assess quality. Combining multiple measures reduces misclassification. Advances in knowledge: A new automated quality control method was developed, which trained machine learning classifiers using QR results.

Glucose metabolism in tumours is reprogrammed away from oxidative metabolism, even in the presence of oxygen. Non-invasive imaging techniques can probe these alterations in cancer metabolism providing tools to detect tumours and their response to therapy. Although Positron Emission Tomography with ((18)F)2-fluoro-2-deoxy-D-glucose ((18)F-FDG PET) is an established clinical tool to probe cancer metabolism, it has poor spatial resolution and soft tissue contrast, utilizes ionizing radiation and only probes glucose uptake and phosphorylation and not further downstream metabolism. Magnetic Resonance Spectroscopy (MRS) has the capability to non-invasively detect and distinguish molecules within tissue but has low sensitivity and can only detect selected nuclei. Dynamic Nuclear Polarization (DNP) is a technique which greatly increases the signal-to-noise ratio (SNR) achieved with MR by significantly increasing nuclear spin polarization and this method has now been translated into human imaging. This review provides a brief overview of this process, also termed Hyperpolarized Carbon-13 Magnetic Resonance Spectroscopic Imaging (HP (13)C-MRSI), its applications in preclinical imaging, an outline of the current human trials that are ongoing, as well as future potential applications in oncology.