Layer-specific analysis of femorotibial cartilage t2 relaxation time based on registration of segmented double echo steady state (dess) to multi-echo-spin-echo (mese) images

Fürst, David; Wirth, W.; Chaudhari, Akshay; Eckstein, F.

doi:10.1007/s10334-020-00852-6

Cited by 5 publications

(7 citation statements)

References 35 publications

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“…Femorotibial cartilage thickness segmentation in the FNIH Biomarker study relied on sagittal double‐echo steady‐state (DESS) imaging, with blinding to group assignment and order of acquisition ( 1 ). To extract cartilage T2, existing cartilage segmentations of the DESS (see Supplementary Figure 1 , available on the Arthritis Care & Research website at http://onlinelibrary.wiley.com/doi/10.1002/acr.24627 ) were registered to the MESE MRIs using a recently validated algorithm ( 14 ). The segmentations comprised the entire medial tibia (MT) and lateral tibia (LT) and the central (weight‐bearing) part of the medial femoral (cMF) and lateral femoral (cLF) condyles, defined as 75% of the distance between the intercondylar notch and the posterior end of the condyles.…”

Section: Methodsmentioning

confidence: 99%

“…The segmentations comprised the entire medial tibia (MT) and lateral tibia (LT) and the central (weight‐bearing) part of the medial femoral (cMF) and lateral femoral (cLF) condyles, defined as 75% of the distance between the intercondylar notch and the posterior end of the condyles. The registration process required automated trimming of the DESS segmentations in the joint periphery and in the depth of the cartilage because we discovered an underestimation of the total cartilage thickness by the MESE ( 14 ). The quality of the registration results was validated visually and quantitatively by checking the final Mattes Mutual Information metrices ( 15 ).…”

Section: Methodsmentioning

confidence: 99%

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Association of Superficial Cartilage Transverse Relaxation Time With Osteoarthritis Disease Progression: Data From the Foundation for the National Institutes of Health Biomarker Study of the Osteoarthritis Initiative

Fuerst

Wirth

Gaisberger

et al. 2022

Arthritis Care & Research

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Objective. To study whether layer-specific cartilage transverse relaxation time (T2) and/or longitudinal change is associated with clinically relevant knee osteoarthritis (OA) disease progression.Methods. The Foundation for the National Institutes of Health Biomarker Consortium was a nested case-control study on 600 knees from 600 Osteoarthritis Initiative participants. Progressor knees had both medial tibiofemoral radiographic joint space width (JSW) loss (≥0.7 mm) and a persistent increase in Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain score (≥9 on a 0-100 scale) at 24-48 months from baseline (n = 194). Multiecho spin-echo (MESE) magnetic resonance images (MRIs) for cartilage T2 analysis had been acquired in the right knees only (97 progressor knees). These were compared to 104 control knees without JSW or pain progression. Fifty-three knees had JSW progression, and 57 pain progression only. Cartilage thickness segmentations obtained from double-echo steady-state MRI were matched to MESE MRI to extract superficial and deep femorotibial cartilage T2. Superficial medial femorotibial compartment (MFTC) T2 at baseline was the primary, and change in deep MFTC T2 between baseline and 12 months was the secondary analytic outcome of this post hoc exploratory study.Results. Baseline superficial MFTC T2 was significantly elevated in progressor knees (adjusted mean 47.2 msec [95% confidence interval (95% CI) 46.5, 48.0]) and JSW progression only knees (adjusted mean 47.3 msec [95% CI 46.3, 48.3]), respectively, versus non-progressor knees (45.8 msec [95% CI 45.0, 46.5]) after adjustment for age, sex, body mass index, WOMAC pain score, and medial joint space narrowing grade (analysis of covariance). Change in T2 was not significantly associated with case status.Conclusion. Baseline superficial, but not deep, medial cartilage T2 is associated with clinically relevant disease progression in knee OA.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Association of Superficial Cartilage Transverse Relaxation Time With Osteoarthritis Disease Progression: Data From the Foundation for the National Institutes of Health Biomarker Study of the Osteoarthritis Initiative

Fuerst

Wirth

Gaisberger

et al. 2022

Arthritis Care & Research

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“…MR thermometry as an alternative approach suffers from a coarse temperature resolution [22]. The advent of artificial intelligence (AI) and machine learning in MRI [23][24][25][26][27][28][29][30][31][32][33][34][35][36] has opened up new avenues for the prediction of various imaging characteristics, among them the recent prediction of local SAR in prostate imaging [37][38][39][40], as well as the prediction of temperature rise in the brain for 33 different tissue types [41].…”

Section: Introductionmentioning

confidence: 99%

MRSaiFE: An AI-Based Approach Towards the Real-Time Prediction of Specific Absorption Rate

Robb¹,

Kainz

Chaudhari³

et al. 2021

IEEE Access

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The purpose of this study is to investigate feasibility of estimating the specific absorption rate (SAR) in MRI in real time. To this goal, SAR maps are predicted from 3T-and 7T-simulated magnetic resonance (MR) images in 10 realistic human body models via a convolutional neural network. Twodimensional (2-D) U-Net architectures with varying contraction layers and different convolutional filters were designed to estimate the SAR distribution in realistic body models. Sim4Life (ZMT, Switzerland) was used to create simulated anatomical images and SAR maps at 3T and 7T imaging frequencies for Duke, Ella, Charlie, and Pregnant Women (at 3, 7, and 9 month gestational stages) body models. Mean squared error (MSE) was used as the cost function and the structural similarity index (SSIM) was reported. A 2-D U-Net with 4 contracting (and 4 expanding) layers and 64 convolutional filters at the initial stage showed the best compromise to estimate SAR distributions. Adam optimizer outperformed stochastic gradient descent (SGD) for all cases with an average SSIM of 90.5∓3.6 % and an average MSE of 0.7∓0.6% for head images at 7T, and an SSIM of >85.1∓6.2 % and an MSE of 0.4∓0.4% for 3T body imaging. Algorithms estimated the SAR maps for 224x224 slices under 30 ms. The proposed methodology shows promise to predict real-time SAR in clinical imaging settings without using extra mapping techniques or patient-specific calibrations.

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“…Prior studies have used image registration to transfer the segmentation of morphological images to corresponding MESE images of the same patient taken in succession. [24][25][26][27][28] However, registration can be imperfect, partially due to the potential for non-affine movements of the knee throughout the acquisition time of the two images (e.g., flexion or extension of the joint or compression of soft tissues). The risk of patient movement and registration error may be higher when the time between the acquisition of the morphological image and T2 image is longer.…”

Section: Introductionmentioning

confidence: 99%

“…Prior studies have used image registration to transfer the segmentation of morphological images to corresponding MESE images of the same patient taken in succession [17][18][19] . However, registration can be imperfect, partially due to the potential for non-affine movement of the knee throughout the acquisition time of the two images.…”

Section: Introductionmentioning

confidence: 99%

Open Source Software for Automatic Subregional Assessment of Knee Cartilage Degradation Using Quantitative T2 Relaxometry and Deep Learning

et al. 2021

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Objective We evaluated a fully automated femoral cartilage segmentation model for measuring T2 relaxation values and longitudinal changes using multi-echo spin-echo (MESE) magnetic resonance imaging (MRI). We open sourced this model and developed a web app available at https://kl.stanford.edu into which users can drag and drop images to segment them automatically. Design We trained a neural network to segment femoral cartilage from MESE MRIs. Cartilage was divided into 12 subregions along medial-lateral, superficial-deep, and anterior-central-posterior boundaries. Subregional T2 values and four-year changes were calculated using a radiologist’s segmentations (Reader 1) and the model’s segmentations. These were compared using 28 held-out images. A subset of 14 images were also evaluated by a second expert (Reader 2) for comparison. Results Model segmentations agreed with Reader 1 segmentations with a Dice score of 0.85 ± 0.03. The model’s estimated T2 values for individual subregions agreed with those of Reader 1 with an average Spearman correlation of 0.89 and average mean absolute error (MAE) of 1.34 ms. The model’s estimated four-year change in T2 for individual subregions agreed with Reader 1 with an average correlation of 0.80 and average MAE of 1.72 ms. The model agreed with Reader 1 at least as closely as Reader 2 agreed with Reader 1 in terms of Dice score (0.85 vs. 0.75) and subregional T2 values. Conclusions Assessments of cartilage health using our fully automated segmentation model agreed with those of an expert as closely as experts agreed with one another. This has the potential to accelerate osteoarthritis research.

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Layer-specific analysis of femorotibial cartilage t2 relaxation time based on registration of segmented double echo steady state (dess) to multi-echo-spin-echo (mese) images

Cited by 5 publications

References 35 publications

Association of Superficial Cartilage Transverse Relaxation Time With Osteoarthritis Disease Progression: Data From the Foundation for the National Institutes of Health Biomarker Study of the Osteoarthritis Initiative

Association of Superficial Cartilage Transverse Relaxation Time With Osteoarthritis Disease Progression: Data From the Foundation for the National Institutes of Health Biomarker Study of the Osteoarthritis Initiative

MRSaiFE: An AI-Based Approach Towards the Real-Time Prediction of Specific Absorption Rate

Open Source Software for Automatic Subregional Assessment of Knee Cartilage Degradation Using Quantitative T2 Relaxometry and Deep Learning

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