50 research outputs found
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Quantitative Magnetic Resonance Imaging and Analysis of Articular Cartilage and Osteoarthritis
MRI plays an important role in the continuing search for a sensitive osteoarthritis (OA) imaging biomarker able to detect early, pre-morphological alterations in cartilage composition. Determining the compositional recovery pattern of cartilage following acute joint loading could potentially present a more sensitive biomarker for defining cartilage health [1]. However, only a limited amount of studies have assessed both the immediate effect of joint loading on cartilage, as well as its post-loading recovery. In addition, when assessing the compositional responses of cartilage to joint loading, previous studies usually did not incorporate the measurement error of the used quantitative MRI technique into their analysis. Therefore, an uncertainty persists whether or not compositional MRI techniques are sensitive enough to measure changes in water and macromolecular content of cartilage, or if previous studies were merely measuring noise. Consequently, an objective of this thesis is to increase our understanding of and reliability in quantitative T2 and T1ρ relaxation time mapping to detect compositional responses of cartilage following a joint loading activity.
Furthermore, to obtain the quantitative morphological and compositional measures of cartilage, detailed region-specific delineation of cartilage is required. This delineation (or segmentation) of cartilage is laborious and time-consuming as it is usually performed manually by an expert observer. Many new advances in image analysis, particularly those in convolutional neural networks (CNNs) and deep learning, have enabled a time-efficient semi- or fully-automated alternative to this process [2, 3]. This thesis explores the utility of deep CNNs generated segmentations for accurate surface-based analysis of cartilage morphology and composition from knee MRIs as well as of cortical bone thickness from knee CTs.
Chapter 1 will provide an introduction into the structure and biomechanics of articular cartilage and the role of MRI in imaging the degenerative joint disorder, osteoarthritis as well as the effects of different joint loading activities on cartilage morphology and composition.
Chapter 2 explains the principle of MRI and the pulse sequences used in the following chapter for the morphometric and compositional assessment of articular cartilage.
Chapter 3 describes the use of 3D Cartilage Surface Mapping (3D-CaSM) [3] to assess variations in cartilage T1ρ and T2 relaxation times of young, healthy participants following a mild, unilateral stepping activity. By evaluating and incorporating the intrasessional repeatability of the T1ρ and T2 mapping techniques, I aim to highlight those cartilage areas experiencing exercise-induced compositional changes greater than measurement error.
A significant amount of time is needed to manually segment the regions-of-interest required to perform the 3D-CaSM used in Chapter 3. Therefore, in Chapter 4, I assessed the use of deep convolutional neural networks for automating the segmentation process for multiple knee joint tissues simultaneous and increase the time-efficiency for evaluating knee MR datasets. I evaluated the use of a conditional Generative Adversarial Network (cGAN) as a potentially improved method for automated segmentation compared to the widely used convolutional neural network, U-Net.
In Chapter 5 I combined the 3D-CaSM and automated segmentation methods presented in Chapters 3 and 4, respectively to assess the use of fully automatic segmentations of femoral and tibial bone-cartilage structures for accurate surface-based analysis of cartilage morphology and composition on knee MR images. This was performed on publicly available data from the Osteoarthritis Initiative, a multicentre observational study with expert manual segmentations provided by the Zuse Institute in Berlin.
Chapter 6 describes an automated pipeline for subchondral cortical bone thickness mapping from knee CT data. I developed a method of using automated segmentations of articular cartilage and bone from knee MRI data to determine the periarticular bone surface which is covered by cartilage. This surface was then used to perform cortical bone thickness measurements on corresponding CT data. I validated this pipeline using data from the EU-funded, multi-centre observational study called Applied Private-Public partneRship enabling OsteoArthritis Clinical Headway (APPROACH).
Chapter 7 summarises the main conclusions and contributions of the works presented in this thesis as well as providing directions for future work.PhD Studentship funded by GlaxoSmithKlin
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
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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
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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
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After the Foxconn suicides in China: a roundtable on labor, the state and civil society in global electronics
We seek to tackle myriad problems of a global production system in which China is the world’s largest producer and exporter of consumer electronics products. Dying for an iPhone simultaneously addresses the challenges facing Chinese workers while locating them within the global economy through an assessment of the relationship between Foxconn (the largest electronics manufacturer) and Apple (one of the richest corporations). Eight researchers from Asia, Europe and North America discuss two main questions: How do tech behemoths and the Chinese state shape labor relations in transnational manufacturing? What roles can workers, public sector buyers, non-governmental organizations and consumers play in holding multinational corporations and states accountable for human rights violations and assuring the protection of worker interests? We also reflect on the possibility that national governments, the electronics industry and civil society groups can collaborate to contribute to improved labor rights in China and the world
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
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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