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Chen Z, Cheng J, Wu H. Application of the Five-Step Phase-Shifting Method in Reflective Ghost Imaging for Efficient Phase Reconstruction. SENSORS (BASEL, SWITZERLAND) 2024; 24:320. [PMID: 38257413 PMCID: PMC11154415 DOI: 10.3390/s24020320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 01/02/2024] [Accepted: 01/03/2024] [Indexed: 01/24/2024]
Abstract
The conventional approach to phase reconstruction in Reflective Ghost Imaging (RGI) typically involves the introduction of three reference screens into the reference path, deeming the Fourier transform step indispensable. However, this method introduces complexity to the system and raises concerns regarding potential errors in phase retrieval. In response to these challenges, we advocate for adopting the Five-Step Phase-Shifting (FSPS) method in the RGI system. This method presents two key advantages over traditional approaches: (1) It streamlines the phase reconstruction process by eliminating the requirement for a Fourier inverse transform. (2) It avoids the need to insert objects into the reference optical path, simplifying the computation of reference optical path intensity and enabling seamless application to Computational Ghost Imaging (CGI), overcoming the constraints of Dual-Arm Ghost Imaging (DAGI). We substantiate the theoretical proposition through numerical simulations involving two intricate objects. Furthermore, our discussion delves into exploring the influence of varying reflective angles on the phase reconstruction performance.
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Affiliation(s)
- Ziyan Chen
- Guangdong Provincial Key Laboratory of Cyber-Physical System, School of Automation, Guangdong University of Technology, Guangzhou 510006, China;
- School of Computer, Guangdong University of Technology, Guangzhou 510006, China
| | - Jing Cheng
- School of Physics, South China University of Technology, Guangzhou 510641, China;
| | - Heng Wu
- Guangdong Provincial Key Laboratory of Cyber-Physical System, School of Automation, Guangdong University of Technology, Guangzhou 510006, China;
- School of Computer, Guangdong University of Technology, Guangzhou 510006, China
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Arana Peña LM, Donato S, Bonazza D, Brombal L, Martellani F, Arfelli F, Tromba G, Longo R. Multiscale X-ray phase-contrast tomography: From breast CT to micro-CT for virtual histology. Phys Med 2023; 112:102640. [PMID: 37441823 DOI: 10.1016/j.ejmp.2023.102640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 05/31/2023] [Accepted: 07/04/2023] [Indexed: 07/15/2023] Open
Abstract
Phase-contrast imaging techniques address the issue of poor soft-tissue contrast encountered in traditional X-ray imaging. This can be accomplished with the propagation-based phase-contrast technique by employing a coherent photon beam, which is available at synchrotron facilities, as well as long sample-to-detector distances. This study demonstrates the optimization of propagation-based phase-contrast computed tomography (CT) techniques for multiscale X-ray imaging of the breast at the Elettra synchrotron facility (Trieste, Italy). Two whole breast mastectomy samples were acquired with propagation-based breast-CT using a monochromatic synchrotron beam at a pixel size of 60 µm. Paraffin-embedded blocks sampled from the same tissues were scanned with propagation-based micro-CT imaging using a polychromatic synchrotron beam at a pixel size of 4 µm. Images of both methodologies and of the same sample were spatially registered. The resulting images showed the transition from whole breast imaging with propagation-based breast-CT methodology to virtual histology with propagation-based micro-CT imaging of the same sample. Additionally, conventional histological images were matched to virtual histology images. Phase-contrast images offer a high resolution with low noise, which allows for a highly precise match between virtual and conventional histology. Furthermore, those techniques allow a clear discernment of breast structures, lesions, and microcalcifications, being a promising clinically-compatible tool for breast imaging in a multiscale approach, to either assist in the detection of cancer in full volume breast samples or to complement structure identification in paraffin-embedded breast tissue samples.
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Affiliation(s)
- L M Arana Peña
- Department of Physics, University of Trieste, Via Alfonso Valerio 2, Trieste I-34127, Italy; INFN Division of Trieste, 34127 Trieste, Italy; Elettra-Sincrotrone Trieste, SS 14 Km 163,5, AREA Science Park, 34149 Basovizza, (Trieste), Italy
| | - S Donato
- Department of Physics and STAR Lab, University of Calabria, Via P. Bucci 31C, Rende, (CS), I-87036, Italy; INFN Division of Frascati, Via E. Fermi 54, Frascati I-00044, Italy.
| | - D Bonazza
- Unit of Surgical Pathology, Cattinara Hospital, Azienda Sanitaria Universitaria Giuliana Isontina (ASUGI), Strada di Fiume, 447, Trieste I-34149, Italy
| | - L Brombal
- Department of Physics, University of Trieste, Via Alfonso Valerio 2, Trieste I-34127, Italy; INFN Division of Trieste, 34127 Trieste, Italy
| | - F Martellani
- Unit of Surgical Pathology, Cattinara Hospital, Azienda Sanitaria Universitaria Giuliana Isontina (ASUGI), Strada di Fiume, 447, Trieste I-34149, Italy
| | - F Arfelli
- Department of Physics, University of Trieste, Via Alfonso Valerio 2, Trieste I-34127, Italy; INFN Division of Trieste, 34127 Trieste, Italy
| | - G Tromba
- Elettra-Sincrotrone Trieste, SS 14 Km 163,5, AREA Science Park, 34149 Basovizza, (Trieste), Italy
| | - R Longo
- Department of Physics, University of Trieste, Via Alfonso Valerio 2, Trieste I-34127, Italy; INFN Division of Trieste, 34127 Trieste, Italy
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Deshpande R, Avachat A, Brooks FJ, Anastasio MA. Investigating the robustness of a deep learning-based method for quantitative phase retrieval from propagation-based x-ray phase contrast measurements under laboratory conditions. Phys Med Biol 2023; 68:10.1088/1361-6560/acc2aa. [PMID: 36889005 PMCID: PMC10405978 DOI: 10.1088/1361-6560/acc2aa] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 03/08/2023] [Indexed: 03/10/2023]
Abstract
Objective.Quantitative phase retrieval (QPR) in propagation-based x-ray phase contrast imaging of heterogeneous and structurally complicated objects is challenging under laboratory conditions due to partial spatial coherence and polychromaticity. A deep learning-based method (DLBM) provides a nonlinear approach to this problem while not being constrained by restrictive assumptions about object properties and beam coherence. The objective of this work is to assess a DLBM for its applicability under practical scenarios by evaluating its robustness and generalizability under typical experimental variations.Approach.Towards this end, an end-to-end DLBM was employed for QPR under laboratory conditions and its robustness was investigated across various system and object conditions. The robustness of the method was tested via varying propagation distances and its generalizability with respect to object structure and experimental data was also tested.Main results.Although the end-to-end DLBM was stable under the studied variations, its successful deployment was found to be affected by choices pertaining to data pre-processing, network training considerations and system modeling.Significance.To our knowledge, we demonstrated for the first time, the potential applicability of an end-to-end learning-based QPR method, trained on simulated data, to experimental propagation-based x-ray phase contrast measurements acquired under laboratory conditions with a commercial x-ray source and a conventional detector. We considered conditions of polychromaticity, partial spatial coherence, and high noise levels, typical to laboratory conditions. This work further explored the robustness of this method to practical variations in propagation distances and object structure with the goal of assessing its potential for experimental use. Such an exploration of any DLBM (irrespective of its network architecture) before practical deployment provides an understanding of its potential behavior under experimental settings.
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Affiliation(s)
- Rucha Deshpande
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, United States of America
| | - Ashish Avachat
- Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, PA, United States of America
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, United States of America
| | - Frank J Brooks
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, United States of America
| | - Mark A Anastasio
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, United States of America
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Salinas F, Solís-Prosser MA. Morphological variations to a ptychographic algorithm. APPLIED OPTICS 2022; 61:6561-6570. [PMID: 36255881 DOI: 10.1364/ao.462173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 07/10/2022] [Indexed: 06/16/2023]
Abstract
Ptychography is a technique widely used in microscopy for achieving high-resolution imaging. This method relies on computational processing of images gathered from diffraction patterns produced by several partial illuminations of a sample. We numerically studied the effect of using different shapes for illuminating the aforementioned sample: convex shapes, such as circles and regular polygons, and unconnected shapes that resemble a QR code. Our results suggest that the use of unconnected shapes seems to outperform convex shapes in terms of convergence and, in some cases, accuracy.
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Shi C, Xiao Y, Chen Z. Dual-domain sparse-view CT reconstruction with Transformers. Phys Med 2022; 101:1-7. [PMID: 35849908 DOI: 10.1016/j.ejmp.2022.07.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 07/04/2022] [Accepted: 07/08/2022] [Indexed: 10/17/2022] Open
Abstract
PURPOSE Computed Tomography (CT) has been widely used in the medical field. Sparse-view CT is an effective and feasible method to reduce the radiation dose. However, the conventional filtered back projection (FBP) algorithm will suffer from severe artifacts in sparse-view CT. Iterative reconstruction algorithms have been adopted to remove artifacts, but they are time-consuming due to repeated projection and back projection and may cause blocky effects. To overcome the difficulty in sparse-view CT, we proposed a dual-domain sparse-view CT algorithm CT Transformer (CTTR) and paid attention to sinogram information. METHODS CTTR treats sinograms as sentences and enhances reconstructed images with sinogram's characteristics. We qualitatively evaluate the CTTR, an iterative method TVM-POCS, a convolutional neural network based method FBPConvNet in terms of a reduction in artifacts and a preservation of details. Besides, we also quantitatively evaluate these methods in terms of RMSE, PSNR and SSIM. RESULTS We evaluate our method on the Lung Image Database Consortium image collection with different numbers of projection views and noise levels. Experiment studies show that, compared with other methods, CTTR can reduce more artifacts and preserve more details on various scenarios. Specifically, CTTR improves the FBPConvNet performance of PSNR by 0.76dB with 30 projections. CONCLUSIONS The performance of our proposed CTTR is better than the method based on CNN in the case of extremely sparse views both on visual results and quantitative evaluation. Our proposed method provides a new idea for the application of Transformers to CT image processing.
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Affiliation(s)
- Changrong Shi
- Department of Engineering Physics, Tsinghua University, Beijing, 100084, China; Key Laboratory of Particle & Radiation Imaging (Tsinghua University), Ministry of Education, Beijing, China
| | - Yongshun Xiao
- Department of Engineering Physics, Tsinghua University, Beijing, 100084, China; Key Laboratory of Particle & Radiation Imaging (Tsinghua University), Ministry of Education, Beijing, China.
| | - Zhiqiang Chen
- Department of Engineering Physics, Tsinghua University, Beijing, 100084, China; Key Laboratory of Particle & Radiation Imaging (Tsinghua University), Ministry of Education, Beijing, China
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