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

Quantitative analysis of the ACL and PCL using T1rho and T2 relaxation time mapping: an exploratory, cross-sectional comparison between OA and healthy control knees.

BACKGROUND: Quantitative magnetic resonance imaging (MRI) methods such as T1rho and T2 mapping are sensitive to changes in tissue composition, however their use in cruciate ligament assessment has been limited to studies of asymptomatic populations or patients with posterior cruciate ligament tears only. The aim of this preliminary study was to compare T1rho and T2 relaxation times of the anterior cruciate ligament (ACL) and posterior cruciate ligament (PCL) between subjects with mild-to-moderate knee osteoarthritis (OA) and healthy controls. METHODS: A single knee of 15 patients with mild-to-moderate knee OA (Kellgren-Lawrence grades 2-3) and of 6 age-matched controls was imaged using a 3.0 T MRI. Three-dimensional (3D) fat-saturated spoiled gradient recalled-echo images were acquired for morphological assessment and T1ρ- and T2-prepared pseudo-steady-state 3D fast spin echo images for compositional assessment of the cruciate ligaments. Manual segmentation of whole ACL and PCL, as well as proximal / middle / distal thirds of both ligaments was carried out by two readers using ITK-SNAP and mean relaxation times were recorded. Variation between thirds of the ligament were assessed using repeated measures ANOVAs and differences in these variations between groups using a Kruskal-Wallis test. Inter- and intra-rater reliability were assessed using intraclass correlation coefficients (ICCs). RESULTS: In OA knees, both T1rho and T2 values were significantly higher in the distal ACL when compared to the rest of the ligament with the greatest differences in T1rho (e.g. distal mean = 54.5 ms, proximal = 47.0 ms, p < 0.001). The variation of T2 values within the PCL was lower in OA knees (OA: distal vs middle vs proximal mean = 28.5 ms vs 29.1 ms vs 28.7 ms, p = 0.748; Control: distal vs middle vs proximal mean = 26.4 ms vs 32.7 ms vs 33.3 ms, p = 0.009). ICCs were excellent for the majority of variables. CONCLUSION: T1rho and T2 mapping of the cruciate ligaments is feasible and reliable. Changes within ligaments associated with OA may not be homogeneous. This study is an important step forward in developing a non-invasive, radiological biomarker to assess the ligaments in diseased human populations in-vivo.Declarations Ethics approval and consent to participate This study was approved by the East of England Cambridge Central Research Ethics Committee and written informed consent was given by all subjects included in the study. All methods were carried out in accordance with relevant guidelines and regulations. Consent for publication Not Applicable Availability of data and materials The datasets generated and analysed during the current study are not publicly available due to unattained permission from participants and research ethics committee but could be made available from JWM (email: [email protected]). Competing interests JWM, DAK and JDK acknowledge funding support from GlaxoSmithKline for their studentships and fellowships, respectively. JWM is an employee of AstraZeneca. CDSR, VAC and SMM have no competing interests to declare. Acknowledgements The Addenbrooke's Hospital Magnetic Resonance Imaging and Spectroscopy (MRIS) staff are thanked for their help with arranging and conducting the study MRI examinations. We also acknowledge the support of the Addenbrooke's Charitable Trust and the National Institute for Health Research Cambridge Biomedical Research Centre. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. Funding The study was funded by an Experimental Medicine Initiative PhD studentship from the University of Cambridge [grant number RG81329] and by GlaxoSmithKline [grant number RG87552]. Authors' contributions Writing of original draft manuscript: CDSR. Study design and coordination: CDSR, JWM, JDK and SMM. Data acquisition: JWM and JDK. Data curation, analysis and interpretation: CDSR, JWM, VAC, JDK, DAK and SMM. Statistical analysis: CDSR and JWM. Review and editing of manuscript: JWM, VAC, JDK, DAK and SMM. All authors read and approved the final manuscript

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

Effectively Measuring Exercise-Related Variations in T1ρ and T2 Relaxation Times of Healthy Articular Cartilage.

BACKGROUND: Determining the compositional response of articular cartilage to dynamic joint-loading using MRI may be a more sensitive assessment of cartilage status than conventional static imaging. However, distinguishing the effects of joint-loading vs. inherent measurement variability remains difficult, as the repeatability of these quantitative methods is often not assessed or reported. PURPOSE: To assess exercise-induced changes in femoral, tibial, and patellar articular cartilage composition and compare these against measurement repeatability. STUDY TYPE: Prospective observational study. POPULATION: Phantom and 19 healthy participants. FIELD STRENGTH/SEQUENCE: 3T; 3D fat-saturated spoiled gradient recalled-echo; T1ρ - and T2 -prepared pseudosteady-state 3D fast spin echo. ASSESSMENT: The intrasessional repeatability of T1ρ and T2 relaxation mapping, with and without knee repositioning between two successive measurements, was determined in 10 knees. T1ρ and T2 relaxation mapping of nine knees was performed before and at multiple timepoints after a 5-minute repeated, joint-loading stepping activity. 3D surface models were created from patellar, femoral, and tibial articular cartilage. STATISTICAL TESTS: Repeatability was assessed using root-mean-squared-CV (RMS-CV). Using Bland-Altman analysis, thresholds defined as the smallest detectable difference (SDD) were determined from the repeatability data with knee repositioning. RESULTS: Without knee repositioning, both surface-averaged T1ρ and T2 were very repeatable on all cartilage surfaces, with RMS-CV SDD) average exercise-induced in T1ρ and T2 of femoral (-8.0% and -5.3%), lateral tibial (-6.9% and -5.9%), medial tibial (+5.8% and +2.9%), and patellar (-7.9% and +2.8%) cartilage were observed. DATA CONCLUSION: Joint-loading with a stepping activity resulted in T1ρ and T2 changes above background measurement error. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY STAGE: 1 J. MAGN. RESON. IMAGING 2020;52:1753-1764.GlaxoSmithKline National Institute of Health Research (NIHR) Cambridge Biomedical Research Centr

Leptin may play a role in bone microstructural alterations in obese children.

Bone mass is low and fracture risk is higher in obese children. Hormonal changes in relation to skeletal microstructure and biomechanics have not been studied in obese children

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

Combined 23 Na and 13 C imaging at 3.0 Tesla using a single‐tuned large FOV birdcage coil

Purpose: An unmet need in carbon‐13 (13C)‐MRI is a transmit system that provides uniform excitation across a large FOV and can accommodate patients of wide‐ranging body habitus. Due to the small difference between the resonant frequencies, sodium‐23 (23Na) coil developments can inform 13C coil design while being simpler to assess due to the higher naturally abundant 23Na signal. Here we present a removable 23Na birdcage, which also allows operation as a 13C abdominal coil. Methods: We demonstrate a quadrature‐driven 4‐rung 23Na birdcage coil of 50 cm in length for both 23Na and 13C abdominal imaging. The coil transmit efficiencies and B 1 + maps were compared to a linearly driven 13C Helmholtz‐based (clamshell) coil. SNR was investigated with 23Na and 13C data using an 8‐channel 13C receive array within the 23Na birdcage. Results: The 23Na birdcage longitudinal FOV was > 40 cm, whereas the 13C clamshell was < 32 cm. The transmit efficiency of the birdcage at the 23Na frequency was 0.65 µT/sqrt(W), similar to the clamshell for 13C. However, the coefficient of variation of 23Na‐ B 1 + was 16%, nearly half that with the 13C clamshell. The 8‐channel 13C receive array combined with the 23Na birdcage coil generated a greater than twofold increase in 23Na‐SNR from the central abdomen compared with the birdcage alone. Discussion: This 23Na birdcage coil has a larger FOV and improved B 1 + uniformity when compared to the widely used clamshell coil design while also providing similar transmit efficiency. The coil has the potential to be used for both 23Na and 13C imaging

Magnetic resonance fingerprinting of the pancreas at 1.5 T and 3.0 T

Funder: GlaxoSmithKline; doi: http://dx.doi.org/10.13039/100004330Funder: National Institute of Health Research (NIHR) Cambridge Biomedical Research CentreFunder: Addenbrooke's Charitable Trust, Cambridge University Hospitals; doi: http://dx.doi.org/10.13039/501100002927Abstract: Magnetic resonance imaging of the pancreas is increasingly used as an important diagnostic modality for characterisation of pancreatic lesions. Pancreatic MRI protocols are mostly qualitative due to time constraints and motion sensitivity. MR Fingerprinting is an innovative acquisition technique that provides qualitative data and quantitative parameter maps from a single free‐breathing acquisition with the potential to reduce exam times. This work investigates the feasibility of MRF parameter mapping for pancreatic imaging in the presence of free-breathing exam. Sixteen healthy participants were prospectively imaged using MRF framework. Regions-of-interest were drawn in multiple solid organs including the pancreas and T1 and T2 values determined. MRF T1 and T2 mapping was performed successfully in all participants (acquisition time:2.4–3.6 min). Mean pancreatic T1 values were 37–43% lower than those of the muscle, spleen, and kidney at both 1.5 and 3.0 T. For these organs, the mean pancreatic T2 values were nearly 40% at 1.5 T and < 12% at 3.0 T. The feasibility of MRF at 1.5 T and 3 T was demonstrated in the pancreas. By enabling fast and free-breathing quantitation, MRF has the potential to add value during the clinical characterisation and grading of pathological conditions, such as pancreatitis or cancer

The Seventh Data Release of the Sloan Digital Sky Survey

This paper describes the Seventh Data Release of the Sloan Digital Sky Survey (SDSS), marking the completion of the original goals of the SDSS and the end of the phase known as SDSS-II. It includes 11663 deg^2 of imaging data, with most of the roughly 2000 deg^2 increment over the previous data release lying in regions of low Galactic latitude. The catalog contains five-band photometry for 357 million distinct objects. The survey also includes repeat photometry over 250 deg^2 along the Celestial Equator in the Southern Galactic Cap. A coaddition of these data goes roughly two magnitudes fainter than the main survey. The spectroscopy is now complete over a contiguous area of 7500 deg^2 in the Northern Galactic Cap, closing the gap that was present in previous data releases. There are over 1.6 million spectra in total, including 930,000 galaxies, 120,000 quasars, and 460,000 stars. The data release includes improved stellar photometry at low Galactic latitude. The astrometry has all been recalibrated with the second version of the USNO CCD Astrograph Catalog (UCAC-2), reducing the rms statistical errors at the bright end to 45 milli-arcseconds per coordinate. A systematic error in bright galaxy photometr is less severe than previously reported for the majority of galaxies. Finally, we describe a series of improvements to the spectroscopic reductions, including better flat-fielding and improved wavelength calibration at the blue end, better processing of objects with extremely strong narrow emission lines, and an improved determination of stellar metallicities. (Abridged)Comment: 20 pages, 10 embedded figures. Accepted to ApJS after minor correction