Suchergebnisse

PURPOSE: Multi-slice ungated double inversion recovery has been proposed as an alternative time-efficient and effective sequence for black-blood carotid imaging. The purpose of this study is to evaluate the comparative repeatability of this multi-contrast sequence with respect to a single slice double inversion recovery prepared gated sequence. MATERIALS AND METHODS: Ten healthy volunteers and three patients with Doppler ultrasound defined carotid artery stenosis >30% were recruited. T₁-weighted (T₁W) and T₂W fast spin-echo (FSE) images were acquired centered at the carotid bifurcation with and without cardiac gating. Repeat imaging was performed without patient repositioning to determine the variations in vessel wall measurement and signal intensity due to gating, while negating variations as a result of slice misalignment and anatomical displacement relative to the receiver coil. The distributions and the repeatability of lumen area, vessel wall area, signal and contrast-to-noise ratio (SNR/CNR) of the vessel wall and adjacent muscle were reported. RESULTS: The T₁W ungated sequence generally had comparable wall SNR/CNR with respect to the gated sequence, however the muscle SNR was lower (P = 0.013). The T₂W ungated multi-slice sequence had lower SNR/CNR than the gated single slice sequence (P < 0.001), but with equivalent effective wall CNR (P = 0.735). Vessel area measurements using the gated/ungated sequences were equivalent. Ungated sequences had better repeatability in SNR/CNR than the gated sequences with borderline and statistically significant differences. The repeatability of T₂W wall area measurement was better using the ungated sequences (P = 0.02), and the repeatability of the remaining vessel area measurements were equivalent. CONCLUSIONS: Ungated sequences can achieve comparable SNR/CNR and equivalent carotid vessel area measurements than gated sequences with improved repeatability of SNR/CNR. Ungated sequences are good alternatives of gated sequences for vessel area measurement and plaque composition quantification. ; This research is partly supported by ARTreat European Union Frame Project 7 and the National Institute of Health Research, Cambridge Biomedical Research Center grant.

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: 3000031984