Rationale And Objectives: To compare Hounsfield unit (HU) data obtained from true-unenhanced (TUE) and virtual-unenhanced (VUE) imaging obtained with a fast kv-switching dual-energy computed tomography (CT) scanner using multimaterial decomposition algorithm.
Materials And Methods: In this Institutional Review Board-approved, Health Insurance Portability and Accountability Act-compliant, retrospective cohort study, CT scans of 19 patients undergoing multiphasic renal protocol abdominal CT on a fast kv-switching dual-energy CT scanner were reviewed. CT numbers were measured on the matched TUE and VUE generated using a multimaterial decomposition algorithm with selective iodine suppression, and postcontrast images at predefined locations in seven organs. Six hundred sixty regions of interest were placed at 132 locations. Agreement was assessed with paired t test, Pearson's correlation, and Bland-Altman analysis.
Results: Mean TUE and VUE measurements were not significantly different in the corticomedullary (P = 0.25) or nephrographic (P = 0.10) phases. There was a strong correlation between TUE and VUE CT numbers (corticomedullary: r = 0.90, nephrographic: r = 0.90, each P < 0.001). Discrepancies ≥5 HU occurred 46 times (35%, 46 of 132) in the corticomedullary phase and 44 times (33%, 44 of 132) in the nephrographic phase. Discrepancies ≥10 HU occurred in 7% (9 of 132 in both corticomedullary and nephrographic phases). Interphase, intrasubject VUE CT numbers were strongly correlated (r = 0.93, P < 0.001), but discrepancies ≥5 HU (22% [29 of 132]) and ≥10 HU (2% [3 of 132]) occurred. There was no significant correlation between the true postcontrast CT number and the magnitude of VUE-TUE discrepancy (r = -0.04, P = 0.6).
Conclusion: CT numbers on VUE images generated from fast kv-switching dual-energy CT scans strongly correlate with TUE CT numbers on a population basis, but commonly vary 5-9 HU on a per-patient basis.
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http://dx.doi.org/10.1016/j.acra.2016.09.002 | DOI Listing |
Sci Rep
December 2024
Health & Medical Equipment Business, Samsung Electronics Co., Ltd, 8, Gumi-ro, Bundang-gu, Seongnam- si, 13638, Gyeonggi-do, Republic of Korea.
The photon-counting detector computed tomography (PCD-CT) is a promising new technology that provides more spectral information in medical imaging. PCD-CT enables bedside imaging in the neuro intensive care unit (neuro ICU) for patients with life-threatening conditions such as brain hemorrhage and ischemic stroke. The primary purpose of this study is to evaluate a multi-material decomposition algorithm available on PCD-CT, dubbed MD Plus, to differentiate between contrast agent and hemorrhage in hyperdense lesions.
View Article and Find Full Text PDFJ Med Imaging (Bellingham)
December 2024
University of Houston, Department of Physics, Houston, Texas, United States.
Purpose: Photon counting detectors offer promising advancements in computed tomography (CT) imaging by enabling the quantification and three-dimensional imaging of contrast agents and tissue types through simultaneous multi-energy projections from broad X-ray spectra. However, the accuracy of these decomposition methods hinges on precise composite spectral attenuation values that one must reconstruct from spectral micro-CT. Errors in such estimations could be due to effects such as beam hardening, object scatter, or detector sensor-related spectral distortions such as fluorescence.
View Article and Find Full Text PDFMed Phys
December 2024
Research Center for Advanced Detection Materials and Medical Imaging Devices, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China.
Background: Recently, the popularity of dual-layer flat-panel detector (DL-FPD) based dual-energy cone-beam CT (CBCT) imaging has been increasing. However, the image quality of dual-energy CBCT remains constrained by the Compton scattered x-ray photons.
Purpose: The objective of this study is to develop a novel scatter correction method, named e-Grid, for DL-FPD based CBCT imaging.
Med Phys
November 2024
Research Center for Advanced Detection Materials and Medical Imaging Devices, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China.
Background: Multi-material decomposition is an interesting topic in dual-energy CT (DECT) imaging; however, the accuracy and performance may be limited using the conventional algorithms.
Purpose: In this work, a novel multi-material decomposition network (MMD-Net) is proposed to improve the multi-material decomposition performance of DECT imaging.
Methods: To achieve dual-energy multi-material decomposition, a deep neural network, named as MMD-Net, is proposed in this work.
Eur Radiol Exp
October 2024
Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA.
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