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Objective. Image quality in whole-body MRI (WB-MRI) may be degraded by faulty radiofrequency (RF) coil elements or mispositioning of the coil arrays. Phantom-based quality control (QC) is used to identify broken RF coil elements but the frequency of these acquisitions is limited by scanner and staff availability. This work aimed to develop a scan-specific QC acquisition and processing pipeline to detect broken RF coil elements, which is sufficiently rapid to be added to the clinical WB-MRI protocol. The purpose of this is to improve the quality of WB-MRI by reducing the number of patient examinations conducted with suboptimal equipment. Approach. A rapid acquisition (14 s additional acquisition time per imaging station) was developed that identifies broken RF coil elements by acquiring images from each individual coil element and using the integral body coil. This acquisition was added to one centre's clinical WB-MRI protocol for one year (892 examinations) to evaluate the effect of this scan-specific QC. To demonstrate applicability in multi-centre imaging trials, the technique was also implemented on scanners from three manufacturers. Main results . Over the course of the study RF coil elements were flagged as potentially broken on five occasions, with the faults confirmed in four of those cases. The method had a precision of 80% and a recall of 100% for detecting faulty RF coil elements. The coil array positioning measurements were consistent across scanners and have been used to define the expected variation in signal. Significance . The technique demonstrated here can identify faulty RF coil elements and positioning errors and is a practical addition to the clinical WB-MRI protocol. This approach was fully implemented on systems from two manufacturers and partially implemented on a third. It has potential to reduce the number of clinical examinations conducted with suboptimal hardware and improve image quality across multi-centre studies., (Creative Commons Attribution license.)

Objectives: MAchine Learning In MyelomA Response (MALIMAR) is an observational clinical study combining "real-world" and clinical trial data, both retrospective and prospective. Images were acquired on three MRI scanners over a 10-year window at two institutions, leading to a need for extensive curation., Methods: Curation involved image aggregation, pseudonymisation, allocation between project phases, data cleaning, upload to an XNAT repository visible from multiple sites, annotation, incorporation of machine learning research outputs and quality assurance using programmatic methods., Results: A total of 796 whole-body MR imaging sessions from 462 subjects were curated. A major change in scan protocol part way through the retrospective window meant that approximately 30% of available imaging sessions had properties that differed significantly from the remainder of the data. Issues were found with a vendor-supplied clinical algorithm for "composing" whole-body images from multiple imaging stations. Historic weaknesses in a digital video disk (DVD) research archive (already addressed by the mid-2010s) were highlighted by incomplete datasets, some of which could not be completely recovered. The final dataset contained 736 imaging sessions for 432 subjects. Software was written to clean and harmonise data. Implications for the subsequent machine learning activity are considered., Conclusions: MALIMAR exemplifies the vital role that curation plays in machine learning studies that use real-world data. A research repository such as XNAT facilitates day-to-day management, ensures robustness and consistency and enhances the value of the final dataset. The types of process described here will be vital for future large-scale multi-institutional and multi-national imaging projects., Critical Relevance Statement: This article showcases innovative data curation methods using a state-of-the-art image repository platform; such tools will be vital for managing the large multi-institutional datasets required to train and validate generalisable ML algorithms and future foundation models in medical imaging., Key Points: • Heterogeneous data in the MALIMAR study required the development of novel curation strategies. • Correction of multiple problems affecting the real-world data was successful, but implications for machine learning are still being evaluated. • Modern image repositories have rich application programming interfaces enabling data enrichment and programmatic QA, making them much more than simple "image marts"., (© 2024. The Author(s).)

Artificial intelligence (AI) methods applied to healthcare problems have shown enormous potential to alleviate the burden of health services worldwide and to improve the accuracy and reproducibility of predictions. In particular, developments in computer vision are creating a paradigm shift in the analysis of radiological images, where AI tools are already capable of automatically detecting and precisely delineating tumours. However, such tools are generally developed in technical departments that continue to be siloed from where the real benefit would be achieved with their usage. Significant effort still needs to be made to make these advancements available, first in academic clinical research and ultimately in the clinical setting. In this paper, we demonstrate a prototype pipeline based entirely on open-source software and free of cost to bridge this gap, simplifying the integration of tools and models developed within the AI community into the clinical research setting, ensuring an accessible platform with visualisation applications that allow end-users such as radiologists to view and interact with the outcome of these AI tools.

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, H 0 :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)., (© 2023. International Cancer Imaging Society (ICIS).)

Recurrence occurs in up to 36% of patients treated with curative-intent radiotherapy for NSCLC. Identifying patients at higher risk of recurrence for more intensive surveillance may facilitate the earlier introduction of the next line of treatment. We aimed to use radiotherapy planning CT scans to develop radiomic classification models that predict overall survival (OS), recurrence-free survival (RFS) and recurrence two years post-treatment for risk-stratification. A retrospective multi-centre study of >900 patients receiving curative-intent radiotherapy for stage I-III NSCLC was undertaken. Models using radiomic and/or clinical features were developed, compared with 10-fold cross-validation and an external test set, and benchmarked against TNM-stage. Respective validation and test set AUCs (with 95% confidence intervals) for the radiomic-only models were: (1) OS: 0.712 (0.592-0.832) and 0.685 (0.585-0.784), (2) RFS: 0.825 (0.733-0.916) and 0.750 (0.665-0.835), (3) Recurrence: 0.678 (0.554-0.801) and 0.673 (0.577-0.77). For the combined models: (1) OS: 0.702 (0.583-0.822) and 0.683 (0.586-0.78), (2) RFS: 0.805 (0.707-0.903) and 0·755 (0.672-0.838), (3) Recurrence: 0·637 (0.51-0.·765) and 0·738 (0.649-0.826). Kaplan-Meier analyses demonstrate OS and RFS difference of >300 and >400 days respectively between low and high-risk groups. We have developed validated and externally tested radiomic-based prediction models. Such models could be integrated into the routine radiotherapy workflow, thus informing a personalised surveillance strategy at the point of treatment. Our work lays the foundations for future prospective clinical trials for quantitative personalised risk-stratification for surveillance following curative-intent radiotherapy for NSCLC., (© 2022. The Author(s).)

Purpose : XNAT is an informatics software platform to support imaging research, particularly in the context of large, multicentre studies of the type that are essential to validate quantitative imaging biomarkers. XNAT provides import, archiving, processing and secure distribution facilities for image and related study data. Until recently, however, modern data visualisation and annotation tools were lacking on the XNAT platform. We describe the background to, and implementation of, an integration of the Open Health Imaging Foundation (OHIF) Viewer into the XNAT environment. We explain the challenges overcome and discuss future prospects for quantitative imaging studies. Materials and methods : The OHIF Viewer adopts an approach based on the DICOM web protocol. To allow operation in an XNAT environment, a data-routing methodology was developed to overcome the mismatch between the DICOM and XNAT information models and a custom viewer panel created to allow navigation within the viewer between different XNAT projects, subjects and imaging sessions. Modifications to the development environment were made to allow developers to test new code more easily against a live XNAT instance. Major new developments focused on the creation and storage of regions-of-interest (ROIs) and included: ROI creation and editing tools for both contour- and mask-based regions; a "smart CT" paintbrush tool; the integration of NVIDIA's Artificial Intelligence Assisted Annotation (AIAA); the ability to view surface meshes, fractional segmentation maps and image overlays; and a rapid image reader tool aimed at radiologists. We have incorporated the OHIF microscopy extension and, in parallel, introduced support for microscopy session types within XNAT for the first time. Results: Integration of the OHIF Viewer within XNAT has been highly successful and numerous additional and enhanced tools have been created in a programme started in 2017 that is still ongoing. The software has been downloaded more than 3700 times during the course of the development work reported here, demonstrating the impact of the work. Conclusions : The OHIF open-source, zero-footprint web viewer has been incorporated into the XNAT platform and is now used at many institutions worldwide. Further innovations are envisaged in the near future.

Genetic intra-tumour heterogeneity fuels clonal evolution, but our understanding of clinically relevant clonal dynamics remain limited. We investigated spatial and temporal features of clonal diversification in clear cell renal cell carcinoma through a combination of modelling and real tumour analysis. We observe that the mode of tumour growth, surface or volume, impacts the extent of subclonal diversification, enabling interpretation of clonal diversity in patient tumours. Specific patterns of proliferation and necrosis explain clonal expansion and emergence of parallel evolution and microdiversity in tumours. In silico time-course studies reveal the appearance of budding structures before detectable subclonal diversification. Intriguingly, we observe radiological evidence of budding structures in early-stage clear cell renal cell carcinoma, indicating that future clonal evolution may be predictable from imaging. Our findings offer a window into the temporal and spatial features of clinically relevant clonal evolution., (© 2021. The Author(s).)

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 delivering precision medicine. The authors provide a practical approach for successfully implementing a radiomic workflow from planning and conceptualization through manuscript writing. Applications in oncology typically are either classification tasks that involve computing the probability of a sample belonging to a category, such as benign versus malignant, or prediction of clinical events with a time-to-event analysis, such as overall survival. The radiomic workflow is multidisciplinary, involving radiologists and data and imaging scientists, and follows a stepwise process involving tumor segmentation, image preprocessing, feature extraction, model development, and validation. Images are curated and processed before segmentation, which can be performed on tumors, tumor subregions, or peritumoral zones. Extracted features typically describe the distribution of signal intensities and spatial relationship of pixels within a region of interest. To improve model performance and reduce overfitting, redundant and nonreproducible features are removed. Validation is essential to estimate model performance in new data and can be performed iteratively on samples of the dataset (cross-validation) or on a separate hold-out dataset by using internal or external data. A variety of noncommercial and commercial radiomic software applications can be used. Guidelines and artificial intelligence checklists are useful when planning and writing up radiomic studies. Although interest in the field continues to grow, radiologists should be familiar with potential pitfalls to ensure that meaningful conclusions can be drawn. Online supplemental material is available for this article. Published under a CC BY 4.0 license.

Background: Most MRI radiomics studies to date, even multi-centre ones, have used "pure" datasets deliberately accrued from single-vendor, single-field-strength scanners. This does not reflect aspirations for the ultimate generalisability of AI models. We therefore investigated the development of a radiomics signature from heterogeneous data originating on six different imaging platforms, for a breast cancer exemplar, in order to provide input into future discussions of the viability of radiomics in "real-world" scenarios where image data are not controlled by specific trial protocols but reflective of routine clinical practice., Methods: One hundred fifty-six patients with pathologically proven breast cancer underwent multi-contrast MRI prior to neoadjuvant chemotherapy and/or surgery. From these, 92 patients were identified for whom T2-weighted, diffusion-weighted and contrast-enhanced T1-weighted sequences were available, as well as key clinicopathological variables. Regions-of-interest were drawn on the above image types and, from these, semantic and calculated radiomics features were derived. Classification models using a variety of methods, both with and without recursive feature elimination, were developed to predict pathological nodal status. Separately, we applied the same methods to analyse the information carried by the radiomic features regarding the originating scanner type and field strength. Repeated, ten-fold cross-validation was employed to verify the results. In parallel work, survival modelling was performed using random survival forests., Results: Prediction of nodal status yielded mean cross-validated AUC values of 0.735 ± 0.15 (SD) for clinical variables alone, 0.673 ± 0.16 (SD) for radiomic features only, and 0.764 ± 0.16 (SD) for radiomics and clinical features together. Prediction of scanner platform from the radiomics features yielded extremely high values of AUC between 0.91 and 1 for the different classes examined indicating the presence of confounding features for the nodal status classification task. Survival analysis, gave out-of-bag prediction errors of 19.3% (clinical features only), 36.9-51.8% (radiomic features from different combinations of image contrasts), and 26.7-35.6% (clinical plus radiomics features)., Conclusions: Radiomic classification models whose predictive ability was consistent with previous single-vendor, single-field strength studies have been obtained from multi-vendor, multi-field-strength data, despite clear confounding information being present. However, our sample size was too small to obtain useful survival modelling results.

The magnetic field in magnetic resonance imaging guided radiotherapy (MRgRT) delivery systems influences charged-particle trajectories and hence the three-dimensional (3D) radiation dose distributions. This study investigated the dose-response as well as dose-rate and fractionation dependencies of silicone-based 3D radiochromic dosimeters for photon irradiation in a magnetic field using a 0.35 T MRgRT system. We found a linear dose response up to 22.6 Gy and no significant dose-rate dependency as a function of depth. A difference in optical response was observed for dosimeters irradiated in a single compared to multiple fractions. The dosimeter showed clinical potential for verification of MRgRT delivery., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2020 The Authors.)

Background: Textural features extracted from MRI potentially provide prognostic information additional to volume for influencing surgical management of cervical cancer., Purpose: To identify textural features that differ between cervical tumors above and below the volume threshold of eligibility for trachelectomy and determine their value in predicting recurrence in patients with low-volume tumors., Methods: Of 378 patients with Stage1-2 cervical cancer imaged prospectively (3T, endovaginal coil), 125 had well-defined, histologically-confirmed squamous or adenocarcinomas with >100 voxels (>0.07 cm 3 ) suitable for radiomic analysis. Regions-of-interest outlined the whole tumor on T2-W images and apparent diffusion coefficient (ADC) maps. Textural features based on grey-level co-occurrence matrices were compared (Mann-Whitney test with Bonferroni correction) between tumors greater (n = 46) or less (n = 79) than 4.19 cm 3 . Clustering eliminated correlated variables. Significantly different features were used to predict recurrence (regression modelling) in surgically-treated patients with low-volume tumors and compared with a model using clinico-pathological features., Results: Textural features (Dissimilarity, Energy, ClusterProminence, ClusterShade, InverseVariance, Autocorrelation) in 6 of 10 clusters from T2-W and ADC data differed between high-volume (mean ± SD 15.3 ± 11.7 cm 3 ) and low-volume (mean ± SD 1.3 ± 1.2 cm 3 ) tumors. (p < 0.02). In low-volume tumors, predicting recurrence was indicated by: Dissimilarity, Energy (ADC-radiomics, AUC = 0.864); Dissimilarity, ClusterProminence, InverseVariance (T2-W-radiomics, AUC = 0.808); Volume, Depth of Invasion, LymphoVascular Space Invasion (clinico-pathological features, AUC = 0.794). Combining ADC-radiomic (but not T2-radiomic) and clinico-pathological features improved prediction of recurrence compared to the clinico-pathological model (AUC = 0.916, p = 0.006). Findings were supported by bootstrap re-sampling (n = 1000)., Conclusion: Textural features from ADC maps and T2-W images differ between high- and low-volume tumors and potentially predict recurrence in low-volume tumors., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Endogenous hormones are associated with breast cancer risk, but little is known about their role on breast tissue composition, a strong risk predictor. This study aims to investigate the relationship between growth and sex hormone levels and breast tissue composition in young nulliparous women., Methods: A cross-sectional study of 415 young (age ∼21.5 years) nulliparous women from an English prebirth cohort underwent a MRI examination of their breasts to estimate percent-water (a proxy for mammographic percent density) and provided a blood sample to measure plasma levels of growth factors (insulin-like growth factor-I, insulin-like growth factor-II, insulin growth factor-binding protein-3, growth hormone) and, if not on hormonal contraception ( n = 117) sex hormones (dehydroepiandrosterone, androstenedione, testosterone, estrone, estadiol, sex hormone-binding globulin, prolactin). Testosterone ( n = 330) and sex hormone-binding globulin ( n = 318) were also measured at age 15.5 years. Regression models were used to estimate the relative difference (RD) in percent-water associated with one SD increment in hormone levels., Results: Estradiol at age 21.5 and sex hormone-binding globulin at age 21.5 were positively associated with body mass index (BMI)-adjusted percent-water [RD (95% confidence interval (CI)): 3% (0%-7%) and 3% (1%-5%), respectively]. There was a positive nonlinear association between androstenedione at age 21.5 and percent-water. Insulin-like growth factor-I and growth hormone at age 21.5 were also positively associated with BMI-adjusted percent-water [RD (95% CI): 2% (0%-4%) and 4% (1%-7%), respectively]., Conclusions: The findings suggest that endogenous hormones affect breast tissue composition in young nulliparous women., Impact: The well-established associations of childhood growth and development with breast cancer risk may be partly mediated by the role of endogenous hormones on breast tissue composition., (©2018 American Association for Cancer Research.)

Mammographic percent density, the proportion of fibroglandular tissue in the breast, is a strong risk factor for breast cancer, but its determinants in young women are unknown. We examined associations of magnetic resonance imaging (MRI) breast-tissue composition at age 21 years with prospectively collected measurements of body size and composition from birth to early adulthood and markers of puberty (all standardized) in a sample of 500 nulliparous women from a prebirth cohort of children born in Avon, United Kingdom, in 1991-1992 and followed up to 2011-2014. Linear models were fitted to estimate relative change in MRI percent water, which is equivalent to mammographic percent density, associated with a 1-standard-deviation increase in the exposure of interest. In mutually adjusted analyses, MRI percent water was positively associated with birth weight (relative change (RC) = 1.03, 95% confidence interval (CI): 1.00, 1.06) and pubertal height growth (RC = 1.07, 95% CI: 1.02, 1.13) but inversely associated with pubertal weight growth (RC = 0.86, 95% CI: 0.84, 0.89) and changes in dual-energy x-ray absorptiometry percent body fat mass (e.g., for change between ages 11 years and 13.5 years, RC = 0.96, 95% CI: 0.93, 0.99). Ages at thelarche and menarche were positively associated with MRI percent water, but these associations did not persist upon adjustment for height and weight growth. These findings support the hypothesis that growth trajectories influence breast-tissue composition in young women, whereas puberty plays no independent role.

Dosimetric quality assurance (QA) of the new Elekta Unity (MR-linac) will differ from the QA performed of a conventional linac due to the constant magnetic field, which creates an electron return effect (ERE). In this work we aim to validate PRESAGE ® dosimetry in a transverse magnetic field, and assess its use to validate the research version of the Monaco TPS of the MR-linac. Cylindrical samples of PRESAGE ® 3D dosimeter separated by an air gap were irradiated with a cobalt-60 unit, while placed between the poles of an electromagnet at 0.5 T and 1.5 T. This set-up was simulated in EGSnrc/Cavity Monte Carlo (MC) code and relative dose distributions were compared with measurements using 1D and 2D gamma criteria of 3% and 1.5 mm. The irradiation conditions were adapted for the MR-linac and compared with Monaco TPS simulations. Measured and EGSnrc/Cavity simulated profiles showed good agreement with a gamma passing rate of 99.9% for 0.5 T and 99.8% for 1.5 T. Measurements on the MR-linac also compared well with Monaco TPS simulations, with a gamma passing rate of 98.4% at 1.5 T. Results demonstrated that PRESAGE ® can accurately measure dose and detect the ERE, encouraging its use as a QA tool to validate the Monaco TPS of the MR-linac for clinically relevant dose distributions at tissue-air boundaries.

Purpose: To compare two methods of automatic breast segmentation with each other and with manual segmentation in a large subject cohort. To discuss the factors involved in selecting the most appropriate algorithm for automatic segmentation and, in particular, to investigate the appropriateness of overlap measures (e.g., Dice and Jaccard coefficients) as the primary determinant in algorithm selection., Methods: Two methods of breast segmentation were applied to the task of calculating MRI breast density in 200 subjects drawn from the Avon Longitudinal Study of Parents and Children, a large cohort study with an MRI component. A semiautomated, bias-corrected, fuzzy C-means (BC-FCM) method was combined with morphological operations to segment the overall breast volume from in-phase Dixon images. The method makes use of novel, problem-specific insights. The resulting segmentation mask was then applied to the corresponding Dixon water and fat images, which were combined to give Dixon MRI density values. Contemporaneously acquired T 1 - and T 2 -weighted image datasets were analyzed using a novel and fully automated algorithm involving image filtering, landmark identification, and explicit location of the pectoral muscle boundary. Within the region found, fat-water discrimination was performed using an Expectation Maximization-Markov Random Field technique, yielding a second independent estimate of MRI density., Results: Images are presented for two individual women, demonstrating how the difficulty of the problem is highly subject-specific. Dice and Jaccard coefficients comparing the semiautomated BC-FCM method, operating on Dixon source data, with expert manual segmentation are presented. The corresponding results for the method based on T 1 - and T 2 -weighted data are slightly lower in the individual cases shown, but scatter plots and interclass correlations for the cohort as a whole show that both methods do an excellent job in segmenting and classifying breast tissue., Conclusions: Epidemiological results demonstrate that both methods of automated segmentation are suitable for the chosen application and that it is important to consider a range of factors when choosing a segmentation algorithm, rather than focus narrowly on a single metric such as the Dice coefficient., (© 2017 The Authors. Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.)

Purpose To assess the repeatability of apparent diffusion coefficient (ADC) estimates in extracranial soft-tissue diffusion-weighted magnetic resonance imaging across a wide range of imaging protocols and patient populations. Materials and Methods Nine prospective patient studies and one prospective volunteer study, performed between 2006 and 2016 with research ethics committee approval and written informed consent from each subject, were included in this single-institution study. A total of 141 tumors and healthy organs were imaged twice (interval between repeated examinations, 45 minutes to 10 days, depending the on study) to assess the repeatability of median and mean ADC estimates. The Levene test was used to determine whether ADC repeatability differed between studies. The Pearson linear correlation coefficient was used to assess correlation between coefficient of variation (CoV) and the year the study started, study size, and volumes of tumors and healthy organs. The repeatability of ADC estimates from small, medium, and large tumors and healthy organs was assessed irrespective of study, and the Levene test was used to determine whether ADC repeatability differed between these groups. Results CoV aggregated across all studies was 4.1% (range for each study, 1.7%-6.5%). No correlation was observed between CoV and the year the study started or study size. CoV was weakly correlated with volume (r = -0.5, P = .1). Repeatability was significantly different between small, medium, and large tumors (P < .05), with the lowest CoV (2.6%) for large tumors. There was a significant difference in repeatability between studies-a difference that did not persist after the study with the largest tumors was excluded. Conclusion ADC is a robust imaging metric with excellent repeatability in extracranial soft tissues across a wide range of tumor sites, sizes, patient populations, and imaging protocol variations. Online supplemental material is available for this article.

Density assessment and lesion localization in breast MRI require accurate segmentation of breast tissues. A fast, computerized algorithm for volumetric breast segmentation, suitable for multi-centre data, has been developed, employing 3D bias-corrected fuzzy c-means clustering and morphological operations. The full breast extent is determined on T1-weighted images without prior information concerning breast anatomy. Left and right breasts are identified separately using automatic detection of the midsternum. Statistical analysis of breast volumes from eighty-two women scanned in a UK multi-centre study of MRI screening shows that the segmentation algorithm performs well when compared with manually corrected segmentation, with high relative overlap (RO), high true-positive volume fraction (TPVF) and low false-positive volume fraction (FPVF), and has an overall performance of RO 0.94 ± 0.05, TPVF 0.97 ± 0.03 and FPVF 0.04 ± 0.06, respectively (training: 0.93 ± 0.05, 0.97 ± 0.03 and 0.04 ± 0.06; test: 0.94 ± 0.05, 0.98 ± 0.02 and 0.05 ± 0.07)., Competing Interests: The authors disclosed no potential conflicts of interest.

Background: Breast density, the amount of fibroglandular tissue in the adult breast for a women's age and body mass index, is a strong biomarker of susceptibility to breast cancer, which may, like breast cancer risk itself, be influenced by events early in life. In the present study, we investigated the association between pre-natal exposures and breast tissue composition., Methods: A sample of 500 young, nulliparous women (aged approximately 21 years) from a U.K. pre-birth cohort underwent a magnetic resonance imaging examination of their breasts to estimate percent water, a measure of the relative amount of fibroglandular tissue equivalent to mammographic percent density. Information on pre-natal exposures was collected throughout the mothers' pregnancy and shortly after delivery. Regression models were used to investigate associations between percent water and pre-natal exposures. Mediation analysis, and a systematic review and meta-analysis of the published literature, were also conducted., Results: Adjusted percent water in young women was positively associated with maternal height (p for linear trend [p t ] = 0.005), maternal mammographic density in middle age (p t  = 0.018) and the participant's birth size (p t  < 0.001 for birthweight). A 1-SD increment in weight (473 g), length (2.3 cm), head circumference (1.2 cm) and Ponderal Index (4.1 g/cm 3 ) at birth were associated with 3 % (95 % CI 2-5 %), 2 % (95 % CI 0-3 %), 3 % (95 % CI 1-4 %) and 1 % (95 % CI 0-3 %), respectively, increases in mean adjusted percent water. The effect of maternal height on the participants' percent water was partly mediated through birth size, but there was little evidence that the effect of birthweight was primarily mediated via adult body size. The meta-analysis supported the study findings, with breast density being positively associated with birth size., Conclusions: These findings provide strong evidence of pre-natal influences on breast tissue composition. The positive association between birth size and relative amount of fibroglandular tissue indicates that breast density and breast cancer risk may share a common pre-natal origin.

Synchrotron microbeam radiation therapy (MRT) is an advanced form of radiotherapy for which it is extremely difficult to provide adequate quality assurance. This may delay or limit its clinical uptake, particularly in the paediatric patient populations for whom it could be especially suitable. This study investigates the extent to which new developments in 3D dosimetry using optical computed tomography (CT) can visualise MRT dose distributions, and assesses what further developments are necessary before fully quantitative 3D measurements can be achieved. Two experiments are reported. In the first cylindrical samples of the radiochromic polymer PRESAGE(®) were irradiated with different complex MRT geometries including multiport treatments of collimated 'pencil' beams, interlaced microplanar arrays and a multiport treatment using an anthropomorphic head phantom. Samples were scanned using transmission optical CT. In the second experiment, optical CT measurements of the biologically important peak-to-valley dose ratio (PVDR) were compared with expected values from Monte Carlo simulations. The depth-of-field (DOF) of the optical CT system was characterised using a knife-edge method and the possibility of spatial resolution improvement through deconvolution of a measured point spread function (PSF) was investigated. 3D datasets from the first experiment revealed excellent visualisation of the 50 μm beams and various discrepancies from the planned delivery dose were found. The optical CT PVDR measurements were found to be consistently 30% of the expected Monte Carlo values and deconvolution of the microbeam profiles was found to lead to increased noise. The reason for the underestimation of the PVDR by optical CT was attributed to lack of spatial resolution, supported by the results of the DOF characterisation. Solutions are suggested for the outstanding challenges and the data are shown already to be useful in identifying potential treatment anomalies.

Methods of monitoring drug toxicity in off-target organs are very important in the development of effective and safe drugs. Standard 2-D techniques, such as histology, are prone to sampling errors and can miss important information. We demonstrate a novel application of optical computed tomography (CT) imaging to quantitatively assess, in 3-D, the response of adult murine spleen to off-target drug toxicity induced by treatment with the vascular disrupting agent ZD6126. Reconstructed images from optical CT scans sensitive to haemoglobin absorption reveal detailed, high-contrast 3-D maps of splenic structure and microvasculature. A significant difference in total splenic volume was found between vehicle and ZD6126-treated cohorts, with mean volumes of 61±3mm(3) and 44±3mm(3) respectively (both n=3, p=0.05). Textural statistics for each sample were calculated using grey-level co-occurrence matrices (GLCMs). Standard 2-D GLCM analysis was found to be slice-dependent while 3-D GLCM contrast and homogeneity analysis resulted in separation of the vehicle and ZD6126-treated cohorts over a range of length scales., (Copyright © 2015. Published by Elsevier Inc.)

Previous research on optical computed tomography (CT) microscopy in the context of the synchrotron microbeam has shown the potential of the technique and demonstrated high quality images, but has left two questions unanswered: (i) are the images suitably quantitative for 3D dosimetry? and (ii) what is the impact on the spatial resolution of the system of the limited depth-of-field of the microscope optics? Cuvette and imaging studies are reported here that address these issues. Two sets of cuvettes containing the radiochromic plastic PRESAGE® were irradiated at the ID17 biomedical beamline of the European Synchrotron Radiation facility over the ranges 0-20 and 0-35 Gy and a third set of cuvettes was irradiated over the range 0-20 Gy using a standard medical linac. In parallel, three cylindrical PRESAGE® samples of diameter 9.7 mm were irradiated with test patterns that allowed the quantitative capabilities of the optical CT microscope to be verified, and independent measurements of the imaging modulation transfer function (MTF) to be made via two different methods. Both spectrophotometric analysis and imaging gave a linear dose response, with gradients ranging from 0.036-0.041 cm(-1) Gy(-1) in the three sets of cuvettes and 0.037 (optical CT units) Gy(-1) for the imaging. High-quality, quantitative imaging results were obtained throughout the 3D volume, as illustrated by depth-dose profiles. These profiles are shown to be monoexponential, and the linear attention coefficient of PRESAGE® for the synchrotron-generated x-ray beam is measured to be (0.185 ± 0.02) cm(-1) in excellent agreement with expectations. Low-level (<5%) residual image artefacts are discussed in detail. It was possible to resolve easily slit patterns of width 37 µm (which are smaller than many of the microbeams used on ID-17), but some uncertainty remains as to whether the low values of MTF for the higher spatial frequencies are scanner related or a result of genuine (but non-ideal) dose distributions. We conclude that microscopy images from our scanner do indeed have intensities that are proportional to spectrophotometric optical density and can thus be used as the basis for accurate dosimetry. However, further investigations are necessary before the microscopy images can be used to make the quantitative measures of peak-to-valley ratios for small-diameter microbeams. We suggest various strategies for moving forward and are optimistic about the future potential of this system.

Medical image analysis remains a challenging application area for artificial intelligence. When applying machine learning, obtaining ground-truth labels for supervised learning is more difficult than in many more common applications of machine learning. This is especially so for datasets with abnormalities, as tissue types and the shapes of the organs in these datasets differ widely. However, organ detection in such an abnormal dataset may have many promising potential real-world applications, such as automatic diagnosis, automated radiotherapy planning, and medical image retrieval, where new multimodal medical images provide more information about the imaged tissues for diagnosis. Here, we test the application of deep learning methods to organ identification in magnetic resonance medical images, with visual and temporal hierarchical features learned to categorize object classes from an unlabeled multimodal DCE-MRI dataset so that only a weakly supervised training is required for a classifier. A probabilistic patch-based method was employed for multiple organ detection, with the features learned from the deep learning model. This shows the potential of the deep learning model for application to medical images, despite the difficulty of obtaining libraries of correctly labeled training datasets and despite the intrinsic abnormalities present in patient datasets.

Purpose: Gold nanoparticles (AuNps), because of their high atomic number (Z), have been demonstrated to absorb low-energy X-rays preferentially, compared with tissue, and may be used to achieve localized radiation dose enhancement in tumors. The purpose of this study is to introduce the first example of a novel multicompartment radiochromic radiation dosimeter and to demonstrate its applicability for 3-dimensional (3D) dosimetry of nanoparticle-enhanced radiation therapy., Methods and Materials: A novel multicompartment phantom radiochromic dosimeter was developed. It was designed and formulated to mimic a tumor loaded with AuNps (50 nm in diameter) at a concentration of 0.5 mM, surrounded by normal tissues. The novel dosimeter is referred to as the Sensitivity Modulated Advanced Radiation Therapy (SMART) dosimeter. The dosimeters were irradiated with 100-kV and 6-MV X-ray energies. Dose enhancement produced from the interaction of X-rays with AuNps was calculated using spectrophotometric and cone-beam optical computed tomography scanning by quantitatively comparing the change in optical density and 3D datasets of the dosimetric measurements between the tissue-equivalent (TE) and TE/AuNps compartments. The interbatch and intrabatch variability and the postresponse stability of the dosimeters with AuNps were also assessed., Results: Radiation dose enhancement factors of 1.77 and 1.11 were obtained using 100-kV and 6-MV X-ray energies, respectively. The results of this study are in good agreement with previous observations; however, for the first time we provide direct experimental confirmation and 3D visualization of the radiosensitization effect of AuNps. The dosimeters with AuNps showed small (<3.5%) interbatch variability and negligible (<0.5%) intrabatch variability., Conclusions: The SMART dosimeter yields experimental insights concerning the spatial distributions and elevated dose in nanoparticle-enhanced radiation therapy, which cannot be performed using any of the current methods. The authors concluded that it can be used as a novel independent method for nanoparticle-enhanced radiation therapy dosimetry., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Picture archiving and communication systems (PACS) provide limited flexibility for the development of novel research methods. By contrast, the research model of data access is more flexible but has vulnerabilities in numerous areas. No single monolithic application can fulfill the diverse and rapidly changing needs of the clinical imaging research community. Instead, the focus should be on the interoperability of preexisting systems. To a large extent, this can be achieved by means of a unified interface for storing and retrieving data. The concept of a research PACS combines the advantages of the clinical and research models of data access while eliminating the disadvantages. A research PACS streamlines the data management process. Instead of a single software program, it consists of a confederation of independent applications brought together by the ability to store and retrieve data in a common database. A prototype research PACS has been developed that is based on the Extensible Neuroimaging Archive Toolkit (XNAT) in association with two new in-house tools: a data selection tool and a data archiving tool. By taking as an example the comparison of regions of interest in multifunctional liver data, it was demonstrated that this framework allows a number of in-house and open-source applications originally designed to work on a stand-alone basis to be integrated into a unified workflow, with minimal redevelopment effort.

Optical computed tomography has now become a well-established method for making empirical measurements of 3D dose distributions in radiotherapy treatment verification. The requirement for effective refractive index matching as part of the scanning process has long been an inconvenience for users, limiting the speed of sample throughput. We propose a new method for reconstructing data that takes explicit account of the refracted path of the light rays and demonstrate theoretically the conditions under which there are sufficient data to create a good reconstruction. Examples of the performance of the algorithm are given. For smoothly varying data, reconstructed images of very high quality are obtained, with RMS deviation of under 1% from the original, provided that the irradiated region lies entirely within a critical radius. For the dosimeter material PRESAGE, this critical value is approximately 0.65 of the sample radius. Regions outside this are not reconstructed successfully, but we argue that there are many cases where this disadvantage is outweighed by the benefits of the technique.

Optical computed tomography (CT), in conjunction with radiochromic gels and plastics, shows great potential for radiation therapy dose verification in 3D. However, an effective quality assurance (QA) regime for the various scanners currently available still remains to be developed. We show how the favourable properties of the PRESAGE® radiochromic polymer may be exploited to create highly sophisticated QA phantoms. Five 60 mm diameter cylindrical PRESAGE® samples were irradiated using the x-ray microbeam radiation therapy facility on the ID-17 biomedical beamline at the European Synchrotron Radiation Facility. Samples were then imaged on the University of Surrey parallel-beam optical CT scanner. The sample irradiations were designed to allow a variety of tests to be performed, including assessments of linearity, modulation transfer function (three independent measurements), geometric distortion and the effect of treatment fractionation. It is clear that, although the synchrotron method produces extremely high-quality test objects, it is not practical on a routine basis, because of its reliance on a highly specialized radiation source. Hence, we investigated a second possibility: three PRESAGE® samples were illuminated with ultraviolet light of wavelength 365 nm, using cheap masks created by laser-printing patterns onto overhead projector acetate sheets. There was good correlation between optical densities measured by the CT scanner and the expected UV 'dose' delivered. The results are encouraging and a proposal is made for a scanner test regime based on calibrated and well-characterized PRESAGE® samples.

X-ray microbeam radiation therapy (MRT) is a novel form of treatment, currently in its preclinical stage, which uses microplanar x-ray beams from a synchrotron radiation source. It is important to perform accurate dosimetry on these microbeams, but, to date, there has been no accurate enough method available for making 3D dose measurements with isotropic, high spatial resolution to verify the results of Monte Carlo dose simulations. Here, we investigate the potential of optical computed tomography for satisfying these requirements. The construction of a simple optical CT microscopy (optical projection tomography) system from standard commercially available hardware is described. The measurement of optical densities in projection data is shown to be highly linear (r2=0.999). The depth-of-field (DOF) of the imaging system is calculated based on the previous literature and measured experimentally using a commercial DOF target. It is shown that high quality images can be acquired despite the evident lack of telecentricity and despite DOF of the system being much lower than the sample diameter. Possible reasons for this are discussed. Results are presented for a complex irradiation of a 22 mm diameter cylinder of the radiochromic polymer PRESAGE, demonstrating the exquisite 'dose-painting' abilities available in the MRT hutch of beamline ID-17 at the European Synchrotron Radiation Facility. Dose distributions in this initial experiment are equally well resolved on both an optical CT scan and a corresponding transmission image of radiochromic film, down to a line width of 83 microm (6 lp mm(-1)) with an MTF value of 0.40. A group of 33 microm wide lines was poorly resolved on both the optical CT and film images, and this is attributed to an incorrect exposure time calculation, leading to under-delivery of dose. Image artefacts in the optical CT scan are discussed. PRESAGE irradiated using the microbeam facility is proposed as a suitable material for producing phantom samples for quantitative characterization of optical CT microscopy systems.

Over recent decades, modern protocols of external beam radiotherapy have been developed that involve very steep dose gradients and are thus extremely sensitive to errors in treatment delivery. A recent credentialling study by the Radiological Physics Center at the MD Anderson Cancer Center (Texas, USA) has noted potentially significant inaccuracies in test treatments at a variety of institutions. 3-D radiation dosimetry (often referred to as "gel dosimetry") may have an important role in commissioning new treatment protocols, to help prevent this type of error. This article discusses the various techniques of 3-D radiation dosimetry, with a focus on the types of radiosensitive samples used and on the optical computed tomography readout technique.

3D measurement of optical attenuation is of interest in a variety of fields of biomedical importance, including spectrophotometry, optical projection tomography (OPT) and analysis of 3D radiation dosimeters. Accurate, precise and economical 3D measurements of optical density (OD) are a crucial step in enabling 3D radiation dosimeters to enter wider use in clinics. Polymer gels and Fricke gels, as well as dosimeters not based around gels, have been characterized for 3D dosimetry over the last two decades. A separate problem is the verification of the best readout method. A number of different imaging modalities (magnetic resonance imaging (MRI), optical CT, x-ray CT and ultrasound) have been suggested for the readout of information from 3D dosimeters. To date only MRI and laser-based optical CT have been characterized in detail. This paper describes some initial steps we have taken in establishing charge coupled device (CCD)-based optical CT as a viable alternative to MRI for readout of 3D radiation dosimeters. The main advantage of CCD-based optical CT over traditional laser-based optical CT is a speed increase of at least an order of magnitude, while the simplicity of its architecture would lend itself to cheaper implementation than both MRI and laser-based optical CT if the camera itself were inexpensive enough. Specifically, we study the following aspects of optical metrology, using high quality test targets: (i) calibration and quality of absorbance measurements and the camera requirements for 3D dosimetry; (ii) the modulation transfer function (MTF) of individual projections; (iii) signal-to-noise ratio (SNR) in the projection and reconstruction domains; (iv) distortion in the projection domain, depth-of-field (DOF) and telecentricity. The principal results for our current apparatus are as follows: (i) SNR of optical absorbance in projections is better than 120:1 for uniform phantoms in absorbance range 0.3 to 1.6 (and better than 200:1 for absorbances 1.0 to 3.5 with the test target and a novel absorbance range extension method), (ii) the spatial resolution is shown to be at worst 0.5 mm (and often better than this) with an associated DOF of 8 cm, (iii) the SNR of uniform phantoms in reconstruction domain is above 80:1 (one standard deviation) over an absorbance dynamic range of 0.3 to 1.6, (iv) the apparatus is telecentric and without distortion. Finally, a sample scan and reconstruction of a scan of a PRESAGE dosimeter are shown, demonstrating the capabilities of the apparatus.

Optical computed tomography (optical-CT) of 3D radiation dosimeters is a promising avenue for delivering an economic and reliable quality control of radiotherapy treatments such as intensity modulated radiotherapy, brachytherapy and stereotactic radiosurgery. The main problems in transferring 3D dosimeters to clinical setting have been in (1) the complexity of manufacture and behaviour of 3D dosimeters and (2) time-consuming readout and analysis of 3D dosimeters. This paper addresses the readout problem by showing that fast (20 min tomography scan), precise (projection absorbance signal-to-noise ratio is greater than 100:1 across the absorbance range 0.2 to 1.5) and accurate (good linearity in the calibration curve) measurements are possible using a novel method of optically scanning a laser beam across the 3D dosimeter.

Optical tomography of gel dosimeters is a promising and cost-effective avenue for quality control of radiotherapy treatments such as intensity-modulated radiotherapy (IMRT). Systems based on a laser coupled to a photodiode have so far shown the best results within the context of optical scanning of radiosensitive gels, but are very slow ( approximately 9 min per slice) and poorly suited to measurements that require many slices. Here, we describe a fast, three-dimensional (3D) optical computed tomography (optical-CT) apparatus, based on a broad, collimated beam, obtained from a high power LED and detected by a charged coupled detector (CCD). The main advantages of such a system are (i) an acquisition speed approximately two orders of magnitude higher than a laser-based system when 3D data are required, and (ii) a greater simplicity of design. This paper advances our previous work by introducing a new design of focusing optics, which take information from a suitably positioned focal plane and project an image onto the CCD. An analysis of the ray optics is presented, which explains the roles of telecentricity, focusing, acceptance angle and depth-of-field (DOF) in the formation of projections. A discussion of the approximation involved in measuring the line integrals required for filtered backprojection reconstruction is given. Experimental results demonstrate (i) the effect on projections of changing the position of the focal plane of the apparatus, (ii) how to measure the acceptance angle of the optics, and (iii) the ability of the new scanner to image both absorbing and scattering gel phantoms. The quality of reconstructed images is very promising and suggests that the new apparatus may be useful in a clinical setting for fast and accurate 3D dosimetry.

Radiotherapy treatment planning relies on the use of geometrically correct images. This paper presents a fully automatic tool for correcting MR images for the effects of B(0) inhomogeneities. The post-processing method is based on the gradient-reversal technique of Chang and Fitzpatrick (1992 IEEE Trans. Med. Imaging 11 319-29) which combines two identical images acquired with a forward- and a reversed read gradient. This paper demonstrates how maximization of mutual information for registration of forward and reverse read gradient images allows the elimination of user interaction for the correction. Image quality is preserved to a degree not reported previously.

MR images are known to be distorted because of both gradient nonlinearity and imperfections in the B0 field, the latter caused either by an imperfect shim or sample-induced distortions. This paper describes in detail a method for correcting the gradient warp distortion, based on a direct field mapping using a custom-built phantom with three orthogonal grids of fluid-filled rods. The key advance of the current work over previous contributions is the large volume of the mapping phantom and the large distortions (>25 mm) corrected, making the method suitable for use with large field of view, extra-cranial images. Experimental measurements on the Siemens AS25 gradient set, as installed on a Siemens Vision scanner, are compared with a theoretical description of the gradient set, based on the manufacturer's spherical harmonic coefficients. It was found that over a volume of 320x200x340 mm3 distortions can be successfully mapped to within the voxel resolution of the raw imaging data, whilst outside this volume, correction is still good but some systematic errors are present. The phenomenon of through-plane distortion (also known as 'slice warp') is examined in detail, and the perturbation it causes to the measurements is quantified and corrected. At the very edges of the region of support provided by the phantom, through-plane distortion is extreme and only partially corrected by the present method. Solutions to this problem are discussed. Both phantom and patient data demonstrate the efficacy of the gradient warp correction.