Matthew R. Orton

We present a compartmental modelling approach to analyse radioactive time activity curves for first pass kinetics of [ ^99m Tc]sestamibi in the heart. Reparametrizing the kinetic equations using the Nernst membrane-potential equation provides a novel means of non-invasively estimating the sarcolemmal ( E _m ) and mitochondrial (ΔΨ _m ) membrane potentials in the heart. A Markov Chain Monte Carlo (MCMC) fitting approach was applied to data derived from established interventions in Langendorff perfused rat hearts where the sarcolemmal membrane was depolarised using hyperkalaemic Krebs Henseleit buffers; the mitochondrial membrane was depolarised using carbonylcyanide-3-chlorophenylhydrazone (CCCP); or both membranes were depolarised using their combination. Translating this approach to single photon emission planar scintigraphy kinetics from healthy rats allowed an estimate of these membrane potentials (voltages) in vivo for the first time; the values were E _m = –62 ± 5 mV and ΔΨ _m = –151 ± 5 mV (n = 4, mean ± SD).

Background: The aim of this work is to evaluate the performance of radiomics predictions for a range of molecular, genomic and clinical targets in patients with clear cell renal cell carcinoma (ccRCC) and demonstrate the impact of novel feature selection strategies and sub-segmentations on model interpretability.Methods: Contrast-enhanced CT scans from the first 101 patients recruited to the TRACERx Renal Cancer study (NCT03226886) were used to derive radiomics classification models to predict 20 molecular, histopathology and clinical target variables. Manual 3D segmentation was used in conjunction with automatic sub-segmentation to generate radiomics features from the core, rim, high and low enhancing sub-regions, and the whole tumour. Comparisons were made between two classification model pipelines: a Conventional pipeline reflecting common radiomics practice, and a Proposed pipeline including two novel feature selection steps designed to improve model interpretability. For both pipelines nested cross-validation was used to estimate prediction performance and tune model hyper-parameters, and permutation testing was used to evaluate the statistical significance of the estimated performance measures. Further model robustness assessments were conducted by evaluating model variability across the cross-validation folds.Results: Classification performance was significant (p < 0.05, H₀:AUROC = 0.5) for 11 of 20 targets using either pipeline and for these targets the AUROCs were within ± 0.05 for the two pipelines, except for one target where the Proposed pipeline performance increased by > 0.1. Five of these targets (necrosis on histology, presence of renal vein invasion, overall histological stage, linear evolutionary subtype and loss of 9p21.3 somatic alteration marker) had AUROC > 0.8. Models derived using the Proposed pipeline contained fewer feature groups than the Conventional pipeline, leading to more straightforward model interpretations without loss of performance. Sub-segmentations lead to improved performance and/or improved interpretability when predicting the presence of sarcomatoid differentiation and tumour stage.Conclusions: Use of the Proposed pipeline, which includes the novel feature selection methods, leads to more interpretable models without compromising prediction performance.Trial registration: NCT03226886 (TRACERx Renal)

Objectives: Myxoid liposarcomas (MLS) show enhanced response to radiotherapy due to their distinctive vascular pattern and therefore could be effectively treated with lower radiation doses. This is a descriptive study to explore the use of functional MRI to identify response in a uniform cohort of MLS patients treated with reduced dose radiotherapy Methods: 10 patients with MLS were imaged pre-, during, and post-radiotherapy receiving reduced dose radiotherapy and the response to treatment was histopathologically assessed post-radiotherapy. Apparent diffusion coefficient (ADC), T2* relaxation time, volume transfer constant (Ktrans), initial area under the gadolinium curve over 60 s (IAUGC60) and (Gd) were estimated for a central tumour volume. Results: All parameters showed large inter- and intrasubject variabilities. Pre-treatment (Gd), IAUGC60 and Ktrans were significantly different between responders and non-responders. Post-radiotherapy reductions from baseline were demonstrated for T2*, (Gd), IAUGC60 and Ktrans for responders. No statistically significant ADC differences were demonstrated between the two response groups. Significantly greater early tumour volume reductions were observed for responders. Conclusions: MLS are heterogenous lesions, characterised by a slow gradual contrast-agent uptake. Pre-treatment vascular parameters, early changes to tumour volume, vascular parameters and T2* have potential in identifying response to treatment. The delayed (Gd) is a suitable descriptive parameter, relying simply on T1 measurements. Volume changes precede changes in MLS functionality and could be used to identify early response. Advances in knowledge: MLS are are characterised by slow gradual contrast-agent uptake. Measurement of the delayed contrast-agent uptake (Gd) is simple to implement and able to discriminate response.

Radiomics refers to the extraction of mineable data from medical imaging and has been applied within oncology to improve diagnosis, prognostication, and clinical decision support, with the goal of ... Radiomics refers to the extraction of mineable data from medical imaging and has been applied within oncology to improve diagnosis, prognostication, and clinical decision support, with the goal of ... Created with BioRender.com

Objective: To assess intra- and inter-reader variability of apparent diffusion coefficient (ADC) and fat fraction (FF) measurement in focal myeloma bone lesions and the influence of lesion size. Methods: 22 myeloma patients with focal active disease on whole body MRI were included. Two readers outlined a small (5–10 mm) and large lesion (>10 mm) in each subject on derived ADC and FF maps; one reader performed this twice. Intra- and inter-reader agreement for small and large lesion groups were calculated for derived statistics from each map using within-subject standard deviation, coefficient of variation, interclass correlation coefficient measures, and visualized with Bland–Altman plots. Results: For mean ADC, intra- and inter-reader repeatability demonstrated equivalently low coefficient of variation (3.0–3.6%) and excellent interclass correlation coefficient (0.975–0.982) for both small and large lesions. For mean FF, intra- and inter-reader repeatability was significantly poorer for small lesions compared to large lesions (intra-reader within-subject standard variation estimate is 2.7 times higher for small lesions than large lesions (p = 0.0071), and for inter-reader variations is 3.8 times higher (p = 0.0070)). Conclusion: There is excellent intra- and inter-reader agreement for mean ADC estimates, even for lesions as small as 5 mm. For FF measurements, there is a significant increase in coefficient of variation for smaller lesions, suggesting lesions >10 mm should be selected for lesion FF measurement. Advances in knowledge: ADC measurements of focal myeloma have excellent intra- and inter-reader agreement. FF measurements are more susceptible to lesion size as intra- and inter-reader agreement is significantly impaired in lesions less than 10 mm.

Objectives: To investigate the feasibility of accurately quantifying the concentration of MRI contrast agent in flowing blood by measuring its T1 in a large vessel. Such measures are often used to obtain patient-specific arterial input functions for the accurate fitting of pharmacokinetic models to dynamic contrast enhanced MRI data. Flow is known to produce errors with this technique, but these have so far been poorly quantified and characterised in the context of pulsatile flow with a rapidly changing T1 as would be expected in vivo. Methods: A phantom was developed which used a mechanical pump to pass fluid at physiologically relevant rates. Measurements of T1 were made using high temporal resolution gradient recalled sequences suitable for DCE-MRI of both constant and pulsatile flow. These measures were used to validate a virtual phantom that was then used to simulate the expected errors in the measurement of an AIF in vivo. Results: The relationship between measured T1 values and flow velocity was found to be non-linear. The subsequent error in quantification of contrast agent concentration in a measured AIF was shown. Conclusions: The T1 measurement of flowing blood using standard DCE- MRI sequences are subject to large measurement errors which are non-linear in relation to flow velocity. Advances in knowledge: This work qualitatively and quantitatively demonstrates the difficulties of accurately measuring the T1 of flowing blood using DCE-MRI over a wide range of physiologically realistic flow velocities and pulsatilities. Sources of error are identified and proposals made to reduce these.

Despite the utility of tumour characterisation using quantitative parameter maps from multi-b-value diffusion-weighted MRI (DWI), clinicians often prefer the use of the image with highest diffusion-weighting (b-value), for instance for defining regions of interest (ROIs). However, these images are typically degraded by noise, as they do not utilize the information from the full acquisition. We present a principal component analysis (PCA) approach for model-free denoising of DWI data. PCA-denoising was compared to synthetic MRI, where a diffusion model is fitted for each voxel and a denoised image at a given b-value is generated from the model fit. A quantitative comparison of systematic and random errors was performed on data simulated using several diffusion models (mono-exponential, bi-exponential, stretched-exponential and kurtosis). A qualitative visual comparison was also performed for in vivo images in six healthy volunteers and three pancreatic cancer patients. In simulations, the reduction in random errors from PCA-denoising was substantial (up to 55%) and similar to synthetic MRI (up to 53%). Model-based synthetic MRI denoising resulted in substantial (up to 29% of signal) systematic errors, whereas PCA-denoising was able to denoise without introducing systematic errors (less than 2%). In vivo, the signal-to-noise ratio (SNR) and sharpness of PCA-denoised images were superior to synthetic MRI, resulting in clearer tumour boundaries. In the presence of motion, PCA-denoising did not cause image blurring, unlike image averaging or synthetic MRI. Multi-b-value MRI can be denoised model-free with our PCA-denoising strategy that reduces noise to a level similar to synthetic MRI, but without introducing systematic errors associated with the synthetic MRI method.

Background: Interpretation of diffusion in conjunction with T₂‐weighted MRI is essential for assessing prostate cancer; however, the combination of apparent diffusion coefficient (ADC) with quantitative T₂ mapping remains unexplored.Purpose: To document the T₂ components and ADC of untreated and irradiated nonmalignant prostate tissue as a measure of their glandular luminal and cellular compartments and to compare values with those of tumor.Study Type: Prospective.Population: Twenty‐four men with prostate cancer (14 untreated; 10 with biochemical recurrence following radiation therapy).Field Strength/Sequences: Endorectal 3 T MRI including a 32‐echo gradient echo and spin echo (GRASE) and an 8 b‐value diffusion‐weighted sequence.Assessment: Regions of interest were drawn on ADC maps and T₂‐weighted images around focal lesions in areas of biopsy‐positive prostate cancer and in nonmalignant areas of untreated and irradiated peripheral zone (PZ), and untreated transitional zone (TZ). Multiecho T₂ data were fitted with mono‐/biexponential decay and nonnegative least squares functions. The luminal water fraction (LWF) was derived.Statistical Tests: The preference between mono‐ and biexponential decay was assessed using the Bayesian information criterion. Differences in fitted parameters between tissue types were compared (paired t‐test within groups, Kruskal–Wallis and Wilcoxon rank‐sum test between groups) and correlations between ADC and T₂ components assessed (Spearman rank correlation test).Results: LWF in tumor (0.09) was significantly lower than in PZ or TZ (0.27 and 0.18, P < 0.01, respectively), but tumor values were comparable to nonmalignant irradiated prostate (0.08). The short T₂ relaxation rate was lower in tumor than in nonmalignant untreated or irradiated tissue (significant compared with TZ, P = 0.01). There was a strong correlation between LWF and ADC in normal untreated tissue (r = 0.88, P < 0.001). This relationship was absent in nonmalignant irradiated prostrate (r = –0.35, P = 0.42) and in tumor (r = –0.04, P = 0.88).Data Conclusion: T₂ components in conjunction with ADC can be used to characterize untreated and irradiated nonmalignant prostate and tumor. LWF is most useful at discriminating tumor in the untreated prostate.Level of Evidence: 2Technical Efficacy: Stage 2J. Magn. Reson. Imaging 2019;50:619–627.