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Williams MC, Weir-McCall JR, Baldassarre LA, De Cecco CN, Choi AD, Dey D, Dweck MR, Isgum I, Kolossvary M, Leipsic J, Lin A, Lu MT, Motwani M, Nieman K, Shaw L, van Assen M, Nicol E. Artificial intelligence and machine learning for cardiovascular computed tomography (CCT): A white paper of the society of cardiovascular computed tomography (SCCT). J Cardiovasc Comput Tomogr 2024:S1934-5925(24)00405-2. [PMID: 39214777 DOI: 10.1016/j.jcct.2024.08.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 08/03/2024] [Accepted: 08/05/2024] [Indexed: 09/04/2024]
Affiliation(s)
| | | | - Lauren A Baldassarre
- Section of Cardiovascular Medicine and Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | | | - Andrew D Choi
- The George Washington University School of Medicine, Washington, USA
| | - Damini Dey
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, USA
| | - Marc R Dweck
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Ivana Isgum
- Amsterdam University Medical Center, University of Amsterdam, Netherlands
| | - Márton Kolossvary
- Gottsegen National Cardiovascular Center, Budapest, Hungary, and Physiological Controls Research Center, University Research and Innovation Center, Óbuda University, Budapest, Hungary
| | | | - Andrew Lin
- Victorian Heart Institute and Monash Health Heart, Victorian Heart Hospital, Monash University, Australia
| | - Michael T Lu
- Massachusetts General Hospital Cardiovascular Imaging Research Center/Harvard Medical School, USA
| | | | | | - Leslee Shaw
- Icahn School of Medicine at Mount Sinai, New York, USA
| | | | - Edward Nicol
- Royal Brompton Hospital, Guys and St Thomas' NHS Foundation Trust, London, UK; School of Biomedical Engineering and Imaging Sciences, King's College London, UK
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Kawai N, Noda Y, Nakamura F, Kaga T, Suzuki R, Miyoshi T, Mori F, Hyodo F, Kato H, Matsuo M. Low-tube-voltage whole-body CT angiography with extremely low iodine dose: a comparison between hybrid-iterative reconstruction and deep-learning image-reconstruction algorithms. Clin Radiol 2024; 79:e791-e798. [PMID: 38403540 DOI: 10.1016/j.crad.2024.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 01/29/2024] [Accepted: 02/07/2024] [Indexed: 02/27/2024]
Abstract
AIM To evaluate arterial enhancement, its depiction, and image quality in low-tube potential whole-body computed tomography (CT) angiography (CTA) with extremely low iodine dose and compare the results with those obtained by hybrid-iterative reconstruction (IR) and deep-learning image-reconstruction (DLIR) methods. MATERIALS AND METHODS This prospective study included 34 consecutive participants (27 men; mean age, 74.2 years) who underwent whole-body CTA at 80 kVp for evaluating aortic diseases between January and July 2020. Contrast material (240 mg iodine/ml) with simultaneous administration of its quarter volume of saline, which corresponded to 192 mg iodine/ml, was administered. CT raw data were reconstructed using adaptive statistical IR-Veo of 40% (hybrid-IR), DLIR with medium- (DLIR-M), and high-strength level (DLIR-H). A radiologist measured CT attenuation of the arteries and background noise, and the signal-to-noise ratio (SNR) was then calculated. Two reviewers qualitatively evaluated the arterial depictions and diagnostic acceptability on axial, multiplanar-reformatted (MPR), and volume-rendered (VR) images. RESULTS Mean contrast material volume and iodine weight administered were 64.1 ml and 15.4 g, respectively. The SNRs of the arteries were significantly higher in the following order of the DLIR-H, DLIR-M, and hybrid-IR (p<0.001). Depictions of six arteries on axial, three arteries on MPR, and four arteries on VR images were significantly superior in the DLIR-M or hybrid-IR than in the DLIR-H (p≤0.009 for each). Diagnostic acceptability was significantly better in the DLIR-M and DLIR-H than in the hybrid-IR (p<0.001-0.005). CONCLUSION DLIR-M showed well-balanced arterial depictions and image quality compared with the hybrid-IR and DLIR-H.
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Affiliation(s)
- N Kawai
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan
| | - Y Noda
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
| | - F Nakamura
- Department of Radiology, Gifu Municipal Hospital, 7-1 Kashima, Gifu 500-8513, Japan
| | - T Kaga
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan
| | - R Suzuki
- Department of Radiology Services, Gifu University Hospital, 1-1 Yanagido, Gifu 501-1194, Japan
| | - T Miyoshi
- Department of Radiology Services, Gifu University Hospital, 1-1 Yanagido, Gifu 501-1194, Japan
| | - F Mori
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan
| | - F Hyodo
- Department of Pharmacology, Graduate School of Medicine, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan; Center for One Medicine Innovative Translational Research (COMIT), Institute for Advanced Study, Gifu University, Japan
| | - H Kato
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan
| | - M Matsuo
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan
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Yuan D, Wang L, Lyu P, Zhang Y, Gao J, Liu J. Evaluation of image quality on low contrast media with deep learning image reconstruction algorithm in prospective ECG-triggering coronary CT angiography. THE INTERNATIONAL JOURNAL OF CARDIOVASCULAR IMAGING 2024; 40:1377-1388. [PMID: 38722507 DOI: 10.1007/s10554-024-03113-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 04/08/2024] [Indexed: 06/29/2024]
Abstract
To assess the impact of low-dose contrast media (CM) injection protocol with deep learning image reconstruction (DLIR) algorithm on image quality in coronary CT angiography (CCTA). In this prospective study, patients underwent CCTA were prospectively and randomly assigned to three groups with different contrast volume protocols (at 320mgI/mL concentration and constant flow rate of 5ml/s). After pairing basic information, 210 patients were enrolled in this study: Group A, 0.7mL/kg (n = 70); Group B, 0.6mL/kg (n = 70); Group C, 0.5mL/kg (n = 70). All patients were examined via a prospective ECG-triggered scan protocol within one heartbeat. A high level DLIR (DLIR-H) algorithm was used for image reconstruction with a thickness and interval of 0.625mm. The CT values of ascending aorta (AA), descending aorta (DA), three main coronary arteries, pulmonary artery (PA), and superior vena cava (SVC) were measured and analyzed for objective assessment. Two radiologists assessed the image quality and diagnostic confidence using a 5-point Likert scale. The CM doses were 46.81 ± 6.41mL, 41.96 ± 7.51mL and 34.65 ± 5.38mL for Group A, B and C, respectively. The objective assessments on AA, DA and the three main coronary arteries and the overall subjective scoring showed no significant difference among the three groups (all p > 0.05). The subjective assessment proved that excellent CCTA images can be obtained from the three different contrast media protocols. There were no significant differences in intracoronary attenuation values between the higher HR subgroup and the lower HR subgroup among three groups. CCTA reconstructed with DLIR could be realized with adequate enhancement in coronary arteries, excellent image quality and diagnostic confidence at low contrast dose of a 0.5mL/kg. The use of lower tube voltages may further reduce the contrast dose requirement.
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Affiliation(s)
- Dian Yuan
- The Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1, Eastern Jianshe Road, Zhengzhou, Henan Province, 450052, China
| | - Luotong Wang
- CT Imaging Research Center, GE Healthcare China, Beijing, 100176, China
| | - Peijie Lyu
- The Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1, Eastern Jianshe Road, Zhengzhou, Henan Province, 450052, China
| | - Yonggao Zhang
- The Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1, Eastern Jianshe Road, Zhengzhou, Henan Province, 450052, China
| | - Jianbo Gao
- The Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1, Eastern Jianshe Road, Zhengzhou, Henan Province, 450052, China
| | - Jie Liu
- The Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1, Eastern Jianshe Road, Zhengzhou, Henan Province, 450052, China.
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Zhang Q, Lin Y, Zhang H, Ding J, Pan J, Zhang S. The application value of a vendor-specific deep learning image reconstruction algorithm in "triple low" head and neck computed tomography angiography. Quant Imaging Med Surg 2024; 14:2955-2967. [PMID: 38617163 PMCID: PMC11007512 DOI: 10.21037/qims-23-1602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 03/04/2024] [Indexed: 04/16/2024]
Abstract
Background Head and neck computed tomography angiography (CTA) technology has become the noninvasive imaging method of choice for the diagnosis and long-term follow-up of vascular lesions of the head and neck. However, issues of radiation safety and contrast nephropathy associated with CTA examinations remain concerns. In recent years, deep learning image reconstruction (DLIR) algorithms have been increasingly used in clinical studies, demonstrating their potential for dose optimization. This study aimed to investigate the value of using a DLIR algorithm to reduce radiation and contrast doses in head and neck CTA. Methods A total of 100 patients were prospectively enrolled and randomly divided into two groups. Group A (50 patients) consisted of those who underwent 70-kVp CTA with a low contrast volume and injection rate and who were classified according to the reconstruction algorithm into subgroups A1 [DLIR at high weighting (DLIR-H)], A2 [DLIR at low weighting (DLIR-L)], and A3 [volume-based adaptive statistical iterative reconstruction with 50% weighting (ASIR-V50%)]. Meanwhile, group B (50 patients) consisted of those who underwent standard radiation and contrast doses at 100 kVp with ASIR-V50% reconstruction. The computed tomography (CT) attenuation, background noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and subjective image quality score (SIQS) were statistically compared for several vessels among the four groups. Results Group A showed significant reductions in contrast dosage, injection rate, and radiation dose of 36.09%, 20.88%, and 47.80%, respectively, compared to group B (all P<0.001). The four groups differed significantly in terms of background noise (all P<0.05) with group A1 having the lowest value. Group A1 also had significantly higher SNR and CNR values compared to group B in all vessels (all P<0.05) except the M1 of the middle cerebral artery for the SNR. Group A1 also had the highest SIQS, followed by the A2, B, and A3 groups. The SIQS showed good agreement between the two reviewers in all groups, with κ values between 0.88 and 1. Conclusions Compared to the standard-dose protocol using 100 kVp and ASIR-V50%, a protocol of 70 kVp combined with DLIR-H significantly reduces the radiation dose, contrast dose, and injection rate in head and neck CTA while still significantly improving image quality for patients with a standard body size.
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Affiliation(s)
- Qiushuang Zhang
- Department of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
| | - Youyou Lin
- Department of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
- Department of Radiology, Enze Hospital, Taizhou Enze Medical Center (Group), Taizhou, China
| | - Hailun Zhang
- Department of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
| | - Jianrong Ding
- Department of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
- Key Laboratory of Evidence-Based Radiology of Taizhou, Linhai, China
| | - Jingli Pan
- Department of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
| | - Shuai Zhang
- CT Imaging Research Center, GE HealthCare China, Shanghai, China
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Wang H, Yue S, Liu N, Chen Y, Zhan P, Liu X, Shang B, Wang L, Li Z, Gao J, Lyu P. Deep learning reconstruction vs standard reconstruction for abdominal CT: the influence of BMI. Eur Radiol 2024; 34:1614-1623. [PMID: 37650972 DOI: 10.1007/s00330-023-10179-0] [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: 12/15/2022] [Revised: 07/17/2023] [Accepted: 08/03/2023] [Indexed: 09/01/2023]
Abstract
OBJECTIVE This study aimed to evaluate the image quality and lesion conspicuity of the deep learning image reconstruction (DLIR) algorithm compared with standard image reconstruction algorithms on abdominal enhanced computed tomography (CT) scanning with a wide range of body mass indexes (BMIs). METHODS A total of 112 participants who underwent contrast-enhanced abdominal CT scans were divided into three groups according to BMIs: the 80-kVp group (BMI ≤ 23.9 kg/m2), 100-kVp group (BMI 24-28.9 kg/m2), and 120-kVp group (BMI ≥ 29 kg/m2). All images were reconstructed using filtered back projection (FBP), adaptive statistical iterative reconstruction-V of 50% level (IR), and DLIR at low, medium, and high levels (DL, DM, and DH, respectively). Subjective noise, artifact, overall image quality, and low- and high-contrast hepatic lesion conspicuity were all graded on a 5-point scale. The CT attenuation value (in HU), image noise, and contrast-to-noise ratio (CNR) were quantified and compared. RESULTS DM and DH improved the qualitative and quantitative parameters compared with FBP and IR for all three BMI groups. DH had the lowest image noise and highest CNR value, while DM had the highest subjective overall image quality and low- and high-contrast lesion conspicuity scores for the three BMI groups. Based on the FBP, the improvement in image quality and lesion conspicuity of DM and DH images was greater in the 80-kVp group than in the 100-kVp and 120-kVp groups. CONCLUSION For all BMIs, DLIR improves both image quality and hepatic lesion conspicuity, of which DM would be the best choice to balance both. CLINICAL RELEVANCE STATEMENT The study suggests that utilizing DLIR, particularly at the medium level, can significantly enhance image quality and lesion visibility on abdominal CT scans across a wide range of BMIs. KEY POINTS • DLIR improved the image quality and lesion conspicuity across a wide range of BMIs. • DLIR at medium level had the highest subjective parameters and lesion conspicuity scores among all reconstruction levels. • On the basis of the FBP, the 80-kVp group had improved image quality and lesion conspicuity more than the 100-kVp and 120-kVp groups.
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Affiliation(s)
- Huixia Wang
- The Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1, Eastern Jianshe Road, Zhengzhou, 450052, Henan Province, China
| | - Songwei Yue
- The Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1, Eastern Jianshe Road, Zhengzhou, 450052, Henan Province, China
| | - Nana Liu
- The Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1, Eastern Jianshe Road, Zhengzhou, 450052, Henan Province, China
| | - Yan Chen
- The Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1, Eastern Jianshe Road, Zhengzhou, 450052, Henan Province, China
| | - Pengchao Zhan
- The Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1, Eastern Jianshe Road, Zhengzhou, 450052, Henan Province, China
| | - Xing Liu
- The Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1, Eastern Jianshe Road, Zhengzhou, 450052, Henan Province, China
| | - Bo Shang
- The Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1, Eastern Jianshe Road, Zhengzhou, 450052, Henan Province, China
| | - Luotong Wang
- CT Imaging Research Center, GE Healthcare China, Beijing, 100176, China
| | - Zhen Li
- The Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, No.1, Eastern Jianshe Road, Zhengzhou, 450052, Henan Province, China
| | - Jianbo Gao
- The Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1, Eastern Jianshe Road, Zhengzhou, 450052, Henan Province, China.
| | - Peijie Lyu
- The Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1, Eastern Jianshe Road, Zhengzhou, 450052, Henan Province, China.
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Ng CKC. Generative Adversarial Network (Generative Artificial Intelligence) in Pediatric Radiology: A Systematic Review. CHILDREN (BASEL, SWITZERLAND) 2023; 10:1372. [PMID: 37628371 PMCID: PMC10453402 DOI: 10.3390/children10081372] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 08/07/2023] [Accepted: 08/09/2023] [Indexed: 08/27/2023]
Abstract
Generative artificial intelligence, especially with regard to the generative adversarial network (GAN), is an important research area in radiology as evidenced by a number of literature reviews on the role of GAN in radiology published in the last few years. However, no review article about GAN in pediatric radiology has been published yet. The purpose of this paper is to systematically review applications of GAN in pediatric radiology, their performances, and methods for their performance evaluation. Electronic databases were used for a literature search on 6 April 2023. Thirty-seven papers met the selection criteria and were included. This review reveals that the GAN can be applied to magnetic resonance imaging, X-ray, computed tomography, ultrasound and positron emission tomography for image translation, segmentation, reconstruction, quality assessment, synthesis and data augmentation, and disease diagnosis. About 80% of the included studies compared their GAN model performances with those of other approaches and indicated that their GAN models outperformed the others by 0.1-158.6%. However, these study findings should be used with caution because of a number of methodological weaknesses. For future GAN studies, more robust methods will be essential for addressing these issues. Otherwise, this would affect the clinical adoption of the GAN-based applications in pediatric radiology and the potential advantages of GAN could not be realized widely.
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Affiliation(s)
- Curtise K. C. Ng
- Curtin Medical School, Curtin University, GPO Box U1987, Perth, WA 6845, Australia; or ; Tel.: +61-8-9266-7314; Fax: +61-8-9266-2377
- Curtin Health Innovation Research Institute (CHIRI), Faculty of Health Sciences, Curtin University, GPO Box U1987, Perth, WA 6845, Australia
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Wang H, Li X, Wang T, Li J, Sun T, Chen L, Cheng Y, Jia X, Niu X, Guo J. The value of using a deep learning image reconstruction algorithm of thinner slice thickness to balance the image noise and spatial resolution in low-dose abdominal CT. Quant Imaging Med Surg 2023; 13:1814-1824. [PMID: 36915333 PMCID: PMC10006151 DOI: 10.21037/qims-22-353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Accepted: 11/03/2022] [Indexed: 12/03/2022]
Abstract
Background Traditional reconstruction techniques have certain limitations in balancing image quality and reducing radiation dose. The deep learning image reconstruction (DLIR) algorithm opens the door to a new era of medical image reconstruction. The purpose of the study was to evaluate the DLIR images at 1.25 mm thickness in balancing image noise and spatial resolution in low-dose abdominal computed tomography (CT) in comparison with the conventional adaptive statistical iterative reconstruction-V at 40% strength (ASIR-V40%) at 5 and 1.25 mm. Methods This retrospective study included 89 patients who underwent low-dose abdominal CT. Five sets of images were generated using ASIR-V40% at a 5 mm slice thickness and 1.25 mm (high-resolution) with DLIR at 1.25 mm using 3 strengths: low (DLIR-L), medium (DLIR-M), and high (DLIR-H). Qualitative evaluation was performed for image noise, artifacts, and visualization of small structures, while quantitative evaluation was performed for standard deviation (SD), signal-to-noise ratio (SNR), and spatial resolution (defined as the edge rising slope). Results At 1.25 mm, DLIR-M and DLIR-H images had significantly lower noise (SD in fat: 14.29±3.37 and 9.65±3.44 HU, respectively), higher SNR for liver (3.70±0.78 and 5.64±1.20, respectively), and higher overall image quality (4.30±0.44 and 4.67±0.40, respectively) than did the respective values in ASIR-V40% images (20.60±4.04 HU, 2.60±0.63, and 3.77±0.43; all P values <0.05). Compared with the 5 mm ASIR-V40% images, the 1.25 mm DLIR-H images had lower noise (SD: 9.65±3.44 vs. 13.63±10.03 HU), higher SNR (5.64±1.20 vs. 4.69±1.28), and higher overall image quality scores (4.67±0.40 vs. 3.94±0.46) (all P values <0.001). In addition, DLIR-L, DLIR-M, and DLIR-H images had a significantly higher spatial resolution in terms of edge rising slope (59.66±21.46, 58.52±17.48, and 59.26±13.33, respectively, vs. 33.79±9.23) and significantly higher image quality scores in the visualization of fine structures (4.43±0.50, 4.41±0.49, and 4.38±0.49, respectively vs. 2.62±0.49) than did the 5 mm ASIR-V40 images. Conclusions The 1.25 mm DLIR-M and DLIR-H images had significantly reduced image noise and improved SNR and overall image quality compared to the 1.25 mm ASIR-V40% images, and they had significantly improved the spatial resolution and visualization of fine structures compared to the 5 mm ASIR-V40% images. DLIR-H images had further reduced image noise compared with the 5 mm ASIR-V40% images, and DLIR-H was the most effective technique at balancing the image noise and spatial resolution in low-dose abdominal CT.
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Affiliation(s)
- Huan Wang
- Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Xinyu Li
- Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Tianze Wang
- Department of Neurosurgery, Xi'an Jiaotong University School of Medicine, Xi'an, China
| | - Jianying Li
- GE Healthcare, Computed Tomography Research Center, Beijing, China
| | - Tianze Sun
- Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Lihong Chen
- Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Yannan Cheng
- Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Xiaoqian Jia
- Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Xinyi Niu
- Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Jianxin Guo
- Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
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Contrast-Enhanced Chest Computed Tomography (CT) Scan with Low Radiation and Total Iodine Dose for Lung Cancer Detection Using Adaptive Statistical Iterative Reconstruction. IRANIAN JOURNAL OF RADIOLOGY 2022. [DOI: 10.5812/iranjradiol-126572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Background: Contrast-enhanced chest computed tomography (CT) is useful for the detection and follow-up of patients with lung cancer. However, reaching balance between diagnostic image quality, radiation dose, and iodixanol dose is a cause of concern. Objectives: To investigate the clinical value of adaptive statistical iterative reconstruction (ASIR) in reducing the iodixanol content and radiation dose during contrast-enhanced chest CT scan for patients diagnosed with lung masses/nodules based on the analysis of image quality. Methods: This prospective study was conducted on 80 patients diagnosed with nodules or masses, who required contrast-enhanced chest CT scans. The experimental group (n = 40) was subjected to iohexol at a high concentration (350 mgI/L) with a tube voltage of 120 kVp and a filter back projection (FBP) reconstruction algorithm. The comparison group (n = 40) was subject to iodixanol at a lower concentration (270 mgI/L) with a tube voltage of 100 kVp and ASIR (blending ratio, 40%). The radiation dose and total iodixanol content, as well as subjective and objective evaluations of image quality, were analyzed and compared. Results: The two groups obtained non-significantly different subjective scores for five structures detected in the lung window and five structures detected in the mediastinal window, as well as the overall image (P > 0.05 for all). Both the two-group images obtained diagnosis-acceptable scores (≥ 3 points) on displays of 10 structures and overall image quality. The mean CT value of vessels (100 kVp vs. 120 kVp: 314.90 ± 23.42 vs. 308.93 ± 21.40; P > 0.05), standard deviation (13.03 ± 0.88 vs.12.83 ± 0.90; P > 0.05), and contrast-to-noise ratio (20.77 ± 2.20 vs. 20.36 ± 1.94; P > 0.05) were not significantly different between two groups. However, the CT dose index, dose-length product, effective dose, and total iodine dose were reduced by 27.58%, 36.65%, 36.59%, and 22.86% in the 100-kVp group compared to the 120-kVp group. Conclusions: The ASIR showed great potential in reducing the radiation dose and iodine contrast dose, while maintaining good image quality and providing strong confidence for the diagnosis of lung cancer.
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Ng CKC. Artificial Intelligence for Radiation Dose Optimization in Pediatric Radiology: A Systematic Review. CHILDREN 2022; 9:children9071044. [PMID: 35884028 PMCID: PMC9320231 DOI: 10.3390/children9071044] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 07/11/2022] [Accepted: 07/11/2022] [Indexed: 01/19/2023]
Abstract
Radiation dose optimization is particularly important in pediatric radiology, as children are more susceptible to potential harmful effects of ionizing radiation. However, only one narrative review about artificial intelligence (AI) for dose optimization in pediatric computed tomography (CT) has been published yet. The purpose of this systematic review is to answer the question “What are the AI techniques and architectures introduced in pediatric radiology for dose optimization, their specific application areas, and performances?” Literature search with use of electronic databases was conducted on 3 June 2022. Sixteen articles that met selection criteria were included. The included studies showed deep convolutional neural network (CNN) was the most common AI technique and architecture used for dose optimization in pediatric radiology. All but three included studies evaluated AI performance in dose optimization of abdomen, chest, head, neck, and pelvis CT; CT angiography; and dual-energy CT through deep learning image reconstruction. Most studies demonstrated that AI could reduce radiation dose by 36–70% without losing diagnostic information. Despite the dominance of commercially available AI models based on deep CNN with promising outcomes, homegrown models could provide comparable performances. Future exploration of AI value for dose optimization in pediatric radiology is necessary due to small sample sizes and narrow scopes (only three modalities, CT, positron emission tomography/magnetic resonance imaging and mobile radiography, and not all examination types covered) of existing studies.
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Affiliation(s)
- Curtise K. C. Ng
- Curtin Medical School, Curtin University, GPO Box U1987, Perth, WA 6845, Australia; or ; Tel.: +61-8-9266-7314; Fax: +61-8-9266-2377
- Curtin Health Innovation Research Institute (CHIRI), Faculty of Health Sciences, Curtin University, GPO Box U1987, Perth, WA 6845, Australia
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Liao J, Huang L, Qu M, Chen B, Wang G. Artificial Intelligence in Coronary CT Angiography: Current Status and Future Prospects. Front Cardiovasc Med 2022; 9:896366. [PMID: 35783834 PMCID: PMC9247240 DOI: 10.3389/fcvm.2022.896366] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 05/18/2022] [Indexed: 12/28/2022] Open
Abstract
Coronary heart disease (CHD) is the leading cause of mortality in the world. Early detection and treatment of CHD are crucial. Currently, coronary CT angiography (CCTA) has been the prior choice for CHD screening and diagnosis, but it cannot meet the clinical needs in terms of examination quality, the accuracy of reporting, and the accuracy of prognosis analysis. In recent years, artificial intelligence (AI) has developed rapidly in the field of medicine; it played a key role in auxiliary diagnosis, disease mechanism analysis, and prognosis assessment, including a series of studies related to CHD. In this article, the application and research status of AI in CCTA were summarized and the prospects of this field were also described.
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Affiliation(s)
- Jiahui Liao
- Department of Radiology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
- School of Biomedical Engineering, Guangzhou Xinhua University, Guangzhou, China
| | - Lanfang Huang
- Department of Radiology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Meizi Qu
- Department of Radiology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Binghui Chen
- Department of Radiology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
- *Correspondence: Binghui Chen
| | - Guojie Wang
- Department of Radiology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
- Guojie Wang
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Yao Y, Guo B, Li J, Yang Q, Li X, Deng L. The influence of a deep learning image reconstruction algorithm on the image quality and auto-analysis of pulmonary nodules at ultra-low dose chest CT: a phantom study. Quant Imaging Med Surg 2022; 12:2777-2791. [PMID: 35502370 PMCID: PMC9014152 DOI: 10.21037/qims-21-815] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 01/24/2022] [Indexed: 08/16/2023]
Abstract
BACKGROUND To investigate the effect of a new deep learning image reconstruction (DLIR) algorithm on the detection, characterization and image quality of pulmonary nodules (PNs) in ultra-low dose chest computed tomography (CT) in comparison with the adaptive statistical iterative reconstruction (ASIR-V) algorithm. METHODS Nine artificial pulmonary nodules [six ground glass nodules (GGNs) and three solid nodules (SNs); density: -800 HU, -630 HU, 100 HU; diameter: 12 mm, 10 mm, 8 mm] were randomly placed in a thorax anthropomorphic phantom (Lungman, Kyoto Kagaku Inc.) and scanned on a 256-row CT (Revolution CT, GE Healthcare). Eight scans were performed at 70 kVp with different tube currents (20, 30, 50, 70, 90, 100, 120, 150 mA). Raw data were reconstructed using the filtered back projection (FBP), ASIR-V (30%, 50%, 80%) and DLIR (Low, Medium, High; TrueFidelity™) at 0.625 mm thickness. The effective radiation dose was recorded. All images were automatically analyzed using a commercially available artificial intelligence software (Intelligent 4D Imaging System for Chest CT 5.5, YITU Healthcare) and CT value, standard deviation (SD), long and short diameters of each nodule and SD of air (background) were measured. The detection rate, deformation degree (long diameter/short diameter), signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of pulmonary nodules were calculated. RESULTS Nodule CT values were the same in all mA settings for all three types of reconstruction algorithms (all P>0.05). DLIR groups had significantly lower SD and higher SNR and CNR values, with better overall image quality than ASIR-V and FBP groups at each mA, ranging from 65-85% reduction in SD, 67-83% increase in SNR with DLIR-H over 50%ASIR-V and 75-91% reduction in SD and 77-89% increase in SNR with DLIR-H over FBP (all P<0.05). At ultra-low dose conditions (30 mA), the DLIR-H images had the highest detection rate of PNs (100%). In addition, the DLIR-M had a minimal negative effect on the characterization of PNs. CONCLUSIONS DLIR algorithm can be a potential reconstruction technique to optimize image quality and improve detection rate of PNs in ultra-low dose lung screening.
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Affiliation(s)
- Yue Yao
- Department of Radiology, the Second Affiliated Hospital, Xi’an Jiaotong University, Xi’an, China
| | - Baobin Guo
- Department of Radiology, the Second Affiliated Hospital, Xi’an Jiaotong University, Xi’an, China
| | | | - Quanxin Yang
- Department of Radiology, the Second Affiliated Hospital, Xi’an Jiaotong University, Xi’an, China
| | - Xiaohui Li
- Department of Radiology, the Second Affiliated Hospital, Xi’an Jiaotong University, Xi’an, China
| | - Lei Deng
- Department of Radiology, the Second Affiliated Hospital, Xi’an Jiaotong University, Xi’an, China
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