arXiv subjects

Joshua D. Trzasko

Publications and source records attributed to Joshua D. Trzasko.

3 recordsLinked to original sources

arXiv2026

Reducing Spectral Oscillations for Robust Reference Frequency-Based Ultrasound Attenuation Estimation in Harmonic Imaging

Ultrasound attenuation coefficient estimation (ACE) has emerged as a quantitative imaging biomarker for noninvasive assessment of hepatic steatosis. A system-independent technique based on spectral normalization, known as the reference frequency method (RFM), was previously proposed to estimate ACE without requiring a well-calibrated reference phantom. Furthermore, incorporating harmonic imaging can significantly suppress reverberation signals. In previous clinical study, RFM has achieved high correlation with MRI-PDFF, demonstrating its potential for clinical application. However, a major challenge of RFM is the presence of oscillations in the frequency power-ratio decay curves (FPDCs), which can distort the linear fitting used to estimate the attenuation coefficient and consequently degrade ACE accuracy. These oscillations arise from constructive and destructive interference among backscattered echoes, resulting in oscillatory fluctuations in the measured power spectrum that propagate into the FPDCs. We propose a transmission scheme combining multiple frequencies and steering angles to mitigate oscillations in the FPDCs. Averaging these FPDCs suppresses the interference-induced oscillations while preserving the attenuation-dependent decay trend, thereby improving linearity and the accuracy of ACE results. In in-vitro experiments using calibrated phantoms (0.5 and 0.76 dB/cm/MHz) demonstrated that the proposed method improved FPDC linearity and ACE accuracy, achieving an R2 of 0.99 and attenuation coefficient estimates of 0.51 and 0.77 dB/cm/MHz, versus an R2 of 0.89 and estimates of 0.56 and 0.70 dB/cm/MHz for conventional RFM. The proposed method also demonstrated superior performance in a pilot patient study (n=15), achieving a stronger correlation with MRI-PDFF (R = 0.89 vs. 0.83) while reducing inter-measurement variability, indicating improved robustness and clinical potential.

physics.med-ph↗

arXiv2022

Deep learning-based reconstruction of highly accelerated 3D MRI

Purpose: To accelerate brain 3D MRI scans by using a deep learning method for reconstructing images from highly-undersampled multi-coil k-space data Methods: DL-Speed, an unrolled optimization architecture with dense skip-layer connections, was trained on 3D T1-weighted brain scan data to reconstruct complex-valued images from highly-undersampled k-space data. The trained model was evaluated on 3D MPRAGE brain scan data retrospectively-undersampled with a 10-fold acceleration, compared to a conventional parallel imaging method with a 2-fold acceleration. Scores of SNR, artifacts, gray/white matter contrast, resolution/sharpness, deep gray-matter, cerebellar vermis, anterior commissure, and overall quality, on a 5-point Likert scale, were assessed by experienced radiologists. In addition, the trained model was tested on retrospectively-undersampled 3D T1-weighted LAVA (Liver Acquisition with Volume Acceleration) abdominal scan data, and prospectively-undersampled 3D MPRAGE and LAVA scans in three healthy volunteers and one, respectively. Results: The qualitative scores for DL-Speed with a 10-fold acceleration were higher than or equal to those for the parallel imaging with 2-fold acceleration. DL-Speed outperformed a compressed sensing method in quantitative metrics on retrospectively-undersampled LAVA data. DL-Speed was demonstrated to perform reasonably well on prospectively-undersampled scan data, realizing a 2-5 times reduction in scan time. Conclusion: DL-Speed was shown to accelerate 3D MPRAGE and LAVA with up to a net 10-fold acceleration, achieving 2-5 times faster scans compared to conventional parallel imaging and acceleration, while maintaining diagnostic image quality and real-time reconstruction. The brain scan data-trained DL-Speed also performed well when reconstructing abdominal LAVA scan data, demonstrating versatility of the network.

eess.IV↗

arXiv2012

Unbiased Risk Estimates for Singular Value Thresholding and Spectral Estimators

In an increasing number of applications, it is of interest to recover an approximately low-rank data matrix from noisy observations. This paper develops an unbiased risk estimate---holding in a Gaussian model---for any spectral estimator obeying some mild regularity assumptions. In particular, we give an unbiased risk estimate formula for singular value thresholding (SVT), a popular estimation strategy which applies a soft-thresholding rule to the singular values of the noisy observations. Among other things, our formulas offer a principled and automated way of selecting regularization parameters in a variety of problems. In particular, we demonstrate the utility of the unbiased risk estimation for SVT-based denoising of real clinical cardiac MRI series data. We also give new results concerning the differentiability of certain matrix-valued functions.

math.ST↗