Editorial for "Diffusion Tensor Imaging for Quantitative Assessment of Anterior Cruciate Ligament Injury Grades and Graft".

Tears to the anterior cruciate ligament (ACL) are common and serious knee injuries which tend to occur in young, active individuals. They result in functional impairment and require a period of relative immobilisation followed by rehabilitation, often leading to surgery. Individuals suffering from an ACL injury also have a higher risk of developing osteoarthritis as a long-term consequence(1, 2). ACL reconstructive surgery using a tendon graft remains the clinical standard of care to provide stability to the knee joint and allow patients to return to sport quicker. However, the question of when to allow patients to return to high-level sport remains hotly debated, as the risk of sustaining a second ACL rupture following reconstructive surgery is highest within the subsequent two years(3). While conventional MRI methods continue to provide high diagnostic structural information for ACL injuries, they are unable to deliver advanced quantitative measures required for biological tissue characterisation and longitudinal observation of graft maturation. Promising techniques such as diffusion tensor imaging (DTI), are used for research purposes only and have not yet made the translation into routine clinical application.University of Cambridg

Reproducibility of magnetic resonance fingerprinting-based T 1 mapping of the healthy prostate at 1.5 and 3.0 T: A proof-of-concept study

Funder: Cancer Research UK; funder-id: http://dx.doi.org/10.13039/501100000289Funder: National Institute of Health Research Cambridge Biomedical Research CentreFunder: Cancer Research UK and the Engineering and Physical Sciences Research Council Imaging Centre in Cambridge and ManchesterFunder: Cambridge Experimental Cancer Medicine CentreFacilitating clinical translation of quantitative imaging techniques has been suggested as means of improving interobserver agreement and diagnostic accuracy of multiparametric magnetic resonance imaging (mpMRI) of the prostate. One such technique, magnetic resonance fingerprinting (MRF), has significant competitive advantages over conventional mapping techniques in terms of its multi-site reproducibility, short scanning time and inherent robustness to motion. It has also been shown to improve the detection of clinically significant prostate cancer when added to standard mpMRI sequences, however, the existing studies have all been conducted on 3.0 T MRI systems, limiting the technique’s use on 1.5 T MRI scanners that are still more widely used for prostate imaging across the globe. The aim of this proof-of-concept study was, therefore, to evaluate the cross-system reproducibility of prostate MRF T1 in healthy volunteers (HVs) using 1.5 and 3.0 T MRI systems. The initial validation of MRF T1 against gold standard inversion recovery fast spin echo (IR-FSE) T1 in the ISMRM/NIST MRI system revealed a strong linear correlation between phantom-derived MRF and IR-FSE T1 values was observed at both field strengths (R2 = 0.998 at 1.5T and R2 = 0.993 at 3T; p = < 0.0001 for both). In young HVs, inter-scanner CVs demonstrated marginal differences across all tissues with the highest difference of 3% observed in fat (2% at 1.5T vs 5% at 3T). At both field strengths, MRF T1 could confidently differentiate prostate peripheral zone from transition zone, which highlights the high quantitative potential of the technique given the known difficulty of tissue differentiation in this age group. The high cross-system reproducibility of MRF T1 relaxometry of the healthy prostate observed in this preliminary study, therefore, supports the technique’s prospective clinical validation as part of larger trials employing 1.5 T MRI systems, which are still widely used clinically for routine mpMRI of the prostate

The optimisation of deep neural networks for segmenting multiple knee joint tissues from MRIs.

Automated semantic segmentation of multiple knee joint tissues is desirable to allow faster and more reliable analysis of large datasets and to enable further downstream processing e.g. automated diagnosis. In this work, we evaluate the use of conditional Generative Adversarial Networks (cGANs) as a robust and potentially improved method for semantic segmentation compared to other extensively used convolutional neural network, such as the U-Net. As cGANs have not yet been widely explored for semantic medical image segmentation, we analysed the effect of training with different objective functions and discriminator receptive field sizes on the segmentation performance of the cGAN. Additionally, we evaluated the possibility of using transfer learning to improve the segmentation accuracy. The networks were trained on i) the SKI10 dataset which comes from the MICCAI grand challenge "Segmentation of Knee Images 2010″, ii) the OAI ZIB dataset containing femoral and tibial bone and cartilage segmentations of the Osteoarthritis Initiative cohort and iii) a small locally acquired dataset (Advanced MRI of Osteoarthritis (AMROA) study) consisting of 3D fat-saturated spoiled gradient recalled-echo knee MRIs with manual segmentations of the femoral, tibial and patellar bone and cartilage, as well as the cruciate ligaments and selected peri-articular muscles. The Sørensen-Dice Similarity Coefficient (DSC), volumetric overlap error (VOE) and average surface distance (ASD) were calculated for segmentation performance evaluation. DSC ≥ 0.95 were achieved for all segmented bone structures, DSC ≥ 0.83 for cartilage and muscle tissues and DSC of ≈0.66 were achieved for cruciate ligament segmentations with both cGAN and U-Net on the in-house AMROA dataset. Reducing the receptive field size of the cGAN discriminator network improved the networks segmentation performance and resulted in segmentation accuracies equivalent to those of the U-Net. Pretraining not only increased segmentation accuracy of a few knee joint tissues of the fine-tuned dataset, but also increased the network's capacity to preserve segmentation capabilities for the pretrained dataset. cGAN machine learning can generate automated semantic maps of multiple tissues within the knee joint which could increase the accuracy and efficiency for evaluating joint health.European Union's Horizon 2020 Framework Programme [grant number 761214] Addenbrooke’s Charitable Trust (ACT) National Institute of Health Research (NIHR) Cambridge Biomedical Research Centre University of Cambridge Cambridge University Hospitals NHS Foundation Trust GSK VARSITY: PHD STUDENTSHIP Funder reference: 300003198

Feasibility of Quantitative Magnetic Resonance Fingerprinting in Ovarian Tumors for T1 and T2 Mapping in a PET/MR Setting.

Multiparametric magnetic resonance imaging (MRI) can be used to characterize many cancer subtypes including ovarian cancer. Quantitative mapping of MRI relaxation values, such as T 1 and T 2 mapping, is promising for improving tumor assessment beyond conventional qualitative T 1- and T 2-weighted images. However, quantitative MRI relaxation mapping methods often involve long scan times due to sequentially measuring many parameters. Magnetic resonance fingerprinting (MRF) is a new method that enables fast quantitative MRI by exploiting the transient signals caused by the variation of pseudorandom sequence parameters. These transient signals are then matched to a simulated dictionary of T 1 and T 2 values to create quantitative maps. The ability of MRF to simultaneously measure multiple parameters, could represent a new approach to characterizing cancer and assessing treatment response. This feasibility study investigates MRF for simultaneous T 1, T 2, and relative proton density (rPD) mapping using ovarian cancer as a model system

The effect of gadolinium-based contrast agent administration on magnetic resonance fingerprinting-based T 1 relaxometry in patients with prostate cancer

Abstract: Magnetic resonance fingerprinting (MRF) is a rapidly developing fast quantitative mapping technique able to produce multiple property maps with reduced sensitivity to motion. MRF has shown promise in improving the diagnosis of clinically significant prostate cancer but requires further validation as part of a prostate multiparametric (mp) MRI protocol. mpMRI protocol mandates the inclusion of dynamic contrast enhanced (DCE) imaging, known for its significant T1 shortening effect. MRF could be used to measure both pre- and post-contrast T1 values, but its utility must be assessed. In this proof-of-concept study, we sought to evaluate the variation in MRF T1 measurements post gadolinium-based contrast agent (GBCA) injection and the utility of such T1 measurements to differentiate peripheral and transition zone tumours from normal prostatic tissue. We found that the T1 variation in all tissues increased considerably post-GBCA following the expected significant T1 shortening effect, compromising the ability of MRF T1 to identify transition zone lesions. We, therefore, recommend performing MRF T1 prior to DCE imaging to maintain its benefit for improving detection of both peripheral and transition zone lesions while reducing additional scanning time. Demonstrating the effect of GBCA on MRF T1 relaxometry in patients also paves the way for future clinical studies investigating the added value of post-GBCA MRF in PCa, including its dynamic analysis as in DCE-MRF

Improving the quantitative classification of Erlenmeyer flask deformities

Abstract: The Erlenmeyer flask deformity is a common skeletal modeling deformity, but current classification systems are binary and may restrict its utility as a predictor of associated skeletal conditions. A quantifiable 3-point system of severity classification could improve its predictive potential in disease. Ratios were derived from volumes of regions of interests drawn in 50 Gaucher’s disease patients. ROIs were drawn from the distal physis to 2 cm proximal, 2 cm to 4 cm, and 4 cm to 6 cm. Width was also measured at each of these boundaries. Two readers rated these 100 femurs using a 3-point scale of severity classification. Weighted kappa indicated reliability and one-way analysis of variance characterized ratio differences across the severity scale. Accuracy analyses allowed determination of clinical cutoffs for each ratio. Pearson’s correlations assessed the associations of volume and width with a shape-based concavity metric of the femur. The volume ratio incorporating the metaphyseal region from 0 to 2 cm and the diametaphyseal region at 4–6 cm was most accurate at distinguishing femurs on the 3-point scale. Receiver operating characteristic curves for this ratio indicated areas of 0.95 to distinguish normal and mild femurs and 0.93 to distinguish mild and severe femurs. Volume was moderately associated with the degree of femur concavity. The proposed volume ratio method is an objective, proficient method at distinguishing severities of the Erlenmeyer flask deformity with the potential for automation. This may have application across diseases associated with the deformity and deficient osteoclast-mediated modeling of growing bone

Author Correction: Ultra Short Echo Time MRI of Iron-Labelled Mesenchymal Stem Cells in an Ovine Osteochondral Defect Model.

An amendment to this paper has been published and can be accessed via a link at the top of the paper

Segmentation of knee MRI data with convolutional neural networks for semi-automated three-dimensional surface-based analysis of cartilage morphology and composition

Objective: To assess automatic segmentations for surface-based analysis of cartilage morphology and composition on knee magnetic resonance (MR) images. Methods: 2D and 3D U-Nets were trained on double echo steady state (DESS) images from the publicly available Osteoarthritis Initiative (OAI) dataset with femoral and tibial bone and cartilage segmentations provided by the Zuse Institute Berlin (ZIB). The U-Nets were used to perform automatic segmentation of femoral and tibial bone-cartilage structures (bone and cartilage segmentations combined into one structure) from the DESS images. T2-weighted images from the OAI dataset were registered to the DESS images and used for T2 map calculation. Using the 3D cartilage surface mapping (3D-CaSM) method, surface-based analysis of cartilage morphology (thickness) and composition (T2) was performed using both manual and network-generated segmentations from OAI ZIB testing images. Bland-Altman analyses were performed to evaluate the accuracy of the extracted cartilage thickness and T2 measurements from both U-Nets compared to manual segmentations. Results: Bland-Altman analysis showed a mean bias [95% limits of agreement] for femoral and tibial cartilage thickness measurements ranging between -0.12 to 0.33 [-0.28, 0.96] mm with 2D U-Net and 0.07 to 0.14 [-0.14, 0.39] mm with 3D U-Net. For T2, the mean bias [95% limits of agreement] ranged between -0.16 to 1.32 [-4.71, 4.83] ms with 2D U-Net and -0.05 to 0.46 [-2.47, 3.39] ms with 3D U-Net. Conclusions: While both 2D and 3D U-Nets exemplified the time-efficiency benefit of using deep learning methods for generating the required segmentations, segmentations from 3D U-Nets demonstrated higher accuracy in the extracted thickness and T2 features using 3D-CaSM compared to the segmentations from 2D U-Nets