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Langius-Wiffen E, Nijholt IM, van Dijk RA, de Boer E, Nijboer-Oosterveld J, Veldhuis WB, de Jong PA, Boomsma MF. An artificial intelligence algorithm for pulmonary embolism detection on polychromatic computed tomography: performance on virtual monochromatic images. Eur Radiol 2024; 34:384-390. [PMID: 37542651 DOI: 10.1007/s00330-023-10048-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 06/19/2023] [Accepted: 06/22/2023] [Indexed: 08/07/2023]
Abstract
OBJECTIVES Virtual monochromatic images (VMI) are increasingly used in clinical practice as they improve contrast-to-noise ratio. However, due to their different appearances, the performance of artificial intelligence (AI) trained on conventional CT images may worsen. The goal of this study was to assess the performance of an established AI algorithm trained on conventional polychromatic computed tomography (CT) images (CPI) to detect pulmonary embolism (PE) on VMI. METHODS Paired 60 kiloelectron volt (keV) VMI and CPI of 114 consecutive patients suspected of PE, obtained with a detector-based spectral CT scanner, were retrospectively analyzed by an established AI algorithm. The CT pulmonary angiography (CTPA) were classified as positive or negative for PE on a per-patient level. The reference standard was established using a comprehensive method that combined the evaluation of the attending radiologist and three experienced cardiothoracic radiologists aided by two different detection tools. Sensitivity, specificity, positive and negative predictive values and likelihood ratios of the algorithm on VMI and CPI were compared. RESULTS The prevalence of PE according to the reference standard was 35.1% (40 patients). None of the diagnostic accuracy measures of the algorithm showed a significant difference between CPI and VMI. Sensitivity was 77.5% (95% confidence interval (CI) 64.6-90.4%) and 85.0% (73.9-96.1%) (p = 0.08) on CPI and VMI respectively and specificity 96.0% (91.4-100.0%) and 94.6% (89.4-99.7%) (p = 0.32). CONCLUSIONS Diagnostic performance of the AI algorithm that was trained on CPI did not drop on VMI, which is reassuring for its use in clinical practice. CLINICAL RELEVANCE STATEMENT A commercially available AI algorithm, trained on conventional polychromatic CTPA, could be safely used on virtual monochromatic images. This supports the sustainability of AI-aided detection of PE on CT despite ongoing technological advances in medical imaging, although monitoring in daily practice will remain important. KEY POINTS • Diagnostic accuracy of an AI algorithm trained on conventional polychromatic images to detect PE did not drop on virtual monochromatic images. • Our results are reassuring as innovations in hardware and reconstruction in CT are continuing, whilst commercial AI algorithms that are trained on older generation data enter healthcare.
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Affiliation(s)
- Eline Langius-Wiffen
- Department of Radiology, Isala Hospital, Dr. Van Heesweg 2, 8025 AB, Zwolle, The Netherlands.
| | - Ingrid M Nijholt
- Department of Radiology, Isala Hospital, Dr. Van Heesweg 2, 8025 AB, Zwolle, The Netherlands
| | - Rogier A van Dijk
- Department of Radiology, Isala Hospital, Dr. Van Heesweg 2, 8025 AB, Zwolle, The Netherlands
| | - Erwin de Boer
- Department of Radiology, Isala Hospital, Dr. Van Heesweg 2, 8025 AB, Zwolle, The Netherlands
| | | | - Wouter B Veldhuis
- Department of Radiology, University Medical Centre Utrecht, Utrecht, the Netherlands
| | - Pim A de Jong
- Department of Radiology, University Medical Centre Utrecht, Utrecht, the Netherlands
| | - Martijn F Boomsma
- Department of Radiology, Isala Hospital, Dr. Van Heesweg 2, 8025 AB, Zwolle, The Netherlands
- Division of Imaging and Oncology, University Medical Centre Utrecht, Utrecht, The Netherlands
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Penso M, Babbaro M, Moccia S, Baggiano A, Carerj ML, Guglielmo M, Fusini L, Mushtaq S, Andreini D, Pepi M, Pontone G, Caiani EG. A deep-learning approach for myocardial fibrosis detection in early contrast-enhanced cardiac CT images. Front Cardiovasc Med 2023; 10:1151705. [PMID: 37424918 PMCID: PMC10325686 DOI: 10.3389/fcvm.2023.1151705] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 06/12/2023] [Indexed: 07/11/2023] Open
Abstract
Aims Diagnosis of myocardial fibrosis is commonly performed with late gadolinium contrast-enhanced (CE) cardiac magnetic resonance (CMR), which might be contraindicated or unavailable. Coronary computed tomography (CCT) is emerging as an alternative to CMR. We sought to evaluate whether a deep learning (DL) model could allow identification of myocardial fibrosis from routine early CE-CCT images. Methods and results Fifty consecutive patients with known left ventricular (LV) dysfunction (LVD) underwent both CE-CMR and (early and late) CE-CCT. According to the CE-CMR patterns, patients were classified as ischemic (n = 15, 30%) or non-ischemic (n = 35, 70%) LVD. Delayed enhancement regions were manually traced on late CE-CCT using CE-CMR as reference. On early CE-CCT images, the myocardial sectors were extracted according to AHA 16-segment model and labeled as with scar or not, based on the late CE-CCT manual tracing. A DL model was developed to classify each segment. A total of 44,187 LV segments were analyzed, resulting in accuracy of 71% and area under the ROC curve of 76% (95% CI: 72%-81%), while, with the bull's eye segmental comparison of CE-CMR and respective early CE-CCT findings, an 89% agreement was achieved. Conclusions DL on early CE-CCT acquisition may allow detection of LV sectors affected with myocardial fibrosis, thus without additional contrast-agent administration or radiational dose. Such tool might reduce the user interaction and visual inspection with benefit in both efforts and time.
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Affiliation(s)
- Marco Penso
- Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy
- Department of Electronics, Information and Biomedical Engineering, Politecnico di Milano, Milan, Italy
| | - Mario Babbaro
- Department of Cardiology, IRCCS Policlinico San Donato, Milan, Italy
| | - Sara Moccia
- The BioRobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant’Anna, Pisa, Italy
| | - Andrea Baggiano
- Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy
- Cardiovascular Section, Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
| | - Maria Ludovica Carerj
- Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy
- Department of Biomedical Sciences and Morphological and Functional Imaging, “G. Martino” University Hospital Messina, Messina, Italy
| | - Marco Guglielmo
- Department of Cardiology, Division of Heart and Lungs, Utrecht University, Utrecht University Medical Center, Utrecht, Netherlands
- Department of Cardiology, Haga Teaching Hospital, The Hague, Netherlands
| | - Laura Fusini
- Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy
- Department of Electronics, Information and Biomedical Engineering, Politecnico di Milano, Milan, Italy
| | - Saima Mushtaq
- Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy
| | - Daniele Andreini
- Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy
- Cardiovascular Section, Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
| | - Mauro Pepi
- Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy
| | - Gianluca Pontone
- Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy
| | - Enrico G. Caiani
- Department of Electronics, Information and Biomedical Engineering, Politecnico di Milano, Milan, Italy
- Department of Cardiology, Istituto Auxologico Italiano IRCCS, Milan, Italy
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Wang T, Yue Y, Fan Z, Jia Z, Yu X, Liu C, Hou Y. Spectral Dual-Layer Computed Tomography Can Predict the Invasiveness of Ground-Glass Nodules: A Diagnostic Model Combined with Thymidine Kinase-1. J Clin Med 2023; 12:jcm12031107. [PMID: 36769756 PMCID: PMC9917490 DOI: 10.3390/jcm12031107] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 01/26/2023] [Accepted: 01/27/2023] [Indexed: 02/04/2023] Open
Abstract
OBJECTIVES Few studies have explored the use of spectral dual-layer detector-based computed tomography (SDCT) parameters, thymidine kinase-1 (TK1), and tumor abnormal protein (TAP) for the detection of ground-glass nodules (GGNs). Therefore, we aimed to evaluate the quantitative and qualitative parameters generated from SDCT for predicting the pathological subtypes of GGN-featured lung adenocarcinoma combined with TK1 and TAP. MATERIAL AND METHODS Between July 2021 and September 2022, 238 patients with GGNs were retrospectively enrolled in this study. SDCT and tests for TK1 and TAP were performed preoperatively, and the lesions were divided into glandular precursor lesions (PGL), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC), according to the pathological results. A receiver operating characteristic (ROC) curve was used to compare the diagnostic performance of these parameters. Multivariate logistic regression analysis was performed to construct a joint diagnostic model and create a nomogram. RESULTS This study included 238 GGNs, including 41 atypical adenomatous hyperplasias (AAH), 62 adenocarcinomas in situ (AIS), 49 MIA, and 86 IAC, with a high proportion of women, non-smokers, and pure ground-glass nodule (pGGN). CT100 keV (a/v), electronic density (EDW) (a/v), Daverage, Dsolid, TK1, and TAP of MIA and IAC were higher than those of PGL. The effective atomic number (Zeff (a/v)) was lower in MIA and IAC than in PGL (all p < 0.05). Logistic regression analysis showed that Zeff (a), EDW (a), TK1, Daverage, and internal bronchial morphology were crucial factors in predicting the aggressiveness of GGN. Zeff (a) had the highest diagnostic performance with an area under the ROC curve (AUC) = 0.896, followed by EDW (a) (AUC = 0.838) and CT100 keVa (AUC = 0.819). The diagnostic model and nomogram constructed using these five parameters (Zeff (a) + EDW (a) + CT100 keVa + Daverage + TK1) had an AUC = 0.933, which was higher than the individual parameters (p < 0.05). CONCLUSIONS Multiple quantitative and functional parameters can be selected based on SDCT, especially Zeff (a) and EDW (a), which have high sensitivity and specificity for predicting GGNs' invasiveness. Additionally, the combination of TK1 can further improve diagnostic performance, and using a nomogram is helpful for individualized predictions.
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Affiliation(s)
- Tong Wang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang 110004, China
| | - Yong Yue
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang 110004, China
| | - Zheng Fan
- Department of Orthopedics, Shengjing Hospital of China Medical University, Shenyang 110004, China
| | - Zheng Jia
- Philips (China) Investment Co., Ltd., Shanghai 200072, China
| | - Xiuze Yu
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang 110004, China
| | - Chen Liu
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang 110004, China
| | - Yang Hou
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang 110004, China
- Correspondence: ; Tel.: +86-96615-73218
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Langius-Wiffen E, Nijholt IM, de Boer E, Nijboer-Oosterveld J, Huurman L, Rozema I, Walen S, van den Berg JWK, de Jong PA, Boomsma MF. Computer-aided Pulmonary Embolism Detection on Virtual Monochromatic Images Compared to Conventional CT Angiography. Radiology 2021; 301:420-422. [PMID: 34491128 DOI: 10.1148/radiol.2021204620] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Eline Langius-Wiffen
- From the Departments of Radiology (E.L.W., I.M.N., E.d.B., J.N.O., L.H., I.R., M.F.B.) and Pulmonology (S.W., J.W.K.v.d.B.), Isala Hospital, Dokter van Heesweg 2, 8025 AB Zwolle, the Netherlands; and Department of Radiology, University Medical Centre Utrecht, Utrecht, the Netherlands (P.A.d.J.)
| | - Ingrid M Nijholt
- From the Departments of Radiology (E.L.W., I.M.N., E.d.B., J.N.O., L.H., I.R., M.F.B.) and Pulmonology (S.W., J.W.K.v.d.B.), Isala Hospital, Dokter van Heesweg 2, 8025 AB Zwolle, the Netherlands; and Department of Radiology, University Medical Centre Utrecht, Utrecht, the Netherlands (P.A.d.J.)
| | - Erwin de Boer
- From the Departments of Radiology (E.L.W., I.M.N., E.d.B., J.N.O., L.H., I.R., M.F.B.) and Pulmonology (S.W., J.W.K.v.d.B.), Isala Hospital, Dokter van Heesweg 2, 8025 AB Zwolle, the Netherlands; and Department of Radiology, University Medical Centre Utrecht, Utrecht, the Netherlands (P.A.d.J.)
| | - Jacqueline Nijboer-Oosterveld
- From the Departments of Radiology (E.L.W., I.M.N., E.d.B., J.N.O., L.H., I.R., M.F.B.) and Pulmonology (S.W., J.W.K.v.d.B.), Isala Hospital, Dokter van Heesweg 2, 8025 AB Zwolle, the Netherlands; and Department of Radiology, University Medical Centre Utrecht, Utrecht, the Netherlands (P.A.d.J.)
| | - Lisa Huurman
- From the Departments of Radiology (E.L.W., I.M.N., E.d.B., J.N.O., L.H., I.R., M.F.B.) and Pulmonology (S.W., J.W.K.v.d.B.), Isala Hospital, Dokter van Heesweg 2, 8025 AB Zwolle, the Netherlands; and Department of Radiology, University Medical Centre Utrecht, Utrecht, the Netherlands (P.A.d.J.)
| | - Ilse Rozema
- From the Departments of Radiology (E.L.W., I.M.N., E.d.B., J.N.O., L.H., I.R., M.F.B.) and Pulmonology (S.W., J.W.K.v.d.B.), Isala Hospital, Dokter van Heesweg 2, 8025 AB Zwolle, the Netherlands; and Department of Radiology, University Medical Centre Utrecht, Utrecht, the Netherlands (P.A.d.J.)
| | - Stefan Walen
- From the Departments of Radiology (E.L.W., I.M.N., E.d.B., J.N.O., L.H., I.R., M.F.B.) and Pulmonology (S.W., J.W.K.v.d.B.), Isala Hospital, Dokter van Heesweg 2, 8025 AB Zwolle, the Netherlands; and Department of Radiology, University Medical Centre Utrecht, Utrecht, the Netherlands (P.A.d.J.)
| | - Jan W K van den Berg
- From the Departments of Radiology (E.L.W., I.M.N., E.d.B., J.N.O., L.H., I.R., M.F.B.) and Pulmonology (S.W., J.W.K.v.d.B.), Isala Hospital, Dokter van Heesweg 2, 8025 AB Zwolle, the Netherlands; and Department of Radiology, University Medical Centre Utrecht, Utrecht, the Netherlands (P.A.d.J.)
| | - Pim A de Jong
- From the Departments of Radiology (E.L.W., I.M.N., E.d.B., J.N.O., L.H., I.R., M.F.B.) and Pulmonology (S.W., J.W.K.v.d.B.), Isala Hospital, Dokter van Heesweg 2, 8025 AB Zwolle, the Netherlands; and Department of Radiology, University Medical Centre Utrecht, Utrecht, the Netherlands (P.A.d.J.)
| | - Martijn F Boomsma
- From the Departments of Radiology (E.L.W., I.M.N., E.d.B., J.N.O., L.H., I.R., M.F.B.) and Pulmonology (S.W., J.W.K.v.d.B.), Isala Hospital, Dokter van Heesweg 2, 8025 AB Zwolle, the Netherlands; and Department of Radiology, University Medical Centre Utrecht, Utrecht, the Netherlands (P.A.d.J.)
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Computed Tomography Structured Reporting in the Staging of Lymphoma: A Delphi Consensus Proposal. J Clin Med 2021; 10:jcm10174007. [PMID: 34501455 PMCID: PMC8432477 DOI: 10.3390/jcm10174007] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 08/30/2021] [Accepted: 08/31/2021] [Indexed: 12/17/2022] Open
Abstract
Structured reporting (SR) in radiology is becoming increasingly necessary and has been recognized recently by major scientific societies. This study aims to build structured CT-based reports for lymphoma patients during the staging phase to improve communication between radiologists, members of multidisciplinary teams, and patients. A panel of expert radiologists, members of the Italian Society of Medical and Interventional Radiology (SIRM), was established. A modified Delphi process was used to develop the SR and to assess a level of agreement for all report sections. The Cronbach's alpha (Cα) correlation coefficient was used to assess internal consistency for each section and to measure quality analysis according to the average inter-item correlation. The final SR version was divided into four sections: (a) Patient Clinical Data, (b) Clinical Evaluation, (c) Imaging Protocol, and (d) Report, including n = 13 items in the "Patient Clinical Data" section, n = 8 items in the "Clinical Evaluation" section, n = 9 items in the "Imaging Protocol" section, and n = 32 items in the "Report" section. Overall, 62 items were included in the final version of the SR. A dedicated section of significant images was added as part of the report. In the first Delphi round, all sections received more than a good rating (≥3). The overall mean score of the experts and the sum of score for structured report were 4.4 (range 1-5) and 1524 (mean value of 101.6 and standard deviation of 11.8). The Cα correlation coefficient was 0.89 in the first round. In the second Delphi round, all sections received more than an excellent rating (≥4). The overall mean score of the experts and the sum of scores for structured report were 4.9 (range 3-5) and 1694 (mean value of 112.9 and standard deviation of 4.0). The Cα correlation coefficient was 0.87 in this round. The highest overall means value, highest sum of scores of the panelists, and smallest standard deviation values of the evaluations in this round reflect the increase of the internal consistency and agreement among experts in the second round compared to first round. The accurate statement of imaging data given to referring physicians is critical for patient care; the information contained affects both the decision-making process and the subsequent treatment. The radiology report is the most important source of clinical imaging information. It conveys critical information about the patient's health and the radiologist's interpretation of medical findings. It also communicates information to the referring physicians and records this information for future clinical and research use. The present SR was generated based on a multi-round consensus-building Delphi exercise and uses standardized terminology and structures, in order to adhere to diagnostic/therapeutic recommendations and facilitate enrolment in clinical trials, to reduce any ambiguity that may arise from non-conventional language, and to enable better communication between radiologists and clinicians.
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Tsurusaki M, Sofue K, Hori M, Sasaki K, Ishii K, Murakami T, Kudo M. Dual-Energy Computed Tomography of the Liver: Uses in Clinical Practices and Applications. Diagnostics (Basel) 2021; 11:diagnostics11020161. [PMID: 33499201 PMCID: PMC7912647 DOI: 10.3390/diagnostics11020161] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 01/10/2021] [Accepted: 01/20/2021] [Indexed: 02/06/2023] Open
Abstract
Dual-energy computed tomography (DECT) is an imaging technique based on data acquisition at two different energy settings. Recent advances in CT have allowed data acquisitions and simultaneous analyses of X-rays at two energy levels, and have resulted in novel developments in the field of abdominal imaging. The use of low and high X-ray tube voltages in DECT provide fused images that improve the detection of liver tumors owing to the higher contrast-to-noise ratio (CNR) of the tumor compared with the liver. The use of contrast agents in CT scanning improves image quality by enhancing the CNR and signal-to-noise ratio while reducing beam-hardening artifacts. DECT can improve detection and characterization of hepatic abnormalities, including mass lesions. The technique can also be used for the diagnosis of steatosis and iron overload. This article reviews and illustrates the different applications of DECT in liver imaging.
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Affiliation(s)
- Masakatsu Tsurusaki
- Department of Radiology, Faculty of Medicine, Kindai University, Osakasayama 589-8511, Japan;
- Correspondence: ; Tel.: +81-72-366-0221 (ext. 3133); Fax: +81-72-367-1685
| | - Keitaro Sofue
- Department of Radiology, Graduate School of Medicine, Kobe University, Kobe 650-0017, Japan; (K.S.); (M.H.); (T.M.)
| | - Masatoshi Hori
- Department of Radiology, Graduate School of Medicine, Kobe University, Kobe 650-0017, Japan; (K.S.); (M.H.); (T.M.)
| | - Kosuke Sasaki
- CT Research Group, GE Healthcare Japan, Hino 191-8503, Japan;
| | - Kazunari Ishii
- Department of Radiology, Faculty of Medicine, Kindai University, Osakasayama 589-8511, Japan;
| | - Takamichi Murakami
- Department of Radiology, Graduate School of Medicine, Kobe University, Kobe 650-0017, Japan; (K.S.); (M.H.); (T.M.)
| | - Masatoshi Kudo
- Department of Gastroenterology and Hepatology, Kindai University, Faculty of Medicine, Osakasayama 589-8511, Japan;
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Building a dual-energy CT service line in abdominal radiology. Eur Radiol 2020; 31:4330-4339. [PMID: 33210201 DOI: 10.1007/s00330-020-07441-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 10/08/2020] [Accepted: 10/23/2020] [Indexed: 10/23/2022]
Abstract
As the access of radiology practices to dual-energy CT (DECT) has increased worldwide, seamless integration into clinical workflows and optimized use of this technology are desirable. In this article, we provide basic concepts of commercially available DECT hardware implementations, discuss financial and logistical aspects, provide tips for protocol building and image routing strategies, and review radiation dose considerations to establish a DECT service line in abdominal imaging. KEY POINTS: • Tube-based and detector-based DECT implementations with varying features and strengths are available on the imaging market. • Thorough assessment of financial and logistical aspects is key to successful implementation of a DECT service line. • Optimized protocol building and image routing strategies are of critical importance for effective use and seamless inception of DECT in routine clinical workflows.
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Skornitzke S, Kauczor HU, Stiller W. Virtual monoenergetic reconstructions of dynamic DECT acquisitions for calculation of perfusion maps of blood flow: Quantitative comparison to conventional, dynamic 80 kV p CT perfusion. Eur J Radiol 2020; 131:109262. [PMID: 32942200 DOI: 10.1016/j.ejrad.2020.109262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 07/09/2020] [Accepted: 08/27/2020] [Indexed: 10/23/2022]
Abstract
PURPOSE Investigation of potential improvements in dynamic CT perfusion measurements by exploitation of improved visualization of contrast agent in virtual monoenergetic reconstructions of images acquired with dual-energy computed tomography (DECT). METHOD For 17 patients with pancreatic carcinoma, dynamic dual-source DECT acquisitions were performed at 80kVp/Sn140kVp every 1.5 s over 51 s. Virtual monoenergetic images (VMI) were reconstructed for photon energies between 40 keV and 150 keV (5 keV steps). Using the maximum-slope model, perfusion maps of blood flow were calculated from VMIs and 80kVp images and compared quantitatively with regard to blood flow measured in regions of interest in healthy tissue and carcinoma, standard deviation (SD), and absolute-difference-to-standard-deviation ratio (ADSDR) of measurements. RESULTS On average, blood flow calculated from VMIs increased with increasing energy levels from 114.3 ± 37.2 mL/100 mL/min (healthy tissue) and 45.6 ± 25.3 mL/100 mL/min (carcinoma) for 40 keV to 128.6 ± 58.9 mL/100 mL/min (healthy tissue) and 75.5 ± 49.8 mL/100 mL/min (carcinoma) for 150 keV, compared to 114.2 ± 37.4 mL/100 mL/min (healthy tissue) and 46.5 ± 26.6 mL/100 mL/min (carcinoma) for polyenergetic 80kVp. Differences in blood flow between tissue types were significant for all energies. Differences between perfusion maps calculated from VMIs and 80kVp images were not significant below 110 keV. SD and ADSDR were significantly better for perfusion maps calculated from VMIs at energies between 40 keV and 55 keV than for those calculated from 80kVp images. Compared to effective dose of dynamic 80kVp acquisitions (4.6 ± 2.2mSv), dose of dynamic DECT/VMI acquisitions (8.0 ± 3.7mSv) was higher. CONCLUSIONS Perfusion maps of blood flow based on low-energy VMIs between 40 keV and 55 keV offer improved robustness and quality of quantitative measurements over those calculated from 80kVp image data (reference standard), albeit at increased patient radiation exposure.
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Affiliation(s)
- Stephan Skornitzke
- Diagnostic and Interventional Radiology (DIR), Heidelberg University Hospital, Im Neuenheimer Feld 110, 69120 Heidelberg, Germany.
| | - Hans-Ulrich Kauczor
- Diagnostic and Interventional Radiology (DIR), Heidelberg University Hospital, Im Neuenheimer Feld 110, 69120 Heidelberg, Germany.
| | - Wolfram Stiller
- Diagnostic and Interventional Radiology (DIR), Heidelberg University Hospital, Im Neuenheimer Feld 110, 69120 Heidelberg, Germany.
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Schicchi N, Fogante M, Palumbo P, Agliata G, Esposto Pirani P, Di Cesare E, Giovagnoni A. The sub-millisievert era in CTCA: the technical basis of the new radiation dose approach. Radiol Med 2020; 125:1024-1039. [PMID: 32930945 DOI: 10.1007/s11547-020-01280-1] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Accepted: 09/03/2020] [Indexed: 12/12/2022]
Abstract
Computed tomography coronary angiography (CTCA) has become a cornerstone in the diagnostic process of the heart disease. Although the cardiac imaging with interventional procedures is responsible for approximately 40% of the cumulative effective dose in medical imaging, a relevant radiation dose reduction over the last decade was obtained, with the beginning of the sub-mSv era in CTCA. The main technical basis to obtain a radiation dose reduction in CTCA is the use of a low tube voltage, the adoption of a prospective electrocardiogram-triggering spiral protocol and the application of the tube current modulation with the iterative reconstruction technique. Nevertheless, CTCA examinations are characterized by a wide range of radiation doses between different radiology departments. Moreover, the dose exposure in CTCA is extremely important because the benefit-risk calculus in comparison with other modalities also depends on it. Finally, because anatomical evaluation not adequately predicts the hemodynamic relevance of coronary stenosis, a low radiation dose in routine CTCA would allow the greatest use of the myocardial CT perfusion, fractional flow reserve-CT, dual-energy CT and artificial intelligence, to shift focus from morphological assessment to a comprehensive morphological and functional evaluation of the stenosis. Therefore, the aim of this work is to summarize the correct use of the technical basis in order that CTCA becomes an established examination for assessment of the coronary artery disease with low radiation dose.
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Affiliation(s)
- Nicolò Schicchi
- Radiology Department, Azienda Ospedaliero Universitaria "Ospedali Riuniti", 60126, Ancona, Italy
| | - Marco Fogante
- Radiology Department, Azienda Ospedaliero Universitaria "Ospedali Riuniti", 60126, Ancona, Italy.
| | - Pierpaolo Palumbo
- Radiology Department, Azienda Ospedaliero Universitaria "San Salvatore", 60126, L'Aquila, Italy
| | - Giacomo Agliata
- Radiology Department, Azienda Ospedaliero Universitaria "Ospedali Riuniti", 60126, Ancona, Italy
| | - Paolo Esposto Pirani
- Radiology Department, Azienda Ospedaliero Universitaria "Ospedali Riuniti", 60126, Ancona, Italy
| | - Ernesto Di Cesare
- Radiology Department, Azienda Ospedaliero Universitaria "San Salvatore", 60126, L'Aquila, Italy
| | - Andrea Giovagnoni
- Radiology Department, Azienda Ospedaliero Universitaria "Ospedali Riuniti", 60126, Ancona, Italy
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