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Choi HU, Cho J, Hwang J, Lee S, Chang W, Park JH, Lee KH. Diagnostic performance and image quality of an image-based denoising algorithm applied to radiation dose-reduced CT in diagnosing acute appendicitis. Abdom Radiol (NY) 2024; 49:1839-1849. [PMID: 38411690 PMCID: PMC11213764 DOI: 10.1007/s00261-024-04246-3] [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: 11/10/2023] [Revised: 02/07/2024] [Accepted: 02/07/2024] [Indexed: 02/28/2024]
Abstract
PURPOSE To evaluate diagnostic performance and image quality of ultralow-dose CT (ULDCT) in diagnosing acute appendicitis with an image-based deep-learning denoising algorithm (IDLDA). METHODS This retrospective multicenter study included 180 patients (mean ± standard deviation, 29 ± 9 years; 91 female) who underwent contrast-enhanced 2-mSv CT for suspected appendicitis from February 2014 to August 2016. We simulated ULDCT from 2-mSv CT, reducing the dose by at least 50%. Then we applied an IDLDA on ULDCT to produce denoised ULDCT (D-ULDCT). Six radiologists with different experience levels (three board-certified radiologists and three residents) independently reviewed the ULDCT and D-ULDCT. They rated the likelihood of appendicitis and subjective image qualities (subjective image noise, diagnostic acceptability, and artificial sensation). One radiologist measured image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). We used the receiver operating characteristic (ROC) analyses, Wilcoxon's signed-rank tests, and paired t-tests. RESULTS The area under the ROC curves (AUC) for diagnosing appendicitis ranged 0.90-0.97 for ULDCT and 0.94-0.97 for D-ULDCT. The AUCs of two residents were significantly higher on D-ULDCT (AUC difference = 0.06 [95% confidence interval, 0.01-0.11; p = .022] and 0.05 [0.00-0.10; p = .046], respectively). D-ULDCT provided better subjective image noise and diagnostic acceptability to all six readers. However, the response of board-certified radiologists and residents differed in artificial sensation (all p ≤ .003). D-ULDCT showed significantly lower image noise, higher SNR, and higher CNR (all p < .001). CONCLUSION An IDLDA can provide better ULDCT image quality and enhance diagnostic performance for less-experienced radiologists.
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Affiliation(s)
- Hyeon Ui Choi
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea
| | - Jungheum Cho
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea.
| | - Jinhee Hwang
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea
- Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea
| | - Seungjae Lee
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea
- Institute of Health and Environment, Seoul National University, Seoul, Korea
| | - Won Chang
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea
| | - Ji Hoon Park
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea
- Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea
- Department of Medical Device Development, Seoul National University College of Medicine, Seoul, Korea
| | - Kyoung Ho Lee
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea
- Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea
- Department of Medical Device Development, Seoul National University College of Medicine, Seoul, Korea
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Plajer D, Hahn M, Chaika M, Mader M, Mueck J, Nikolaou K, Afat S, Brendlin AS. Deep-learning denoising minimizes radiation exposure in neck CT beyond the limits of conventional reconstruction. Eur J Radiol 2024; 178:111523. [PMID: 39013270 DOI: 10.1016/j.ejrad.2024.111523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 04/15/2024] [Accepted: 05/20/2024] [Indexed: 07/18/2024]
Abstract
BACKGROUND Neck computed tomography (NCT) is essential for diagnosing suspected neck tumors and abscesses, but radiation exposure can be an issue. In conventional reconstruction techniques, limiting radiation dose comes at the cost of diminished diagnostic accuracy. Therefore, this study aimed to evaluate the effects of an AI-based denoising post-processing software solution in low-dose neck computer tomography. MATERIALS AND METHODS From 01 September 2023 to 01 December 2023, we retrospectively included patients with clinically suspected neck tumors from the same single-source scanner. The scans were reconstructed using Advanced Modeled Iterative Reconstruction (Original) at 100% and simulated 50% and 25% radiation doses. Each dataset was post-processed using a novel denoising software solution (Denoising). Three radiologists with varying experience levels subjectively rated image quality, diagnostic confidence, sharpness, and contrast for all pairwise combinations of radiation dose and reconstruction mode in a randomized, blinded forced-choice setup. Objective image quality was assessed using ROI measurements of mean CT numbers, noise, and a contrast-to-noise ratio (CNR). An adequately corrected mixed-effects analysis was used to compare objective and subjective image quality. RESULTS At each radiation dose level, pairwise comparisons showed significantly lower image noise and higher CNR for Denoising than for Original (p < 0.001). In subjective analysis, image quality, diagnostic confidence, sharpness, and contrast were significantly higher for Denoising than for Original at 100 and 50 % (p < 0.001). However, there were no significant differences in the subjective ratings between Original 100 % and Denoising 25 % (p = 0.906). CONCLUSIONS The investigated denoising algorithm enables diagnostic-quality neck CT images with radiation doses reduced to 25% of conventional levels, significantly minimizing patient exposure.
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Affiliation(s)
- David Plajer
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls University, Hoppe-Seyler-Str. 3, 72076 Tuebingen, Germany
| | - Marlene Hahn
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls University, Hoppe-Seyler-Str. 3, 72076 Tuebingen, Germany
| | - Marianna Chaika
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls University, Hoppe-Seyler-Str. 3, 72076 Tuebingen, Germany
| | - Markus Mader
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls University, Hoppe-Seyler-Str. 3, 72076 Tuebingen, Germany
| | - Jonas Mueck
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls University, Hoppe-Seyler-Str. 3, 72076 Tuebingen, Germany
| | - Konstantin Nikolaou
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls University, Hoppe-Seyler-Str. 3, 72076 Tuebingen, Germany
| | - Saif Afat
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls University, Hoppe-Seyler-Str. 3, 72076 Tuebingen, Germany.
| | - Andreas S Brendlin
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls University, Hoppe-Seyler-Str. 3, 72076 Tuebingen, Germany
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Chen H, Li Q, Zhou L, Li F. Deep learning-based algorithms for low-dose CT imaging: A review. Eur J Radiol 2024; 172:111355. [PMID: 38325188 DOI: 10.1016/j.ejrad.2024.111355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 01/05/2024] [Accepted: 01/31/2024] [Indexed: 02/09/2024]
Abstract
The computed tomography (CT) technique is extensively employed as an imaging modality in clinical settings. The radiation dose of CT, however, is significantly high, thereby raising concerns regarding the potential radiation damage it may cause. The reduction of X-ray exposure dose in CT scanning may result in a significant decline in imaging quality, thereby elevating the risk of missed diagnosis and misdiagnosis. The reduction of CT radiation dose and acquisition of high-quality images to meet clinical diagnostic requirements have always been a critical research focus and challenge in the field of CT. Over the years, scholars have conducted extensive research on enhancing low-dose CT (LDCT) imaging algorithms, among which deep learning-based algorithms have demonstrated superior performance. In this review, we initially introduced the conventional algorithms for CT image reconstruction along with their respective advantages and disadvantages. Subsequently, we provided a detailed description of four aspects concerning the application of deep neural networks in LDCT imaging process: preprocessing in the projection domain, post-processing in the image domain, dual-domain processing imaging, and direct deep learning-based reconstruction (DLR). Furthermore, an analysis was conducted to evaluate the merits and demerits of each method. The commercial and clinical applications of the LDCT-DLR algorithm were also presented in an overview. Finally, we summarized the existing issues pertaining to LDCT-DLR and concluded the paper while outlining prospective trends for algorithmic advancement.
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Affiliation(s)
- Hongchi Chen
- School of Medical Information Engineering, Gannan Medical University, Ganzhou 341000, China
| | - Qiuxia Li
- School of Medical Information Engineering, Gannan Medical University, Ganzhou 341000, China
| | - Lazhen Zhou
- School of Medical Information Engineering, Gannan Medical University, Ganzhou 341000, China
| | - Fangzuo Li
- School of Medical Information Engineering, Gannan Medical University, Ganzhou 341000, China; Key Laboratory of Prevention and Treatment of Cardiovascular and Cerebrovascular Diseases, Ministry of Education, Gannan Medical University, Ganzhou 341000, China.
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Kazimierczak W, Kazimierczak N, Wilamowska J, Wojtowicz O, Nowak E, Serafin Z. Enhanced visualization in endoleak detection through iterative and AI-noise optimized spectral reconstructions. Sci Rep 2024; 14:3845. [PMID: 38360941 PMCID: PMC10869818 DOI: 10.1038/s41598-024-54502-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 02/13/2024] [Indexed: 02/17/2024] Open
Abstract
To assess the image quality parameters of dual-energy computed tomography angiography (DECTA) 40-, and 60 keV virtual monoenergetic images (VMIs) combined with deep learning-based image reconstruction model (DLM) and iterative reconstructions (IR). CT scans of 28 post EVAR patients were enrolled. The 60 s delayed phase of DECTA was evaluated. Objective [noise, contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR)] and subjective (overall image quality and endoleak conspicuity - 3 blinded readers assessment) image quality analyses were performed. The following reconstructions were evaluated: VMI 40, 60 keV VMI; IR VMI 40, 60 keV; DLM VMI 40, 60 keV. The noise level of the DLM VMI images was approximately 50% lower than that of VMI reconstruction. The highest CNR and SNR values were measured in VMI DLM images. The mean CNR in endoleak in 40 keV was accounted for as 1.83 ± 1.2; 2.07 ± 2.02; 3.6 ± 3.26 in VMI, VMI IR, and VMI DLM, respectively. The DLM algorithm significantly reduced noise and increased lesion conspicuity, resulting in higher objective and subjective image quality compared to other reconstruction techniques. The application of DLM algorithms to low-energy VMIs significantly enhances the diagnostic value of DECTA in evaluating endoleaks. DLM reconstructions surpass traditional VMIs and IR in terms of image quality.
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Affiliation(s)
- Wojciech Kazimierczak
- Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067, Bydgoszcz, Poland.
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009, Bydgoszcz, Poland.
- University Hospital No 1 in Bydgoszcz, Marii Skłodowskiej - Curie 9, 85-094, Bydgoszcz, Poland.
| | - Natalia Kazimierczak
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009, Bydgoszcz, Poland
| | - Justyna Wilamowska
- Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067, Bydgoszcz, Poland
- University Hospital No 1 in Bydgoszcz, Marii Skłodowskiej - Curie 9, 85-094, Bydgoszcz, Poland
| | - Olaf Wojtowicz
- Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067, Bydgoszcz, Poland
- University Hospital No 1 in Bydgoszcz, Marii Skłodowskiej - Curie 9, 85-094, Bydgoszcz, Poland
| | - Ewa Nowak
- University Hospital No 1 in Bydgoszcz, Marii Skłodowskiej - Curie 9, 85-094, Bydgoszcz, Poland
| | - Zbigniew Serafin
- Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067, Bydgoszcz, Poland
- University Hospital No 1 in Bydgoszcz, Marii Skłodowskiej - Curie 9, 85-094, Bydgoszcz, Poland
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Sadia RT, Chen J, Zhang J. CT image denoising methods for image quality improvement and radiation dose reduction. J Appl Clin Med Phys 2024; 25:e14270. [PMID: 38240466 PMCID: PMC10860577 DOI: 10.1002/acm2.14270] [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: 09/18/2023] [Revised: 12/15/2023] [Accepted: 12/28/2023] [Indexed: 02/13/2024] Open
Abstract
With the ever-increasing use of computed tomography (CT), concerns about its radiation dose have become a significant public issue. To address the need for radiation dose reduction, CT denoising methods have been widely investigated and applied in low-dose CT images. Numerous noise reduction algorithms have emerged, such as iterative reconstruction and most recently, deep learning (DL)-based approaches. Given the rapid advancements in Artificial Intelligence techniques, we recognize the need for a comprehensive review that emphasizes the most recently developed methods. Hence, we have performed a thorough analysis of existing literature to provide such a review. Beyond directly comparing the performance, we focus on pivotal aspects, including model training, validation, testing, generalizability, vulnerability, and evaluation methods. This review is expected to raise awareness of the various facets involved in CT image denoising and the specific challenges in developing DL-based models.
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Affiliation(s)
- Rabeya Tus Sadia
- Department of Computer ScienceUniversity of KentuckyLexingtonKentuckyUSA
| | - Jin Chen
- Department of Medicine‐NephrologyUniversity of Alabama at BirminghamBirminghamAlabamaUSA
| | - Jie Zhang
- Department of RadiologyUniversity of KentuckyLexingtonKentuckyUSA
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Kang HJ, Lee JM, Park SJ, Lee SM, Joo I, Yoon JH. Image Quality Improvement of Low-dose Abdominal CT using Deep Learning Image Reconstruction Compared with the Second Generation Iterative Reconstruction. Curr Med Imaging 2024; 20:e250523217310. [PMID: 37231764 DOI: 10.2174/1573405620666230525104809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 03/23/2023] [Accepted: 04/06/2023] [Indexed: 05/27/2023]
Abstract
BACKGROUND Whether deep learning-based CT reconstruction could improve lesion conspicuity on abdominal CT when the radiation dose is reduced is controversial. OBJECTIVES To determine whether DLIR can provide better image quality and reduce radiation dose in contrast-enhanced abdominal CT compared with the second generation of adaptive statistical iterative reconstruction (ASiR-V). AIMS This study aims to determine whether deep-learning image reconstruction (DLIR) can improve image quality. METHOD In this retrospective study, a total of 102 patients were included, who underwent abdominal CT using a DLIR-equipped 256-row scanner and routine CT of the same protocol on the same vendor's 64-row scanner within four months. The CT data from the 256-row scanner were reconstructed into ASiR-V with three blending levels (AV30, AV60, and AV100), and DLIR images with three strength levels (DLIR-L, DLIR-M, and DLIR-H). The routine CT data were reconstructed into AV30, AV60, and AV100. The contrast-to-noise ratio (CNR) of the liver, overall image quality, subjective noise, lesion conspicuity, and plasticity in the portal venous phase (PVP) of ASiR-V from both scanners and DLIR were compared. RESULTS The mean effective radiation dose of PVP of the 256-row scanner was significantly lower than that of the routine CT (6.3±2.0 mSv vs. 2.4±0.6 mSv; p< 0.001). The mean CNR, image quality, subjective noise, and lesion conspicuity of ASiR-V images of the 256-row scanner were significantly lower than those of ASiR-V images at the same blending factor of routine CT, but significantly improved with DLIR algorithms. DLIR-H showed higher CNR, better image quality, and subjective noise than AV30 from routine CT, whereas plasticity was significantly better for AV30. CONCLUSION DLIR can be used for improving image quality and reducing radiation dose in abdominal CT, compared with ASIR-V.
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Affiliation(s)
- Hyo-Jin Kang
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Jeong Min Lee
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea
| | - Sae Jin Park
- Department of Radiology, G&E alphadom medical center, Seongnam, Korea
| | - Sang Min Lee
- Department of Radiology, Cha Gangnam Medical Center, Seoul, Korea
| | - Ijin Joo
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Jeong Hee Yoon
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
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Fujiwara M, Shiotsuki K, Kawano M, Noto D, Maruyama K, Miyazaki M. [Physical Properties of Small Focal Spot Imaging with Deep Learning Reconstruction in Chest-abdominal Plain CT]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2023; 79:1344-1351. [PMID: 37880079 DOI: 10.6009/jjrt.2023-1400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2023]
Abstract
PURPOSE The aim of this study was to compare the physical properties of small focal spot imaging with deep learning reconstruction (DLR) and small or large focal spot imaging with hybrid iterative reconstruction (IR) in chest-abdominal plain computed tomography. METHOD In small focal spot imaging using DLR and hybrid IR, tube currents were set at 350 mA. For the large focal spot imaging using hybrid IR, the tube current was set at 360, 400, 450, and 500 mA. The spatial frequencies with 50% task transfer function (TTF) for delrin and acrylic were calculated to compare spatial resolution properties for lung and soft tissue in the chest. Additionally, the low-contrast object-specific contrast-to-noise ratio (CNRLO) was measured as noise property was measured for a 7-mm module with a CT value contrast of 10 HU in the abdomen. RESULT Spatial frequencies with 50% TTF for delrin and acrylic were found to be greater in small focal spot imaging using DLR compared to those in small and large focal spot imaging using hybrid IR. Moreover, the CNRLO obtained from small focal spot imaging with DLR was also nearly equivalent to that of large focal spot imaging with hybrid IR at tube currents of 450 and 500 mA. CONCLUSION In chest-abdominal plain computed tomography, small focal spot imaging with DLR has been demonstrated to exhibit greater spatial resolution properties compared to small and large focal spot imaging with hybrid IR, with equivalent or better noise performance.
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Affiliation(s)
| | | | - Mizuki Kawano
- Department of Radiology, JA Oita Koseiren Tsurumi Hospital
| | | | - Kenta Maruyama
- Department of Radiology, JA Oita Koseiren Tsurumi Hospital
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Kim DY, Oh HW, Suh CH. Reporting Quality of Research Studies on AI Applications in Medical Images According to the CLAIM Guidelines in a Radiology Journal With a Strong Prominence in Asia. Korean J Radiol 2023; 24:1179-1189. [PMID: 38016678 DOI: 10.3348/kjr.2023.1027] [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: 10/20/2023] [Revised: 10/25/2023] [Accepted: 10/26/2023] [Indexed: 11/30/2023] Open
Abstract
OBJECTIVE We aimed to evaluate the reporting quality of research articles that applied deep learning to medical imaging. Using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) guidelines and a journal with prominence in Asia as a sample, we intended to provide an insight into reporting quality in the Asian region and establish a journal-specific audit. MATERIALS AND METHODS A total of 38 articles published in the Korean Journal of Radiology between June 2018 and January 2023 were analyzed. The analysis included calculating the percentage of studies that adhered to each CLAIM item and identifying items that were met by ≤ 50% of the studies. The article review was initially conducted independently by two reviewers, and the consensus results were used for the final analysis. We also compared adherence rates to CLAIM before and after December 2020. RESULTS Of the 42 items in the CLAIM guidelines, 12 items (29%) were satisfied by ≤ 50% of the included articles. None of the studies reported handling missing data (item #13). Only one study respectively presented the use of de-identification methods (#12), intended sample size (#19), robustness or sensitivity analysis (#30), and full study protocol (#41). Of the studies, 35% reported the selection of data subsets (#10), 40% reported registration information (#40), and 50% measured inter and intrarater variability (#18). No significant changes were observed in the rates of adherence to these 12 items before and after December 2020. CONCLUSION The reporting quality of artificial intelligence studies according to CLAIM guidelines, in our study sample, showed room for improvement. We recommend that the authors and reviewers have a solid understanding of the relevant reporting guidelines and ensure that the essential elements are adequately reported when writing and reviewing the manuscripts for publication.
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Affiliation(s)
- Dong Yeong Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | | | - Chong Hyun Suh
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
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Cao J, Mroueh N, Pisuchpen N, Parakh A, Lennartz S, Pierce TT, Kambadakone AR. Can 1.25 mm thin-section images generated with Deep Learning Image Reconstruction technique replace standard-of-care 5 mm images in abdominal CT? Abdom Radiol (NY) 2023; 48:3253-3264. [PMID: 37369922 DOI: 10.1007/s00261-023-03992-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 06/14/2023] [Accepted: 06/15/2023] [Indexed: 06/29/2023]
Abstract
BACKGROUND CT image reconstruction has evolved from filtered back projection to hybrid- and model-based iterative reconstruction. Deep learning-based image reconstruction is a relatively new technique that uses deep convolutional neural networks to improve image quality. OBJECTIVE To evaluate and compare 1.25 mm thin-section abdominal CT images reconstructed with deep learning image reconstruction (DLIR) with 5 mm thick images reconstructed with adaptive statistical iterative reconstruction (ASIR-V). METHODS This retrospective study included 52 patients (31 F; 56.9±16.9 years) who underwent abdominal CT scans between August-October 2019. Image reconstruction was performed to generate 5 mm images at 40% ASIR-V and 1.25 mm DLIR images at three strengths (low [DLIR-L], medium [DLIR-M], and high [DLIR-H]). Qualitative assessment was performed to determine image noise, contrast, visibility of small structures, sharpness, and artifact based on a 5-point-scale. Image preference determination was based on a 3-point-scale. Quantitative assessment included measurement of attenuation, image noise, and contrast-to-noise ratios (CNR). RESULTS Thin-section images reconstructed with DLIR-M and DLIR-H yielded better image quality scores than 5 mm ASIR-V reconstructed images. Mean qualitative scores of DLIR-H for noise (1.77 ± 0.71), contrast (1.6 ± 0.68), small structure visibility (1.42 ± 0.66), sharpness (1.34 ± 0.55), and image preference (1.11 ± 0.34) were the best (p<0.05). DLIR-M yielded intermediate scores. All DLIR reconstructions showed superior ratings for artifacts compared to ASIR-V (p<0.05), whereas each DLIR group performed comparably (p>0.05, 0.405-0.763). In the quantitative assessment, there were no significant differences in attenuation values between all reconstructions (p>0.05). However, DLIR-H demonstrated the lowest noise (9.17 ± 3.11) and the highest CNR (CNRliver = 26.88 ± 6.54 and CNRportal vein = 7.92 ± 3.85) (all p<0.001). CONCLUSION DLIR allows generation of thin-section (1.25 mm) abdominal CT images, which provide improved image quality with higher inter-reader agreement compared to 5 mm thick images reconstructed with ASIR-V. CLINICAL IMPACT Improved image quality of thin-section CT images reconstructed with DLIR has several benefits in clinical practice, such as improved diagnostic performance without radiation dose penalties.
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Affiliation(s)
- Jinjin Cao
- Abdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, White 270, Boston, MA, 02114-2696, USA
| | - Nayla Mroueh
- Abdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, White 270, Boston, MA, 02114-2696, USA
| | - Nisanard Pisuchpen
- Abdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, White 270, Boston, MA, 02114-2696, USA
- Department of Radiology, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand
| | - Anushri Parakh
- Abdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, White 270, Boston, MA, 02114-2696, USA
| | - Simon Lennartz
- Abdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, White 270, Boston, MA, 02114-2696, USA
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Kerpener Straße 62, 50937, Cologne, Germany
| | - Theodore T Pierce
- Abdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, White 270, Boston, MA, 02114-2696, USA
| | - Avinash R Kambadakone
- Abdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, White 270, Boston, MA, 02114-2696, USA.
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Nagata M, Ichikawa Y, Domae K, Yoshikawa K, Kanii Y, Yamazaki A, Nagasawa N, Ishida M, Sakuma H. Application of Deep Learning-Based Denoising Technique for Radiation Dose Reduction in Dynamic Abdominal CT: Comparison with Standard-Dose CT Using Hybrid Iterative Reconstruction Method. J Digit Imaging 2023; 36:1578-1587. [PMID: 36944812 PMCID: PMC10406991 DOI: 10.1007/s10278-023-00808-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 03/08/2023] [Accepted: 03/08/2023] [Indexed: 03/23/2023] Open
Abstract
The purpose is to evaluate whether deep learning-based denoising (DLD) algorithm provides sufficient image quality for abdominal computed tomography (CT) with a 30% reduction in radiation dose, compared to standard-dose CT reconstructed with conventional hybrid iterative reconstruction (IR). The subjects consisted of 50 patients who underwent abdominal CT with standard dose and reconstructed with hybrid IR (ASiR-V50%) and another 50 patients who underwent abdominal CT with approximately 30% less dose and reconstructed with ASiR-V50% and DLD at low-, medium- and high-strength (DLD-L, DLD-M and DLD-H, respectively). The standard deviation of attenuation in liver parenchyma was measured as image noise. Contrast-to-noise ratio (CNR) for portal vein on portal venous phase was calculated. Lesion conspicuity in 23 abdominal solid mass on the reduced-dose CT was rated on a 5-point scale: 0 (best) to -4 (markedly inferior). Compared with hybrid IR of standard-dose CT, DLD-H of reduced-dose CT provided significantly lower image noise (portal phase: 9.0 (interquartile range, 8.7-9.4) HU vs 12.0 (11.4-12.7) HU, P < 0.0001) and significantly higher CNR (median, 5.8 (4.4-7.4) vs 4.3 (3.3-5.3), P = 0.0019). As for DLD-M of reduced-dose CT, no significant difference was found in image noise and CNR compared to hybrid IR of standard-dose CT (P > 0.99). Lesion conspicuity scores for DLD-H and DLD-M were significantly better than hybrid IR (P < 0.05). Dynamic contrast-enhanced abdominal CT acquired with approximately 30% lower radiation dose and generated with the DLD algorithm exhibit lower image noise and higher CNR compared to standard-dose CT with hybrid IR.
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Affiliation(s)
- Motonori Nagata
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, 514-8507 Tsu, Mie Japan
| | - Yasutaka Ichikawa
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, 514-8507 Tsu, Mie Japan
| | - Kensuke Domae
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, 514-8507 Tsu, Mie Japan
| | - Kazuya Yoshikawa
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, 514-8507 Tsu, Mie Japan
| | - Yoshinori Kanii
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, 514-8507 Tsu, Mie Japan
| | - Akio Yamazaki
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, 514-8507 Tsu, Mie Japan
| | - Naoki Nagasawa
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, 514-8507 Tsu, Mie Japan
| | - Masaki Ishida
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, 514-8507 Tsu, Mie Japan
| | - Hajime Sakuma
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, 514-8507 Tsu, Mie Japan
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11
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Jeon PH, Jeon SH, Ko D, An G, Shim H, Otgonbaatar C, Son K, Kim D, Ko SM, Chung MA. Assessment of Image Quality of Coronary CT Angiography Using Deep Learning-Based CT Reconstruction: Phantom and Patient Studies. Diagnostics (Basel) 2023; 13:diagnostics13111862. [PMID: 37296714 DOI: 10.3390/diagnostics13111862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 04/11/2023] [Accepted: 05/23/2023] [Indexed: 06/12/2023] Open
Abstract
BACKGROUND In coronary computed tomography angiography (CCTA), the main issue of image quality is noise in obese patients, blooming artifacts due to calcium and stents, high-risk coronary plaques, and radiation exposure to patients. OBJECTIVE To compare the CCTA image quality of deep learning-based reconstruction (DLR) with that of filtered back projection (FBP) and iterative reconstruction (IR). METHODS This was a phantom study of 90 patients who underwent CCTA. CCTA images were acquired using FBP, IR, and DLR. In the phantom study, the aortic root and the left main coronary artery in the chest phantom were simulated using a needleless syringe. The patients were classified into three groups according to their body mass index. Noise, the signal-to-noise ratio (SNR), and the contrast-to-noise ratio (CNR) were measured for image quantification. A subjective analysis was also performed for FBP, IR, and DLR. RESULTS According to the phantom study, DLR reduced noise by 59.8% compared to FBP and increased SNR and CNR by 121.4% and 123.6%, respectively. In a patient study, DLR reduced noise compared to FBP and IR. Furthermore, DLR increased the SNR and CNR more than FBP and IR. In terms of subjective scores, DLR was higher than FBP and IR. CONCLUSION In both phantom and patient studies, DLR effectively reduced image noise and improved SNR and CNR. Therefore, the DLR may be useful for CCTA examinations.
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Affiliation(s)
- Pil-Hyun Jeon
- Department of Radiology, Wonju Severance Christian Hospital, Wonju 26426, Republic of Korea
| | - Sang-Hyun Jeon
- Department of Radiology, Wonju Severance Christian Hospital, Wonju 26426, Republic of Korea
| | - Donghee Ko
- Department of Radiology, Wonju Severance Christian Hospital, Wonju 26426, Republic of Korea
| | - Giyong An
- Department of Radiology, Wonju Severance Christian Hospital, Wonju 26426, Republic of Korea
| | - Hackjoon Shim
- Medical Imaging AI Research Center, Canon Medical System, Seoul 08826, Republic of Korea
| | - Chuluunbaatar Otgonbaatar
- Department of Radiology, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
| | - Kihong Son
- Medical Information Research Section, Electronics and Telecommunications Research Institute, Daejeon 34129, Republic of Korea
| | - Daehong Kim
- Department of Radiological Science, Eulji University, Seongnam 13135, Republic of Korea
| | - Sung Min Ko
- Department of Radiology, Wonju Severance Christian Hospital, Wonju 26426, Republic of Korea
| | - Myung-Ae Chung
- Department of Bigdata Medical Convergence, Eulji University, Seongnam 13135, Republic of Korea
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12
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Kędzierski B, Macek P, Dziadkowiec-Macek B, Truszkiewicz K, Poręba R, Gać P. Radiation Doses in Cardiovascular Computed Tomography. Life (Basel) 2023; 13:life13040990. [PMID: 37109519 PMCID: PMC10141413 DOI: 10.3390/life13040990] [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: 02/16/2023] [Revised: 04/03/2023] [Accepted: 04/07/2023] [Indexed: 04/29/2023] Open
Abstract
We discussed the contemporary views on the effects of ionising radiation on living organisms and the process of estimating radiation doses in CT examinations and the definitions of the CTDI, CTDIvol, DLP, SSDE, ED. We reviewed the reports from large analyses on the radiation doses in CT examinations of the coronary arteries prior to TAVI procedures, including the CRESCENT, PROTECTION, German Cardiac CT Registry studies. These studies were carried out over the last 10 years and can help confront the daily practice of performing cardiovascular CT examinations in most centres. The reference dose levels for these examinations were also collected. The methods to optimise the radiation dose included tube voltage reduction, ECG-monitored tube current modulation, iterative and deep learning reconstruction techniques, a reduction in the scan range, prospective study protocols, automatic exposure control, heart rate control, rational use of the calcium score, multi-slices and dual-source and wide-field tomography. We also present the studies that indicated the need to raise the organ conversion factor for cardiovascular studies from the 0.014-0.017 mSv/mGy*cm used for chest studies to date to a value of 0.0264-0.03 mSv/mGy*cm.
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Affiliation(s)
- Bartłomiej Kędzierski
- Department of Radiology and Imaging Diagnostics, Emergency Medicine Center, Marciniak Lower Silesian Specialist Hospital, Fieldorfa 2, 54-049 Wrocław, Poland
| | - Piotr Macek
- Department of Internal Medicine, Occupational Diseases, Hypertension and Clinical Oncology, Wroclaw Medical University, Borowska 213, 50-556 Wrocław, Poland
| | - Barbara Dziadkowiec-Macek
- Department of Internal Medicine, Occupational Diseases, Hypertension and Clinical Oncology, Wroclaw Medical University, Borowska 213, 50-556 Wrocław, Poland
| | - Krystian Truszkiewicz
- Department of Radiology and Imaging Diagnostics, Emergency Medicine Center, Marciniak Lower Silesian Specialist Hospital, Fieldorfa 2, 54-049 Wrocław, Poland
| | - Rafał Poręba
- Department of Internal Medicine, Occupational Diseases, Hypertension and Clinical Oncology, Wroclaw Medical University, Borowska 213, 50-556 Wrocław, Poland
| | - Paweł Gać
- Department of Population Health, Division of Environmental Health and Occupational Medicine, Wroclaw Medical University, Mikulicza-Radeckiego 7, 50-368 Wrocław, Poland
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13
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Jung Y, Hur J, Han K, Imai Y, Hong YJ, Im DJ, Lee KH, Desnoyers M, Thomsen B, Shigemasa R, Um K, Jang K. Radiation dose reduction using deep learning-based image reconstruction for a low-dose chest computed tomography protocol: a phantom study. Quant Imaging Med Surg 2023; 13:1937-1947. [PMID: 36915339 PMCID: PMC10006148 DOI: 10.21037/qims-22-618] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 12/20/2022] [Indexed: 02/04/2023]
Abstract
Background The aim of this study was to compare the dose reduction potential and image quality of deep learning-based image reconstruction (DLIR) with those of filtered back-projection (FBP) and iterative reconstruction (IR) and to determine the clinically usable dose of DLIR for low-dose chest computed tomography (LDCT) scans. Methods Multi-slice computed tomography (CT) scans of a chest phantom were performed with various tube voltages and tube currents, and the images were reconstructed using seven methods to control the amount of noise reduction: FBP, three stages of IR, and three stages of DLIR. For subjective image analysis, four radiologists compared 48 image data sets with reference images and rated on a 5-point scale. For quantitative image analysis, the signal to noise ratio (SNR), contrast to noise ratio (CNR), nodule volume, and nodule diameter were measured. Results In the subjective analysis, DLIR-Low (0.46 mGy), DLIR-Medium (0.31 mGy), and DLIR-High (0.18 mGy) images showed similar quality to the FBP (2.47 mGy) image. Under the same dose conditions, the SNR and CNR were higher with DLIR-High than with FBP and all the IR methods (all P<0.05). The nodule volume and size with DLIR-High were significantly closer to the real volume than with FBP and all the IR methods (all P<0.001). Conclusions DLIR can improve the image quality of LDCT compared to FBP and IR. In addition, the appropriate effective dose for LDCT would be 0.24 mGy with DLIR-High.
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Affiliation(s)
- Yunsub Jung
- Research Team, GE Healthcare Korea, Seoul, Republic of Korea
| | - Jin Hur
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Kyunghwa Han
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | | | - Yoo Jin Hong
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Dong Jin Im
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Kye Ho Lee
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.,Department of Radiology, Dankook University Hospital, Cheonan, Republic of Korea
| | | | - Brian Thomsen
- Global Research Team, GE Healthcare US, Milwaukee, WI, USA
| | - Risa Shigemasa
- Global Research Team, GE Healthcare US, Milwaukee, WI, USA
| | - Kyounga Um
- Research Team, GE Healthcare Korea, Seoul, Republic of Korea
| | - Kyungeun Jang
- Research Team, GE Healthcare Korea, Seoul, Republic of Korea
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14
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De Santis D, Polidori T, Tremamunno G, Rucci C, Piccinni G, Zerunian M, Pugliese L, Del Gaudio A, Guido G, Barbato L, Laghi A, Caruso D. Deep learning image reconstruction algorithm: impact on image quality in coronary computed tomography angiography. LA RADIOLOGIA MEDICA 2023; 128:434-444. [PMID: 36847992 PMCID: PMC10119038 DOI: 10.1007/s11547-023-01607-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 02/03/2023] [Indexed: 03/01/2023]
Abstract
PURPOSE To perform a comprehensive intraindividual objective and subjective image quality evaluation of coronary CT angiography (CCTA) reconstructed with deep learning image reconstruction (DLIR) and to assess correlation with routinely applied hybrid iterative reconstruction algorithm (ASiR-V). MATERIAL AND METHODS Fifty-one patients (29 males) undergoing clinically indicated CCTA from April to December 2021 were prospectively enrolled. Fourteen datasets were reconstructed for each patient: three DLIR strength levels (DLIR_L, DLIR_M, and DLIR_H), ASiR-V from 10% to 100% in 10%-increment, and filtered back-projection (FBP). Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) determined objective image quality. Subjective image quality was assessed with a 4-point Likert scale. Concordance between reconstruction algorithms was assessed by Pearson correlation coefficient. RESULTS DLIR algorithm did not impact vascular attenuation (P ≥ 0.374). DLIR_H showed the lowest noise, comparable with ASiR-V 100% (P = 1) and significantly lower than other reconstructions (P ≤ 0.021). DLIR_H achieved the highest objective quality, with SNR and CNR comparable to ASiR-V 100% (P = 0.139 and 0.075, respectively). DLIR_M obtained comparable objective image quality with ASiR-V 80% and 90% (P ≥ 0.281), while achieved the highest subjective image quality (4, IQR: 4-4; P ≤ 0.001). DLIR and ASiR-V datasets returned a very strong correlation in the assessment of CAD (r = 0.874, P = 0.001). CONCLUSION DLIR_M significantly improves CCTA image quality and has very strong correlation with routinely applied ASiR-V 50% dataset in the diagnosis of CAD.
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Affiliation(s)
- Domenico De Santis
- Radiology Unit, Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Tiziano Polidori
- Radiology Unit, Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Giuseppe Tremamunno
- Radiology Unit, Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Carlotta Rucci
- Radiology Unit, Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Giulia Piccinni
- Radiology Unit, Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Marta Zerunian
- Radiology Unit, Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Luca Pugliese
- Radiology Unit, Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Antonella Del Gaudio
- Radiology Unit, Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Gisella Guido
- Radiology Unit, Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Luca Barbato
- Radiology Unit, Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Andrea Laghi
- Radiology Unit, Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy.
| | - Damiano Caruso
- Radiology Unit, Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
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15
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Lee HJ, Kim JS, Lee JK, Lee HA, Pak S. Ultra-low-dose hepatic multiphase CT using deep learning-based image reconstruction algorithm focused on arterial phase in chronic liver disease: A non-inferiority study. Eur J Radiol 2023; 159:110659. [PMID: 36584563 DOI: 10.1016/j.ejrad.2022.110659] [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: 10/04/2022] [Revised: 12/07/2022] [Accepted: 12/14/2022] [Indexed: 12/24/2022]
Abstract
PURPOSE This study determined whether image quality and detectability of ultralow-dose hepatic multiphase CT (ULDCT, 33.3% dose) using a vendor-agnostic deep learning model(DLM) are noninferior to those of standard-dose CT (SDCT, 100% dose) using model-based iterative reconstruction(MBIR) in patients with chronic liver disease focusing on arterial phase. METHODS Sixty-seven patients underwent hepatic multiphase CT using a dual-source scanner to obtain two different radiation dose CT scans (100%, SDCT and 33.3%, ULDCT). ULDCT using DLM and SDCT using MBIR were compared. A margin of -0.5 for the difference between the two protocols was pre-defined as noninferiority of the overall image quality of the arterial phase image. Quantitative image analysis (signal to noise ratio[SNR] and contrast to noise ratio[CNR]) was also conducted. The detectability of hepatic arterial focal lesions was compared using the Jackknife free-response receiver operating characteristic analysis. Non-inferiority was satisfied if the margin of the lower limit of 95%CI of the difference in figure-of-merit was less than -0.1. RESULTS Mean overall arterial phase image quality scores with ULDCT using DLM and SDCT using MBIR were 4.35 ± 0.57 and 4.08 ± 0.58, showing noninferiority (difference: -0.269; 95 %CI, -0.374 to -0.164). ULDCT using DLM showed a significantly superior contrast-to-noise ratio of arterial enhancing lesion (p < 0.05). Figure-of-merit for detectability of arterial hepatic focal lesion was 0.986 for ULDCT using DLM and 0.963 for SDCT using MBIR, showing noninferiority (difference: -0.023, 95 %CI: -0.016 to 0.063). CONCLUSION ULDCT using DLM with 66.7% dose reduction showed non-inferior overall image quality and detectability of arterial focal hepatic lesion compared to SDCT using MBIR.
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Affiliation(s)
- Hyun Joo Lee
- Department of Radiology, College of Medicine, Ewha Womans University, Seoul, Republic of Korea
| | - Jin Sil Kim
- Department of Radiology, College of Medicine, Ewha Womans University, Seoul, Republic of Korea.
| | - Jeong Kyong Lee
- Department of Radiology, College of Medicine, Ewha Womans University, Seoul, Republic of Korea
| | - Hye Ah Lee
- Clinical Trial Center, Mokdong Hospital, Ewha Womans University, Seoul, Republic of Korea
| | - Seongyong Pak
- Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology,Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
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16
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Tian Q, Li X, Li J, Cheng Y, Niu X, Zhu S, Xu W, Guo J. Image quality improvement in low-dose chest CT with deep learning image reconstruction. J Appl Clin Med Phys 2022; 23:e13796. [PMID: 36210060 PMCID: PMC9797160 DOI: 10.1002/acm2.13796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Revised: 07/10/2022] [Accepted: 09/06/2022] [Indexed: 01/01/2023] Open
Abstract
OBJECTIVES To investigate the clinical utility of deep learning image reconstruction (DLIR) for improving image quality in low-dose chest CT in comparison with 40% adaptive statistical iterative reconstruction-Veo (ASiR-V40%) algorithm. METHODS This retrospective study included 86 patients who underwent low-dose CT for lung cancer screening. Images were reconstructed with ASiR-V40% and DLIR at low (DLIR-L), medium (DLIR-M), and high (DLIR-H) levels. CT value and standard deviation of lung tissue, erector spinae muscles, aorta, and fat were measured and compared across the four reconstructions. Subjective image quality was evaluated by two blind readers from three aspects: image noise, artifact, and visualization of small structures. RESULTS The effective dose was 1.03 ± 0.36 mSv. There was no significant difference in CT values of erector spinae muscles and aorta, whereas the maximum difference for lung tissue and fat was less than 5 HU among the four reconstructions. Compared with ASiR-V40%, the DLIR-L, DLIR-M, and DLIR-H reconstructions reduced the noise in aorta by 11.44%, 33.03%, and 56.1%, respectively, and had significantly higher subjective quality scores in image artifacts (all p < 0.001). ASiR-V40%, DLIR-L, and DLIR-M had equivalent score in visualizing small structures (all p > 0.05), whereas DLIR-H had slightly lower score. CONCLUSIONS Compared with ASiR-V40%, DLIR significantly reduces image noise in low-dose chest CT. DLIR strength is important and should be adjusted for different diagnostic needs in clinical application.
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Affiliation(s)
- Qian Tian
- Department of RadiologyThe First Affiliated Hospital of Xi'an Jiaotong UniversityXi'anShaanxiP. R. China
| | - Xinyu Li
- Department of RadiologyThe First Affiliated Hospital of Xi'an Jiaotong UniversityXi'anShaanxiP. R. China
| | - Jianying Li
- GE Healthcare, Computed Tomography Research CenterBeijingP. R. China
| | - Yannan Cheng
- Department of RadiologyThe First Affiliated Hospital of Xi'an Jiaotong UniversityXi'anShaanxiP. R. China
| | - Xinyi Niu
- Department of RadiologyThe First Affiliated Hospital of Xi'an Jiaotong UniversityXi'anShaanxiP. R. China
| | - Shumeng Zhu
- Department of RadiologyThe First Affiliated Hospital of Xi'an Jiaotong UniversityXi'anShaanxiP. R. China
| | - Wenting Xu
- Department of RadiologyThe First Affiliated Hospital of Xi'an Jiaotong UniversityXi'anShaanxiP. R. China
| | - Jianxin Guo
- Department of RadiologyThe First Affiliated Hospital of Xi'an Jiaotong UniversityXi'anShaanxiP. R. China
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Dillman JR, Somasundaram E, Brady SL, He L. Current and emerging artificial intelligence applications for pediatric abdominal imaging. Pediatr Radiol 2022; 52:2139-2148. [PMID: 33844048 DOI: 10.1007/s00247-021-05057-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 01/25/2021] [Accepted: 03/16/2021] [Indexed: 12/12/2022]
Abstract
Artificial intelligence (AI) uses computers to mimic cognitive functions of the human brain, allowing inferences to be made from generally large datasets. Traditional machine learning (e.g., decision tree analysis, support vector machines) and deep learning (e.g., convolutional neural networks) are two commonly employed AI approaches both outside and within the field of medicine. Such techniques can be used to evaluate medical images for the purposes of automated detection and segmentation, classification tasks (including diagnosis, lesion or tissue characterization, and prediction), and image reconstruction. In this review article we highlight recent literature describing current and emerging AI methods applied to abdominal imaging (e.g., CT, MRI and US) and suggest potential future applications of AI in the pediatric population.
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Affiliation(s)
- Jonathan R Dillman
- Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave., Cincinnati, OH, 45229, USA. .,Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
| | - Elan Somasundaram
- Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave., Cincinnati, OH, 45229, USA.,Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Samuel L Brady
- Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave., Cincinnati, OH, 45229, USA.,Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Lili He
- Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave., Cincinnati, OH, 45229, USA.,Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
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18
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Zhang JZ, Ganesh H, Raslau FD, Nair R, Escott E, Wang C, Wang G, Zhang J. Deep learning versus iterative reconstruction on image quality and dose reduction in abdominal CT: a live animal study. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac7999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 06/16/2022] [Indexed: 11/12/2022]
Abstract
Abstract
Objective. While simulated low-dose CT images and phantom studies cannot fully approximate subjective and objective effects of deep learning (DL) denoising on image quality, live animal models may afford this assessment. This study is to investigate the potential of DL in CT dose reduction on image quality compared to iterative reconstruction (IR). Approach. The upper abdomen of a live 4 year old sheep was scanned on a CT scanner at different exposure levels. Images were reconstructed using FBP and ADMIRE with 5 strengths. A modularized DL network with 5 modules was used for image reconstruction via progressive denoising. Radiomic features were extracted from a region over the liver. Concordance correlation coefficient (CCC) was applied to quantify agreement between any two sets of radiomic features. Coefficient of variation was calculated to measure variation in a radiomic feature series. Structural similarity index (SSIM) was used to measure the similarity between any two images. Diagnostic quality, low-contrast detectability, and image texture were qualitatively evaluated by two radiologists. Pearson correlation coefficient was computed across all dose-reconstruction/denoising combinations. Results. A total of 66 image sets, with 405 radiomic features extracted from each, are analyzed. IR and DL can improve diagnostic quality and low-contrast detectability and similarly modulate image texture features. In terms of SSIM, DL has higher potential in preserving image structure. There is strong correlation between SSIM and radiologists’ evaluations for diagnostic quality (0.559) and low-contrast detectability (0.635) but moderate correlation for texture (0.313). There is moderate correlation between CCC of radiomic features and radiologists’ evaluation for diagnostic quality (0.397), low-contrast detectability (0.417), and texture (0.326), implying that improvement of image features may not relate to improvement of diagnostic quality. Conclusion. DL shows potential to further reduce radiation dose while preserving structural similarity, while IR is favored by radiologists and more predictably alters radiomic features.
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19
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Son W, Kim M, Hwang JY, Kim YW, Park C, Choo KS, Kim TU, Jang JY. Comparison of a Deep Learning-Based Reconstruction Algorithm with Filtered Back Projection and Iterative Reconstruction Algorithms for Pediatric Abdominopelvic CT. Korean J Radiol 2022; 23:752-762. [PMID: 35695313 PMCID: PMC9240291 DOI: 10.3348/kjr.2021.0466] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 02/26/2022] [Accepted: 03/29/2022] [Indexed: 11/15/2022] Open
Abstract
Objective To compare a deep learning-based reconstruction (DLR) algorithm for pediatric abdominopelvic computed tomography (CT) with filtered back projection (FBP) and iterative reconstruction (IR) algorithms. Materials and Methods Post-contrast abdominopelvic CT scans obtained from 120 pediatric patients (mean age ± standard deviation, 8.7 ± 5.2 years; 60 males) between May 2020 and October 2020 were evaluated in this retrospective study. Images were reconstructed using FBP, a hybrid IR algorithm (ASiR-V) with blending factors of 50% and 100% (AV50 and AV100, respectively), and a DLR algorithm (TrueFidelity) with three strength levels (low, medium, and high). Noise power spectrum (NPS) and edge rise distance (ERD) were used to evaluate noise characteristics and spatial resolution, respectively. Image noise, edge definition, overall image quality, lesion detectability and conspicuity, and artifacts were qualitatively scored by two pediatric radiologists, and the scores of the two reviewers were averaged. A repeated-measures analysis of variance followed by the Bonferroni post-hoc test was used to compare NPS and ERD among the six reconstruction methods. The Friedman rank sum test followed by the Nemenyi-Wilcoxon-Wilcox all-pairs test was used to compare the results of the qualitative visual analysis among the six reconstruction methods. Results The NPS noise magnitude of AV100 was significantly lower than that of the DLR, whereas the NPS peak of AV100 was significantly higher than that of the high- and medium-strength DLR (p < 0.001). The NPS average spatial frequencies were higher for DLR than for ASiR-V (p < 0.001). ERD was shorter with DLR than with ASiR-V and FBP (p < 0.001). Qualitative visual analysis revealed better overall image quality with high-strength DLR than with ASiR-V (p < 0.001). Conclusion For pediatric abdominopelvic CT, the DLR algorithm may provide improved noise characteristics and better spatial resolution than the hybrid IR algorithm.
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Affiliation(s)
- Wookon Son
- Department of Radiology, Pusan National University Yangsan Hospital, Yangsan, Korea
| | - MinWoo Kim
- School of Biomedical Convergence Engineering, Pusan National University, Busan, Korea
| | - Jae-Yeon Hwang
- Department of Radiology, Pusan National University Yangsan Hospital, Yangsan, Korea.,Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, College of Medicine, Pusan National University, Yangsan, Korea.
| | - Yong-Woo Kim
- Department of Radiology, Pusan National University Yangsan Hospital, Yangsan, Korea
| | - Chankue Park
- Department of Radiology, Pusan National University Yangsan Hospital, Yangsan, Korea
| | - Ki Seok Choo
- Department of Radiology, Pusan National University Yangsan Hospital, Yangsan, Korea
| | - Tae Un Kim
- Department of Radiology, Pusan National University Yangsan Hospital, Yangsan, Korea
| | - Joo Yeon Jang
- Department of Radiology, Pusan National University Yangsan Hospital, Yangsan, Korea
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Seo JY, Joo I, Yoon JH, Kang HJ, Kim S, Kim JH, Ahn C, Lee JM. Deep learning-based reconstruction of virtual monoenergetic images of kVp-switching dual energy CT for evaluation of hypervascular liver lesions: Comparison with standard reconstruction technique. Eur J Radiol 2022; 154:110390. [PMID: 35724579 DOI: 10.1016/j.ejrad.2022.110390] [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: 09/08/2021] [Revised: 01/12/2022] [Accepted: 05/31/2022] [Indexed: 11/26/2022]
Abstract
OBJECTIVE To investigate clinical applicability of deep learning(DL)-based reconstruction of virtual monoenergetic images(VMIs) of arterial phase liver CT obtained by rapid kVp-switching dual-energy CT for evaluation of hypervascular liver lesions. MATERIALS AND METHODS We retrospectively included 109 patients who had available late arterial phase liver CT images of the liver obtained with a rapid switching kVp DECT scanner for suspicious intra-abdominal malignancies. Two VMIs of 70 keV and 40 keV were reconstructed using adaptive statistical iterative reconstruction (ASiR-V) for arterial phase scans. VMIs at 40 keV were additionally reconstructed with a vendor-agnostic DL-based reconstruction technique (ClariCT.AI, ClariPi, DL 40 keV). Qualitative, quantitative image quality and subjective diagnostic acceptability were compared according to reconstruction techniques. RESULTS In qualitative analysis, DL 40 keV images showed less image noise (4.55 vs 3.11 vs 3.95, p < 0.001), better image sharpness (4.75 vs 4.16 vs 4.3, p < 0.001), better image contrast (4.98 vs 4.72 vs 4.19, p < 0.017), better lesion conspicuity (4.61 vs 4.23 vs 3.4, p < 0.001) and diagnostic acceptability (4.59 vs 3.88 vs 4.09, p < 0.001) compared with ASiR-V 40 keV or 70 keV image sets. In quantitative analysis, DL 40 keV significantly reduced image noise relative to ASiR-V 40 keV images (49.9%, p < 0.001) and ASiR-V 70 keV images (85.2%, p = 0.012). DL 40 keV images showed significantly higher CNRlesion to the liver and SNRliver than ASiR-V 40 keV image and 70 keV images (p < 0.001). CONCLUSION DL-based reconstruction of 40 keV images using vendor-agnostic software showed greater noise reduction, better lesion conspicuity, image contrast, image sharpness, and higher overall image diagnostic acceptability than ASiR for 40 keV or 70 keV images in patients with hypervascular liver lesions.
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Affiliation(s)
- June Young Seo
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Ijin Joo
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jeong Hee Yoon
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Hyo Jin Kang
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Sewoo Kim
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jong Hyo Kim
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea; Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea; Center for Medical-IT Convergence Technology Research, Advanced Institutes of Convergence Technology, Suwon, Republic of Korea; Research Institute, ClariPi, Seoul, Republic of Korea
| | - Chulkyun Ahn
- Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea; Research Institute, ClariPi, Seoul, Republic of Korea
| | - Jeong Min Lee
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
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Zhang K, Shi X, Xie SS, Sun JH, Liu ZH, Zhang S, Song JY, Shen W. Deep learning image reconstruction in pediatric abdominal and chest computed tomography: a comparison of image quality and radiation dose. Quant Imaging Med Surg 2022; 12:3238-3250. [PMID: 35655845 PMCID: PMC9131348 DOI: 10.21037/qims-21-936] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Accepted: 03/18/2022] [Indexed: 08/27/2023]
Abstract
BACKGROUND Studies on the application of deep learning image reconstruction (DLIR) in pediatric computed tomography (CT) are limited and have so far been mostly based on phantom. The purpose of this study was to compare the image quality and radiation dose of DLIR with that of adaptive statistical iterative reconstruction-Veo (ASiR-V) during abdominal and chest CT for the pediatric population. METHODS A pediatric phantom was used for the pilot study, and 20 children were recruited for clinical verification. The preset scan parameter noise index (NI) was 5, 8, 11, 13, 15, and 18 for the phantom study, and 8 and 13 for the clinical pediatric study. We reconstructed CT images with ASiR-V 30%, ASiR-V 70%, DLIR-M (medium) and DLIR-H (high). The regions of interest (ROI) were marked on the organs of the abdomen (liver, kidney, and subcutaneous fat) and the chest (lung, mediastinum, and spine). The CT dose index volume (CTDIvol), CT value, image noise (N), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured and calculated. The subjective image quality was assessed by 3 radiologists blindly using a 5-point scale. The dose reduction efficiency of DLIR was estimated. RESULTS In the phantom study, the interobserver assessment of the data measurement demonstrated good agreement [intraclass correlation coefficient (ICC) =0.814 for abdomen, 0.801 for chest]. Within the same dose level, the N, SNR, and CNR were statistically different among reconstructions, while the CT value remained the same. The N increased and SNR decreased as the radiation dose decreased. The DLIR-H performed better than ASiR-V when the radiation dose was reduced, without sacrificing image quality. In the patient study, the interobserver assessment of the data measurement demonstrated good agreement (ICC =0.774 for abdomen, 0.751 for chest). DLIR-H had the highest subjective and objective scores in the abdomen. CONCLUSIONS Application of DLIR could help to reduce radiation dose without sacrificing the image quality of pediatric CT scans. Further clinical validation is required.
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Affiliation(s)
- Kun Zhang
- Department of Radiology, Tianjin First Central Hospital, Tianjin Institute of Imaging Medicine, School of Medicine, Nankai University, Tianjin, China
| | - Xiang Shi
- First Central Clinical College, Tianjin Medical University, Tianjin, China
| | - Shuang-Shuang Xie
- Department of Radiology, Tianjin First Central Hospital, Tianjin Institute of Imaging Medicine, School of Medicine, Nankai University, Tianjin, China
| | - Ji-Hang Sun
- Imaging Center, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China
| | - Zhuo-Heng Liu
- GE Healthcare, Computed Tomography Research Center, Beijing, China
| | - Shuai Zhang
- GE Healthcare, Computed Tomography Research Center, Beijing, China
| | - Jia-Yang Song
- GE Healthcare, Computed Tomography Research Center, Beijing, China
| | - Wen Shen
- Department of Radiology, Tianjin First Central Hospital, Tianjin Institute of Imaging Medicine, School of Medicine, Nankai University, Tianjin, China
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The use of deep learning methods in low-dose computed tomography image reconstruction: a systematic review. COMPLEX INTELL SYST 2022. [DOI: 10.1007/s40747-022-00724-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
AbstractConventional reconstruction techniques, such as filtered back projection (FBP) and iterative reconstruction (IR), which have been utilised widely in the image reconstruction process of computed tomography (CT) are not suitable in the case of low-dose CT applications, because of the unsatisfying quality of the reconstructed image and inefficient reconstruction time. Therefore, as the demand for CT radiation dose reduction continues to increase, the use of artificial intelligence (AI) in image reconstruction has become a trend that attracts more and more attention. This systematic review examined various deep learning methods to determine their characteristics, availability, intended use and expected outputs concerning low-dose CT image reconstruction. Utilising the methodology of Kitchenham and Charter, we performed a systematic search of the literature from 2016 to 2021 in Springer, Science Direct, arXiv, PubMed, ACM, IEEE, and Scopus. This review showed that algorithms using deep learning technology are superior to traditional IR methods in noise suppression, artifact reduction and structure preservation, in terms of improving the image quality of low-dose reconstructed images. In conclusion, we provided an overview of the use of deep learning approaches in low-dose CT image reconstruction together with their benefits, limitations, and opportunities for improvement.
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Feasibility of Deep Learning-Based Noise and Artifact Reduction in Coronal Reformation of Contrast-Enhanced Chest Computed Tomography. J Comput Assist Tomogr 2022; 46:593-603. [PMID: 35617647 DOI: 10.1097/rct.0000000000001326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE This study aimed to evaluate the feasibility of a deep learning method for imaging artifact and noise reduction in coronal reformation of contrast-enhanced chest computed tomography (CT). METHODS A total of 19,052 coronal reformatted chest CT images of 110 CT image sets (55 pairs of concordant 16- and 320-row CT image sets) were included and used to train a deep learning algorithm for artifact and noise correction. For internal validation, 4093 coronal reformatted CT images of 25 patients from 16-row CT images underwent correction processing. For external validation, chest CT images of 30 patients (1028 coronal reformatted CT images), acquired in other institutions using different scanners, were subjected to correction processing. For both validations, image quality was compared between original ("CTorigin") and deep learning-based corrected ("CTcorrect") CT images. Quantitative analysis for stair-step artifact (coefficient of variance of CT density on coronal reformation), image noise, signal-to-noise ratio, and contrast-to-noise ratio were evaluated. Subjective image quality scores were assigned for image contrast, artifact, and conspicuity of major structures. RESULTS CTcorrect showed significantly reduced stair-step artifact (mean coefficient of variance: CTorigin 7.35 ± 2.0 vs CTcorrect 5.17 ± 2.4, P < 0.001) and image noise and improved signal-to-noise ratio and contrast-to-noise ratio in the aorta, pulmonary artery, and liver, compared with those of CTorigin (P < 0.01). On subjective analysis, CTcorrect had higher image contrast, lower artifact, and better conspicuity than CTorigin. Most results of the external validation were consistent with those obtained from the internal validation, except for those concerning the pulmonary artery. CONCLUSIONS Deep learning-based artifact correction significantly improved the image quality of coronal reformation chest CT by reducing image noise and artifacts.
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Evaluation of Abdominal CT Obtained Using a Deep Learning-Based Image Reconstruction Engine Compared with CT Using Adaptive Statistical Iterative Reconstruction. J Belg Soc Radiol 2022; 106:15. [PMID: 35480337 PMCID: PMC8992765 DOI: 10.5334/jbsr.2638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 03/24/2022] [Indexed: 11/20/2022] Open
Abstract
Purpose: Materials and Methods: Results: Conclusions:
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Brendlin AS, Estler A, Plajer D, Lutz A, Grözinger G, Bongers MN, Tsiflikas I, Afat S, Artzner CP. AI Denoising Significantly Enhances Image Quality and Diagnostic Confidence in Interventional Cone-Beam Computed Tomography. Tomography 2022; 8:933-947. [PMID: 35448709 PMCID: PMC9031402 DOI: 10.3390/tomography8020075] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 03/15/2022] [Accepted: 03/18/2022] [Indexed: 11/16/2022] Open
Abstract
(1) To investigate whether interventional cone-beam computed tomography (cbCT) could benefit from AI denoising, particularly with respect to patient body mass index (BMI); (2) From 1 January 2016 to 1 January 2022, 100 patients with liver-directed interventions and peri-procedural cbCT were included. The unenhanced mask run and the contrast-enhanced fill run of the cbCT were reconstructed using weighted filtered back projection. Additionally, each dataset was post-processed using a novel denoising software solution. Place-consistent regions of interest measured signal-to-noise ratio (SNR) per dataset. Corrected mixed-effects analysis with BMI subgroup analyses compared objective image quality. Multiple linear regression measured the contribution of “Radiation Dose”, “Body-Mass-Index”, and “Mode” to SNR. Two radiologists independently rated diagnostic confidence. Inter-rater agreement was measured using Spearman correlation (r); (3) SNR was significantly higher in the denoised datasets than in the regular datasets (p < 0.001). Furthermore, BMI subgroup analysis showed significant SNR deteriorations in the regular datasets for higher patient BMI (p < 0.001), but stable results for denoising (p > 0.999). In regression, only denoising contributed positively towards SNR (0.6191; 95%CI 0.6096 to 0.6286; p < 0.001). The denoised datasets received overall significantly higher diagnostic confidence grades (p = 0.010), with good inter-rater agreement (r ≥ 0.795, p < 0.001). In a subgroup analysis, diagnostic confidence deteriorated significantly for higher patient BMI (p < 0.001) in the regular datasets but was stable in the denoised datasets (p ≥ 0.103).; (4) AI denoising can significantly enhance image quality in interventional cone-beam CT and effectively mitigate diagnostic confidence deterioration for rising patient BMI.
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Park HJ, Choi SY, Lee JE, Lim S, Lee MH, Yi BH, Cha JG, Min JH, Lee B, Jung Y. Deep learning image reconstruction algorithm for abdominal multidetector CT at different tube voltages: assessment of image quality and radiation dose in a phantom study. Eur Radiol 2022; 32:3974-3984. [DOI: 10.1007/s00330-021-08459-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 10/15/2021] [Accepted: 10/27/2021] [Indexed: 11/29/2022]
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AI Denoising Significantly Improves Image Quality in Whole-Body Low-Dose Computed Tomography Staging. Diagnostics (Basel) 2022; 12:diagnostics12010225. [PMID: 35054391 PMCID: PMC8774552 DOI: 10.3390/diagnostics12010225] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 01/13/2022] [Accepted: 01/14/2022] [Indexed: 12/10/2022] Open
Abstract
(1) Background: To evaluate the effects of an AI-based denoising post-processing software solution in low-dose whole-body computer tomography (WBCT) stagings; (2) Methods: From 1 January 2019 to 1 January 2021, we retrospectively included biometrically matching melanoma patients with clinically indicated WBCT staging from two scanners. The scans were reconstructed using weighted filtered back-projection (wFBP) and Advanced Modeled Iterative Reconstruction strength 2 (ADMIRE 2) at 100% and simulated 50%, 40%, and 30% radiation doses. Each dataset was post-processed using a novel denoising software solution. Five blinded radiologists independently scored subjective image quality twice with 6 weeks between readings. Inter-rater agreement and intra-rater reliability were determined with an intraclass correlation coefficient (ICC). An adequately corrected mixed-effects analysis was used to compare objective and subjective image quality. Multiple linear regression measured the contribution of “Radiation Dose”, “Scanner”, “Mode”, “Rater”, and “Timepoint” to image quality. Consistent regions of interest (ROI) measured noise for objective image quality; (3) Results: With good–excellent inter-rater agreement and intra-rater reliability (Timepoint 1: ICC ≥ 0.82, 95% CI 0.74–0.88; Timepoint 2: ICC ≥ 0.86, 95% CI 0.80–0.91; Timepoint 1 vs. 2: ICC ≥ 0.84, 95% CI 0.78–0.90; all p ≤ 0.001), subjective image quality deteriorated significantly below 100% for wFBP and ADMIRE 2 but remained good–excellent for the post-processed images, regardless of input (p ≤ 0.002). In regression analysis, significant increases in subjective image quality were only observed for higher radiation doses (≥0.78, 95%CI 0.63–0.93; p < 0.001), as well as for the post-processed images (≥2.88, 95%CI 2.72–3.03, p < 0.001). All post-processed images had significantly lower image noise than their standard counterparts (p < 0.001), with no differences between the post-processed images themselves. (4) Conclusions: The investigated AI post-processing software solution produces diagnostic images as low as 30% of the initial radiation dose (3.13 ± 0.75 mSv), regardless of scanner type or reconstruction method. Therefore, it might help limit patient radiation exposure, especially in the setting of repeated whole-body staging examinations.
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Deep learning versus iterative image reconstruction algorithm for head CT in trauma. Emerg Radiol 2022; 29:339-352. [PMID: 34984574 PMCID: PMC8917108 DOI: 10.1007/s10140-021-02012-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Accepted: 12/19/2021] [Indexed: 10/27/2022]
Abstract
PURPOSE To compare the image quality between a deep learning-based image reconstruction algorithm (DLIR) and an adaptive statistical iterative reconstruction algorithm (ASiR-V) in noncontrast trauma head CT. METHODS Head CT scans from 94 consecutive trauma patients were included. Images were reconstructed with ASiR-V 50% and the DLIR strengths: low (DLIR-L), medium (DLIR-M), and high (DLIR-H). The image quality was assessed quantitatively and qualitatively and compared between the different reconstruction algorithms. Inter-reader agreement was assessed by weighted kappa. RESULTS DLIR-M and DLIR-H demonstrated lower image noise (p < 0.001 for all pairwise comparisons), higher SNR of up to 82.9% (p < 0.001), and higher CNR of up to 53.3% (p < 0.001) compared to ASiR-V. DLIR-H outperformed other DLIR strengths (p ranging from < 0.001 to 0.016). DLIR-M outperformed DLIR-L (p < 0.001) and ASiR-V (p < 0.001). The distribution of reader scores for DLIR-M and DLIR-H shifted towards higher scores compared to DLIR-L and ASiR-V. There was a tendency towards higher scores with increasing DLIR strengths. There were fewer non-diagnostic CT series for DLIR-M and DLIR-H compared to ASiR-V and DLIR-L. No images were graded as non-diagnostic for DLIR-H regarding intracranial hemorrhage. The inter-reader agreement was fair-good between the second most and the less experienced reader, poor-moderate between the most and the less experienced reader, and poor-fair between the most and the second most experienced reader. CONCLUSION The image quality of trauma head CT series reconstructed with DLIR outperformed those reconstructed with ASiR-V. In particular, DLIR-M and DLIR-H demonstrated significantly improved image quality and fewer non-diagnostic images. The improvement in qualitative image quality was greater for the second most and the less experienced readers compared to the most experienced reader.
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Hasegawa A, Ishihara T, Thomas MA, Pan T. Noise reduction profile: A new method for evaluation of noise reduction techniques in CT. Med Phys 2021; 49:186-200. [PMID: 34837717 PMCID: PMC9300212 DOI: 10.1002/mp.15382] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Revised: 10/26/2021] [Accepted: 11/22/2021] [Indexed: 12/19/2022] Open
Abstract
Purpose Noise power spectrum (NPS) is a commonly used performance metric to evaluate noise‐reduction techniques (NRT) in imaging systems. The images reconstructed with and without an NRT can be compared via their NPS to better understand the NRT's effects on image noise. However, when comparing NPSs, simple visual assessments or a comparison of NPS peaks or medians are often used. These assessments make it difficult to objectively evaluate the effect of noise reduction across all spatial frequencies. In this work, we propose a new noise reduction profile (NRP) to facilitate a more complete and objective evaluation of NPSs for a range of NRTs used specifically in computed tomography (CT). Methods and materials The homogeneous section of the ACR or Catphan phantoms was scanned on different CT scanners equipped with the following NRTs: AIDR3D, AiCE, ASiR, ASiR‐V, TrueFidelity, iDose, SAFIRE, and ADMIRE. The images were then reconstructed with all strengths of each NRT in reference to the baseline filtered back projection (FBP) images. One set of the baseline FBP images was also processed with PixelShine, an NRT based on artificial intelligence. The NPSs of the images before and after noise reduction were calculated in both the xy‐plane and along the z‐direction. The difference in the logarithmic scale between each NPS (baseline FBP and NRT) was then calculated and deemed the NRP. Furthermore, the relationship between the NRP and NPS peak positions was mathematically analyzed. Results Each NRT has its own unique NRP. By comparing the NPS and NRP for each NRT, it was found that NRP is related to the peak shift of NPS. Additionally, under the assumption that the NPS has one peak and is differentiable, a relationship was mathematically derived between the slope of the NRP at the peak position of the NPS before noise reduction and the shift of the NPS peak position after noise reduction. Conclusions A new metric, NRP, was proposed based on NPS to objectively evaluate and compare methods for noise reduction in CT. The NRP can be used to compare the effects of various NRTs on image noise in both the xy‐plane and z‐direction. It also enables unbiased assessment of the detailed noise reduction properties of each NRT over all relevant spatial frequencies.
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Affiliation(s)
- Akira Hasegawa
- Department of Radiological Technology, National Cancer Center Japan, Tokyo, Japan.,AlgoMedica, Inc., Sunnyvale, California, USA
| | - Toshihiro Ishihara
- Department of Radiological Technology, National Cancer Center Japan, Tokyo, Japan
| | - M Allan Thomas
- Department of Imaging Physics, M.D. Anderson Cancer Center, University of Texas, Houston, Texas, USA
| | - Tinsu Pan
- Department of Imaging Physics, M.D. Anderson Cancer Center, University of Texas, Houston, Texas, USA
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Image quality in liver CT: low-dose deep learning vs standard-dose model-based iterative reconstructions. Eur Radiol 2021; 32:2865-2874. [PMID: 34821967 DOI: 10.1007/s00330-021-08380-0] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 09/15/2021] [Accepted: 10/04/2021] [Indexed: 10/19/2022]
Abstract
OBJECTIVES To compare the overall image quality and detectability of significant (malignant and pre-malignant) liver lesions of low-dose liver CT (LDCT, 33.3% dose) using deep learning denoising (DLD) to standard-dose CT (SDCT, 100% dose) using model-based iterative reconstruction (MBIR). METHODS In this retrospective study, CT images of 80 patients with hepatic focal lesions were included. For noninferiority analysis of overall image quality, a margin of - 0.5 points (scored in a 5-point scale) for the difference between scan protocols was pre-defined. Other quantitative or qualitative image quality assessments were performed. Additionally, detectability of significant liver lesions was compared, with 64 pairs of CT, using the jackknife alternative free-response ROC analysis, with noninferior margin defined by the lower limit of 95% confidence interval (CI) of the difference of figure-of-merit less than - 0.1. RESULTS The mean overall image quality scores with LDCT and SDCT were 3.77 ± 0.38 and 3.94 ± 0.34, respectively, demonstrating a difference of - 0.17 (95% CI: - 0.21 to - 0.12), which did not cross the predefined noninferiority margin of - 0.5. Furthermore, LDCT showed significantly superior quantitative results of liver lesion contrast to noise ratio (p < 0.05). However, although LDCT scored higher than the average score in qualitative image quality assessments, they were significantly lower than those of SDCT (p < 0.05). Figure-of-merit for lesion detection was 0.859 for LDCT and 0.878 for SDCT, showing noninferiority (difference: - 0.019, 95% CI: - 0.058 to 0.021). CONCLUSION LDCT using DLD with 67% radiation dose reduction showed non-inferior overall image quality and lesion detectability, compared to SDCT. KEY POINTS • Low-dose liver CT using deep learning denoising (DLD), at 67% dose reduction, provided non-inferior overall image quality compared to standard-dose CT using model-based iterative reconstruction (MBIR). • Low-dose CT using DLD showed significantly less noise and higher CNR lesion to liver than standard-dose CT using MBIR and demonstrated at least average image quality score among all readers, albeit with lower scores than standard-dose CT using MBIR. • Low-dose liver CT showed noninferior detectability for malignant and pre-malignant liver lesions, compared to standard-dose CT.
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Diagnosis of Cardiac Rehabilitation after Percutaneous Coronary Intervention in Acute Myocardial Infarction Patients by Emission Computed Tomography Image Features under Filtered Back Projection Reconstruction Algorithm. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:6844549. [PMID: 34777737 PMCID: PMC8578695 DOI: 10.1155/2021/6844549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 09/29/2021] [Accepted: 09/30/2021] [Indexed: 11/18/2022]
Abstract
This study aimed to explore the application value of emission computed tomography (ECT) imaging technology based on filtered back projection reconstruction algorithm (FBP) in cardiac function examination after percutaneous coronary intervention (PCI) in patients with acute myocardial infarction (AMI). Eighty patients with myocardial infarction diagnosed by medical history, electrocardiograph (ECG), and myocardial enzyme admitted to hospital from February 2018 to February 2019 were selected as the research objects. All patients underwent PCI seven days after the onset of myocardial infarction. ECT was performed for all patients before and after surgery. In addition, all ECT images were processed by the FBP reconstruction algorithm. On this basis, preoperative and postoperative cardiac surgery function and ischemia of the patients were diagnosed. Then, the diagnostic results were compared with the results of coronary angiography and echocardiogram. The results showed that all patients had a total of 541 segments before PCI surgery. ECT examination revealed 294 abnormal segments of the ventricular wall, with a total score of 585 points. A total of 100 segments were scored with 1 point, a total of 194 segments were scored with 2 points, and a total of 50 segments were scored with 3 points. After PCI, the number of abnormal segments was reduced to 58, with a total score of 193. There were 6 segments with a score of 1, 44 segments with a score of 2, and 5 segments with a score of 3. The left ventricular diastolic volume (EDV), left ventricular systolic volume (ESV), stroke volume (CO), and ejection fraction (EF) of the patients before the operation were 148 ± 16 mL, 77 ± 14.5 mL, 4.29 ± 0.37 L/min, and 41.9 ± 8%, respectively. The EDV, ESV, CO, and EF of the patients after surgery were 132 ± 16 mL, 62 ± 13 mL, 4.89 ± 0.71, and 53 ± 6%, respectively. Significant changes occurred in various systolic function parameters before and after surgery, P < 0.05. The standardized regression coefficients of the three groups were 0.32, 0.41, and 0.47, respectively, P < 0.05, which indicated that the greater the coronary artery stenosis rate, the higher the diagnostic coincidence rate of left anterior descending limb (LAD), left circumflex branch (LCX), and left coronary artery (RCA). The conformity of ECT imaging in the LCX group for diagnosis of myocardial ischemia was higher than that of UCG, P < 0.05. To sum up, the ECT technology based on the FBP reconstruction algorithm had a good application prospect in the diagnosis of cardiac function recovery in AMI patients after PCI.
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Yoon H, Kim J, Lim HJ, Lee MJ. Image quality assessment of pediatric chest and abdomen CT by deep learning reconstruction. BMC Med Imaging 2021; 21:146. [PMID: 34629049 PMCID: PMC8503996 DOI: 10.1186/s12880-021-00677-2] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 09/28/2021] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Efforts to reduce the radiation dose have continued steadily, with new reconstruction techniques. Recently, image denoising algorithms using artificial neural networks, termed deep learning reconstruction (DLR), have been applied to CT image reconstruction to overcome the drawbacks of iterative reconstruction (IR). The purpose of our study was to compare the objective and subjective image quality of DLR and IR on pediatric abdomen and chest CT images. METHODS This retrospective study included pediatric body CT images from February 2020 to October 2020, performed on 51 patients (34 boys and 17 girls; age 1-18 years). Non-contrast chest CT (n = 16), contrast-enhanced chest CT (n = 12), and contrast-enhanced abdomen CT (n = 23) images were included. Standard 50% adaptive statistical iterative reconstruction V (ASIR-V) images were compared to images with 100% ASIR-V and DLR at medium and high strengths. Attenuation, noise, contrast to noise ratio (CNR), and signal to noise (SNR) measurements were performed. Overall image quality, artifacts, and noise were subjectively assessed by two radiologists using a four-point scale (superior, average, suboptimal, and unacceptable). A phantom scan was performed including the dose range of the clinical images used in our study, and the noise power spectrum (NPS) was calculated. Quantitative and qualitative parameters were compared using repeated-measures analysis of variance (ANOVA) with Bonferroni correction and Wilcoxon signed-rank tests. RESULTS DLR had better CNR and SNR than 50% ASIR-V in both pediatric chest and abdomen CT images. When compared with 50% ASIR-V, high strength DLR was associated with noise reduction in non-contrast chest CT (33.0%), contrast-enhanced chest CT (39.6%), and contrast-enhanced abdomen CT (38.7%) with increases in CNR at 149.1%, 105.8%, and 53.1% respectively. The subjective assessment of overall image quality and the noise was also better on DLR images (p < 0.001). However, there was no significant difference in artifacts between reconstruction methods. From NPS analysis, DLR methods showed a pattern of reducing the magnitude of noise while maintaining the texture. CONCLUSION Compared with 50% ASIR-V, DLR improved pediatric body CT images with significant noise reduction. However, artifacts were not improved by DLR, regardless of strength.
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Affiliation(s)
- Haesung Yoon
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea
| | - Jisoo Kim
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea
| | - Hyun Ji Lim
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea
| | - Mi-Jung Lee
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea.
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Noda Y, Iritani Y, Kawai N, Miyoshi T, Ishihara T, Hyodo F, Matsuo M. Deep learning image reconstruction for pancreatic low-dose computed tomography: comparison with hybrid iterative reconstruction. Abdom Radiol (NY) 2021; 46:4238-4244. [PMID: 33973060 DOI: 10.1007/s00261-021-03111-x] [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: 01/24/2021] [Revised: 04/12/2021] [Accepted: 04/27/2021] [Indexed: 12/12/2022]
Abstract
PURPOSE To evaluate image quality, image noise, and conspicuity of pancreatic ductal adenocarcinoma (PDAC) in pancreatic low-dose computed tomography (LDCT) reconstructed using deep learning image reconstruction (DLIR) and compare with those of images reconstructed using hybrid iterative reconstruction (IR). METHODS Our institutional review board approved this prospective study. Written informed consent was obtained from all patients. Twenty-eight consecutive patients with PDAC undergoing chemotherapy (14 men and 14 women; mean age, 68.4 years) underwent pancreatic LDCT for therapy evaluation. The LDCT images were reconstructed using 40% adaptive statistical iterative reconstruction-Veo (hybrid-IR) and DLIR at medium and high levels (DLIR-M and DLIR-H). The image noise, diagnostic acceptability, and conspicuity of PDAC were qualitatively assessed using a 5-point scale. CT numbers of the abdominal aorta, portal vein, pancreas, PDAC, background noise, signal-to-noise ratio (SNR) of the anatomical structures, and tumor-to-pancreas contrast-to-noise ratio (CNR) were calculated. Qualitative and quantitative parameters were compared between the hybrid-IR, DLIR-M, and DLIR-H images. RESULTS CT dose-index volumes and dose-length product in pancreatic LDCT were 2.3 ± 1.0 mGy and 74.9 ± 37.0 mGy•cm, respectively. The image noise, diagnostic acceptability, and conspicuity of PDAC were significantly better in DLIR-H than those in hybrid-IR and DLIR-M (all P < 0.001). The background noise was significantly lower in the DLIR-H images (P < 0.001) and resulted in improved SNRs (P < 0.001) and CNR (P < 0.001) compared with those in the hybrid-IR and DLIR-M images. CONCLUSION DLIR significantly reduced image noise and improved image quality in pancreatic LDCT images compared with hybrid-IR.
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Choi H, Chang W, Kim JH, Ahn C, Lee H, Kim HY, Cho J, Lee YJ, Kim YH. Dose reduction potential of vendor-agnostic deep learning model in comparison with deep learning-based image reconstruction algorithm on CT: a phantom study. Eur Radiol 2021; 32:1247-1255. [PMID: 34390372 PMCID: PMC8364308 DOI: 10.1007/s00330-021-08199-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 06/11/2021] [Accepted: 07/02/2021] [Indexed: 12/25/2022]
Abstract
Objectives To compare the dose reduction potential (DRP) of a vendor-agnostic deep learning model (DLM, ClariCT.AI) with that of a vendor-specific deep learning–based image reconstruction algorithm (DLR, TrueFidelity™). Methods Computed tomography (CT) images of a multi-sized image quality phantom (Mercury v4.0) were acquired under six radiation dose levels (0.48/0.97/1.93/3.87/7.74/15.47 mGy) and were reconstructed using filtered back projection (FBP) and three strength levels of the DLR (low/medium/high). The FBP images were denoised using the DLM. For all DLM and DLR images, the detectability index (d′) (a task-based detection performance metric) was obtained, under various combinations of three target sizes (10/5/1 mm), five inlets (CT value difference with the background; −895/50/90/335/1000 HU), five phantom diameters (36/31/26/21/16 cm), and six radiation dose levels. Dose reduction potential (DRP) measures the dose reduction made by using DLM or DLR, while yielding d′ equivalent to that of FBP at full dose. Results The DRPs of the DLM, DLR-low, DLR-medium, and DLR-high were 86% (81–88%), 60% (46–67%), 76% (60–81%), and 87% (78–92%), respectively. For 10-mm targets, the DRP of the DLM (87%) was higher than that of all DLR algorithms (58–86%). However, for smaller targets (5 mm/1 mm), the DRPs of the DLR-high (89/88%) were greater than those of the DLM (87/84%). Conclusion The dose reduction potential of the vendor-agnostic DLM was shown to be comparable to that of the vendor-specific DLR at high strength and superior to those of the DLRs at medium and low strengths. Key Points • DRP of the vendor-agnostic model was comparable to that of high-strength vendor-specific model and superior to those of medium- and low-strength models. • Under various radiation dose levels, the deep learning model shows higher detectability indexes compared to FBP. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-021-08199-9.
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Affiliation(s)
- Hyunsu Choi
- Department of Radiology, Seoul National University Bundang Hospital, 82, Gumi-ro-173-beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, Republic of Korea
| | - Won Chang
- Department of Radiology, Seoul National University Bundang Hospital, 82, Gumi-ro-173-beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, Republic of Korea.
| | - Jong Hyo Kim
- Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center, 101 Daehak-ro, Jongno-gu, Seoul, Republic of Korea
- Department of Transdisciplinary Studies, Program in Biomedical Radiation Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea
| | - Chulkyun Ahn
- Department of Transdisciplinary Studies, Program in Biomedical Radiation Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea
| | - Heejin Lee
- Department of Applied bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea
| | - Hae Young Kim
- Department of Radiology, Seoul National University Bundang Hospital, 82, Gumi-ro-173-beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, Republic of Korea
| | - Jungheum Cho
- Department of Radiology, Seoul National University Bundang Hospital, 82, Gumi-ro-173-beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, Republic of Korea
| | - Yoon Jin Lee
- Department of Radiology, Seoul National University Bundang Hospital, 82, Gumi-ro-173-beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, Republic of Korea
| | - Young Hoon Kim
- Department of Radiology, Seoul National University Bundang Hospital, 82, Gumi-ro-173-beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, Republic of Korea
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Hasegawa A, Ishihara T, Allan Thomas M, Pan T. Scanner dependence of adaptive statistical iterative reconstruction with 3D noise power spectrum central frequency and noise magnitude ratios. Med Phys 2021; 48:4993-5003. [PMID: 34287936 DOI: 10.1002/mp.15104] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 06/27/2021] [Accepted: 06/27/2021] [Indexed: 11/09/2022] Open
Abstract
PURPOSE In this study, the noise reduction properties of the adaptive statistical iterative reconstruction (IR) on two different CT scanners of 64 and 256-slice were compared and their differences were assessed. METHODS AND MATERIALS The homogeneous module of the ACR CT phantom was scanned on the 64 and 256 slices CT scanners from the same vendor in the range of 15-40 mA. On each scanner, the data were reconstructed using filtered back projection (FBP) and at all strengths of IR with the STANDARD kernel. For each reconstruction, a 3D noise power spectrum (NPS) was calculated and the central frequency ratio in the xy plane (CFRxy ), CFR in the z-direction (CFRz ), and noise magnitude ratio (NMR) were derived. CFR is the central frequency ratio of NPS between the denoised image and the FBP image, and NMR is the ratio of the areas under the NPS curves. Ideally, both CFRxy and CFRz should be near 1, indicating minimal texture changes in both xy and z directions, while NMR should be as close to 0 as possible, indicating more noise reduction. RESULTS When comparing strengths with equivalent impact on noise texture, IR on the 64-slice reduced the noise magnitude in the xy plane more than that on the 256-slice. In the z-direction, the IR on the 256-slice produced a central frequency shift on the 256-slice but not on the 64-slice. In addition, the noise reduction effects of the IR on the 256-slice were affected when radiation exposure was below 2.0 mGy, but there was no observable dose-dependence on the 64-slice. CONCLUSIONS Our noise property analysis revealed that iterative reconstructions on different scanner platforms from the same vendor can be distinct, with unique effects on the noise texture and magnitude in CT images. The IR on a 64-slice scanner provides slightly enhanced noise reduction and maintains a noise reduction rate independent of dose, unlike the one on a 256-slice scanner. Notably, the IR on the 64-slice scanner was a 2D noise reduction technique (NRT), while the one on the 256-slice was a 3D NRT. These observations showcase the impact of different NRTs on clinical CT images, even when comparing the same NRT on different scanners.
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Affiliation(s)
- Akira Hasegawa
- Department of Radiological Technology, National Cancer Center Japan, Tokyo, Japan.,AlgoMedica, Inc., Sunnyvale, California, USA
| | - Toshihiro Ishihara
- Department of Radiological Technology, National Cancer Center Japan, Tokyo, Japan
| | - Matthew Allan Thomas
- Department of Imaging Physics, M.D. Anderson Cancer Center, University of Texas, Houston, Texas, USA
| | - Tinsu Pan
- Department of Imaging Physics, M.D. Anderson Cancer Center, University of Texas, Houston, Texas, USA
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Lyu P, Neely B, Solomon J, Rigiroli F, Ding Y, Schwartz FR, Thomsen B, Lowry C, Samei E, Marin D. Effect of deep learning image reconstruction in the prediction of resectability of pancreatic cancer: Diagnostic performance and reader confidence. Eur J Radiol 2021; 141:109825. [PMID: 34144309 DOI: 10.1016/j.ejrad.2021.109825] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 05/11/2021] [Accepted: 06/09/2021] [Indexed: 11/28/2022]
Abstract
OBJECTIVE To assess the diagnostic performance and reader confidence in determining the resectability of pancreatic cancer at computed tomography (CT) using a new deep learning image reconstruction (DLIR) algorithm. METHODS A retrospective review was conduct of on forty-seven patients with pathologically confirmed pancreatic cancers who underwent baseline multiphasic contrast-enhanced CT scan. Image data sets were reconstructed using filtered back projection (FBP), hybrid model-based adaptive statistical iterative reconstruction (ASiR-V) 60 %, and DLIR "TrueFidelity" at low(L), medium(M), and high strength levels(H). Four board-certified abdominal radiologists reviewed the CT images and classified cancers as resectable, borderline resectable, or unresectable. Diagnostic performance and reader confidence for categorizing the resectability of pancreatic cancer were evaluated based on the reference standards, and the interreader agreement was assessed using Fleiss k statistics. RESULTS For prediction of margin-negative resections(ie, R0), the average area under the receiver operating characteristic curve was significantly higher with DLIR-H (0.91; 95 % confidence interval [CI]: 0.79, 0.98) than FBP (0.75; 95 % CI:0.60, 0.86) and ASiR-V (0.81; 95 % CI:0.67, 0.91) (p = 0.030 and 0.023 respectively). Reader confidence scores were significantly better using DLIR compared to FBP and ASiR-V 60 % and increased linearly with the increase of DLIR strength level (all p < 0.001). Among the image reconstructions, DLIR-H showed the highest interreader agreement in the resectability classification and lowest subject variability in the reader confidence. CONCLUSIONS The DLIR-H algorithm may improve the diagnostic performance and reader confidence in the CT assignment of the local resectability of pancreatic cancer while reducing the interreader variability.
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Affiliation(s)
- Peijie Lyu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China; Department of Radiology, Duke University Medical Center, Durham, NC, USA.
| | - Ben Neely
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
| | - Justin Solomon
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, 2424 Erwin Road, Suite 302, Durham, NC, USA
| | - Francesca Rigiroli
- Department of Radiology, Duke University Medical Center, Durham, NC, USA
| | - Yuqin Ding
- Department of Radiology, Duke University Medical Center, Durham, NC, USA; Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai, China
| | | | - Brian Thomsen
- Senior Research Manager, CT, GE Healthcare, 3000 N Grandview Blvd, Waukesha, WI, USA
| | - Carolyn Lowry
- Duke Imaging Services Cary Parkway, Duke University Health System, INC, 3700 NW Cary Parkway Suite120, Cary, NC, USA
| | - Ehsan Samei
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, 2424 Erwin Road, Suite 302, Durham, NC, USA
| | - Daniele Marin
- Department of Radiology, Duke University Medical Center, Durham, NC, USA
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Sinogram-based deep learning image reconstruction technique in abdominal CT: image quality considerations. Eur Radiol 2021; 31:8342-8353. [PMID: 33893535 DOI: 10.1007/s00330-021-07952-4] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 03/09/2021] [Accepted: 03/26/2021] [Indexed: 10/21/2022]
Abstract
OBJECTIVES To investigate the image quality and perception of a sinogram-based deep learning image reconstruction (DLIR) algorithm for single-energy abdominal CT compared to standard-of-care strength of ASIR-V. METHODS In this retrospective study, 50 patients (62% F; 56.74 ± 17.05 years) underwent portal venous phase. Four reconstructions (ASIR-V at 40%, and DLIR at three strengths: low (DLIR-L), medium (DLIR-M), and high (DLIR-H)) were generated. Qualitative and quantitative image quality analysis was performed on the 200 image datasets. Qualitative scores were obtained for image noise, contrast, small structure visibility, sharpness, and artifact by three blinded radiologists on a 5-point scale (1, excellent; 5, very poor). Radiologists also indicated image preference on a 3-point scale (1, most preferred; 3, least preferred). Quantitative assessment was performed by measuring image noise and contrast-to-noise ratio (CNR). RESULTS DLIR had better image quality scores compared to ASIR-V. Scores on DLIR-H for noise (1.40 ± 0.53), contrast (1.41 ± 0.55), small structure visibility (1.51 ± 0.61), and sharpness (1.60 ± 0.54) were the best (p < 0.05) followed by DLIR-M (1.85 ± 0.52, 1.66 ± 0.57, 1.69 ± 0.59, 1.68 ± 0.46), DLIR-L (2.29 ± 0.58, 1.96 ± 0.61, 1.90 ± 0.65, 1.86 ± 0.46), and ASIR-V (2.86 ± 0.67, 2.55 ± 0.58, 2.34 ± 0.66, 2.01 ± 0.36). Ratings for artifacts were similar for all reconstructions (p > 0.05). DLIRs did not influence subjective textural perceptions and were preferred over ASIR-V from the beginning. All DLIRs had a higher CNR (26.38-102.30%) and lower noise (20.64-48.77%) than ASIR-V. DLIR-H had the best objective scores. CONCLUSION Sinogram-based deep learning image reconstructions were preferred over iterative reconstruction subjectively and objectively due to improved image quality and lower noise, even in large patients. Use in clinical routine may allow for radiation dose reduction. KEY POINTS • Deep learning image reconstructions (DLIRs) have a higher contrast-to-noise ratio compared to medium-strength hybrid iterative reconstruction techniques. • DLIR may be advantageous in patients with large body habitus due to a lower image noise. • DLIR can enable further optimization of radiation doses used in abdominal CT.
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Deep learning-based denoising algorithm in comparison to iterative reconstruction and filtered back projection: a 12-reader phantom study. Eur Radiol 2021; 31:8755-8764. [PMID: 33885958 DOI: 10.1007/s00330-021-07810-3] [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: 09/25/2020] [Revised: 01/02/2021] [Accepted: 02/17/2021] [Indexed: 10/21/2022]
Abstract
OBJECTIVES (1) To compare low-contrast detectability of a deep learning-based denoising algorithm (DLA) with ADMIRE and FBP, and (2) to compare image quality parameters of DLA with those of reconstruction methods from two different CT vendors (ADMIRE, IMR, and FBP). MATERIALS AND METHODS Using abdominal CT images of 100 patients reconstructed via ADMIRE and FBP, we trained DLA by feeding FBP images as input and ADMIRE images as the ground truth. To measure the low-contrast detectability, the randomized repeat scans of Catphan® phantom were performed under various conditions of radiation exposures. Twelve radiologists evaluated the presence/absence of a target on a five-point confidence scale. The multi-reader multi-case area under the receiver operating characteristic curve (AUC) was calculated, and non-inferiority tests were performed. Using American College of Radiology CT accreditation phantom, contrast-to-noise ratio, target transfer function, noise magnitude, and detectability index (d') of DLA, ADMIRE, IMR, and FBPs were computed. RESULTS The AUC of DLA in low-contrast detectability was non-inferior to that of ADMIRE (p < .001) and superior to that of FBP (p < .001). DLA improved the image quality in terms of all physical measurements compared to FBPs from both CT vendors and showed profiles of physical measurements similar to those of ADMIRE. CONCLUSIONS The low-contrast detectability of the proposed deep learning-based denoising algorithm was non-inferior to that of ADMIRE and superior to that of FBP. The DLA could successfully improve image quality compared with FBP while showing the similar physical profiles of ADMIRE. KEY POINTS • Low-contrast detectability in the images denoised using the deep learning algorithm was non-inferior to that in the images reconstructed using standard algorithms. • The proposed deep learning algorithm showed similar profiles of physical measurements to advanced iterative reconstruction algorithm (ADMIRE).
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Abstract
Abstract
Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy.
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Yan C, Lin J, Li H, Xu J, Zhang T, Chen H, Woodruff HC, Wu G, Zhang S, Xu Y, Lambin P. Cycle-Consistent Generative Adversarial Network: Effect on Radiation Dose Reduction and Image Quality Improvement in Ultralow-Dose CT for Evaluation of Pulmonary Tuberculosis. Korean J Radiol 2021; 22:983-993. [PMID: 33739634 PMCID: PMC8154783 DOI: 10.3348/kjr.2020.0988] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2020] [Revised: 11/22/2020] [Accepted: 12/21/2020] [Indexed: 01/15/2023] Open
Abstract
Objective To investigate the image quality of ultralow-dose CT (ULDCT) of the chest reconstructed using a cycle-consistent generative adversarial network (CycleGAN)-based deep learning method in the evaluation of pulmonary tuberculosis. Materials and Methods Between June 2019 and November 2019, 103 patients (mean age, 40.8 ± 13.6 years; 61 men and 42 women) with pulmonary tuberculosis were prospectively enrolled to undergo standard-dose CT (120 kVp with automated exposure control), followed immediately by ULDCT (80 kVp and 10 mAs). The images of the two successive scans were used to train the CycleGAN framework for image-to-image translation. The denoising efficacy of the CycleGAN algorithm was compared with that of hybrid and model-based iterative reconstruction. Repeated-measures analysis of variance and Wilcoxon signed-rank test were performed to compare the objective measurements and the subjective image quality scores, respectively. Results With the optimized CycleGAN denoising model, using the ULDCT images as input, the peak signal-to-noise ratio and structural similarity index improved by 2.0 dB and 0.21, respectively. The CycleGAN-generated denoised ULDCT images typically provided satisfactory image quality for optimal visibility of anatomic structures and pathological findings, with a lower level of image noise (mean ± standard deviation [SD], 19.5 ± 3.0 Hounsfield unit [HU]) than that of the hybrid (66.3 ± 10.5 HU, p < 0.001) and a similar noise level to model-based iterative reconstruction (19.6 ± 2.6 HU, p > 0.908). The CycleGAN-generated images showed the highest contrast-to-noise ratios for the pulmonary lesions, followed by the model-based and hybrid iterative reconstruction. The mean effective radiation dose of ULDCT was 0.12 mSv with a mean 93.9% reduction compared to standard-dose CT. Conclusion The optimized CycleGAN technique may allow the synthesis of diagnostically acceptable images from ULDCT of the chest for the evaluation of pulmonary tuberculosis.
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Affiliation(s)
- Chenggong Yan
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China.,The D-Lab, Department of Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Jie Lin
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Haixia Li
- Clinical and Technical Solution, Philips Healthcare, Guangzhou, China
| | - Jun Xu
- Department of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Tianjing Zhang
- Clinical and Technical Solution, Philips Healthcare, Guangzhou, China
| | - Hao Chen
- Jiangsu JITRI Sioux Technologies Co., Ltd., Suzhou, China
| | - Henry C Woodruff
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.,Department of Radiology and Nuclear Imaging, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Guangyao Wu
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Siqi Zhang
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yikai Xu
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China.
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.,Department of Radiology and Nuclear Imaging, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
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Addressing signal alterations induced in CT images by deep learning processing: A preliminary phantom study. Phys Med 2021; 83:88-100. [DOI: 10.1016/j.ejmp.2021.02.022] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 02/19/2021] [Accepted: 02/23/2021] [Indexed: 12/13/2022] Open
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Shiri I, Akhavanallaf A, Sanaat A, Salimi Y, Askari D, Mansouri Z, Shayesteh SP, Hasanian M, Rezaei-Kalantari K, Salahshour A, Sandoughdaran S, Abdollahi H, Arabi H, Zaidi H. Ultra-low-dose chest CT imaging of COVID-19 patients using a deep residual neural network. Eur Radiol 2021; 31:1420-1431. [PMID: 32879987 PMCID: PMC7467843 DOI: 10.1007/s00330-020-07225-6] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 08/13/2020] [Accepted: 08/21/2020] [Indexed: 02/07/2023]
Abstract
OBJECTIVES The current study aimed to design an ultra-low-dose CT examination protocol using a deep learning approach suitable for clinical diagnosis of COVID-19 patients. METHODS In this study, 800, 170, and 171 pairs of ultra-low-dose and full-dose CT images were used as input/output as training, test, and external validation set, respectively, to implement the full-dose prediction technique. A residual convolutional neural network was applied to generate full-dose from ultra-low-dose CT images. The quality of predicted CT images was assessed using root mean square error (RMSE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR). Scores ranging from 1 to 5 were assigned reflecting subjective assessment of image quality and related COVID-19 features, including ground glass opacities (GGO), crazy paving (CP), consolidation (CS), nodular infiltrates (NI), bronchovascular thickening (BVT), and pleural effusion (PE). RESULTS The radiation dose in terms of CT dose index (CTDIvol) was reduced by up to 89%. The RMSE decreased from 0.16 ± 0.05 to 0.09 ± 0.02 and from 0.16 ± 0.06 to 0.08 ± 0.02 for the predicted compared with ultra-low-dose CT images in the test and external validation set, respectively. The overall scoring assigned by radiologists showed an acceptance rate of 4.72 ± 0.57 out of 5 for reference full-dose CT images, while ultra-low-dose CT images rated 2.78 ± 0.9. The predicted CT images using the deep learning algorithm achieved a score of 4.42 ± 0.8. CONCLUSIONS The results demonstrated that the deep learning algorithm is capable of predicting standard full-dose CT images with acceptable quality for the clinical diagnosis of COVID-19 positive patients with substantial radiation dose reduction. KEY POINTS • Ultra-low-dose CT imaging of COVID-19 patients would result in the loss of critical information about lesion types, which could potentially affect clinical diagnosis. • Deep learning-based prediction of full-dose from ultra-low-dose CT images for the diagnosis of COVID-19 could reduce the radiation dose by up to 89%. • Deep learning algorithms failed to recover the correct lesion structure/density for a number of patients considered outliers, and as such, further research and development is warranted to address these limitations.
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Affiliation(s)
- Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Azadeh Akhavanallaf
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Amirhossein Sanaat
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Dariush Askari
- Department of Radiology Technology, Shahid Beheshti University of Medical, Tehran, Iran
| | - Zahra Mansouri
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Sajad P Shayesteh
- Department of Physiology, Pharmacology and Medical Physics, Alborz University of Medical Sciences, Karaj, Iran
| | - Mohammad Hasanian
- Department of Radiology, Arak University of Medical Sciences, Arak, Iran
| | - Kiara Rezaei-Kalantari
- Rajaie Cardiovascular, Medical & Research Center, Iran University of Medical Science, Tehran, Iran
| | - Ali Salahshour
- Department of Radiology, Alborz University of Medical Sciences, Karaj, Iran
| | - Saleh Sandoughdaran
- Department of Radiation Oncology, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hamid Abdollahi
- Department of Radiologic Sciences and Medical Physics, Faculty of Allied Medicine, Kerman University of Medical sciences, Kerman, Iran
| | - Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland.
- Geneva University Neurocenter, Geneva University, CH-1205, Geneva, Switzerland.
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands.
- Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
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Zeng L, Xu X, Zeng W, Peng W, Zhang J, Sixian H, Liu K, Xia C, Li Z. Deep learning trained algorithm maintains the quality of half-dose contrast-enhanced liver computed tomography images: Comparison with hybrid iterative reconstruction: Study for the application of deep learning noise reduction technology in low dose. Eur J Radiol 2021; 135:109487. [PMID: 33418383 DOI: 10.1016/j.ejrad.2020.109487] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 12/14/2020] [Accepted: 12/17/2020] [Indexed: 02/05/2023]
Abstract
PURPOSE This study compares the image and diagnostic qualities of a DEep Learning Trained Algorithm (DELTA) for half-dose contrast-enhanced liver computed tomography (CT) with those of a commercial hybrid iterative reconstruction (HIR) method used for standard-dose CT (SDCT). METHODS This study enrolled 207 adults, and they were divided into two groups: SDCT and low-dose CT (LDCT). SDCT was reconstructed using the HIR method (SDCTHIR), and LDCT was reconstructed using both the HIR method (LDCTHIR) and DELTA (LDCTDL). Noise, Hounsfield unit (HU) values, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were compared between three image series. Two radiologists assessed the noise, artefacts, overall image quality, visualisation of critical anatomical structures and lesion detection, characterisation and visualisation. RESULTS The mean effective doses were 5.64 ± 1.96 mSv for SDCT and 2.87 ± 0.87 mSv for LDCT. The noise of LDCTDL was significantly lower than that of SDCTHIR and LDCTHIR. The SNR and CNR of LDCTDL were significantly higher than those of the other two groups. The overall image quality, visualisation of anatomical structures and lesion visualisation between LDCTDL and SDCTHIR were not significantly different. For lesion detection, the sensitivities and specificities of SDCTHIR vs. LDCTDL were 81.9 % vs. 83.7 % and 89.1 % vs. 86.3 %, respectively, on a per-patient basis. SDCTHIR showed 75.4 % sensitivity and 82.6 % specificity for lesion characterisation on a per-patient basis, whereas LDCTDL showed 73.5 % sensitivity and 82.4 % specificity. CONCLUSIONS LDCT with DELTA had approximately 49 % dose reduction compared with SDCT with HIR while maintaining image quality on contrast-enhanced liver CT.
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Affiliation(s)
- Lingming Zeng
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Xu Xu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Wen Zeng
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Wanlin Peng
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Jinge Zhang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Hu Sixian
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Keling Liu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Chunchao Xia
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Zhenlin Li
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.
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Steuwe A, Weber M, Bethge OT, Rademacher C, Boschheidgen M, Sawicki LM, Antoch G, Aissa J. Influence of a novel deep-learning based reconstruction software on the objective and subjective image quality in low-dose abdominal computed tomography. Br J Radiol 2021; 94:20200677. [PMID: 33095654 DOI: 10.1259/bjr.20200677] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVES Modern reconstruction and post-processing software aims at reducing image noise in CT images, potentially allowing for a reduction of the employed radiation exposure. This study aimed at assessing the influence of a novel deep-learning based software on the subjective and objective image quality compared to two traditional methods [filtered back-projection (FBP), iterative reconstruction (IR)]. METHODS In this institutional review board-approved retrospective study, abdominal low-dose CT images of 27 patients (mean age 38 ± 12 years, volumetric CT dose index 2.9 ± 1.8 mGy) were reconstructed with IR, FBP and, furthermore, post-processed using a novel software. For the three reconstructions, qualitative and quantitative image quality was evaluated by means of CT numbers, noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) in six different ROIs. Additionally, the reconstructions were compared using SNR, peak SNR, root mean square error and mean absolute error to assess structural differences. RESULTS On average, CT numbers varied within 1 Hounsfield unit (HU) for the three assessed methods in the assessed ROIs. In soft tissue, image noise was up to 42% lower compared to FBP and up to 27% lower to IR when applying the novel software. Consequently, SNR and CNR were highest with the novel software. For both IR and the novel software, subjective image quality was equal but higher than the image quality of FBP-images. CONCLUSION The assessed software reduces image noise while maintaining image information, even in comparison to IR, allowing for a potential dose reduction of approximately 20% in abdominal CT imaging. ADVANCES IN KNOWLEDGE The assessed software reduces image noise by up to 27% compared to IR and 48% compared to FBP while maintaining the image information.The reduced image noise allows for a potential dose reduction of approximately 20% in abdominal imaging.
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Affiliation(s)
- Andrea Steuwe
- Department of Diagnostic and Interventional Radiology, University Dusseldorf, Medical Faculty, Dusseldorf, Germany
| | - Marie Weber
- Department of Diagnostic and Interventional Radiology, University Dusseldorf, Medical Faculty, Dusseldorf, Germany
| | - Oliver Thomas Bethge
- Department of Diagnostic and Interventional Radiology, University Dusseldorf, Medical Faculty, Dusseldorf, Germany
| | - Christin Rademacher
- Department of Diagnostic and Interventional Radiology, University Dusseldorf, Medical Faculty, Dusseldorf, Germany
| | - Matthias Boschheidgen
- Department of Diagnostic and Interventional Radiology, University Dusseldorf, Medical Faculty, Dusseldorf, Germany
| | - Lino Morris Sawicki
- Department of Diagnostic and Interventional Radiology, University Dusseldorf, Medical Faculty, Dusseldorf, Germany
| | - Gerald Antoch
- Department of Diagnostic and Interventional Radiology, University Dusseldorf, Medical Faculty, Dusseldorf, Germany
| | - Joel Aissa
- Department of Diagnostic and Interventional Radiology, University Dusseldorf, Medical Faculty, Dusseldorf, Germany
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Brady SL, Trout AT, Somasundaram E, Anton CG, Li Y, Dillman JR. Improving Image Quality and Reducing Radiation Dose for Pediatric CT by Using Deep Learning Reconstruction. Radiology 2020; 298:180-188. [PMID: 33201790 DOI: 10.1148/radiol.2020202317] [Citation(s) in RCA: 72] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Background CT deep learning reconstruction (DLR) algorithms have been developed to remove image noise. How the DLR affects image quality and radiation dose reduction has yet to be fully investigated. Purpose To investigate a DLR algorithm's dose reduction and image quality improvement for pediatric CT. Materials and Methods DLR was compared with filtered back projection (FBP), statistical-based iterative reconstruction (SBIR), and model-based iterative reconstruction (MBIR) in a retrospective study by using data from CT examinations of pediatric patients (February to December 2018). A comparison of object detectability for 15 objects (diameter, 0.5-10 mm) at four contrast difference levels (50, 150, 250, and 350 HU) was performed by using a non-prewhitening-matched mathematical observer model with eye filter (d'NPWE), task transfer function, and noise power spectrum analysis. Object detectability was assessed by using area under the curve analysis. Three pediatric radiologists performed an observer study to assess anatomic structures with low object-to-background signal and contrast to noise in the azygos vein, right hepatic vein, common bile duct, and superior mesenteric artery. Observers rated from 1 to 10 (worst to best) for edge definition, quantum noise level, and object conspicuity. Analysis of variance and Tukey honest significant difference post hoc tests were used to analyze differences between reconstruction algorithms. Results Images from 19 patients (mean age, 11 years ± 5 [standard deviation]; 10 female patients) were evaluated. Compared with FBP, SBIR, and MBIR, DLR demonstrated improved object detectability by 51% (16.5 of 10.9), 18% (16.5 of 13.9), and 11% (16.5 of 14.8), respectively. DLR reduced image noise without noise texture effects seen with MBIR. Radiologist ratings were 7 ± 1 (DLR), 6.2 ± 1 (MBIR), 6.2 ± 1 (SBIR), and 4.6 ± 1 (FBP); two-way analysis of variance showed a difference on the basis of reconstruction type (P < .001). Radiologists consistently preferred DLR images (intraclass correlation coefficient, 0.89; 95% CI: 0.83, 0.93). DLR demonstrated 52% (1 of 2.1) greater dose reduction than SBIR. Conclusion The DLR algorithm improved image quality and dose reduction without sacrificing noise texture and spatial resolution. © RSNA, 2020 Online supplemental material is available for this article.
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Affiliation(s)
- Samuel L Brady
- From the Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333, Burnet Ave, Cincinnati, OH 45329; and Department of Radiology, University of Cincinnati Medical School, Cincinnati, Ohio
| | - Andrew T Trout
- From the Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333, Burnet Ave, Cincinnati, OH 45329; and Department of Radiology, University of Cincinnati Medical School, Cincinnati, Ohio
| | - Elanchezhian Somasundaram
- From the Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333, Burnet Ave, Cincinnati, OH 45329; and Department of Radiology, University of Cincinnati Medical School, Cincinnati, Ohio
| | - Christopher G Anton
- From the Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333, Burnet Ave, Cincinnati, OH 45329; and Department of Radiology, University of Cincinnati Medical School, Cincinnati, Ohio
| | - Yinan Li
- From the Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333, Burnet Ave, Cincinnati, OH 45329; and Department of Radiology, University of Cincinnati Medical School, Cincinnati, Ohio
| | - Jonathan R Dillman
- From the Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333, Burnet Ave, Cincinnati, OH 45329; and Department of Radiology, University of Cincinnati Medical School, Cincinnati, Ohio
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CT iterative vs deep learning reconstruction: comparison of noise and sharpness. Eur Radiol 2020; 31:3156-3164. [PMID: 33057781 DOI: 10.1007/s00330-020-07358-8] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 08/26/2020] [Accepted: 09/25/2020] [Indexed: 10/23/2022]
Abstract
OBJECTIVES To compare image noise and sharpness of vessels, liver, and muscle in lower extremity CT angiography between "adaptive statistical iterative reconstruction-V" (ASIR-V) and deep learning reconstruction "TrueFidelity" (TFI). METHODS Thirty-seven patients (mean age, 65.2 years; 32 men) with lower extremity CT angiography were enrolled between November and December 2019. Images were reconstructed with two ASIR-V (blending factor of 80% and 100% (AV-100)) and three TFI (low-, medium-, and high-strength-level (TF-H) settings). Two radiologists evaluated these images for vessels (aorta, femoral artery, and popliteal artery), liver, and psoas muscle. For quantitative analyses, conventional indicators (CT number, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR)) and blur metric values (indicating the degree of image sharpness) of selected regions of interest were determined. For qualitative analyses, the degrees of quantum mottle and blurring were assessed. RESULTS The higher the blending factor in ASIR-V or the strength in TFI, the lower the noise, the higher the SNR and CNR values, and the higher the blur metric values in all structures. The SNR and CNR values of TF-H images were significantly higher than those of AV-80 images and similar to those of AV-100 images. The blur metric values in TFI images were significantly lower than those in ASIR-V images (p < 0.001), indicating increased sharpness. Among all the investigated image procedures, the overall qualitative image quality was best in TF-H images. CONCLUSION TF-H was the most balanced image in terms of image noise and sharpness among the examined image combinations. KEY POINTS • Deep learning image reconstruction "TrueFidelity" is superior to iterative reconstruction "ASIR-V" regarding image noise and sharpness. • The high-strength "TrueFidelity" approach generated the best image quality among the examined image reconstruction procedures. • In iterative and deep learning CT image reconstruction, the higher the blending and strength factors, the lower the image noise and the poorer the image sharpness.
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Kim JH, Yoon HJ, Lee E, Kim I, Cha YK, Bak SH. Validation of Deep-Learning Image Reconstruction for Low-Dose Chest Computed Tomography Scan: Emphasis on Image Quality and Noise. Korean J Radiol 2020; 22:131-138. [PMID: 32729277 PMCID: PMC7772377 DOI: 10.3348/kjr.2020.0116] [Citation(s) in RCA: 80] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Revised: 04/20/2020] [Accepted: 05/18/2020] [Indexed: 12/16/2022] Open
Abstract
OBJECTIVE Iterative reconstruction degrades image quality. Thus, further advances in image reconstruction are necessary to overcome some limitations of this technique in low-dose computed tomography (LDCT) scan of the chest. Deep-learning image reconstruction (DLIR) is a new method used to reduce dose while maintaining image quality. The purposes of this study was to evaluate image quality and noise of LDCT scan images reconstructed with DLIR and compare with those of images reconstructed with the adaptive statistical iterative reconstruction-Veo at a level of 30% (ASiR-V 30%). MATERIALS AND METHODS This retrospective study included 58 patients who underwent LDCT scan for lung cancer screening. Datasets were reconstructed with ASiR-V 30% and DLIR at medium and high levels (DLIR-M and DLIR-H, respectively). The objective image signal and noise, which represented mean attenuation value and standard deviation in Hounsfield units for the lungs, mediastinum, liver, and background air, and subjective image contrast, image noise, and conspicuity of structures were evaluated. The differences between CT scan images subjected to ASiR-V 30%, DLIR-M, and DLIR-H were evaluated. RESULTS Based on the objective analysis, the image signals did not significantly differ among ASiR-V 30%, DLIR-M, and DLIR-H (p = 0.949, 0.737, 0.366, and 0.358 in the lungs, mediastinum, liver, and background air, respectively). However, the noise was significantly lower in DLIR-M and DLIR-H than in ASiR-V 30% (all p < 0.001). DLIR had higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) than ASiR-V 30% (p = 0.027, < 0.001, and < 0.001 in the SNR of the lungs, mediastinum, and liver, respectively; all p < 0.001 in the CNR). According to the subjective analysis, DLIR had higher image contrast and lower image noise than ASiR-V 30% (all p < 0.001). DLIR was superior to ASiR-V 30% in identifying the pulmonary arteries and veins, trachea and bronchi, lymph nodes, and pleura and pericardium (all p < 0.001). CONCLUSION DLIR significantly reduced the image noise in chest LDCT scan images compared with ASiR-V 30% while maintaining superior image quality.
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Affiliation(s)
- Joo Hee Kim
- Department of Radiology, Veterans Health Service Medical Center, Seoul, Korea
| | - Hyun Jung Yoon
- Department of Radiology, Veterans Health Service Medical Center, Seoul, Korea.
| | - Eunju Lee
- Department of Radiology, Veterans Health Service Medical Center, Seoul, Korea
| | - Injoong Kim
- Department of Radiology, Veterans Health Service Medical Center, Seoul, Korea
| | - Yoon Ki Cha
- Department of Radiology, Dongguk University Ilsan Hospital, Goyang, Korea
| | - So Hyeon Bak
- Department of Radiology, Kangwon National University Hospital, Kangwon National University School of Medicine, Chuncheon, Korea
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