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1. Spatial Gradients of Quantitative MRI as Biomarkers for Early Detection of Osteoarthritis: Data From Human Explants and the Osteoarthritis Initiative

   https://doi.org/10.1002/jmri.28471  

   Wilson, Robert L. ; Emery, Nancy C. ; Pierce, David M. ; Neu, Corey P. ( October 2022 , Journal of Magnetic Resonance Imaging)

   Healthy articular cartilage presents structural gradients defined by distinct zonal patterns through the thickness, which may be disrupted in the pathogenesis of several disorders. Analysis of textural patterns using quantitative MRI data may identify structural gradients of healthy or degenerating tissue that correlate with early osteoarthritis (OA).

   To quantify spatial gradients and patterns in MRI data, and to probe new candidate biomarkers for early severity of OA.

   Retrospective study.

   Fourteen volunteers receiving total knee replacement surgery (eight males/two females/four unknown, average age ± standard deviation: 68.1 ± 9.6 years) and 10 patients from the OA Initiative (OAI) with radiographic OA onset (two males/eight females, average age ± standard deviation: 57.7 ± 9.4 years; initial Kellgren‐Lawrence [KL] grade: 0; final KL grade: 3 over the 10‐year study).

   3.0‐T and 14.1‐T, biomechanics‐based displacement‐encoded imaging, fast spin echo, multi‐slice multi‐echoT2mapping.

   We studied structure and strain in cartilage explants from volunteers receiving total knee replacement, or structure in cartilage of OAI patients with progressive OA. We calculated spatial gradients of quantitative MRI measures (eg, T2) normal to the cartilage surface to enhance zonal variations. We compared gradient values against histologically OA severity, conventional relaxometry, and/or KL grades.

   Multiparametric linear regression for evaluation of the relationship between residuals of the mixed effects models and histologically determined OA severity scoring, with a significance threshold atα = 0.05.

   Gradients of individual relaxometry and biomechanics measures significantly correlated with OA severity, outperforming conventional relaxometry and strain metrics. In human explants, analysis of spatial gradients provided the strongest relationship to OA severity (R² = 0.627). Spatial gradients of T2 from OAI data identified variations in radiographic (KL Grade 2) OA severity in single subjects, while conventional T2 alone did not.

   Spatial gradients of quantitative MRI data may improve the predictive power of noninvasive imaging for early‐stage degeneration.

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   Stage 1

    

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2. Simultaneous Mapping of T1 and T2 Using Cardiac Magnetic Resonance Fingerprinting in a Cohort of Healthy Subjects at 1. 5T

   https://doi.org/10.1002/jmri.27155  

   Hamilton, Jesse I. ; Pahwa, Shivani ; Adedigba, Joseph ; Frankel, Samuel ; O'Connor, Gregory ; Thomas, Rahul ; Walker, Jonathan R. ; Killinc, Ozden ; Lo, Wei‐Ching ; Batesole, Joshua ; et al ( March 2020 , Journal of Magnetic Resonance Imaging)

   Cardiac MR fingerprinting (cMRF) is a novel technique for simultaneous T₁and T₂mapping.

   To compare T₁/T₂measurements, repeatability, and map quality between cMRF and standard mapping techniques in healthy subjects.

   Prospective.

   In all, 58 subjects (ages 18–60).

   cMRF, modified Look–Locker inversion recovery (MOLLI), and T₂‐prepared balanced steady‐state free precession (bSSFP) at 1.5T.

   T₁/T₂values were measured in 16 myocardial segments at apical, medial, and basal slice positions. Test–retest and intrareader repeatability were assessed for the medial slice. cMRF and conventional mapping sequences were compared using ordinal and two alternative forced choice (2AFC) ratings.

   Pairedt‐tests, Bland–Altman analyses, intraclass correlation coefficient (ICC), linear regression, one‐way analysis of variance (ANOVA), and binomial tests.

   Average T₁measurements were: basal 1007.4±96.5 msec (cMRF), 990.0±45.3 msec (MOLLI); medial 995.0±101.7 msec (cMRF), 995.6±59.7 msec (MOLLI); apical 1006.6±111.2 msec (cMRF); and 981.6±87.6 msec (MOLLI). Average T₂measurements were: basal 40.9±7.0 msec (cMRF), 46.1±3.5 msec (bSSFP); medial 41.0±6.4 msec (cMRF), 47.4±4.1 msec (bSSFP); apical 43.5±6.7 msec (cMRF), 48.0±4.0 msec (bSSFP). A statistically significant bias (cMRF T₁larger than MOLLI T₁) was observed in basal (17.4 msec) and apical (25.0 msec) slices. For T₂, a statistically significant bias (cMRF lower than bSSFP) was observed for basal (–5.2 msec), medial (–6.3 msec), and apical (–4.5 msec) slices. Precision was lower for cMRF—the average of the standard deviation measured within each slice was 102 msec for cMRF vs. 61 msec for MOLLI T₁, and 6.4 msec for cMRF vs. 4.0 msec for bSSFP T₂. cMRF and conventional techniques had similar test–retest repeatability as quantified by ICC (0.87 cMRF vs. 0.84 MOLLI for T₁; 0.85 cMRF vs. 0.85 bSSFP for T₂). In the ordinal image quality comparison, cMRF maps scored higher than conventional sequences for both T₁(all five features) and T₂(four features).

   This work reports on myocardial T₁/T₂measurements in healthy subjects using cMRF and standard mapping sequences. cMRF had slightly lower precision, similar test–retest and intrareader repeatability, and higher scores for map quality.

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   Stage 1 J. Magn. Reson. Imaging 2020;52:1044–1052.

    

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3. Fully Automatic Knee Bone Detection and Segmentation on Three-Dimensional MRI

   https://doi.org/10.3390/diagnostics12010123  

   Almajalid, Rania ; Zhang, Ming ; Shan, Juan ( January 2022 , Diagnostics)

   In the medical sector, three-dimensional (3D) images are commonly used like computed tomography (CT) and magnetic resonance imaging (MRI). The 3D MRI is a non-invasive method of studying the soft-tissue structures in a knee joint for osteoarthritis studies. It can greatly improve the accuracy of segmenting structures such as cartilage, bone marrow lesion, and meniscus by identifying the bone structure first. U-net is a convolutional neural network that was originally designed to segment the biological images with limited training data. The input of the original U-net is a single 2D image and the output is a binary 2D image. In this study, we modified the U-net model to identify the knee bone structures using 3D MRI, which is a sequence of 2D slices. A fully automatic model has been proposed to detect and segment knee bones. The proposed model was trained, tested, and validated using 99 knee MRI cases where each case consists of 160 2D slices for a single knee scan. To evaluate the model’s performance, the similarity, dice coefficient (DICE), and area error metrics were calculated. Separate models were trained using different knee bone components including tibia, femur, patella, as well as a combined model for segmenting all the knee bones. Using the whole MRI sequence (160 slices), the method was able to detect the beginning and ending bone slices first, and then segment the bone structures for all the slices in between. On the testing set, the detection model accomplished 98.79% accuracy and the segmentation model achieved DICE 96.94% and similarity 93.98%. The proposed method outperforms several state-of-the-art methods, i.e., it outperforms U-net by 3.68%, SegNet by 14.45%, and FCN-8 by 2.34%, in terms of DICE score using the same dataset. 

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4. Robust automatic hexahedral cartilage meshing framework enables population-based computational studies of the knee

   https://doi.org/10.3389/fbioe.2022.1059003  

   Gibbons, Kalin D. ; Malbouby, Vahid ; Alvarez, Oliver ; Fitzpatrick, Clare K. ( December 2022 , Frontiers in Bioengineering and Biotechnology)

   Osteoarthritis of the knee is increasingly prevalent as our population ages, representing an increasing financial burden, and severely impacting quality of life. The invasiveness of in vivo procedures and the high cost of cadaveric studies has left computational tools uniquely suited to study knee biomechanics. Developments in deep learning have great potential for efficiently generating large-scale datasets to enable researchers to perform population-sized investigations, but the time and effort associated with producing robust hexahedral meshes has been a limiting factor in expanding finite element studies to encompass a population. Here we developed a fully automated pipeline capable of taking magnetic resonance knee images and producing a working finite element simulation. We trained an encoder-decoder convolutional neural network to perform semantic image segmentation on the Imorphics dataset provided through the Osteoarthritis Initiative. The Imorphics dataset contained 176 image sequences with varying levels of cartilage degradation. Starting from an open-source swept-extrusion meshing algorithm, we further developed this algorithm until it could produce high quality meshes for every sequence and we applied a template-mapping procedure to automatically place soft-tissue attachment points. The meshing algorithm produced simulation-ready meshes for all 176 sequences, regardless of the use of provided (manually reconstructed) or predicted (automatically generated) segmentation labels. The average time to mesh all bones and cartilage tissues was less than 2 min per knee on an AMD Ryzen 5600X processor, using a parallel pool of three workers for bone meshing, followed by a pool of four workers meshing the four cartilage tissues. Of the 176 sequences with provided segmentation labels, 86% of the resulting meshes completed a simulated flexion-extension activity. We used a reserved testing dataset of 28 sequences unseen during network training to produce simulations derived from predicted labels. We compared tibiofemoral contact mechanics between manual and automated reconstructions for the 24 pairs of successful finite element simulations from this set, resulting in mean root-mean-squared differences under 20% of their respective min-max norms. In combination with further advancements in deep learning, this framework represents a feasible pipeline to produce population sized finite element studies of the natural knee from subject-specific models. 

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5. Democratization of deep learning for segmenting cartilage from MRIs of human knees: Application to data from the osteoarthritis initiative

   https://doi.org/10.1002/jor.25509  

   Rodriguez‐Vila, Borja ; Gonzalez‐Hospital, Vera ; Puertas, Enrique ; Beunza, Juan‐Jose ; Pierce, David M. ( January 2023 , Journal of Orthopaedic Research)

   In this study, we aimed to democratize access to convolutional neural networks (CNN) for segmenting cartilage volumes, generating state‐of‐the‐art results for specialized, real‐world applications in hospitals and research. Segmentation of cross‐sectional and/or longitudinal magnetic resonance (MR) images of articular cartilage facilitates both clinical management of joint damage/disease and fundamental research. Manual delineation of such images is a time‐consuming task susceptible to high intra‐ and interoperator variability and prone to errors. Thus, enabling reliable and efficient analyses of MRIs of cartilage requires automated segmentation of cartilage volumes. Two main limitations arise in the development of hospital‐ or population‐specific deep learning (DL) models for image segmentation: specialized knowledge and specialized hardware. We present a relatively easy and accessible implementation of a DL model to automatically segment MRIs of human knees with state‐of‐the‐art accuracy. In representative examples, we trained CNN models in 6‐8 h and obtained results quantitatively comparable to state‐of‐the‐art for every anatomical structure. We established and evaluated our methods using two publicly available MRI data sets originating from the Osteoarthritis Initiative, Stryker Imorphics, and Zuse Institute Berlin (ZIB), as representative test cases. We use Google Colabfor editing and adapting the Python codes and selecting the runtime environment leveraging high‐performance graphical processing units. We designed our solution for novice users to apply to any data set with relatively few adaptations requiring only basic programming skills. To facilitate the adoption of our methods, we provide a complete guideline for using our methods and software, as well as the software tools themselves. Clinical significance: We establish and detail methods that clinical personal can apply to create their own DL models without specialized knowledge of DL nor specialized hardware/infrastructure and obtain results comparable with the state‐of‐the‐art to facilitate both clinical management of joint damage/disease and fundamental research.

    

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