OBJECTIVES:Clinical translation of advanced MRI techniques can be hindered by the challenges of performing standardized multicentre imaging trials. This work aims to develop and demonstrate an automated tool for monitoring imaging protocol deviations, enabling corrective action to be taken. METHODS:A Python-based tool, integrated into the imaging repository XNAT, was developed to compare DICOM series with an agreed imaging protocol, highlighting missing series and parameter deviations. This was demonstrated through retrospective analysis of a prospectively acquired dataset from a ten-site whole-body (WB) MRI study of patients with multiple myeloma. The acquired data were compared to the relevant radiological guidelines and to the site-specific imaging protocols agreed for the study. RESULTS:The rate of technical software failure was 0% across 174 examinations from 10 sites. The clinical guidelines were followed in 87.9% of examinations and compliance with the site-specific imaging protocol was greater than 75.0% for all parameters. Common deviations included number of averages for diffusion-weighted imaging (DWI) and repetition time for DWI and Dixon: 85.2%, 81.7%, and 75.1%, respectively. There was a statistically significant correlation between protocol compliance and overall exam radiological image quality. CONCLUSIONS:Repository-integrated software is presented for automated monitoring of imaging protocol compliance to support standardization in multicentre studies and clinical translation. ADVANCES IN KNOWLEDGE:This study presents a novel open-source repository-integrated software tool for automatically monitoring compliance with the expected imaging protocol. Standardized acquisition protocols are crucial in multicentre imaging studies and this tool has the potential to enhance research outcomes and support clinical translation.

Background The Myeloma Response Assessment and Diagnosis System (MY-RADS) guidelines establish a standardised acquisition and analysis pipeline for whole-body MRI (WB-MRI) in patients with myeloma. This is the first study to assess image quality in a multi-centre prospective trial using MY-RADS. Methods The cohort consisted of 121 examinations acquired across ten sites with a range of prior WB-MRI experience, three scanner manufacturers and two field strengths. Image quality was evaluated qualitatively by a radiologist and quantitatively using a semi-automated pipeline to quantify common artefacts and image quality issues. The intra- and inter-rater repeatability of qualitative and quantitative scoring was also assessed. Results Qualitative radiological scoring found that the image quality was generally good, with 94% of examinations rated as good or excellent and only one examination rated as non-diagnostic. There was a significant correlation between radiological and quantitative scoring for most measures, and intra- and inter-rater repeatability were generally good. When the quality of an overall examination was low, this was often due to low quality diffusion-weighted imaging (DWI), where signal to noise ratio (SNR), anterior thoracic signal loss and brain geometric distortion were found as significant predictors of examination quality. Conclusions It is possible to successfully deliver a multi-centre WB-MRI study using the MY-RADS protocol involving scanners with a range of manufacturers, models and field strengths. Quantitative measures of image quality were developed and shown to be significantly correlated with radiological assessment. The SNR of DW images was identified as a significant factor affecting overall examination quality. Trial registration ClinicalTrials.gov, NCT03188172 , Registered on 15 June 2017. Critical relevance statement Good overall image quality, assessed both qualitatively and quantitatively, can be achieved in a multi-centre whole-body MRI study using the MY-RADS guidelines. Key points • A prospective multi-centre WB-MRI study using MY-RADS can be successfully delivered. • Quantitative image quality metrics were developed and correlated with radiological assessment. • SNR in DWI was identified as a significant predictor of quality, allowing for rapid quality adjustment. Graphical Abstract

Background Whole-body (WB) MRI, which includes diffusion-weighted imaging (DWI) and T 1 -w Dixon, permits sensitive detection of marrow disease in addition to qualitative and quantitative measurements of disease and response to treatment of bone marrow. We report on the first study to embed standardised WB-MRI within a prospective, multi-centre myeloma clinical trial (IMAGIMM trial, sub-study of OPTIMUM/MUKnine) to explore the use of WB-MRI to detect minimal residual disease after treatment. Methods The standardised MY-RADS WB-MRI protocol was set up on a local 1.5 T scanner. An imaging manual describing the MR protocol, quality assurance/control procedures and data transfer was produced and provided to sites. For non-identical scanners (different vendor or magnet strength), site visits from our physics team were organised to support protocol optimisation. The site qualification process included review of phantom and volunteer data acquired at each site and a teleconference to brief the multidisciplinary team. Image quality of initial patients at each site was assessed. Results WB-MRI was successfully set up at 12 UK sites involving 3 vendor systems and two field strengths. Four main protocols (1.5 T Siemens, 3 T Siemens, 1.5 T Philips and 3 T GE scanners) were generated. Scanner limitations (hardware and software) and scanning time constraint required protocol modifications for 4 sites. Nevertheless, shared methodology and imaging protocols enabled other centres to obtain images suitable for qualitative and quantitative analysis. Conclusions Standardised WB-MRI protocols can be implemented and supported in prospective multi-centre clinical trials. Trial registration NCT 03188172 clinicaltrials.gov; registration date 15th June 2017 https://clinicaltrials.gov/ct2/show/study/NCT03188172

In this acute intervention study, we investigated the potential benefit of ketone supplementation in humans by studying cardiac phosphocreatine to adenosine-triphosphate ratios (PCr/ATP) and skeletal muscle PCr recovery using phosphorus magnetic resonance spectroscopy (31P-MRS) before and after ingestion of a ketone ester drink. We recruited 28 healthy individuals: 12 aged 23–70 years for cardiac 31P-MRS, and 16 aged 60–75 years for skeletal muscle 31P-MRS. Baseline and post-intervention resting cardiac and dynamic skeletal muscle 31P-MRS scans were performed in one visit, where 25 g of the ketone monoester, deltaG®, was administered after the baseline scan. Administration was timed so that post-intervention 31P-MRS would take place 30 min after deltaG® ingestion. The deltaG® ketone drink was well-tolerated by all participants. In participants who provided blood samples, post-intervention blood glucose, lactate and non-esterified fatty acid concentrations decreased significantly (−28.8%, p ≪ 0.001; −28.2%, p = 0.02; and −49.1%, p ≪ 0.001, respectively), while levels of the ketone body D-beta-hydroxybutyrate significantly increased from mean (standard deviation) 0.7 (0.3) to 4.0 (1.1) mmol/L after 30 min (p ≪ 0.001). There were no significant changes in cardiac PCr/ATP or skeletal muscle metabolic parameters between baseline and post-intervention. Acute ketone supplementation caused mild ketosis in blood, with drops in glucose, lactate, and free fatty acids; however, such changes were not associated with changes in 31P-MRS measures in the heart or in skeletal muscle. Future work may focus on the effect of longer-term ketone supplementation on tissue energetics in groups with compromised mitochondrial function.

Cardiovascular magnetic resonance (CMR) imaging is a versatile tool that has established itself as the reference method for functional assessment and tissue characterisation. CMR helps to diagnose, monitor disease course and sub-phenotype disease states. Several emerging CMR methods have the potential to offer a personalised medicine approach to treatment. CMR tissue characterisation is used to assess myocardial oedema, inflammation or thrombus in various disease conditions. CMR derived scar maps have the potential to inform ablation therapy—both in atrial and ventricular arrhythmias. Quantitative CMR is pushing boundaries with motion corrections in tissue characterisation and first-pass perfusion. Advanced tissue characterisation by imaging the myocardial fibre orientation using diffusion tensor imaging (DTI), has also demonstrated novel insights in patients with cardiomyopathies. Enhanced flow assessment using four-dimensional flow (4D flow) CMR, where time is the fourth dimension, allows quantification of transvalvular flow to a high degree of accuracy for all four-valves within the same cardiac cycle. This review discusses these emerging methods and others in detail and gives the reader a foresight of how CMR will evolve into a powerful clinical tool in offering a precision medicine approach to treatment, diagnosis, and detection of disease.

Prostate cancer is the second most commonly diagnosed cancer among men and currently multi-parametric MRI is a promising imaging technique used for clinical workup of prostate cancer. Accurate detection and localisation of the prostate tissue boundary on various MRI scans can be helpful for obtaining a region of interest for Computer Aided Diagnosis systems. In this paper, we present a fully automated detection and segmentation pipeline using a conditional Generative Adversarial Network (cGAN). We investigated the robustness of the cGAN model against adding Gaussian noise or removing noise from the training data. Based on the detection and segmentation metrics, de-noising did not show a significant improvement. However, by including noisy images in the training data, the detection and segmentation performance was improved in each 3D modality, which resulted in comparable to state-of-the-art results.

The manual delineation of region of interest (RoI) in 3D magnetic resonance imaging (MRI) of the prostate is time-consuming and subjective. Correct identification of prostate tissue is helpful to define a precise RoI to be used in CAD systems in clinical practice during diagnostic imaging, radiotherapy and monitoring the progress of disease. Conditional GAN (cGAN), cycleGAN and U-Net models and their performances were studied for the detection and segmentation of prostate tissue in 3D multi-parametric MRI scans. These models were trained and evaluated on MRI data from 40 patients with biopsy-proven prostate cancer. Due to the limited amount of available training data, three augmentation schemes were proposed to artificially increase the training samples. These models were tested on a clinical dataset annotated for this study and on a public dataset (PROMISE12). The cGAN model outperformed the U-Net and cycleGAN predictions owing to the inclusion of paired image supervision. Based on our quantitative results, cGAN gained a Dice score of 0.78 and 0.75 on the private and the PROMISE12 public datasets, respectively.

The assessment of liver percentage fat fraction (%FF) using proton density fat fraction sequences is becoming increasingly accessible. Previous studies have tended to use multiple small ROIs that focus on Couinaud segments. In an effort to simplify day-to-day analysis, this study assesses the impact of using larger, elliptical ROIs focused on a single hepatic lobe. Additionally, we assess the impact of sampling fewer transhepatic slices when measuring %FF. Retrospective analysis of prospectively obtained images from 34 volunteers using an IDEAL IQ sequence. Two observers independently measured %FF using three different protocols: freehand whole-liver ROI (fh-ROI), elliptical-ROI on the right lobe (rt-ROI) and elliptical-ROI on the left lobe (lt-ROI). Inter-observer reliability for all measurements techniques was ‘excellent’ (Spearman’s rank correlation coefficients 0.81–0.98). There was a significant difference (Paired Wilcoxon Test: p < 0.001) between the median %FF obtained using fh-ROI when compared to the rt-ROI method, the maximum mean difference between the two techniques was 2.79% (95% CI). For all sampling methods a Kruskall-Wallis analysis demonstrated no significant difference in mean %FF when the number of slices sampled was reduced from 11 to 1. The mean coefficient of variance increased when more slices were sampled (3 slices = 0.1, 11 slices = 0.17, p < 0.001). Simplified ROIs focused on one hepatic lobe provide %FF measurements that are unlikely to be sufficiently accurate for use in clinical practice. Freehand whole-liver ROIs should be used in preference. A single freehand ROI measurement taken at the level of the hepatic hilum yields a %FF that is representative of the mean whole liver % FF. Multiple slices are needed to measure heterogeneity.

Groupwise non-rigid image alignment is a difficult non-linear optimization problem involving many parameters and often large datasets. Previous methods have explored various metrics and optimization strategies. Good results have been previously achieved with simple metrics, requiring complex optimization, often with many unintuitive parameters that require careful tuning for each dataset. In this chapter, the problem is restructured to use a simpler, iterative optimization algorithm, with very few free parameters. The warps are refined using an iterative Levenberg-Marquardt minimization to the mean, based on updating the locations of a small number of points and incorporating a stiffness constraint. This optimization approach is efficient, has very few free parameters to tune, and the authors show how to tune the few remaining parameters. Results show that the method reliably aligns various datasets including two facial datasets and two medical datasets of prostate and brain MRI images and demonstrates efficiency in terms of performance and a reduction of the computational cost.

We present a novel approach for classifying the Gleason score for prostate tumours based on MRI data. Proposed approach uses three scores: 2, 3 and 4–5 (representing Gleason scores 4 and 5 as one single class). Patches are extracted from annotated MRI data for each of the class. Raw image patches have been used as features, instead of extracting manual hand-crafted features. Each patch is encoded using a dictionary and the encoded feature vector is then used for classification. A voting-based encoding approach is used to transform data from the image domain to more discriminative class-specific representations. Initial investigation demonstrated excellent results (Classification Accuracy equal to 85

Groupwise image alignment automatically provides non-rigid registration across a set of images. It has found applications in facial image analysis and medical image analysis by automatically generating statistical models of shape and appearance. The main approaches used previously include iterative and graph-based approaches. In iterative approaches, the registration of each image is iteratively updated to minimise an error measure across the set. Various metrics and optimisation strategies have been proposed to achieve this. Graph-based methods perform registration of each pair of images in the set, to form a weighted graph of the "distance" between all the images, and then finds the optimal paths between the most central image and every other image. In this paper, we use a graph-based approach to perform initialisation, which is then refined with an iterative approach. Pairwise registration is performed using demons registration, then shortest paths identified in the resulting graph are used to provide an initial warp for each image by concatenating warps along the path. The warps are refined using an iterative Levenberg-Marquardt minimisation to the mean, based on updating the locations of a small number of points and incorporating a stiffness constraint. This optimisation approach is efficient, has very few free parameters to tune and we show how to tune the few remaining parameters. We compare the combined approach to both the iterative and graph-based approaches used independently. Results demonstrate that the combined method improves the alignment of various datasets, including two face datasets and a difficult medical dataset of prostate MRI images.

Objective: To reaudit the use of safety checklists in radiology departments in NHS departments throughout the UK. Methods: This audit was performed on behalf of The Royal College of Radiologists Audit Committee in 2016 and was sent to radiology audit leads at every NHS department in the UK to determine the use of safety checks in various modalities and subspecialties. Free-form text boxes gathered data on problems with checklist implementation. Results: 109/177 (62%) trusts responded. 48% of respondents used safety checklists for all radiological procedures in all modalities. 50% used checklists for some procedures. 2% did not use a checklist. Checklist use had increased since the previous audit (98% 2016, compared to 94% in 2012) but implementation for different procedures remains variable. For example, in ultrasound-guided fine needle and breast stereotactic procedures (49%), use has not increased since 2012. Conclusion: Reasons for not using checklists include a perception that intervention suite checklists were not appropriate for minor procedures and the limited flexibility of radiology information systems. The limitations of checklists are discussed. Advances in knowledge: Our reaudit shows that in spite of increased implementation, use of safety checks is variable. Local ownership and radiology information system flexibility are needed to support the culture of safety processes in radiology departments.

PurposeQuantitative diffusion MRI has frequently been studied as a means of grading prostate cancer. Interpretation of results is complicated by the nature of prostate tissue, which consists of four distinct compartments: vascular, ductal lumen, epithelium, and stroma. Current diffusion measurements are an ill‐defined weighted average of these compartments. In this study, prostate diffusion is analyzed in terms of a model that takes explicit account of tissue compartmentalization, exchange effects, and the non‐Gaussian behavior of tissue diffusion.MethodThe model assumes that exchange between the cellular (ie, stromal plus epithelial) and the vascular and ductal compartments is slow. Ductal and cellular diffusion characteristics are estimated by Monte Carlo simulation and a two‐compartment exchange model, respectively. Vascular pseudodiffusion is represented by an additional signal at b = 0. Most model parameters are obtained either from published data or by comparing model predictions with the published results from 41 studies. Model prediction error is estimated using 10‐fold cross‐validation.ResultsAgreement between model predictions and published results is good. The model satisfactorily explains the variability of ADC estimates found in the literature.ConclusionA reliable model that predicts the diffusion behavior of benign and cancerous prostate tissue of different Gleason scores has been developed. Magn Reson Med 78:316–326, 2017. © 2016 International Society for Magnetic Resonance in Medicine

Purpose: Diffusion MRI is used frequently to assess prostate cancer. The prostate consists of cellular tissue surrounding fluid filled ducts. Here, the diffusion properties of the ductal fluid alone were studied. Monte Carlo simulations were used to investigate ductal residence times to determine whether ducts can be regarded as forming a separate compartment and whether ductal radius could determine the Apparent Diffusion Coefficient (ADC) of the ductal fluid.Methods: Random walks were simulated in cavities. Average residence times were estimated for permeable cavities. Signal reductions resulting from application of a Stejskal-Tanner pulse sequence were calculated in impermeable cavities. Simulations were repeated for cavities of different radii and different diffusion times.Results: Residence times are at least comparable with diffusion times even in relatively high grade tumors. ADCs asymptotically approach theoretical limiting values. At large radii and short diffusion times, ADCs are similar to free diffusion. At small radii and long diffusion times, ADCs are reduced toward zero, and kurtosis approaches a value of -1.2.Conclusions: Restricted diffusion in cavities of similar sizes to prostate ducts may reduce ductal ADCs. This may contribute to reductions in total ADC seen in prostate cancer. Magn Reson Med 77: 1671-1677, 2017. (C) 2016 International Society for Magnetic Resonance in Medicine

The purpose of this work was to refine a previously published model of prostate diffusion by incorporating improved estimates of cellular diffusivity obtained by Monte Carlo simulation.Stromal and epithelial cell size and intracellular volume fraction in different grades of cancer were determined from histological images. Diffusion in different mixtures of cells, corresponding to different tumor grades, was simulated and cellular apparent diffusion coefficient and kurtosis values determined. These values were incorporated into the previously published model of prostate diffusion and model predictions compared with values found in the literature.Stromal cell radius and intracellular volume fraction were 3.74 ± 0.96 μm and 13 ± 3% respectively in normal peripheral zone (PZ), and were similar in all grades of cancer. Epithelial cell radius and intracellular volume fraction were 3.40 ± 0.15 μm and 45 ± 5% respectively in normal PZ, rising to 4.75 ± 0.20 μm and 70 ± 8% in high grade cancer. Cellular apparent diffusion coefficient and kurtosis were 1.02 μm2 ms−1 and 0.58 respectively in normal PZ, and 0.61 μm2 ms−1 and 1.15 in high grade cancer (variation in simulation values are less than 0.1%). Agreement between model predictions and measurements were good, with a mean square error of 0.22 μm2 ms−1.Incorporation of cellular diffusion coefficient and kurtosis values obtained by Monte Carlo simulation into a model of prostate diffusion gives good agreement with published results.

AIM: To compare magnetic resonance imaging (MRI)-derived breast density measurements using automatic segmentation algorithms with radiologist estimations using the Breast Imaging Reporting and Data Systems (BI-RADS) density classification.MATERIALS AND METHODS: Forty women undergoing mammography and dynamic breast MRI as part of their clinical management were recruited. Fate-water separated MRI images derived from a two-point Dixon technique, phase-sensitive reconstruction, and atlas-based segmentation were obtained before and after intravenous contrast medium administration. Breast density was assessed using software from Advanced MR Analytics (AMRA), Linkoping, Sweden, with results compared to the widely used four-quartile quantitative BI-RADS scale.RESULTS: The proportion of glandular tissue in the breast on MRI was derived from the AMRA sequence. The mean unenhanced breast density was 0.31 +/- 0.22 (mean +/- SD; left) and 0.29 +/- 0.21 (right). Mean breast density on post-contrast images was 0.32 +/- 0.19 (left) and 0.32 +/- 0.2 (right). There was "almost perfect" correlation between pre-and post-contrast breast density quantification: Spearman's correlation rho=0.98 (95% confidence intervals [CI]: 0.97 -0.99; left) and rho=0.99 (95% CI: 0.98-0.99; right). The 95% limits of agreement were -0.11 -0.08 (left) and -0.08-0.03 (right). Interobserver reliability for BI-RADS was "substantial": weighted Kappa k -0.8 (95% CI: 0.74-0.87). The Spearman correlation coefficient between BIRADS and MRI breast density was rho=0.73 (95% CI: 0.60-0.82; left) and rho=0.75 (95% CI: 0.63-0.83; right) which was also "substantial".CONCLUSION: The AMRA sequence provides a fully automated, reproducible, objective assessment of fibroglandular breast tissue proportion that correlates well with mammographic assessment of breast density with the added advantage of avoidance of ionising radiation. (C) 2017 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

PURPOSE:In this paper the authors propose a texton based prostate computer aided diagnosis approach which bypasses the typical feature extraction process such as filtering and convolution which can be computationally expensive. The study focuses the peripheral zone because 75% of prostate cancers start within this region and the majority of prostate cancers arising within this region are more aggressive than those arising in the transitional zone.METHODS:For the model development, square patches were extracted at random locations from malignant and benign regions. Subsequently, extracted patches were aggregated and clustered using k-means clustering to generate textons that represent both regions. All textons together form a texton dictionary, which was used to construct a texton map for every peripheral zone in the training images. Based on the texton map, histogram models for each malignant and benign tissue samples were constructed and used as a feature vector to train our classifiers. In the testing phase, four machine learning algorithms were employed to classify each unknown sample tissue based on its corresponding feature vector.RESULTS:The proposed method was tested on 418 T2-W MR images taken from 45 patients. Evaluation results show that the best three classifiers were Bayesian network (Az = 92.8% ± 5.9%), random forest (89.5% ± 7.1%), and k-NN (86.9% ± 7.5%). These results are comparable to the state-of-the-art in the literature.CONCLUSIONS:The authors have developed a prostate computer aided diagnosis method based on textons using a single modality of T2-W MRI without the need for the typical feature extraction methods, such as filtering and convolution. The proposed method could form a solid basis for a multimodality magnetic resonance imaging based systems.

This study aims to investigate the effects of window size on the performance of prostate cancer CAD and to identify discriminant texture descriptors in prostate T2-W MRI. For this purpose we extracted 215 texture features from 418 T2-W MRI images and extracted them using 9 different window sizes (3 × 3 to 19 × 19). The Bayesian Network and Random Forest classifiers were employed to perform the classification. Experimental results suggest that using window size of 9 × 9 and 11 × 11 produced Az > 89%. Also, this study suggests a set of best texture features based on our experimental results.

This study aims to investigate the role of diffusion-weighted imaging (DWI) and T2-weighted imaging (T2WI) in combination for the detection of prostate cancer, specifically assessing the role of high b-values (> 1000 s/mm2), with a systematic review and meta-analysis of the existing published data.The electronic databases MEDLINE, EMBASE, and OpenSIGLE were searched between inception and September 1, 2017. Eligible studies were those that reported the sensitivity and specificity of DWI and T2WI for the diagnosis of prostate cancer by visual assessment using a histopathologic reference standard. The QUADAS-2 critical appraisal tool was used to assess the quality of included studies. A meta-analysis with pooling of sensitivity, specificity, likelihood, and diagnostic odds ratios was undertaken, and a summary receiver-operating characteristics (sROC) curve was constructed. Predetermined subgroup analysis was also performed.Thirty-three studies were included in the final analysis, evaluating 2949 patients. The pooled sensitivity and specificity were 0.69 (95% CI 0.68-0.69) and 0.84 (95% CI 0.83-0.85), respectively, and the sROC AUC was 0.84 (95% CI 0.81-0.87). Subgroup analysis showed significantly better sensitivity with high b-values (> 1000 s/mm2). There was high statistical heterogeneity between studies.The diagnostic accuracy of combined DWI and T2WI is good with high b-values (> 1000 s/mm2) seeming to improve overall sensitivity while maintaining specificity. However, further large-scale studies specifically looking at b-value choice are required before a categorical recommendation can be made.