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

The Stellar Halos of Massive Elliptical Galaxies

We use the Mitchell Spectrograph (formerly VIRUS-P) on the McDonald Observatory 2.7m Harlan J. Smith Telescope to search for the chemical signatures of massive elliptical galaxy assembly. The Mitchell Spectrograph is an integral-field spectrograph with a uniquely wide field of view (107x107 sq arcsec), allowing us to achieve remarkably high signal-to-noise ratios of ~20-70 per pixel in radial bins of 2-2.5 times the effective radii of the eight galaxies in our sample. Focusing on a sample of massive elliptical galaxies with stellar velocity dispersions sigma* > 150 km/s, we study the radial dependence in the equivalent widths (EWs) of key metal absorption lines. By twice the effective radius, the Mgb EWs have dropped by ~50%, and only a weak correlation between sigma* and Mgb EW remains. The Mgb EWs at large radii are comparable to those seen in the centers of elliptical galaxies that are approximately an order of magnitude less massive. We find that the well-known metallicity gradients often observed within an effective radius continue smoothly to 2.5R_e, while the abundance ratio gradients remain flat. Much like the halo of the Milky Way, the stellar halos of our galaxies have low metallicities and high alpha-abundance ratios, as expected for very old stars formed in small stellar systems. Our observations support a picture in which the outer parts of massive elliptical galaxies are built by the accretion of much smaller systems whose star formation history was truncated at early times.Comment: To appear in ApJ, 15 pages, 9 figure

Cell type-specific plasticity of striatal projection neurons in parkinsonism and L-DOPA-induced dyskinesia

The striatum is widely viewed as the fulcrum of pathophysiology in Parkinson’s disease (PD) and L-DOPA-induced dyskinesia (LID). In these disease states, the balance in activity of striatal direct pathway spiny projection neurons (dSPNs) and indirect pathway spiny projection neurons (iSPNs) is disrupted, leading to aberrant action selection. However, it is unclear whether countervailing mechanisms are engaged in these states. Here we report that iSPN intrinsic excitability and excitatory corticostriatal synaptic connectivity were lower in PD models than normal; ​L-DOPA treatment restored these properties. Conversely, dSPN intrinsic excitability was elevated in tissue from PD models and suppressed in LID models. Although the synaptic connectivity of dSPNs did not change in PD models, it fell with ​L-DOPA treatment. In neither case, however, was the strength of corticostriatal connections globally scaled. Thus, SPNs manifested homeostatic adaptations in intrinsic excitability and in the number but not strength of excitatory corticostriatal synapses

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

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

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

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