Partner: Jiang Du

University of California (US)

Recent publications
1.Byra M., Wu M., Zhang X., Jang H., Ma Y-J., Chang E.Y., Shah S., Du J., Knee menisci segmentation and relaxometry of 3D ultrashort echo time cones MR imaging using attention U‐Net with transfer learning, Magnetic Resonance in Medicine, ISSN: 1522-2594, DOI: 10.1002/mrm.27969, Vol.83, No.3, pp.1109-1122, 2020
Abstract:

Purpose: To develop a deep learning-based method for knee menisci segmentation in 3D ultrashort echo time (UTE) cones MR imaging, and to automatically determine MR relaxation times, namely the T1, T1ρ, and T2* parameters, which can be used to assess knee osteoarthritis (OA). Methods: Whole knee joint imaging was performed using 3D UTE cones sequences to collect data from 61 human subjects. Regions of interest (ROIs) were outlined by 2 experienced radiologists based on subtracted T1ρ-weighted MR images. Transfer learning was applied to develop 2D attention U-Net convolutional neural networks for the menisci segmentation based on each radiologist's ROIs separately. Dice scores were calculated to assess segmentation performance. Next, the T1, T1ρ, T2* relaxations, and ROI areas were determined for the manual and automatic segmentations, then compared. Results: The models developed using ROIs provided by 2 radiologists achieved high Dice scores of 0.860 and 0.833, while the radiologists' manual segmentations achieved a Dice score of 0.820. Linear correlation coefficients for the T1, T1ρ, and T2* relaxations calculated using the automatic and manual segmentations ranged between 0.90 and 0.97, and there were no associated differences between the estimated average meniscal relaxation parameters. The deep learning models achieved segmentation performance equivalent to the inter-observer variability of 2 radiologists. Conclusion: The proposed deep learning-based approach can be used to efficiently generate automatic segmentations and determine meniscal relaxations times. The method has the potential to help radiologists with the assessment of meniscal diseases, such as OA.

Keywords:

deep learning, menisci, osteoarthritis, quantitative MR, segmentation

Affiliations:
Byra M.-IPPT PAN
Wu M.-University of California (US)
Zhang X.-University of California (US)
Jang H.-University of California (US)
Ma Y-J.-University of California (US)
Chang E.Y.-University of California (US)
Shah S.-University of California (US)
Du J.-University of California (US)
2.Guo T., Ma Y-J., High R.A., Tang Q., Wong J.H., Byra M., Searleman A.C., To S.C., Wan L., Le N., Du J., Chang E., Assessment of an in vitro model of rotator cuff degeneration using quantitative magnetic resonance and ultrasound imaging with biochemical and histological correlation, European Journal of Radiology, ISSN: 0720-048X, DOI: 10.1016/j.ejrad.2019.108706, Vol.121, pp.108706-1-10, 2019
Abstract:

Purpose: Quantitative imaging methods could improve diagnosis of rotator cuff degeneration, but the capability of quantitative MR and US imaging parameters to detect alterations in collagen is unknown. The goal of this study was to assess quantitative MR and US imaging measures for detecting abnormalities in collagen using an in vitro model of tendinosis with biochemical and histological correlation. Method: 36 pieces of supraspinatus tendons from 6 cadaveric donors were equally distributed into 3 groups (2 subjected to different concentrations of collagenase and a control group). Ultrashort echo time MR and US imaging measures were performed to assess changes at baseline and after 24 h of enzymatic digestion. Biochemical and histological measures, including brightfield, fluorescence, and polarized microscopy, were used to verify the validity of the model and were compared with quantitative imaging parameters. Correlations between the imaging parameters and biochemically measured digestion were analyzed. Results: Among the imaging parameters, macromolecular fraction (MMF), adiabatic T1p, T2*, and backscatter coefficient (BSC) were useful in differentiating between the extent of degeneration among the 3 groups. MMF strongly correlated with collagen loss (r=-0.81; 95% confidence interval [CI]: -0.90,-0.66), while the adiabatic T1p (r = 0.66; CI: 0.42,0.81), T2* (r = 0.58; CI: 0.31,0.76), and BSC (r = 0.51; CI: 0.22,0.72) moderately correlated with collagen loss. Conclusions: MMF, adiabatic T1p, and T2* measured and US BSC can detect alterations in collagen. Of the quantitative MR and US imaging measures evaluated, MMF showed the highest correlation with collagen loss and can be used to assess rotator cuff degeneration.

Keywords:

rotator cuff tendon, tendinopathy, quantitative MRI, UTE, quantitative ultrasound

Affiliations:
Guo T.-University of California (US)
Ma Y-J.-University of California (US)
High R.A.-University of California (US)
Tang Q.-University of California (US)
Wong J.H.-University of California (US)
Byra M.-IPPT PAN
Searleman A.C.-University of California (US)
To S.C.-University of California (US)
Wan L.-University of California (US)
Le N.-University of California (US)
Du J.-University of California (US)
Chang E.-University of California (US)
3.Byra M., Wan L., Wong J.H., Du J., Shah SB., Andre M.P., Chang E.Y., Quantitative ultrasound and b-mode image texture featurescorrelate with collagen and myelin content in human ulnarnerve fascicles, ULTRASOUND IN MEDICINE AND BIOLOGY, ISSN: 0301-5629, DOI: 10.1016/j.ultrasmedbio.2019.02.019, Vol.45, No.7, pp.1830-1840, 2019
Abstract:

We investigate the usefulness of quantitative ultrasound and B-mode texture features for characterization of ulnar nerve fascicles. Ultrasound data were acquired from cadaveric specimens using a nominal 30-MHz probe. Next, the nerves were extracted to prepare histology sections. Eighty-five fascicles were matched between the B-mode images and the histology sections. For each fascicle image, we selected an intra-fascicular region of interest. We used histology sections to determine features related to the concentration of collagen and myelin and ultrasound data to calculate the backscatter coefficient (–24.89 ± 8.31 dB), attenuation coefficient (0.92 ± 0.04 db/cm-MHz), Nakagami parameter (1.01 ± 0.18) and entropy (6.92 ± 0.83), as well as B-mode texture features obtained via the gray-level co-occurrence matrix algorithm. Significant Spearman rank correlations between the combined collagen and myelin concentrations were obtained for the backscatter coefficient (R = –0.68), entropy (R = –0.51) and several texture features. Our study indicates that quantitative ultrasound may potentially provide information on structural components of nerve fascicles.

Keywords:

nerve, quantitative ultrasound, high frequency, histology, pattern recognition, texture analysis

Affiliations:
Byra M.-IPPT PAN
Wan L.-University of California (US)
Wong J.H.-University of California (US)
Du J.-University of California (US)
Shah SB.-University of California (US)
Andre M.P.-University of California (US)
Chang E.Y.-University of California (US)

Conference abstracts
1.Byra M., Wu M., Zhang X., Jang H., Ma Y., Chang E., Shah S., Du J., Assessing the performance of knee meniscus segmentation with deep convolutional neural networks in 3D ultrashort echo time (UTE) Cones MR imaging, 27th ISMRM Annual Meeting & Exhibition, 2019-05-11/05-16, Montreal (CA), pp.1-5, 2019
2.Byra M., Wong J., Shah S., Han A., O Brien W., Du J., Chang E., Andre M., High-frequency quantitative ultrasound and B-mode analysis for characterization of peripheral nerves including carpal tunnel syndrome, ASA, 178th Meeting of the Acoustical Society of America, 2019-12-02/12-06, San Diego (US), DOI: 10.1121/1.5136729, Vol.146, No.4, pp.2809-2809, 2019
Abstract:

We investigated the use of high-frequency quantitative ultrasound (QUS) and B-mode texture features to characterize ulnar and median nerve fascicles using a clinical scanner (Vevo MD) and a 30-MHz center-frequency probe. US correlation with histology was first investigated in the ulnar nerve in situ in cadaveric specimens. 85 fascicles were matched in B-mode images and the histology sections. Collagen and myelin concentrations were quantified from trichrome labeling, and backscatter coefficient (-24.89 ± 8.31 dB), attenuation coefficient (0.92 ± 0.04 dB/cm MHz), Nakagami parameter (1.01 ± 0.18) and entropy (6.92 ± 0.83) were calculated from ultrasound data. B-mode texture features were obtained via the gray-level co-occurrence matrix algorithm. Combined collagen and myelin concentration were significantly correlated with the backscatter coefficient (R = -0.68), entropy (R = -0.51), and several texture features. For the median nerve, we measured backscatter and morphology in 10 patients with carpal tunnel syndrome and 21 healthy volunteers. Significant differences (<0.01) between patients and controls and AUC 0.89–0.94 for QUS biomarkers were observed. Our study indicates that QUS may potentially provide useful information on structural components of even very small nerves (2 × 4 mm) and fascicles for diagnosing and monitoring injury, and surgical planning.

Affiliations:
Byra M.-IPPT PAN
Wong J.-University of California (US)
Shah S.-University of California (US)
Han A.-University of Illinois at Urbana-Champaign (US)
O Brien W.-University of Illinois at Urbana-Champaign (US)
Du J.-University of California (US)
Chang E.-University of California (US)
Andre M.-University of California (US)