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Li H, Li Z, Gao S, Hu J, Yang Z, Peng Y, Sun J. Performance evaluation of deep learning image reconstruction algorithm for dual-energy spectral CT imaging: A phantom study. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2024; 32:513-528. [PMID: 38393883 DOI: 10.3233/xst-230333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/25/2024]
Abstract
OBJECTIVES To evaluate the performance of deep learning image reconstruction (DLIR) algorithm in dual-energy spectral CT (DEsCT) as a function of radiation dose and image energy level, in comparison with filtered-back-projection (FBP) and adaptive statistical iterative reconstruction-V (ASIR-V) algorithms. METHODS An ACR464 phantom was scanned with DEsCT at four dose levels (3.5 mGy, 5 mGy, 7.5 mGy, and 10 mGy). Virtual monochromatic images were reconstructed at five energy levels (40 keV, 50 keV, 68 keV, 74 keV, and 140 keV) using FBP, 50% and 100% ASIR-V, DLIR at low (DLIR-L), medium (DLIR-M), and high (DLIR-H) settings. The noise power spectrum (NPS), task-based transfer function (TTF) and detectability index (d') were computed and compared among reconstructions. RESULTS NPS area and noise increased as keV decreased, with DLIR having slower increase than FBP and ASIR-V, and DLIR-H having the lowest values. DLIR had the best 40 keV/140 keV noise ratio at various energy levels, DLIR showed higher TTF (50%) than ASIR-V for all materials, especially for the soft tissue-like polystyrene insert, and DLIR-M and DLIR-H provided higher d' than DLIR-L, ASIR-V and FBP in all dose and energy levels. As keV increases, d' increased for acrylic insert, and d' of the 50 keV DLIR-M and DLIR-H images at 3.5 mGy (7.39 and 8.79, respectively) were higher than that (7.20) of the 50 keV ASIR-V50% images at 10 mGy. CONCLUSIONS DLIR provides better noise containment for low keV images in DEsCT and higher TTF(50%) for the polystyrene insert over ASIR-V. DLIR-H has the lowest image noise and highest detectability in all dose and energy levels. DEsCT 50 keV images with DLIR-M and DLIR-H show potential for 65% dose reduction over ASIR-V50% withhigher d'.
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Affiliation(s)
- Haoyan Li
- Department of Radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
| | - Zhentao Li
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Shuaiyi Gao
- Department of Radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
| | - Jiaqi Hu
- Department of Radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
| | - Zhihao Yang
- Department of Radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
| | - Yun Peng
- Department of Radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
| | - Jihang Sun
- Department of Radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
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Narita A, Ohsugi Y, Ohkubo M, Fukaya T, Sakai K, Noto Y. Method for measuring noise-power spectrum independent of the effect of extracting the region of interest from a noise image. Radiol Phys Technol 2023; 16:471-477. [PMID: 37515623 DOI: 10.1007/s12194-023-00733-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 07/11/2023] [Accepted: 07/11/2023] [Indexed: 07/31/2023]
Abstract
This study aimed to evaluate the impact of region of interest (ROI) size on noise-power spectrum (NPS) measurement in computed tomography (CT) images and to propose a novel method for measuring NPS independent of ROI size. The NPS was measured using the conventional method with an ROI of size P × P pixels in a uniform region in the CT image; the NPS is referred to as NPSR=P. NPSsR=256, 128, 64, 32, 16, and 8 were obtained and compared to assess their dependency on ROI size. In the proposed method, the true NPS was numerically modeled as an NPSmodel, with adjustable parameters, and a noise image with the property of the NPSmodel was generated. From the generated noise image, the NPS was measured using the conventional method with a P × P pixel ROI size; the obtained NPS was referred to as NPS'R=P. The adjustable parameters of the NPSmodel were optimized such that NPS'R=P was most similar to NPSR=P. When NPS'R=P was almost equivalent to NPSR=P, the NPSmodel was considered the true NPS. NPSsR=256, 128, 64, 32, 16, and 8 obtained using the conventional method were dependent on the ROI size. Conversely, the NPSs (optimized NPSsmodel) measured using the proposed method were not dependent on the ROI size, even when a much smaller ROI (P = 16 or 8) was used. The proposed method for NPS measurement was confirmed to be precise, independent of the ROI size, and useful for measuring local NPSs using a small ROI.
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Affiliation(s)
- Akihiro Narita
- Graduate School of Health Sciences, Niigata University, 2-746 Asahimachi-Dori, Chuo-Ku, Niigata, 951-8518, Japan.
| | - Yuki Ohsugi
- Department of Clinical Support, Niigata University Medical and Dental Hospital, 1-754 Asahimachi-Dori, Chuo-Ku, Niigata, 951-8520, Japan
| | - Masaki Ohkubo
- Graduate School of Health Sciences, Niigata University, 2-746 Asahimachi-Dori, Chuo-Ku, Niigata, 951-8518, Japan
| | - Takahiro Fukaya
- Department of Clinical Support, Niigata University Medical and Dental Hospital, 1-754 Asahimachi-Dori, Chuo-Ku, Niigata, 951-8520, Japan
| | - Kenichi Sakai
- Department of Clinical Support, Niigata University Medical and Dental Hospital, 1-754 Asahimachi-Dori, Chuo-Ku, Niigata, 951-8520, Japan
| | - Yoshiyuki Noto
- Department of Clinical Support, Niigata University Medical and Dental Hospital, 1-754 Asahimachi-Dori, Chuo-Ku, Niigata, 951-8520, Japan
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Zhong J, Wang L, Shen H, Li J, Lu W, Shi X, Xing Y, Hu Y, Ge X, Ding D, Yan F, Du L, Yao W, Zhang H. Improving lesion conspicuity in abdominal dual-energy CT with deep learning image reconstruction: a prospective study with five readers. Eur Radiol 2023; 33:5331-5343. [PMID: 36976337 DOI: 10.1007/s00330-023-09556-6] [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: 10/08/2022] [Revised: 02/07/2023] [Accepted: 02/17/2023] [Indexed: 03/29/2023]
Abstract
OBJECTIVES To evaluate image quality, diagnostic acceptability, and lesion conspicuity in abdominal dual-energy CT (DECT) using deep learning image reconstruction (DLIR) compared to those using adaptive statistical iterative reconstruction-V (Asir-V) at 50% blending (AV-50), and to identify potential factors impacting lesion conspicuity. METHODS The portal-venous phase scans in abdominal DECT of 47 participants with 84 lesions were prospectively included. The raw data were reconstructed to virtual monoenergetic image (VMI) at 50 keV using filtered back-projection (FBP), AV-50, and DLIR at low (DLIR-L), medium (DLIR-M), and high strength (DLIR-H). A noise power spectrum (NPS) was generated. CT number and standard deviation values of eight anatomical sites were measured. Signal-to-noise (SNR), and contrast-to-noise ratio (CNR) values were calculated. Five radiologists assessed image quality in terms of image contrast, image noise, image sharpness, artificial sensation, and diagnostic acceptability, and evaluated the lesion conspicuity. RESULTS DLIR further reduced image noise (p < 0.001) compared to AV-50 while better preserved the average NPS frequency (p < 0.001). DLIR maintained CT number values (p > 0.99) and improved SNR and CNR values compared to AV-50 (p < 0.001). DLIR-H and DLIR-M showed higher ratings in all image quality analyses than AV-50 (p < 0.001). DLIR-H provided significantly better lesion conspicuity than AV-50 and DLIR-M regardless of lesion size, relative CT attenuation to surrounding tissue, or clinical purpose (p < 0.05). CONCLUSIONS DLIR-H could be safely recommended for routine low-keV VMI reconstruction in daily contrast-enhanced abdominal DECT to improve image quality, diagnostic acceptability, and lesion conspicuity. KEY POINTS • DLIR is superior to AV-50 in noise reduction, with less shifts of the average spatial frequency of NPS towards low frequency, and larger improvements of NPS noise, noise peak, SNR, and CNR values. • DLIR-M and DLIR-H generate better image quality in terms of image contrast, noise, sharpness, artificial sensation, and diagnostic acceptability than AV-50, while DLIR-H provides better lesion conspicuity than AV-50 and DLIR-M. • DLIR-H could be safely recommended as a new standard for routine low-keV VMI reconstruction in contrast-enhanced abdominal DECT to provide better lesion conspicuity and better image quality than the standard AV-50.
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Affiliation(s)
- Jingyu Zhong
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Lingyun Wang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Hailin Shen
- Department of Radiology, Suzhou Kowloon Hospital, Shanghai Jiao Tong University School of Medicine, Suzhou, 215028, China
| | - Jianying Li
- Computed Tomography Research Center, GE Healthcare, Beijing, 100176, China
| | - Wei Lu
- Computed Tomography Research Center, GE Healthcare, Shanghai, 201203, China
| | - Xiaomeng Shi
- Department of Materials, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK
| | - Yue Xing
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Yangfan Hu
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Xiang Ge
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Defang Ding
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Lianjun Du
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Weiwu Yao
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China.
| | - Huan Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
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Dabli D, Durand Q, Frandon J, de Oliveira F, Pastor M, Beregi J, Greffier J. Impact of the automatic tube current modulation (ATCM) system on virtual monoenergetic image quality for dual-source CT: A phantom study. Phys Med 2023; 109:102574. [PMID: 37004360 DOI: 10.1016/j.ejmp.2023.102574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 02/23/2023] [Accepted: 03/22/2023] [Indexed: 04/03/2023] Open
Abstract
PURPOSE To assess the impact of the automatic tube current modulation (ATCM) on virtual monoenergetic images (VMIs) quality in dual-source CT(DSCT). MATERIALS AND METHODS Acquisitions were performed on DSCT using the Mercury phantom. The acquisition parameters for an abdomen-pelvic examination with single-energy CT(SECT) and dual-energy CT(DECT) imaging were used. Acquisitions were performed for each imaging mode using fixed mAs and ATCM. The mAs value was set to obtain a volume CT dose index of 11 mGy in fixed mAs acquisitions. This value was used as the reference mAs in ATCM acquisitions. The noise power spectrum and task-based transfer function at 40,50,60 and 70 keV levels were computed on VMIs and SECT images. The detectability index (d') was calculated for a lesion with an iodine concentration of 10 mg/mL. RESULTS The noise magnitude on VMIs was higher with the ATCM system than with fixed mAs for all energy levels and section diameters of 21,26 and 31 cm. The noise texture and spatial resolution were similar between the fixed mAs and ATCM acquisitions for both imaging modes. The d' values were lower for all energy levels with ATCM than with fixed mAs acquisitions for 21 and 26 cm diameters by -39.82 ± 9.32%, similar at 31 cm diameter -4.13 ± 0.24% and higher at 36 cm diameter 10.40 ± 6.69%. It was higher on VMIs at all energy levels compared to SECT images. CONCLUSIONS The ATCM system could be used with DECT imaging to optimize patient exposure without changing the noise texture and spatial resolution of VMIs compared to fixed mAs and SECT.
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Racine D, Mergen V, Viry A, Eberhard M, Becce F, Rotzinger DC, Alkadhi H, Euler A. Photon-Counting Detector CT With Quantum Iterative Reconstruction: Impact on Liver Lesion Detection and Radiation Dose Reduction. Invest Radiol 2023; 58:245-252. [PMID: 36094810 DOI: 10.1097/rli.0000000000000925] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVES To assess image noise, diagnostic performance, and potential for radiation dose reduction of photon-counting detector (PCD) computed tomography (CT) with quantum iterative reconstruction (QIR) in the detection of hypoattenuating and hyperattenuating focal liver lesions compared with energy-integrating detector (EID) CT. MATERIALS AND METHODS A medium-sized anthropomorphic abdominal phantom with liver parenchyma and lesions (diameter, 5-10 mm; hypoattenuating and hyperattenuating from -30 HU to +90 HU at 120 kVp) was used. The phantom was imaged on ( a ) a third-generation dual-source EID-CT (SOMATOM Force, Siemens Healthineers) in the dual-energy mode at 100 and 150 kVp with tin filtration and ( b ) a clinical dual-source PCD-CT at 120 kVp (NAEOTOM Alpha, Siemens). Scans were repeated 10 times for each of 3 different radiation doses of 5, 2.5, and 1.25 mGy. Datasets were reconstructed as virtual monoenergetic images (VMIs) at 60 keV for both scanners and as linear-blended images (LBIs) for EID-CT. For PCD-CT, VMIs were reconstructed with different strength levels of QIR (QIR 1-4) and without QIR (QIR-off). For EID-CT, VMIs and LBIs were reconstructed using advanced modeled iterative reconstruction at a strength level of 3. Noise power spectrum was measured to compare image noise magnitude and texture. A channelized Hotelling model observer was used to assess diagnostic accuracy for lesion detection. The potential for radiation dose reduction using PCD-CT was estimated for the QIR strength level with the highest area under the curve compared with EID-CT for each radiation dose. RESULTS Image noise decreased with increasing QIR level at all radiation doses. Using QIR-4, noise reduction was 41%, 45%, and 59% compared with EID-CT VMIs and 12%, 18%, and 33% compared with EID-CT LBIs at 5, 2.5, and 1.25 mGy, respectively. The peak spatial frequency shifted slightly to lower frequencies at higher QIR levels. Lesion detection accuracy increased at higher QIR levels and was higher for PCD-CT compared with EID-CT VMIs. The improvement in detection with PCD-CT was strongest at the lowest radiation dose, with an area under the receiver operating curve of 0.917 for QIR-4 versus 0.677 for EID-CT VMIs for hyperattenuating lesions, and 0.900 for QIR-4 versus 0.726 for EID-CT VMIs for hypoattenuating lesions. Compared with EID-CT LBIs, detection was higher for QIR 1-4 at 2.5 mGy and for QIR 2-4 at 1.25 mGy (eg, 0.900 for QIR-4 compared with 0.854 for EID-CT LBIs at 1.25 mGy). Radiation dose reduction potential of PCD-CT with QIR-4 was 54% at 5 mGy compared with VMIs and 39% at 2.5 mGy compared with LBIs. CONCLUSIONS Compared with EID-CT, PCD-CT with QIR substantially improved focal liver lesion detection, especially at low radiation dose. This enables substantial radiation dose reduction while maintaining diagnostic accuracy.
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Affiliation(s)
- Damien Racine
- From the Institute of Radiation Physics (IRA), Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne
| | - Victor Mergen
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich
| | - Anaïs Viry
- From the Institute of Radiation Physics (IRA), Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne
| | - Matthias Eberhard
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich
| | - Fabio Becce
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - David C Rotzinger
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Hatem Alkadhi
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich
| | - André Euler
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich
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Nikolau EP, Toia GV, Nett B, Tang J, Szczykutowicz TP. A Characterization of Deep Learning Reconstruction Applied to Dual-Energy Computed Tomography Monochromatic and Material Basis Images. J Comput Assist Tomogr 2023; 47:437-444. [PMID: 36944100 DOI: 10.1097/rct.0000000000001442] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
Abstract
OBJECTIVE Advancements in computed tomography (CT) reconstruction have enabled image quality improvements and dose reductions. Previous advancements have included iterative and model-based reconstruction. The latest image reconstruction advancement uses deep learning, which has been evaluated for polychromatic imaging only. This article characterizes a commercially available deep learning imaging reconstruction applied to dual-energy CT. METHODS Monochromatic, iodine basis, and water basis images were reconstructed with filtered back projection (FBP), iterative (ASiR-V), and deep learning (DLIR) methods in a phantom experiment. Slice thickness, contrast-to-noise ratio, modulation transfer function, and noise power spectrum metrics were used to characterize ASiR-V and DLIR relative to FBP over a range of dose levels, phantom sizes, and iodine concentrations. RESULTS Slice thicknesses for ASiR-V and DLIR demonstrated no statistically significant difference relative to FBP for all measurement conditions. Contrast-to-noise ratio performance for DLIR-high and ASiR-V 40% at 2 mg I/mL on 40-keV images were 162% and 30% higher than FBP, respectively. Task-based modulation transfer function measurements demonstrated no clinically significant change between FBP and ASiR-V and DLIR on monochromatic or iodine basis images. CONCLUSIONS Deep learning image reconstruction enabled better image quality at lower monochromatic energies and on iodine basis images where image contrast is maximized relative to polychromatic or high-energy monochromatic images. Deep learning image reconstruction did not demonstrate thicker slices, decreased spatial resolution, or poor noise texture (ie, "plastic") relative to FBP.
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Affiliation(s)
| | - Giuseppe V Toia
- Radiology University of Wisconsin Madison School of Medicine and Public Health
| | - Brian Nett
- GE Healthcare, Waukesha Wisconsin, Waukesha; and
| | - Jie Tang
- GE Healthcare, Waukesha Wisconsin, Waukesha; and
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Photon Counting Detector CT-Based Virtual Noniodine Reconstruction Algorithm for In Vitro and In Vivo Coronary Artery Calcium Scoring: Impact of Virtual Monoenergetic and Quantum Iterative Reconstructions. Invest Radiol 2023:00004424-990000000-00091. [PMID: 36822677 DOI: 10.1097/rli.0000000000000959] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
OBJECTIVES The aim of this study was to evaluate the impact of virtual monoenergetic imaging (VMI) and quantum iterative reconstruction (QIR) on the accuracy of coronary artery calcium scoring (CACS) using a virtual noniodine (VNI) reconstruction algorithm on a first-generation, clinical, photon counting detector computed tomography system. MATERIALS AND METHODS Coronary artery calcium scoring was evaluated in an anthropomorphic chest phantom simulating 3 different patient sizes by using 2 extension rings (small: 300 × 200 mm, medium: 350 × 250 mm, large: 400 × 300 mm) and in patients (n = 61; final analyses only in patients with coronary calcifications [n = 34; 65.4 ± 10.0 years; 73.5% male]), who underwent nonenhanced and contrast-enhanced, electrocardiogram-gated, cardiac computed tomography on a photon counting detector system. Phantom and patient data were reconstructed using a VNI reconstruction algorithm at different VMI (55-80 keV) and QIR (strength 1-4) levels (CACSVNI). True noncontrast (TNC) scans at 70 keV and QIR "off" were used as reference for phantom and patient studies (CACSTNC). RESULTS In vitro and in vivo CACSVNI showed strong correlation (r > 0.9, P < 0.001 for all) and excellent agreement (intraclass correlation coefficient > 0.9 for all) with CACSTNC at all investigated VMI and QIR levels. Phantom and patient CACSVNI significantly increased with decreasing keV levels (in vitro: from 475.2 ± 26.3 at 80 keV up to 652.5 ± 42.2 at 55 keV; in vivo: from 142.5 [7.4/737.7] at 80 keV up to 248.1 [31.2/1144] at 55 keV; P < 0.001 for all), resulting in an overestimation of CACSVNI at 55 keV compared with CACSTNC at 70 keV in some cases (in vitro: 625.8 ± 24.4; in vivo: 225.4 [35.1/959.7]). In vitro CACS increased with rising QIR at low keV. In vivo scores were significantly higher at QIR 1 compared with QIR 4 only at 60 and 80 keV (60 keV: 220.3 [29.6-1060] vs 219.5 [23.7/1048]; 80 keV: 152.0 [12.0/735.6] vs 142.5 [7.4/737.7]; P < 0.001). CACSVNI was closest to CACSTNC at 60 keV, QIR 2 (+0.1%) in the small; 55 keV, QIR 1 (±0%) in the medium; 55 keV, QIR 4 (-0.1%) in the large phantom; and at 60 keV, QIR 1 (-2.3%) in patients. CONCLUSIONS Virtual monoenergetic imaging reconstructions have a significant impact on CACSVNI. The effects of different QIR levels are less consistent and seem to depend on several individual conditions, which should be further investigated.
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Greffier J, Villani N, Defez D, Dabli D, Si-Mohamed S. Spectral CT imaging: Technical principles of dual-energy CT and multi-energy photon-counting CT. Diagn Interv Imaging 2022; 104:167-177. [PMID: 36414506 DOI: 10.1016/j.diii.2022.11.003] [Citation(s) in RCA: 41] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 11/11/2022] [Indexed: 11/21/2022]
Abstract
Spectral computed tomography (CT) imaging encompasses a unique generation of CT systems based on a simple principle that makes use of the energy-dependent information present in CT images. Over the past two decades this principle has been expanded with the introduction of dual-energy CT systems. The first generation of spectral CT systems, represented either by dual-source or dual-layer technology, opened up a new imaging approach in the radiology community with their ability to overcome the limitations of tissue characterization encountered with conventional CT. Its expansion worldwide can also be considered as an important leverage for the recent groundbreaking technology based on a new chain of detection available on photon counting CT systems, which holds great promise for extending CT towards multi-energy CT imaging. The purpose of this article was to detail the basic principles and techniques of spectral CT with a particular emphasis on the newest technical developments of dual-energy and multi-energy CT systems.
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Greffier J, Barbotteau Y, Gardavaud F. iQMetrix-CT: New software for task-based image quality assessment of phantom CT images. Diagn Interv Imaging 2022; 103:555-562. [DOI: 10.1016/j.diii.2022.05.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 05/27/2022] [Indexed: 01/09/2023]
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Deep learning image reconstruction for improving image quality of contrast-enhanced dual-energy CT in abdomen. Eur Radiol 2022; 32:5499-5507. [PMID: 35238970 DOI: 10.1007/s00330-022-08647-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 02/05/2022] [Accepted: 02/11/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVES To evaluate the usefulness of deep learning image reconstruction (DLIR) to improve the image quality of dual-energy computed tomography (DECT) of the abdomen, compared to hybrid iterative reconstruction (IR). METHODS This study included 40 patients who underwent contrast-enhanced DECT of the abdomen. Virtual monochromatic 40-, 50-, and 70-keV and iodine density images were reconstructed using three reconstruction algorithms, including hybrid IR (ASiR-V50%) and DLIR (TrueFidelity) at medium- and high-strength level (DLIR-M and DLIR-H, respectively). The standard deviation of attenuation in liver parenchyma was measured as image noise. The contrast-to-noise ratio (CNR) for the portal vein on portal venous phase CT was calculated. The vessel conspicuity and overall image quality were graded on a 5-point scale ranging from 1 (poor) to 5 (excellent). The comparative scale of lesion conspicuity in 47 abdominal solid lesions was evaluated on a 5-point scale ranging from 0 (best) to -4 (markedly inferior). RESULTS The image noise of virtual monochromatic 40-, 50 -, and 70-keV and iodine density images was significantly decreased by DLIR compared to hybrid IR (p < 0.0001). The CNR was significantly higher in DLIR-H and DLIR-M than in hybrid IR (p < 0.0001). The vessel conspicuity and overall image quality scores were also significantly greater in DLIR-H and DLIR-M than in hybrid IR (p < 0.05). The lesion conspicuity scores for DLIR-M and DLIR-H were significantly higher than those for hybrid IR in the virtual monochromatic image of all energy levels (p ≤ 0.001). CONCLUSIONS DLIR improves vessel conspicuity, CNR, and lesion conspicuity of virtual monochromatic and iodine density images in abdominal contrast-enhanced DECT, compared to hybrid IR. KEY POINTS • Deep learning image reconstruction (DLIR) is useful for reducing image noise and improving the CNR of visual monochromatic 40-, 50-, and 70-keV images in dual-energy CT. • DLIR can improve lesion conspicuity of abdominal solid lesions on virtual monochromatic images compared to hybrid iterative reconstruction. • DLIR can also be applied to iodine density maps and significantly improves their image quality.
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Greffier J, Viry A, Barbotteau Y, Frandon J, Loisy M, Oliveira F, Beregi JP, Dabli D. Phantom task‐based image quality assessment of three generations of rapid kV‐switching dual‐energy CT systems on virtual monoenergetic images. Med Phys 2022; 49:2233-2244. [DOI: 10.1002/mp.15558] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 02/11/2022] [Accepted: 02/11/2022] [Indexed: 11/10/2022] Open
Affiliation(s)
- Joël Greffier
- Department of medical imaging CHU Nîmes Univ Montpellier, Nîmes Medical Imaging Group Nîmes 2992 France
| | - Anaïs Viry
- Institute of Radiation Physics Lausanne University Hospital and University of Lausanne Rue du Grand‐Pré 1 Lausanne 1007 Switzerland
| | - Yves Barbotteau
- Hôpital Privé Clairval – Service d'Imagerie 317, Bd du Redon Marseille 13009 France
| | - Julien Frandon
- Department of medical imaging CHU Nîmes Univ Montpellier, Nîmes Medical Imaging Group Nîmes 2992 France
| | - Maeliss Loisy
- Department of medical imaging CHU Nîmes Univ Montpellier, Nîmes Medical Imaging Group Nîmes 2992 France
| | - Fabien Oliveira
- Department of medical imaging CHU Nîmes Univ Montpellier, Nîmes Medical Imaging Group Nîmes 2992 France
| | - Jean Paul Beregi
- Department of medical imaging CHU Nîmes Univ Montpellier, Nîmes Medical Imaging Group Nîmes 2992 France
| | - Djamel Dabli
- Department of medical imaging CHU Nîmes Univ Montpellier, Nîmes Medical Imaging Group Nîmes 2992 France
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Greffier J, Si-Mohamed S, Guiu B, Frandon J, Loisy M, de Oliveira F, Douek P, Beregi JP, Dabli D. Comparison of virtual monoenergetic imaging between a rapid kilovoltage switching dual-energy computed tomography with deep-learning and four dual-energy CTs with iterative reconstruction. Quant Imaging Med Surg 2022; 12:1149-1162. [PMID: 35111612 DOI: 10.21037/qims-21-708] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 09/17/2021] [Indexed: 12/18/2022]
Abstract
Background To assess the spectral performance of rapid kV switching dual-energy CT (KVSCT-Canon) equipped with a Deep-Learning spectral reconstruction algorithm on virtual-monoenergetic images at low-energy levels and to compare its performances with four other dual-energy CT (DECT) platforms equipped with iterative reconstruction algorithms. Methods Two CT phantoms were scanned on five DECT platforms: KVSCT-Canon, fast kV-switching CT (KVSCT-GE), split filter CT, dual-source CT (DSCT), and dual-layer CT (DLCT). The classical parameters of abdomen-pelvic examinations were used for all phantom acquisitions, and a CTDIvol close to 10 mGy. For KVSCT-Canon, virtual-monoenergetic images were reconstructed with a clinical slice thickness of 0.5 and 1.5 mm to be close to other platforms. Noise power spectrum (NPS) and task-based transfer function (TTF) were evaluated from 40 to 80 keV of virtual-monoenergetic images. A detectability index (d') was computed to model the detection task of two contrast-enhanced lesions as function of keV. Results For KVSCT-Canon, the noise magnitude and average NPS spatial frequency (fav) decreased from 40 to 70 keV and increased thereafter. Similar noise magnitude outcomes were found for KVSCT-GE but the opposite for fav. For the other DECT platforms, the noise magnitude decreased as the keV increased. For split filter CT, DSCT and DLCT, the fav values increased from 40 to 80 keV. For all DECT platforms, TTF at 50% (f50) decreased as the keV increased, decreasing spatial resolution. For KVSCT-Canon, d' values peaked at 60 and 70 keV for both simulated lesions and from 50 to 70 keV for KVSCT-GE. d' decreased between 40 and 70 keV for DSCT, DLCT and split filter CT. For KVSCT-Canon, the increase in slice thickness decreases noise magnitude, fav and f50 and increases d' values. The highest d' values were found for DLCT at 40 and 50 keV and for KVSCT-Canon at 1.5 mm for other keV. Conclusions For KVSCT-Canon, the detectability of contrast-enhanced lesions was highest at 60 keV. The highest d' values were found for DLCT at 40 and 50 keV and for KVSCT-Canon at 1.5 mm for other keV.
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Affiliation(s)
- Joël Greffier
- Department of Medical Imaging, CHU Nîmes, Univ Montpellier, Nîmes Medical Imaging Group, Nîmes, France
| | - Salim Si-Mohamed
- Department of Radiology, Hospices Civils de Lyon, Lyon, France.,INSA-Lyon, Université Lyon, Université Claude-Bernard Lyon 1, UJM-Saint-Étienne, CNRS, Inserm, CREATIS UMR 5220, Lyon, France
| | - Boris Guiu
- Saint-Eloi University Hospital, Montpellier, France
| | - Julien Frandon
- Department of Medical Imaging, CHU Nîmes, Univ Montpellier, Nîmes Medical Imaging Group, Nîmes, France
| | - Maeliss Loisy
- Department of Medical Imaging, CHU Nîmes, Univ Montpellier, Nîmes Medical Imaging Group, Nîmes, France
| | - Fabien de Oliveira
- Department of Medical Imaging, CHU Nîmes, Univ Montpellier, Nîmes Medical Imaging Group, Nîmes, France
| | - Philippe Douek
- Department of Radiology, Hospices Civils de Lyon, Lyon, France.,INSA-Lyon, Université Lyon, Université Claude-Bernard Lyon 1, UJM-Saint-Étienne, CNRS, Inserm, CREATIS UMR 5220, Lyon, France
| | - Jean-Paul Beregi
- Department of Medical Imaging, CHU Nîmes, Univ Montpellier, Nîmes Medical Imaging Group, Nîmes, France
| | - Djamel Dabli
- Department of Medical Imaging, CHU Nîmes, Univ Montpellier, Nîmes Medical Imaging Group, Nîmes, France
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Editorial Comment: Increased liver lesion visualization on CT without increased cost. AJR Am J Roentgenol 2022; 218:1029. [PMID: 35107314 DOI: 10.2214/ajr.22.27391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Masuda S, Yamada Y, Minamishima K, Owaki Y, Yamazaki A, Jinzaki M. Impact of noise reduction on radiation dose reduction potential of virtual monochromatic spectral images: Comparison of phantom images with conventional 120 kVp images using deep learning image reconstruction and hybrid iterative reconstruction. Eur J Radiol 2022; 149:110198. [DOI: 10.1016/j.ejrad.2022.110198] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 11/18/2021] [Accepted: 02/02/2022] [Indexed: 01/15/2023]
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Cester D, Eberhard M, Alkadhi H, Euler A. Virtual monoenergetic images from dual-energy CT: systematic assessment of task-based image quality performance. Quant Imaging Med Surg 2022; 12:726-741. [PMID: 34993114 DOI: 10.21037/qims-21-477] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 07/27/2021] [Indexed: 12/13/2022]
Abstract
Background To compare task-based image quality (TB-IQ) among virtual monoenergetic images (VMI) and linear-blended images (LBI) from dual-energy CT as a function of contrast task, radiation dose, size, and lesion diameter. Methods A TB-IQ phantom (Mercury Phantom 4.0, Sun Nuclear Corporation) was imaged on a third-generation dual-source dual-energy CT with 100/Sn150 kVp at three volume CT dose levels (5, 10, 15 mGy). Three size sections (diameters 16, 26, 36 cm) with subsections for image noise and spatial resolution analysis were used. High-contrast tasks (e.g., calcium-containing stone and vascular lesion) were emulated using bone and iodine inserts. A low-contrast task (e.g., low-contrast lesion or hematoma) was emulated using a polystyrene insert. VMI at 40-190 keV and LBI were reconstructed. Noise power spectrum (NPS) determined the noise magnitude and texture. Spatial resolution was assessed using the task-transfer function (TTF) of the three inserts. The detectability index (d') served as TB-IQ metric. Results Noise magnitude increased with increasing phantom size, decreasing dose, and decreasing VMI-energy. Overall, noise magnitude was higher for VMI at 40-60 keV compared to LBI (range of noise increase, 3-124%). Blotchier noise texture was found for low and high VMIs (40-60 keV, 130-190 keV) compared to LBI. No difference in spatial resolution was observed for high contrast tasks. d' increased with increasing dose level or lesion diameter and decreasing size. For high-contrast tasks, d' was higher at 40-80 keV and lower at high VMIs. For the low-contrast task, d' was higher for VMI at 70-90 keV and lower at 40-60 keV. Conclusions Task-based image quality differed among VMI-energy and LBI dependent on the contrast task, dose level, phantom size, and lesion diameter. Image quality could be optimized by tailoring VMI-energy to the contrast task. Considering the clinical relevance of iodine, VMIs at 50-60 keV could be proposed as an alternative to LBI.
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Affiliation(s)
- Davide Cester
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Matthias Eberhard
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Hatem Alkadhi
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - André Euler
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
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Greffier J, Frandon J, Sadate A, Akessoul P, Belaouni A, Beregi JP, Dabli D. Impact of four kVp combinations available in a dual-source CT on the spectral performance of abdominal imaging: A task-based image quality assessment on phantom data. J Appl Clin Med Phys 2021; 22:243-254. [PMID: 34312979 PMCID: PMC8364263 DOI: 10.1002/acm2.13369] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 04/24/2021] [Accepted: 07/11/2021] [Indexed: 12/15/2022] Open
Abstract
Purpose To compare the spectral performance of four combinations of kVp available in a third generation dual‐source CT (DSCT) on abdominal imaging. Methods An image‐quality phantom was scanned with a DSCT using four kVp pairs (tube “A” voltage/tube “B” voltage): 100/Sn150 kVp, 90/Sn150 kVp, 80/Sn150 kVp, and 70/Sn150 kVp, classic parameters and dose level for abdomen examination (CTDIvol: 11 mGy). The noise power spectrum (NPS) and the task‐based transfer function (TTF) of two inserts were computed on virtual monochromatic images (VMIs) at 40/50/60/70 keV and for mixed, low‐, and high‐kVp images. Detectability index (d’) was computed on VMIs and mixed images to model the detection task of liver metastasis (LM) and hepatocellular carcinoma (HCC). Iodine quantification accuracy was assessed using the Root Mean Square Deviation (RMSDiodine) and the iodine bias (IB). Results Noise magnitude decreased by −55%± 0% between 40 and 70 keV for all kVp pairs. Compared to 70/Sn150 kVp, noise magnitude was increased by 9% ± 0% with 80/Sn150 kVp, by 16% ± 1% with 90/Sn150 kVp and by 24%± 1% with 100/Sn150 kVp. The average NPS spatial frequency (fav) shifted toward higher frequencies as energy level increased for all kVp pairs. Lowest fav values were found for 70/Sn150 kVp and highest for 100/Sn150 kVp. The value of TTF at 50% (f50) shifted toward lower frequencies with increasing energy level. The highest f50 values occurred for 100/Sn150 kVp and the lowest for 80/Sn150 kVp. For both lesions, d’ was highest for 70/Sn150 kVp and lowest for 100/Sn150 kVp. Compared to 70/Sn150 kVp, d’ decreased by −6% ± 3% with 80/Sn150 kVp, by −11% ± 2% with 90/Sn150 kVp and by −13%± 2% with 100/Sn150 kVp. For all acquisitions, the RSMDiodine and IB were the lowest for 100/Sn150 kVp (0.29 ± 0.10 mg/ml and 0.88 ± 0.30 mg/ml, respectively) and increased when the tube “A” voltage decreased (2.34 ± 0.29 mg/ml for 70/Sn150 kVp and 7.42 ± 0.51 mg/ml respectively). Conclusion 70/Sn150 kVp presented the lowest image noise and highest detectability in VMIs of two small focal liver lesions. 100/Sn150 kVp presented the lowest image noise on mixed images and highest accuracy of iodine quantification in iodine images.
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Affiliation(s)
- Joël Greffier
- Department of medical imaging, Nîmes Medical Imaging Group, CHU Nimes, Univ Montpellier, Nimes, France
| | - Julien Frandon
- Department of medical imaging, Nîmes Medical Imaging Group, CHU Nimes, Univ Montpellier, Nimes, France
| | - Alexandre Sadate
- Department of medical imaging, Nîmes Medical Imaging Group, CHU Nimes, Univ Montpellier, Nimes, France
| | - Philippe Akessoul
- Department of medical imaging, Nîmes Medical Imaging Group, CHU Nimes, Univ Montpellier, Nimes, France
| | - Asmaa Belaouni
- Department of medical imaging, Nîmes Medical Imaging Group, CHU Nimes, Univ Montpellier, Nimes, France
| | - Jean-Paul Beregi
- Department of medical imaging, Nîmes Medical Imaging Group, CHU Nimes, Univ Montpellier, Nimes, France
| | - Djamel Dabli
- Department of medical imaging, Nîmes Medical Imaging Group, CHU Nimes, Univ Montpellier, Nimes, France
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Granata V, Grassi R, Fusco R, Belli A, Cutolo C, Pradella S, Grazzini G, La Porta M, Brunese MC, De Muzio F, Ottaiano A, Avallone A, Izzo F, Petrillo A. Diagnostic evaluation and ablation treatments assessment in hepatocellular carcinoma. Infect Agent Cancer 2021; 16:53. [PMID: 34281580 PMCID: PMC8287696 DOI: 10.1186/s13027-021-00393-0] [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: 05/15/2021] [Accepted: 07/06/2021] [Indexed: 02/07/2023] Open
Abstract
This article provides an overview of diagnostic evaluation and ablation treatment assessment in Hepatocellular Carcinoma (HCC). Only studies, in the English language from January 2010 to January 202, evaluating the diagnostic tools and assessment of ablative therapies in HCC patients were included. We found 173 clinical studies that satisfied the inclusion criteria.HCC may be noninvasively diagnosed by imaging findings. Multiphase contrast-enhanced imaging is necessary to assess HCC. Intravenous extracellular contrast agents are used for CT, while the agents used for MRI may be extracellular or hepatobiliary. Both gadoxetate disodium and gadobenate dimeglumine may be used in hepatobiliary phase imaging. For treatment-naive patients undergoing CT, unenhanced imaging is optional; however, it is required in the post treatment setting for CT and all MRI studies. Late arterial phase is strongly preferred over early arterial phase. The choice of modality (CT, US/CEUS or MRI) and MRI contrast agent (extracelllar or hepatobiliary) depends on patient, institutional, and regional factors. MRI allows to link morfological and functional data in the HCC evaluation. Also, Radiomics is an emerging field in the assessment of HCC patients.Postablation imaging is necessary to assess the treatment results, to monitor evolution of the ablated tissue over time, and to evaluate for complications. Post- thermal treatments, imaging should be performed at regularly scheduled intervals to assess treatment response and to evaluate for new lesions and potential complications.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
| | - Roberta Grassi
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, Naples, Italy
- Italian Society of Medical and Interventional Radiology SIRM, SIRM Foundation, Milan, Italy
| | | | - Andrea Belli
- Division of Hepatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
| | - Carmen Cutolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, Salerno, Italy
| | - Silvia Pradella
- Radiology Division, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Giulia Grazzini
- Radiology Division, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | | | - Maria Chiara Brunese
- Department of Medicine and Health Sciences "Vincenzo Tiberio", University of Molise, Campobasso, Italy
| | - Federica De Muzio
- Department of Medicine and Health Sciences "Vincenzo Tiberio", University of Molise, Campobasso, Italy
| | - Alessandro Ottaiano
- Abdominal Oncology Division, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
| | - Antonio Avallone
- Abdominal Oncology Division, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
| | - Francesco Izzo
- Division of Hepatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
| | - Antonella Petrillo
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
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Papadakis AE, Damilakis J. The effect of tube focal spot size and acquisition mode on task-based image quality performance of a GE revolution HD dual energy CT scanner. Phys Med 2021; 86:75-81. [PMID: 34062336 DOI: 10.1016/j.ejmp.2021.05.025] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 05/05/2021] [Accepted: 05/17/2021] [Indexed: 11/29/2022] Open
Abstract
PURPOSE To assess the task-based performance of images obtained under different focal spot size and acquisition mode on a dual-energy CT scanner. METHODS Axial CT image series of the Catphan phantom were obtained using a tube focus at different sizes. Acquisitions were performed in standard single-energy, high resolution (HR) and dual-energy modes. Images were reconstructed using conventional and high definition (HD) kernels. Task-based transfer function at the 50% level (TTF50%) for teflon, delrin, low density polyethylene (LDPE) and acrylic, as well as image noise and noise texture, were assessed across all focal spots and acquisition modes using Noise Power Spectrum (NPS) analysis. A non-prewhitening mathematical observer model was used to calculate detectability index (dNPW'). RESULTS TTF50% degraded with increasing focal spot size. TTF50% ranged from 0.67 mm-1 for teflon to 0.25 mm-1 for acrylic. For standard kernel, image noise and NPS-determined average spatial frequency were 8.3 HU and 0.29 mm-1, respectively in single-energy, 12.0 HU and 0.37 mm-1 in HR, and 7.9 HU and 0.26 mm-1 in dual-energy mode. For standard kernel, dNPW' was 61 in single-energy and HR mode and reduced to 56 in dual-energy mode. CONCLUSIONS The task-based image quality assessment metrics have shown that spatial resolution is higher for higher image contrast materials and detectability is higher in the standard single-energy mode compared to HR and dual-energy mode. The results of the current study provide CT operators the required knowledge to characterize their CT system towards the optimization of its clinical performance.
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Affiliation(s)
- Antonios E Papadakis
- Medical Physics Department, University General Hospital of Heraklion, Stavrakia 71110, Crete, Greece.
| | - John Damilakis
- Medical Physics Department, University General Hospital of Heraklion, Stavrakia 71110, Crete, Greece
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Ohira S, Koike Y, Akino Y, Kanayama N, Wada K, Ueda Y, Masaoka A, Washio H, Miyazaki M, Koizumi M, Ogawa K, Teshima T. Improvement of image quality for pancreatic cancer using deep learning-generated virtual monochromatic images: Comparison with single-energy computed tomography. Phys Med 2021; 85:8-14. [PMID: 33940528 DOI: 10.1016/j.ejmp.2021.03.035] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 02/25/2021] [Accepted: 03/30/2021] [Indexed: 01/15/2023] Open
Abstract
PURPOSE To construct a deep convolutional neural network that generates virtual monochromatic images (VMIs) from single-energy computed tomography (SECT) images for improved pancreatic cancer imaging quality. MATERIALS AND METHODS Fifty patients with pancreatic cancer underwent a dual-energy CT simulation and VMIs at 77 and 60 keV were reconstructed. A 2D deep densely connected convolutional neural network was modeled to learn the relationship between the VMIs at 77 (input) and 60 keV (ground-truth). Subsequently, VMIs were generated for 20 patients from SECT images using the trained deep learning model. RESULTS The contrast-to-noise ratio was significantly improved (p < 0.001) in the generated VMIs (4.1 ± 1.8) compared to the SECT images (2.8 ± 1.1). The mean overall image quality (4.1 ± 0.6) and tumor enhancement (3.6 ± 0.6) in the generated VMIs assessed on a five-point scale were significantly higher (p < 0.001) than that in the SECT images (3.2 ± 0.4 and 2.8 ± 0.4 for overall image quality and tumor enhancement, respectively). CONCLUSIONS The quality of the SECT image was significantly improved both objectively and subjectively using the proposed deep learning model for pancreatic tumors in radiotherapy.
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Affiliation(s)
- Shingo Ohira
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan; Department of Medical Physics and Engineering, Osaka University Graduate School of Medicine, Suita, Japan.
| | - Yuhei Koike
- Department of Radiology, Kansai Medical University, Osaka, Japan
| | - Yuichi Akino
- Division of Medical Physics, Oncology Center, Osaka University Hospital, Suita, Japan
| | - Naoyuki Kanayama
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Kentaro Wada
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Yoshihiro Ueda
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Akira Masaoka
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Hayate Washio
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Masayoshi Miyazaki
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Masahiko Koizumi
- Department of Medical Physics and Engineering, Osaka University Graduate School of Medicine, Suita, Japan
| | - Kazuhiko Ogawa
- Department of Radiation Oncology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Teruki Teshima
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan
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Impact of dose reduction and the use of an advanced model-based iterative reconstruction algorithm on spectral performance of a dual-source CT system: A task-based image quality assessment. Diagn Interv Imaging 2021; 102:405-412. [PMID: 33820752 DOI: 10.1016/j.diii.2021.03.002] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 03/04/2021] [Accepted: 03/05/2021] [Indexed: 01/14/2023]
Abstract
PURPOSE To assess the impact of dose reduction and the use of an advanced modeled iterative reconstruction algorithm (ADMIRE) on image quality in low-energy monochromatic images from a dual-source dual energy computed tomography CT (DSCT) platform. MATERIALS AND METHODS Acquisitions on an image-quality phantom were performed using DSCT equipment with 100/Sn150 kVp for four dose levels (CTDIvol: 20/11/8/5mGy). Raw data were reconstructed for six energy levels (40/50/60/70/80/100 keV) using filtered back projection and two levels of ADMIRE (A3/A5). Noise power spectrum (NPS) and task-based transfer function (TTF) were calculated on virtual monoenergetic images (VMIs). Detectability index (d') was computed to model the detection task of two enhanced iodine lesions as function of keV. RESULTS Noise-magnitude was significantly reduced between 40 to 70 keV by -56±0% (SD) (range: -56%--55%) with FBP; -56±0% (SD) (-56%--56%) with A3; and -57±1% (SD) (range: -57%--56%) with A5. The average spatial frequency of the NPS peaked at 70 keV and decreased as ADMIRE level increased. TTF values at 50% were greatest at 40 keV and shifted towards lower frequencies as the keV increased. The detectability of both lesions increased with increasing dose level and ADMIRE level. For the simulated lesion with iodine at 2mg/mL, d' values peaked at 70 keV for all reconstruction types, except for A3 at 20 mGy and A5 at 11 and 20 mGy, where d' peaked at 60 keV. For the other simulated lesion, d' values were highest at 40 keV and decreased beyond. CONCLUSION At low keV on VMIs, this study confirms that iterative reconstruction reduces the noise magnitude, improves the spatial resolution and increases the detectability of enhanced iodine lesions.
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Papadakis AE, Damilakis J. Technical Note: Quality assessment of virtual monochromatic spectral images on a dual energy CT scanner. Phys Med 2021; 82:114-121. [DOI: 10.1016/j.ejmp.2021.01.079] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 01/26/2021] [Accepted: 01/29/2021] [Indexed: 12/11/2022] Open
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Greffier J, Si-Mohamed S, Dabli D, de Forges H, Hamard A, Douek P, Beregi JP, Frandon J. Performance of four dual-energy CT platforms for abdominal imaging: a task-based image quality assessment based on phantom data. Eur Radiol 2021; 31:5324-5334. [PMID: 33449188 DOI: 10.1007/s00330-020-07671-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 12/08/2020] [Accepted: 12/23/2020] [Indexed: 10/22/2022]
Abstract
OBJECTIVES To compare the spectral performance of dual-energy CT (DECT) platforms using task-based image quality assessment based on phantom data. MATERIALS AND METHODS Two CT phantoms were scanned on four DECT platforms: fast kV-switching CT (KVSCT), split filter CT (SFCT), dual-source CT (DSCT), and dual-layer CT (DLCT). Acquisitions on each phantom were performed using classical parameters of abdomen-pelvic examination and a CTDIvol at 10 mGy. Noise power spectrum (NPS) and task-based transfer function (TTF) were evaluated from 40 to 140 keV of virtual monoenergetic images. A detectability index (d') was computed to model the detection task of two contrast-enhanced lesions as function of keV. RESULTS The noise magnitude decreased from 40 to 70 keV for all DECT platforms, and the highest noise magnitude values were found for KVSCT and SFCT and the lowest for DSCT and DLCT. The average NPS spatial frequency shifted towards lower frequencies as the energy level increased for all DECT platforms, smoothing the image texture. TTF values decreased with the increase of keV deteriorating the spatial resolution. For both simulated lesions, higher detectability (d' value) was obtained at 40 keV for DLCT, DSCT, and SFCT but at 70 keV for KVSCT. The detectability of both simulated lesions was highest for DLCT and DSCT. CONCLUSION Highest detectability was found for DLCT for the lowest energy levels. The task-based image quality assessment used for the first time for DECT acquisitions showed the benefit of using low keV for the detection of contrast-enhanced lesions. KEY POINTS • Detectability of both simulated contrast-enhanced lesions was higher for dual-layer CT for the lowest energy levels. • The image noise increased and the image texture changed for the lowest energy levels. • The detectability of both simulated contrast-enhanced lesions was highest at 40 keV for all dual-energy CT platforms except for fast kV-switching platform.
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Affiliation(s)
- J Greffier
- Department of Medical Imaging, CHU Nimes, Medical Imaging Group Nimes, EA 2415, Univ Montpellier, Bd Prof Robert Debré, 30029, Nîmes Cedex 9, France.
| | - S Si-Mohamed
- Department of Radiology, Hospices Civils de Lyon, 69500, Lyon, France.,INSA-Lyon, Université Lyon, Université Claude-Bernard Lyon 1, UJM-Saint-Étienne, CNRS, Inserm, CREATIS UMR 5220, U1206, 69621, Lyon, France
| | - D Dabli
- Department of Medical Imaging, CHU Nimes, Medical Imaging Group Nimes, EA 2415, Univ Montpellier, Bd Prof Robert Debré, 30029, Nîmes Cedex 9, France
| | - H de Forges
- Department of Medical Imaging, CHU Nimes, Medical Imaging Group Nimes, EA 2415, Univ Montpellier, Bd Prof Robert Debré, 30029, Nîmes Cedex 9, France
| | - A Hamard
- Department of Medical Imaging, CHU Nimes, Medical Imaging Group Nimes, EA 2415, Univ Montpellier, Bd Prof Robert Debré, 30029, Nîmes Cedex 9, France
| | - P Douek
- Department of Radiology, Hospices Civils de Lyon, 69500, Lyon, France.,INSA-Lyon, Université Lyon, Université Claude-Bernard Lyon 1, UJM-Saint-Étienne, CNRS, Inserm, CREATIS UMR 5220, U1206, 69621, Lyon, France
| | - J P Beregi
- Department of Medical Imaging, CHU Nimes, Medical Imaging Group Nimes, EA 2415, Univ Montpellier, Bd Prof Robert Debré, 30029, Nîmes Cedex 9, France
| | - J Frandon
- Department of Medical Imaging, CHU Nimes, Medical Imaging Group Nimes, EA 2415, Univ Montpellier, Bd Prof Robert Debré, 30029, Nîmes Cedex 9, France
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