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Chang S, Gao Y, Pomeroy MJ, Bai T, Zhang H, Lu S, Pickhardt PJ, Gupta A, Reiter MJ, Gould ES, Liang Z. Exploring Dual-Energy CT Spectral Information for Machine Learning-Driven Lesion Diagnosis in Pre-Log Domain. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:1835-1845. [PMID: 37022248 PMCID: PMC10238622 DOI: 10.1109/tmi.2023.3240847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
In this study, we proposed a computer-aided diagnosis (CADx) framework under dual-energy spectral CT (DECT), which operates directly on the transmission data in the pre-log domain, called CADxDE, to explore the spectral information for lesion diagnosis. The CADxDE includes material identification and machine learning (ML) based CADx. Benefits from DECT's capability of performing virtual monoenergetic imaging with the identified materials, the responses of different tissue types (e.g., muscle, water, and fat) in lesions at each energy can be explored by ML for CADx. Without losing essential factors in the DECT scan, a pre-log domain model-based iterative reconstruction is adopted to obtain decomposed material images, which are then used to generate the virtual monoenergetic images (VMIs) at selected n energies. While these VMIs have the same anatomy, their contrast distribution patterns contain rich information along with the n energies for tissue characterization. Thus, a corresponding ML-based CADx is developed to exploit the energy-enhanced tissue features for differentiating malignant from benign lesions. Specifically, an original image-driven multi-channel three-dimensional convolutional neural network (CNN) and extracted lesion feature-based ML CADx methods are developed to show the feasibility of CADxDE. Results from three pathologically proven clinical datasets showed 4.01% to 14.25% higher AUC (area under the receiver operating characteristic curve) scores than the scores of both the conventional DECT data (high and low energy spectrum separately) and the conventional CT data. The mean gain >9.13% in AUC scores indicated that the energy spectral-enhanced tissue features from CADxDE have great potential to improve lesion diagnosis performance.
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Affiliation(s)
- Shaojie Chang
- Department of Radiology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Yongfeng Gao
- Department of Radiology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Marc J. Pomeroy
- Departments of Radiology and Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794, USA
| | - Ti Bai
- Department of Radiation Oncology, University of Texas Southwestern Medical Centre, Dallas, TX 75390, USA
| | - Hao Zhang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, NY 10065, USA
| | - Siming Lu
- Departments of Radiology and Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794, USA
| | - Perry J. Pickhardt
- Department of Radiology, School of Medicine, University of Wisconsin, Madison, WI 53792, USA
| | - Amit Gupta
- Department of Radiology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Michael J. Reiter
- Department of Radiology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Elaine S. Gould
- Department of Radiology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Zhengrong Liang
- Departments of Radiology and Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794, USA
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Tarkowski P, Czekajska-Chehab E. Dual-Energy Heart CT: Beyond Better Angiography-Review. J Clin Med 2021; 10:jcm10215193. [PMID: 34768713 PMCID: PMC8584316 DOI: 10.3390/jcm10215193] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 11/02/2021] [Accepted: 11/04/2021] [Indexed: 12/30/2022] Open
Abstract
Heart CT has undergone substantial development from the use of calcium scores performed on electron beam CT to modern 256+-row CT scanners. The latest big step in its evolution was the invention of dual-energy scanners with much greater capabilities than just performing better ECG-gated angio-CT. In this review, we present the unique features of dual-energy CT in heart diagnostics.
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Cassetta R, Lehmann M, Haytmyradov M, Patel R, Wang A, Cortesi L, Morf D, Seghers D, Surucu M, Mostafavi H, Roeske JC. Fast-switching dual energy cone beam computed tomography using the on-board imager of a commercial linear accelerator. Phys Med Biol 2020; 65:015013. [PMID: 31775131 DOI: 10.1088/1361-6560/ab5c35] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
To evaluate fast-kV switching (FS) dual energy (DE) cone beam computed tomography (CBCT) using the on-board imager (OBI) of a commercial linear accelerator to produce virtual monoenergetic (VM) and relative electron density (RED) images. Using an polynomial attenuation mapping model, CBCT phantom projections obtained at 80 and 140 kVp with FS imaging, were decomposed into equivalent thicknesses of aluminum (Al) and polymethyl methacrylate (PMMA). All projections were obtained with the titanium foil and bowtie filter in place. Basis material projections were then recombined to create VM images by using the linear attenuation coefficients at the specified energy for each material. Similarly, RED images were produced by replacing the linear attenuation values of Al and PMMA by their respective RED values in the projection space. VM and RED images were reconstructed using Feldkamp-Davis-Kress (FDK) and an iterative algorithm (iCBCT, Varian Medical Systems). Hounsfield units (HU), contrast-to-noise ratio (CNR) and RED values were compared against known values. The results after VM-CBCT production showed good material decomposition and consistent HUVM values, with measured root mean square errors (RMSE) from theoretical values, after FDK reconstruction, of 20.5, 5.7, 12.8 and 21.7 HU for 50, 80, 100 and 150 keV, respectively. The largest CNR improvements, when compared to polychromatic images, were observed for the 50 keV VM images. Image noise was reduced up to 28% in the VM-CBCT images after iterative image reconstruction. RED values measured for our method resulted in a mean percentage error of 0.0% ± 1.8%. This study describes a method to generate VM-CBCT and RED images using FS-DE scans obtained using the OBI of a linac, including the effects of the bowtie filter. The creation of VM and RED images increases the dynamic range of CBCT images, and provides additional data that may be used for adaptive radiotherapy, and on table verification for radiotherapy treatments.
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Affiliation(s)
- Roberto Cassetta
- Division of Medical Physics, Department of Radiation Oncology, Loyola University Medical Center, Maywood, IL, United States of America
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