1
|
van Bergen R, Sun L, Pandey PK, Wang S, Bjegovic K, Gonzalez G, Chen Y, Lopata R, Xiang L. Discrete Wavelet Transformation for the Sensitive Detection of Ultrashort Radiation Pulse with Radiation-induced Acoustics. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2024; 8:76-87. [PMID: 39220226 PMCID: PMC11364354 DOI: 10.1109/trpms.2023.3314339] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Abstract
Radiation-induced acoustics (RIA) shows promise in advancing radiological imaging and radiotherapy dosimetry methods. However, RIA signals often require extensive averaging to achieve reasonable signal-to-noise ratios, which increases patient radiation exposure and limits real-time applications. Therefore, this paper proposes a discrete wavelet transform (DWT) based filtering approach to denoise the RIA signals and avoid extensive averaging. The algorithm was benchmarked against low-pass filters and tested on various types of RIA sources, including low-energy X-rays, high-energy X-rays, and protons. The proposed method significantly reduced the required averages (1000 times less averaging for low-energy X-ray RIA, 32 times less averaging for high-energy X-ray RIA, and 4 times less averaging for proton RIA) and demonstrated robustness in filtering signals from different sources of radiation. The coif5 wavelet in conjunction with the sqtwolog threshold selection algorithm yielded the best results. The proposed DWT filtering method enables high-quality, automated, and robust filtering of RIA signals, with a performance similar to low-pass filtering, aiding in the clinical translation of radiation-based acoustic imaging for radiology and radiation oncology.
Collapse
Affiliation(s)
- Rick van Bergen
- PULS/e lab Eindhoven, Department of Biomedical Engineering, Eindhoven University of Technology, The Netherlands
| | - Leshan Sun
- Department of Biomedical Engineering, University of California Irvine, Irvine, CA 92617
| | - Prabodh Kumar Pandey
- Department of Radiological Sciences, University of California Irvine, Irvine, CA 92617
| | - Siqi Wang
- Department of Biomedical Engineering, University of California Irvine, Irvine, CA 92617
| | - Kristina Bjegovic
- Department of Biomedical Engineering, University of California Irvine, Irvine, CA 92617
| | - Gilberto Gonzalez
- Department of Radiation Oncology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104
| | - Yong Chen
- Department of Radiation Oncology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104
| | - Richard Lopata
- PULS/e lab Eindhoven, Department of Biomedical Engineering, Eindhoven University of Technology, The Netherlands
| | - Liangzhong Xiang
- Department of Biomedical Engineering, University of California Irvine, Irvine, CA 92617.; Department of Radiological Sciences, University of California Irvine, Irvine, CA 92617.; Beckman Laser Institute Medical Clinic, University of California Irvine, Irvine, CA 92612
| |
Collapse
|
2
|
Pandey PK, Wang S, Sun L, Xing L, Xiang L. Model-Based 3-D X-Ray Induced Acoustic Computerized Tomography. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2023; 7:532-543. [PMID: 38046375 PMCID: PMC10691826 DOI: 10.1109/trpms.2023.3238017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
X-ray-induced acoustic (XA) computerized tomography (XACT) is an evolving imaging technique that aims to reconstruct the X-ray energy deposition from XA measurements. Main challenges in XACT are the poor signal-to-noise ratio and limited field-of-view, which cause artifacts in the images. We demonstrate the efficacy of model-based (MB) algorithms for three-dimensional XACT and compare with the traditional algorithms. The MB algorithm is based on iterative, matrix-free approach for regularized-least-squares minimization corresponding to XACT. The matrix-free-LSQR (MF-LSQR) and the non-iterative model-backprojection (MBP) reconstructions were evaluated and compared with universal backprojection (UBP), time-reversal (TR) and fast-Fourier transform (FFT)-based reconstructions for numerical and experimental XACT datasets. The results demonstrate the capability of MF-LSQR algorithm to reduce noisy artifacts thus yielding better reconstructions. MBP and MF-LSQR algorithms perform particularly well with the experimental XACT dataset, where noise in signals significantly affects the reconstruction of the target in UBP and FFT-based reconstructions. The TR reconstruction for experimental XACT are comparable to MF-LSQR, but takes thrice as much time and filters the frequency components greater than maximum frequency supported by the grid, resulting loss of resolution. The MB algorithms are able to overcome the challenges in XACT and hence are vital for the clinical translation of XACT.
Collapse
Affiliation(s)
- Prabodh Kumar Pandey
- Department of Radiological Sciences, University of California, Irvine, CA, 92697, USA
| | - Siqi Wang
- Department of Biomedical Engineering, University of California, Irvine, CA 92617, USA
| | - Leshan Sun
- Department of Biomedical Engineering, University of California, Irvine, CA 92617, USA
| | - Lei Xing
- Department of Radiological Sciences, University of California, Irvine, CA, 92697, USA.; Department of Biomedical Engineering, University of California, Irvine, CA 92617, USA.; Beckman Laser Institute, University of California, Irvine, CA 92612, USA
| | - Liangzhong Xiang
- Division of Medical Physics, Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA,94305, USA
| |
Collapse
|
3
|
Zhang P, Ma C, Song F, Zhang T, Sun Y, Feng Y, He Y, Liu F, Wang D, Zhang G. D2-RecST: Dual-domain joint reconstruction strategy for fluorescence molecular tomography based on image domain and perception domain. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 229:107293. [PMID: 36481532 DOI: 10.1016/j.cmpb.2022.107293] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 10/05/2022] [Accepted: 10/10/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND AND OBJECTIVE Fluorescence molecular tomography (FMT) is a promising molecular imaging modality for quantifying the three-dimensional (3D) distribution of fluorescent probes in small animals. Over the past few years, learning-based FMT reconstruction methods have achieved promising results. However, these methods typically attempt to minimize the mean-squared error (MSE) between the reconstructed image and the ground truth. Although signal-to-noise ratios (SNRs) are improved, they are susceptible to non-uniform artifacts and loss of structural detail, making it extremely challenging to obtain accurate and robust FMT reconstructions under noisy measurements. METHODS We propose a novel dual-domain joint strategy based on the image domain and perception domain for accurate and robust FMT reconstruction. First, we formulate an explicit adversarial learning strategy in the image domain, which greatly facilitates training and optimization through two enhanced networks to improve anti-noise ability. Besides, we introduce a novel transfer learning strategy in the perceptual domain to optimize edge details by providing perceptual priors for fluorescent targets. Collectively, the proposed dual-domain joint reconstruction strategy can significantly eliminate the non-uniform artifacts and effectively preserve the structural edge details. RESULTS Both numerical simulations and in vivo mouse experiments demonstrate that the proposed method markedly outperforms traditional and cutting-edge methods in terms of positioning accuracy, image contrast, robustness, and target morphological recovery. CONCLUSIONS The proposed method achieves the best reconstruction performance and has great potential to facilitate precise localization and 3D visualization of tumors in in vivo animal experiments.
Collapse
Affiliation(s)
- Peng Zhang
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Chenbin Ma
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Fan Song
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Tianyi Zhang
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Yangyang Sun
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Youdan Feng
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Yufang He
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Fei Liu
- Advanced information & Industrial Technology Research Institute, Beijing Information Science & Technology University, Beijing, 100192, China
| | - Daifa Wang
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Guanglei Zhang
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China.
| |
Collapse
|