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Wajer R, Wajer A, Kazimierczak N, Wilamowska J, Serafin Z. The Impact of AI on Metal Artifacts in CBCT Oral Cavity Imaging. Diagnostics (Basel) 2024; 14:1280. [PMID: 38928694 PMCID: PMC11203150 DOI: 10.3390/diagnostics14121280] [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: 05/27/2024] [Revised: 06/13/2024] [Accepted: 06/14/2024] [Indexed: 06/28/2024] Open
Abstract
OBJECTIVE This study aimed to assess the impact of artificial intelligence (AI)-driven noise reduction algorithms on metal artifacts and image quality parameters in cone-beam computed tomography (CBCT) images of the oral cavity. MATERIALS AND METHODS This retrospective study included 70 patients, 61 of whom were analyzed after excluding those with severe motion artifacts. CBCT scans, performed using a Hyperion X9 PRO 13 × 10 CBCT machine, included images with dental implants, amalgam fillings, orthodontic appliances, root canal fillings, and crowns. Images were processed with the ClariCT.AI deep learning model (DLM) for noise reduction. Objective image quality was assessed using metrics such as the differentiation between voxel values (ΔVVs), the artifact index (AIx), and the contrast-to-noise ratio (CNR). Subjective assessments were performed by two experienced readers, who rated overall image quality and artifact intensity on predefined scales. RESULTS Compared with native images, DLM reconstructions significantly reduced the AIx and increased the CNR (p < 0.001), indicating improved image clarity and artifact reduction. Subjective assessments also favored DLM images, with higher ratings for overall image quality and lower artifact intensity (p < 0.001). However, the ΔVV values were similar between the native and DLM images, indicating that while the DLM reduced noise, it maintained the overall density distribution. Orthodontic appliances produced the most pronounced artifacts, while implants generated the least. CONCLUSIONS AI-based noise reduction using ClariCT.AI significantly enhances CBCT image quality by reducing noise and metal artifacts, thereby improving diagnostic accuracy and treatment planning. Further research with larger, multicenter cohorts is recommended to validate these findings.
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Affiliation(s)
- Róża Wajer
- Department of Radiology and Diagnostic Imaging, University Hospital No. 1 in Bydgoszcz, Marii Skłodowskiej—Curie 9, 85-094 Bydgoszcz, Poland; (J.W.); (Z.S.)
| | | | - Natalia Kazimierczak
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland;
| | - Justyna Wilamowska
- Department of Radiology and Diagnostic Imaging, University Hospital No. 1 in Bydgoszcz, Marii Skłodowskiej—Curie 9, 85-094 Bydgoszcz, Poland; (J.W.); (Z.S.)
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
| | - Zbigniew Serafin
- Department of Radiology and Diagnostic Imaging, University Hospital No. 1 in Bydgoszcz, Marii Skłodowskiej—Curie 9, 85-094 Bydgoszcz, Poland; (J.W.); (Z.S.)
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
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Zhang Y, Liu Z, Cheng Y, Wang Z, Li Z, Li J, Shuai T. Deep Learning Image Reconstruction for Transcatheter Aortic Valve Implantation Planning: Image Quality, Diagnostic Performance, Contrast volume and Radiation Dose Assessment. Acad Radiol 2024; 31:2268-2280. [PMID: 38472024 DOI: 10.1016/j.acra.2024.02.026] [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: 12/02/2023] [Revised: 02/13/2024] [Accepted: 02/15/2024] [Indexed: 03/14/2024]
Abstract
RATIONALE AND OBJECTIVES To assess image quality, contrast volume and radiation dose reduction potential and diagnostic performance with the use of high-strength deep learning image reconstruction (DLIR-H) in transcatheter aortic valve implantation (TAVI) planning CT. METHODS We prospectively enrolled 128 patients referred to TAVI-planning CT. Patients were randomly divided into two groups: DLIR-H group (n = 64) and conventional group (n = 64). The DLIR-H group was scanned with tube voltage of 80kVp and body weighted-dependent contrast injection rate of 28mgI/kg/s, images reconstructed using DLIR-H; the conventional group was scanned with 100kVp and contrast injection rate of 40mgI/kg/s, and images reconstructed using adaptive statistical iterative reconstruction-V at 50% (ASIR-V 50%). Radiation dose, contrast volume, contrast injection rate, and image quality were compared between the two groups. The diagnostic performance of TAVI planning CT for coronary stenosis in 115 patients were calculated using invasive coronary angiography as golden standard. RESULTS DLIR-H group significantly reduced radiation dose (4.94 ± 0.39mSv vs. 7.93 ± 1.20mSv, p < 0.001), contrast dose (45.28 ± 5.38 mL vs. 63.26 ± 9.88 mL, p < 0.001), and contrast injection rate (3.1 ± 0.31 mL/s vs. 4.9 ± 0.2 mL/s, p < 0.001) compared to the conventional group. Images in DLIR-H group had significantly higher SNR and CNR (all p < 0.001). For the diagnostic performance on a per-patient basis, TAVI planning CT in the DLIR-H group provided 100% sensitivity, 92.1% specificity, 100% negative predictive value (NPV), and 84.2% positive predictive value for the detection of > 50% stenosis. In the conventional group, the corresponding results were 94.7%, 95.3%, 97.6%, and 90.0%, respectively. CONCLUSION DLIR-H in TAVI-planning CT provides improved image quality with reduced radiation and contrast doses, and enables satisfactory diagnostic performance for coronary arteries stenosis.
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Affiliation(s)
- Yu Zhang
- Department of Radiology, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu, Sichuan 610041, China
| | - Zixuan Liu
- Department of Radiology, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu, Sichuan 610041, China
| | - Yong Cheng
- Department of Radiology, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu, Sichuan 610041, China
| | - Ziwei Wang
- Department of Radiology, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu, Sichuan 610041, China
| | - Zhenlin Li
- Department of Radiology, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu, Sichuan 610041, China
| | | | - Tao Shuai
- Department of Radiology, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu, Sichuan 610041, China.
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Caruso D, De Santis D, Del Gaudio A, Guido G, Zerunian M, Polici M, Valanzuolo D, Pugliese D, Persechino R, Cremona A, Barbato L, Caloisi A, Iannicelli E, Laghi A. Low-dose liver CT: image quality and diagnostic accuracy of deep learning image reconstruction algorithm. Eur Radiol 2024; 34:2384-2393. [PMID: 37688618 PMCID: PMC10957592 DOI: 10.1007/s00330-023-10171-8] [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: 05/09/2023] [Revised: 07/11/2023] [Accepted: 07/20/2023] [Indexed: 09/11/2023]
Abstract
OBJECTIVES To perform a comprehensive within-subject image quality analysis of abdominal CT examinations reconstructed with DLIR and to evaluate diagnostic accuracy compared to the routinely applied adaptive statistical iterative reconstruction (ASiR-V) algorithm. MATERIALS AND METHODS Oncologic patients were prospectively enrolled and underwent contrast-enhanced CT. Images were reconstructed with DLIR with three intensity levels of reconstruction (high, medium, and low) and ASiR-V at strength levels from 10 to 100% with a 10% interval. Three radiologists characterized the lesions and two readers assessed diagnostic accuracy and calculated signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), figure of merit (FOM), and subjective image quality, the latter with a 5-point Likert scale. RESULTS Fifty patients (mean age: 70 ± 10 years, 23 men) were enrolled and 130 liver lesions (105 benign lesions, 25 metastases) were identified. DLIR_H achieved the highest SNR and CNR, comparable to ASiR-V 100% (p ≥ .051). DLIR_M returned the highest subjective image quality (score: 5; IQR: 4-5; p ≤ .001) and significant median increase (29%) in FOM (p < .001). Differences in detection were identified only for lesions ≤ 0.5 cm: 32/33 lesions were detected with DLIR_M and 26 lesions were detected with ASiR-V 50% (p = .031). Lesion accuracy of was 93.8% (95% CI: 88.1, 97.3; 122 of 130 lesions) for DLIR and 87.7% (95% CI: 80.8, 92.8; 114 of 130 lesions) for ASiR-V 50%. CONCLUSIONS DLIR yields superior image quality and provides higher diagnostic accuracy compared to ASiR-V in the assessment of hypovascular liver lesions, in particular for lesions ≤ 0.5 cm. CLINICAL RELEVANCE STATEMENT Deep learning image reconstruction algorithm demonstrates higher diagnostic accuracy compared to iterative reconstruction in the identification of hypovascular liver lesions, especially for lesions ≤ 0.5 cm. KEY POINTS • Iterative reconstruction algorithm impacts image texture, with negative effects on diagnostic capabilities. • Medium-strength deep learning image reconstruction algorithm outperforms iterative reconstruction in the diagnostic accuracy of ≤ 0.5 cm hypovascular liver lesions (93.9% vs 78.8%), also granting higher objective and subjective image quality. • Deep learning image reconstruction algorithm can be safely implemented in routine abdominal CT protocols in place of iterative reconstruction.
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Affiliation(s)
- Damiano Caruso
- Department of Medical-Surgical Sciences and Translational Medicine, Radiology Unit, Sant'Andrea University Hospital, Sapienza University of Rome, Via Di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Domenico De Santis
- Department of Medical-Surgical Sciences and Translational Medicine, Radiology Unit, Sant'Andrea University Hospital, Sapienza University of Rome, Via Di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Antonella Del Gaudio
- Department of Medical-Surgical Sciences and Translational Medicine, Radiology Unit, Sant'Andrea University Hospital, Sapienza University of Rome, Via Di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Gisella Guido
- Department of Medical-Surgical Sciences and Translational Medicine, Radiology Unit, Sant'Andrea University Hospital, Sapienza University of Rome, Via Di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Marta Zerunian
- Department of Medical-Surgical Sciences and Translational Medicine, Radiology Unit, Sant'Andrea University Hospital, Sapienza University of Rome, Via Di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Michela Polici
- Department of Medical-Surgical Sciences and Translational Medicine, Radiology Unit, Sant'Andrea University Hospital, Sapienza University of Rome, Via Di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Daniela Valanzuolo
- Department of Medical-Surgical Sciences and Translational Medicine, Radiology Unit, Sant'Andrea University Hospital, Sapienza University of Rome, Via Di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Dominga Pugliese
- Department of Medical-Surgical Sciences and Translational Medicine, Radiology Unit, Sant'Andrea University Hospital, Sapienza University of Rome, Via Di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Raffaello Persechino
- Department of Medical-Surgical Sciences and Translational Medicine, Radiology Unit, Sant'Andrea University Hospital, Sapienza University of Rome, Via Di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Antonio Cremona
- Department of Medical-Surgical Sciences and Translational Medicine, Radiology Unit, Sant'Andrea University Hospital, Sapienza University of Rome, Via Di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Luca Barbato
- Department of Medical-Surgical Sciences and Translational Medicine, Radiology Unit, Sant'Andrea University Hospital, Sapienza University of Rome, Via Di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Andrea Caloisi
- Department of Medical-Surgical Sciences and Translational Medicine, Radiology Unit, Sant'Andrea University Hospital, Sapienza University of Rome, Via Di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Elsa Iannicelli
- Department of Medical-Surgical Sciences and Translational Medicine, Radiology Unit, Sant'Andrea University Hospital, Sapienza University of Rome, Via Di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Andrea Laghi
- Department of Medical-Surgical Sciences and Translational Medicine, Radiology Unit, Sant'Andrea University Hospital, Sapienza University of Rome, Via Di Grottarossa, 1035-1039, 00189, Rome, Italy.
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Kazimierczak W, Kędziora K, Janiszewska-Olszowska J, Kazimierczak N, Serafin Z. Noise-Optimized CBCT Imaging of Temporomandibular Joints-The Impact of AI on Image Quality. J Clin Med 2024; 13:1502. [PMID: 38592413 PMCID: PMC10932444 DOI: 10.3390/jcm13051502] [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: 01/26/2024] [Revised: 02/28/2024] [Accepted: 03/04/2024] [Indexed: 04/10/2024] Open
Abstract
Background: Temporomandibular joint disorder (TMD) is a common medical condition. Cone beam computed tomography (CBCT) is effective in assessing TMD-related bone changes, but image noise may impair diagnosis. Emerging deep learning reconstruction algorithms (DLRs) could minimize noise and improve CBCT image clarity. This study compares standard and deep learning-enhanced CBCT images for image quality in detecting osteoarthritis-related degeneration in TMJs (temporomandibular joints). This study analyzed CBCT images of patients with suspected temporomandibular joint degenerative joint disease (TMJ DJD). Methods: The DLM reconstructions were performed with ClariCT.AI software. Image quality was evaluated objectively via CNR in target areas and subjectively by two experts using a five-point scale. Both readers also assessed TMJ DJD lesions. The study involved 50 patients with a mean age of 28.29 years. Results: Objective analysis revealed a significantly better image quality in DLM reconstructions (CNR levels; p < 0.001). Subjective assessment showed high inter-reader agreement (κ = 0.805) but no significant difference in image quality between the reconstruction types (p = 0.055). Lesion counts were not significantly correlated with the reconstruction type (p > 0.05). Conclusions: The analyzed DLM reconstruction notably enhanced the objective image quality in TMJ CBCT images but did not significantly alter the subjective quality or DJD lesion diagnosis. However, the readers favored DLM images, indicating the potential for better TMD diagnosis with CBCT, meriting more study.
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Affiliation(s)
- Wojciech Kazimierczak
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
- Department of Interdisciplinary Dentistry, Pomeranian Medical University in Szczecin, 70-111 Szczecin, Poland
| | - Kamila Kędziora
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
| | | | - Natalia Kazimierczak
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | - Zbigniew Serafin
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
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Wang H, Yue S, Liu N, Chen Y, Zhan P, Liu X, Shang B, Wang L, Li Z, Gao J, Lyu P. Deep learning reconstruction vs standard reconstruction for abdominal CT: the influence of BMI. Eur Radiol 2024; 34:1614-1623. [PMID: 37650972 DOI: 10.1007/s00330-023-10179-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 07/17/2023] [Accepted: 08/03/2023] [Indexed: 09/01/2023]
Abstract
OBJECTIVE This study aimed to evaluate the image quality and lesion conspicuity of the deep learning image reconstruction (DLIR) algorithm compared with standard image reconstruction algorithms on abdominal enhanced computed tomography (CT) scanning with a wide range of body mass indexes (BMIs). METHODS A total of 112 participants who underwent contrast-enhanced abdominal CT scans were divided into three groups according to BMIs: the 80-kVp group (BMI ≤ 23.9 kg/m2), 100-kVp group (BMI 24-28.9 kg/m2), and 120-kVp group (BMI ≥ 29 kg/m2). All images were reconstructed using filtered back projection (FBP), adaptive statistical iterative reconstruction-V of 50% level (IR), and DLIR at low, medium, and high levels (DL, DM, and DH, respectively). Subjective noise, artifact, overall image quality, and low- and high-contrast hepatic lesion conspicuity were all graded on a 5-point scale. The CT attenuation value (in HU), image noise, and contrast-to-noise ratio (CNR) were quantified and compared. RESULTS DM and DH improved the qualitative and quantitative parameters compared with FBP and IR for all three BMI groups. DH had the lowest image noise and highest CNR value, while DM had the highest subjective overall image quality and low- and high-contrast lesion conspicuity scores for the three BMI groups. Based on the FBP, the improvement in image quality and lesion conspicuity of DM and DH images was greater in the 80-kVp group than in the 100-kVp and 120-kVp groups. CONCLUSION For all BMIs, DLIR improves both image quality and hepatic lesion conspicuity, of which DM would be the best choice to balance both. CLINICAL RELEVANCE STATEMENT The study suggests that utilizing DLIR, particularly at the medium level, can significantly enhance image quality and lesion visibility on abdominal CT scans across a wide range of BMIs. KEY POINTS • DLIR improved the image quality and lesion conspicuity across a wide range of BMIs. • DLIR at medium level had the highest subjective parameters and lesion conspicuity scores among all reconstruction levels. • On the basis of the FBP, the 80-kVp group had improved image quality and lesion conspicuity more than the 100-kVp and 120-kVp groups.
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Affiliation(s)
- Huixia Wang
- The Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1, Eastern Jianshe Road, Zhengzhou, 450052, Henan Province, China
| | - Songwei Yue
- The Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1, Eastern Jianshe Road, Zhengzhou, 450052, Henan Province, China
| | - Nana Liu
- The Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1, Eastern Jianshe Road, Zhengzhou, 450052, Henan Province, China
| | - Yan Chen
- The Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1, Eastern Jianshe Road, Zhengzhou, 450052, Henan Province, China
| | - Pengchao Zhan
- The Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1, Eastern Jianshe Road, Zhengzhou, 450052, Henan Province, China
| | - Xing Liu
- The Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1, Eastern Jianshe Road, Zhengzhou, 450052, Henan Province, China
| | - Bo Shang
- The Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1, Eastern Jianshe Road, Zhengzhou, 450052, Henan Province, China
| | - Luotong Wang
- CT Imaging Research Center, GE Healthcare China, Beijing, 100176, China
| | - Zhen Li
- The Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, No.1, Eastern Jianshe Road, Zhengzhou, 450052, Henan Province, China
| | - Jianbo Gao
- The Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1, Eastern Jianshe Road, Zhengzhou, 450052, Henan Province, China.
| | - Peijie Lyu
- The Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1, Eastern Jianshe Road, Zhengzhou, 450052, Henan Province, China.
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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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Li J, Zhu J, Zou Y, Zhang G, Zhu P, Wang N, Xie P. Diagnostic CT of colorectal cancer with artificial intelligence iterative reconstruction: A clinical evaluation. Eur J Radiol 2024; 171:111301. [PMID: 38237522 DOI: 10.1016/j.ejrad.2024.111301] [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: 09/06/2023] [Revised: 12/26/2023] [Accepted: 01/07/2024] [Indexed: 02/10/2024]
Abstract
OBJECTIVES To investigate the clinical value of a novel deep-learning based CT reconstruction algorithm, artificial intelligence iterative reconstruction (AIIR), in diagnostic imaging of colorectal cancer (CRC). METHODS This study retrospectively enrolled 217 patients with pathologically confirmed CRC. CT images were reconstructed with the AIIR algorithm and compared with those originally obtained with hybrid iterative reconstruction (HIR). Objective image quality was evaluated in terms of the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Subjective image quality was graded on the conspicuity of tumor margin and enhancement pattern as well as the certainty in diagnosing organ invasion and regional lymphadenopathy. In patients with surgical pathology (n = 116), the performance of diagnosing visceral peritoneum invasion was characterized using receiver operating characteristic (ROC) analysis. Changes of diagnostic thinking in diagnosing hepatic metastases were assessed through lesion classification confidence. RESULTS The SNRs and CNRs on AIIR images were significantly higher than those on HIR images (all p < 0.001). The AIIR was scored higher for all subjective metrics (all p < 0.001) except for the certainty of diagnosing regional lymphadenopathy (p = 0.467). In diagnosing visceral peritoneum invasion, higher area under curve (AUC) of the ROC was found for AIIR than HIR (0.87 vs 0.77, p = 0.001). In assessing hepatic metastases, AIIR was found capable of correcting the misdiagnosis and improving the diagnostic confidence provided by HIR (p = 0.01). CONCLUSIONS Compared to HIR, AIIR offers better image quality, improves the diagnostic performance regarding CRC, and thus has the potential for application in routine abdominal CT.
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Affiliation(s)
- Jiao Li
- Department of Radiology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510655, China; Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510655, China.
| | - Junying Zhu
- Department of Radiology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510655, China; Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510655, China.
| | - Yixuan Zou
- United Imaging Healthcare, Shanghai 201800, China.
| | - Guozhi Zhang
- United Imaging Healthcare, Shanghai 201800, China.
| | - Pan Zhu
- Department of Radiology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510655, China; Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510655, China.
| | - Ning Wang
- Department of Radiology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510655, China; Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510655, China.
| | - Peiyi Xie
- Department of Radiology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510655, China; Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510655, China.
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Rahman H, Khan AR, Sadiq T, Farooqi AH, Khan IU, Lim WH. A Systematic Literature Review of 3D Deep Learning Techniques in Computed Tomography Reconstruction. Tomography 2023; 9:2158-2189. [PMID: 38133073 PMCID: PMC10748093 DOI: 10.3390/tomography9060169] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 11/27/2023] [Accepted: 12/01/2023] [Indexed: 12/23/2023] Open
Abstract
Computed tomography (CT) is used in a wide range of medical imaging diagnoses. However, the reconstruction of CT images from raw projection data is inherently complex and is subject to artifacts and noise, which compromises image quality and accuracy. In order to address these challenges, deep learning developments have the potential to improve the reconstruction of computed tomography images. In this regard, our research aim is to determine the techniques that are used for 3D deep learning in CT reconstruction and to identify the training and validation datasets that are accessible. This research was performed on five databases. After a careful assessment of each record based on the objective and scope of the study, we selected 60 research articles for this review. This systematic literature review revealed that convolutional neural networks (CNNs), 3D convolutional neural networks (3D CNNs), and deep learning reconstruction (DLR) were the most suitable deep learning algorithms for CT reconstruction. Additionally, two major datasets appropriate for training and developing deep learning systems were identified: 2016 NIH-AAPM-Mayo and MSCT. These datasets are important resources for the creation and assessment of CT reconstruction models. According to the results, 3D deep learning may increase the effectiveness of CT image reconstruction, boost image quality, and lower radiation exposure. By using these deep learning approaches, CT image reconstruction may be made more precise and effective, improving patient outcomes, diagnostic accuracy, and healthcare system productivity.
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Affiliation(s)
- Hameedur Rahman
- Department of Computer Games Development, Faculty of Computing & AI, Air University, E9, Islamabad 44000, Pakistan;
| | - Abdur Rehman Khan
- Department of Creative Technologies, Faculty of Computing & AI, Air University, E9, Islamabad 44000, Pakistan;
| | - Touseef Sadiq
- Centre for Artificial Intelligence Research, Department of Information and Communication Technology, University of Agder, Jon Lilletuns vei 9, 4879 Grimstad, Norway
| | - Ashfaq Hussain Farooqi
- Department of Computer Science, Faculty of Computing AI, Air University, Islamabad 44000, Pakistan;
| | - Inam Ullah Khan
- Department of Electronic Engineering, School of Engineering & Applied Sciences (SEAS), Isra University, Islamabad Campus, Islamabad 44000, Pakistan;
| | - Wei Hong Lim
- Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur 56000, Malaysia;
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9
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Sato S, Urikura A, Mimatsu M, Miyamae Y, Jibiki Y, Yamashita M, Ishihara T. Physical characteristics of deep learning-based image processing software in computed tomography: a phantom study. Phys Eng Sci Med 2023; 46:1713-1721. [PMID: 37725313 DOI: 10.1007/s13246-023-01331-7] [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: 09/04/2022] [Accepted: 09/06/2023] [Indexed: 09/21/2023]
Abstract
PURPOSE This study aimed to assess the image characteristics of deep-learning-based image processing software (DLIP; FCT PixelShine, FUJIFILM, Tokyo, Japan) and compare it with filtered back projection (FBP), model-based iterative reconstruction (MBIR), and deep-learning-based reconstruction (DLR). METHODS This phantom study assessed the object-specific spatial resolution (task-based transfer function [TTF]), noise characteristics (noise power spectrum [NPS]), and low-contrast detectability (low-contrast object-specific contrast-to-noise ratio [CNRLO]) at three different output doses (standard: 10 mGy; low: 3.9 mGy; ultralow: 2.0 mGy). The processing strength of DLIPFBP with A1, A4, and A9 was compared with those of FBP, MBIR, and DLR. RESULT The standard dose with high-contrast TTFs of DLIPFBP exceeded that of FBP. Low-contrast TTFs were comparable to or lower than that of FBP. The NPS peak frequency (fP) of DLIPFBP shifts to low spatial frequencies of up to 8.6% at ultralow doses compared to the standard FBP dose. MBIR shifted the most fP compared to FBP-a marked shift of up to 49%. DLIPFBP showed a CNRLO equal to or greater than that of DLR in standard or low doses. In contrast, the CNRLO of the DLIPFBP was equal to or lower than that of the DLR in ultralow doses. CONCLUSION DLIPFBP reduced image noise while maintaining a resolution similar to commercially available MBIR and DLR. The slight spatial frequency shift of fP in DLIPFBP contributed to the noise texture degradation suppression. The NPS suppression in the low spatial frequency range effectively improved the low-contrast detectability.
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Affiliation(s)
- Seiya Sato
- Department of Radiological Technology, Radiological Diagnosis, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan
| | - Atsushi Urikura
- Department of Radiological Technology, Radiological Diagnosis, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan.
| | - Makoto Mimatsu
- Department of Radiological Technology, Radiological Diagnosis, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan
| | - Yuta Miyamae
- Department of Radiological Technology, Radiological Diagnosis, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan
| | - Yuji Jibiki
- Clinical Product Specialist Marketing Group, FUJIFILM Corporation, 7-3, Akasaka 9-Chome Minato-Ku, Tokyo, Japan
| | - Mami Yamashita
- Clinical Product Specialist Marketing Group, FUJIFILM Corporation, 7-3, Akasaka 9-Chome Minato-Ku, Tokyo, Japan
| | - Toshihiro Ishihara
- Department of Radiological Technology, Radiological Diagnosis, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan
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Jo GD, Ahn C, Hong JH, Kim DS, Park J, Kim H, Kim JH, Goo JM, Nam JG. 75% radiation dose reduction using deep learning reconstruction on low-dose chest CT. BMC Med Imaging 2023; 23:121. [PMID: 37697262 PMCID: PMC10494344 DOI: 10.1186/s12880-023-01081-8] [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: 04/14/2023] [Accepted: 08/17/2023] [Indexed: 09/13/2023] Open
Abstract
OBJECTIVE Few studies have explored the clinical feasibility of using deep-learning reconstruction to reduce the radiation dose of CT. We aimed to compare the image quality and lung nodule detectability between chest CT using a quarter of the low dose (QLD) reconstructed with vendor-agnostic deep-learning image reconstruction (DLIR) and conventional low-dose (LD) CT reconstructed with iterative reconstruction (IR). MATERIALS AND METHODS We retrospectively collected 100 patients (median age, 61 years [IQR, 53-70 years]) who received LDCT using a dual-source scanner, where total radiation was split into a 1:3 ratio. QLD CT was generated using a quarter dose and reconstructed with DLIR (QLD-DLIR), while LDCT images were generated using a full dose and reconstructed with IR (LD-IR). Three thoracic radiologists reviewed subjective noise, spatial resolution, and overall image quality, and image noise was measured in five areas. The radiologists were also asked to detect all Lung-RADS category 3 or 4 nodules, and their performance was evaluated using area under the jackknife free-response receiver operating characteristic curve (AUFROC). RESULTS The median effective dose was 0.16 (IQR, 0.14-0.18) mSv for QLD CT and 0.65 (IQR, 0.57-0.71) mSv for LDCT. The radiologists' evaluations showed no significant differences in subjective noise (QLD-DLIR vs. LD-IR, lung-window setting; 3.23 ± 0.19 vs. 3.27 ± 0.22; P = .11), spatial resolution (3.14 ± 0.28 vs. 3.16 ± 0.27; P = .12), and overall image quality (3.14 ± 0.21 vs. 3.17 ± 0.17; P = .15). QLD-DLIR demonstrated lower measured noise than LD-IR in most areas (P < .001 for all). No significant difference was found between QLD-DLIR and LD-IR for the sensitivity (76.4% vs. 72.2%; P = .35) or the AUFROCs (0.77 vs. 0.78; P = .68) in detecting Lung-RADS category 3 or 4 nodules. Under a noninferiority limit of -0.1, QLD-DLIR showed noninferior detection performance (95% CI for AUFROC difference, -0.04 to 0.06). CONCLUSION QLD-DLIR images showed comparable image quality and noninferior nodule detectability relative to LD-IR images.
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Affiliation(s)
- Gyeong Deok Jo
- Department of Radiology, Seoul National University Hospital and College of Medicine, Seoul, 03080, Republic of Korea
| | - Chulkyun Ahn
- Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, 08826, Republic of Korea
- ClariPi Research, Seoul, 03088, Republic of Korea
| | - Jung Hee Hong
- Department of Radiology, Keimyung University Dongsan Hospital, Keimyung University School of Medicine, Daegu, 42601, Republic of Korea
| | - Da Som Kim
- Department of Radiology, Busan Paik Hospital, College of Medicine, Inje University, Busan, 47392, Republic of Korea
| | - Jongsoo Park
- Department of Radiology, Yeungnam University Medical Center, Yeungnam University College of Medicine, Daegu, 42415, Republic of Korea
| | - Hyungjin Kim
- Department of Radiology, Seoul National University Hospital and College of Medicine, Seoul, 03080, Republic of Korea
| | - Jong Hyo Kim
- Department of Radiology, Seoul National University Hospital and College of Medicine, Seoul, 03080, Republic of Korea
- Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, 08826, Republic of Korea
- ClariPi Research, Seoul, 03088, Republic of Korea
| | - Jin Mo Goo
- Department of Radiology, Seoul National University Hospital and College of Medicine, Seoul, 03080, Republic of Korea.
- Cancer Research Institute, Seoul National University, Seoul, 03080, Republic of Korea.
| | - Ju Gang Nam
- Department of Radiology, Seoul National University Hospital and College of Medicine, Seoul, 03080, Republic of Korea.
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Koh S, Lee NK, Kim S, Hong SB, Kim DU, Han SY. The efficacy of low-dose CT with deep learning image reconstruction in the surveillance of incidentally detected pancreatic cystic lesions. Abdom Radiol (NY) 2023; 48:2585-2595. [PMID: 37204510 DOI: 10.1007/s00261-023-03958-2] [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: 03/03/2023] [Revised: 05/11/2023] [Accepted: 05/12/2023] [Indexed: 05/20/2023]
Abstract
PURPOSE To evaluate the efficacy of low-dose CT (LDCT) with deep learning image reconstruction (DLIR) for the surveillance of pancreatic cystic lesions (PCLs) compared with standard-dose CT (SDCT) with adaptive statistical iterative reconstruction (ASIR-V). METHODS The study enrolled 103 patients who underwent pancreatic CT for follow-up of incidentally detected PCLs. The CT protocol included LDCT in the pancreatic phase with 40% ASIR-V, DLIR at medium (DLIR-M) and high levels (DLIR-H), and SDCT in the portal-venous phase with 40% ASIR-V. The overall image quality and conspicuity of PCLs were qualitatively assessed using five-point scales by two radiologists. The size of PCLs, presence of thickened/enhancing walls, enhancing mural nodules, and main pancreatic duct dilatation were reviewed. CT noise and cyst-to-pancreas contrast-to-noise ratio (CNR) were measured. Qualitative and quantitative parameters were analyzed using the chi-squared test, one-way ANOVA, and t-test. Additionally, interobserver agreement was analyzed using the kappa and weighted-kappa statistics. RESULTS The volume CT dose-indexes in LDCT and SDCT were 3.0 ± 0.6 mGy and 8.4 ± 2.9 mGy, respectively. LDCT with DLIR-H showed the highest overall image quality, the lowest noise, and the highest CNR. The PCL conspicuity in LDCT with either DLIR-M or DLIR-H was not significantly different from that in SDCT with ASIR-V. Other findings depicting PCLs also revealed no significant differences between LDCT with DLIR and SDCT with ASIR-V. Moreover, the results revealed good or excellent interobserver agreement. CONCLUSION LDCT with DLIR has a comparable performance with SDCT for the follow-up of incidentally detected PCLs.
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Affiliation(s)
- Sungho Koh
- Department of Radiology, Biomedical Research Institute, Pusan National University Hospital, and Pusan National University School of Medicine, Pusan National University, #179, Gudeok-Ro, Seo-Gu, Busan, 49241, Republic of Korea
| | - Nam Kyung Lee
- Department of Radiology, Biomedical Research Institute, Pusan National University Hospital, and Pusan National University School of Medicine, Pusan National University, #179, Gudeok-Ro, Seo-Gu, Busan, 49241, Republic of Korea.
| | - Suk Kim
- Department of Radiology, Biomedical Research Institute, Pusan National University Hospital, and Pusan National University School of Medicine, Pusan National University, #179, Gudeok-Ro, Seo-Gu, Busan, 49241, Republic of Korea
| | - Seung Baek Hong
- Department of Radiology, Biomedical Research Institute, Pusan National University Hospital, and Pusan National University School of Medicine, Pusan National University, #179, Gudeok-Ro, Seo-Gu, Busan, 49241, Republic of Korea
| | - Dong Uk Kim
- Department of Internal Medicine, Biomedical Research Institute, Pusan National University Hospital, and Pusan National University School of Medicine, Pusan National University, Busan, Republic of Korea
| | - Sung Yong Han
- Department of Internal Medicine, Biomedical Research Institute, Pusan National University Hospital, and Pusan National University School of Medicine, Pusan National University, Busan, Republic of Korea
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Shehata MA, Saad AM, Kamel S, Stanietzky N, Roman-Colon AM, Morani AC, Elsayes KM, Jensen CT. Deep-learning CT reconstruction in clinical scans of the abdomen: a systematic review and meta-analysis. Abdom Radiol (NY) 2023; 48:2724-2756. [PMID: 37280374 DOI: 10.1007/s00261-023-03966-2] [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: 04/11/2023] [Revised: 05/13/2023] [Accepted: 05/16/2023] [Indexed: 06/08/2023]
Abstract
OBJECTIVE To perform a systematic literature review and meta-analysis of the two most common commercially available deep-learning algorithms for CT. METHODS We used PubMed, Scopus, Embase, and Web of Science to conduct systematic searches for studies assessing the most common commercially available deep-learning CT reconstruction algorithms: True Fidelity (TF) and Advanced intelligent Clear-IQ Engine (AiCE) in the abdomen of human participants since only these two algorithms currently have adequate published data for robust systematic analysis. RESULTS Forty-four articles fulfilled inclusion criteria. 32 studies evaluated TF and 12 studies assessed AiCE. DLR algorithms produced images with significantly less noise (22-57.3% less than IR) but preserved a desirable noise texture with increased contrast-to-noise ratios and improved lesion detectability on conventional CT. These improvements with DLR were similarly noted in dual-energy CT which was only assessed for a single vendor. Reported radiation reduction potential was 35.1-78.5%. Nine studies assessed observer performance with the two dedicated liver lesion studies being performed on the same vendor reconstruction (TF). These two studies indicate preserved low contrast liver lesion detection (> 5 mm) at CTDIvol 6.8 mGy (BMI 23.5 kg/m2) to 12.2 mGy (BMI 29 kg/m2). If smaller lesion detection and improved lesion characterization is needed, a CTDIvol of 13.6-34.9 mGy is needed in a normal weight to obese population. Mild signal loss and blurring have been reported at high DLR reconstruction strengths. CONCLUSION Deep learning reconstructions significantly improve image quality in CT of the abdomen. Assessment of other dose levels and clinical indications is needed. Careful choice of radiation dose levels is necessary, particularly for small liver lesion assessment.
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Affiliation(s)
- Mostafa A Shehata
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1473, Houston, TX, 77030-4009, USA
| | | | - Serageldin Kamel
- Department of Lymphoma and Myeloma, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1473, Houston, TX, 77030-4009, USA
| | - Nir Stanietzky
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1473, Houston, TX, 77030-4009, USA
| | | | - Ajaykumar C Morani
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1473, Houston, TX, 77030-4009, USA
| | - Khaled M Elsayes
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1473, Houston, TX, 77030-4009, USA
| | - Corey T Jensen
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1473, Houston, TX, 77030-4009, USA.
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Zhong J, Shen H, Chen Y, Xia Y, Shi X, Lu W, Li J, Xing Y, Hu Y, Ge X, Ding D, Jiang Z, Yao W. Evaluation of Image Quality and Detectability of Deep Learning Image Reconstruction (DLIR) Algorithm in Single- and Dual-energy CT. J Digit Imaging 2023; 36:1390-1407. [PMID: 37071291 PMCID: PMC10406981 DOI: 10.1007/s10278-023-00806-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 03/02/2023] [Accepted: 03/03/2023] [Indexed: 04/19/2023] Open
Abstract
This study is aimed to evaluate effects of deep learning image reconstruction (DLIR) on image quality in single-energy CT (SECT) and dual-energy CT (DECT), in reference to adaptive statistical iterative reconstruction-V (ASIR-V). The Gammex 464 phantom was scanned in SECT and DECT modes at three dose levels (5, 10, and 20 mGy). Raw data were reconstructed using six algorithms: filtered back-projection (FBP), ASIR-V at 40% (AV-40) and 100% (AV-100) strength, and DLIR at low (DLIR-L), medium (DLIR-M), and high strength (DLIR-H), to generate SECT 120kVp images and DECT 120kVp-like images. Objective image quality metrics were computed, including noise power spectrum (NPS), task transfer function (TTF), and detectability index (d'). Subjective image quality evaluation, including image noise, texture, sharpness, overall quality, and low- and high-contrast detectability, was performed by six readers. DLIR-H reduced overall noise magnitudes from FBP by 55.2% in a more balanced way of low and high frequency ranges comparing to AV-40, and improved the TTF values at 50% for acrylic inserts by average percentages of 18.32%. Comparing to SECT 20 mGy AV-40 images, the DECT 10 mGy DLIR-H images showed 20.90% and 7.75% improvement in d' for the small-object high-contrast and large-object low-contrast tasks, respectively. Subjective evaluation showed higher image quality and better detectability. At 50% of the radiation dose level, DECT with DLIR-H yields a gain in objective detectability index compared to full-dose AV-40 SECT images used in daily practice.
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Affiliation(s)
- Jingyu Zhong
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Changning District, No. 1111 Xianxia Road, Shanghai, 200336 China
| | - Hailin Shen
- Department of Radiology, Suzhou Kowloon Hospital, Shanghai Jiao Tong University School of Medicine, Suzhou, 215028 China
| | - Yong Chen
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025 China
| | - Yihan Xia
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025 China
| | - Xiaomeng Shi
- Department of Materials, Imperial College London, South Kensington Campus, London, SW7 2AZ UK
| | - Wei Lu
- Computed Tomography Research Center, GE Healthcare, Shanghai, 201203 China
| | - Jianying Li
- Computed Tomography Research Center, GE Healthcare, Beijing, 100176 China
| | - Yue Xing
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Changning District, No. 1111 Xianxia Road, Shanghai, 200336 China
| | - Yangfan Hu
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Changning District, No. 1111 Xianxia Road, Shanghai, 200336 China
| | - Xiang Ge
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Changning District, No. 1111 Xianxia Road, Shanghai, 200336 China
| | - Defang Ding
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Changning District, No. 1111 Xianxia Road, Shanghai, 200336 China
| | - Zhenming Jiang
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Changning District, No. 1111 Xianxia Road, Shanghai, 200336 China
| | - Weiwu Yao
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Changning District, No. 1111 Xianxia Road, Shanghai, 200336 China
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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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Liao S, Mo Z, Zeng M, Wu J, Gu Y, Li G, Quan G, Lv Y, Liu L, Yang C, Wang X, Huang X, Zhang Y, Cao W, Dong Y, Wei Y, Zhou Q, Xiao Y, Zhan Y, Zhou XS, Shi F, Shen D. Fast and low-dose medical imaging generation empowered by hybrid deep-learning and iterative reconstruction. Cell Rep Med 2023; 4:101119. [PMID: 37467726 PMCID: PMC10394257 DOI: 10.1016/j.xcrm.2023.101119] [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: 10/23/2022] [Revised: 05/16/2023] [Accepted: 06/19/2023] [Indexed: 07/21/2023]
Abstract
Fast and low-dose reconstructions of medical images are highly desired in clinical routines. We propose a hybrid deep-learning and iterative reconstruction (hybrid DL-IR) framework and apply it for fast magnetic resonance imaging (MRI), fast positron emission tomography (PET), and low-dose computed tomography (CT) image generation tasks. First, in a retrospective MRI study (6,066 cases), we demonstrate its capability of handling 3- to 10-fold under-sampled MR data, enabling organ-level coverage with only 10- to 100-s scan time; second, a low-dose CT study (142 cases) shows that our framework can successfully alleviate the noise and streak artifacts in scans performed with only 10% radiation dose (0.61 mGy); and last, a fast whole-body PET study (131 cases) allows us to faithfully reconstruct tumor-induced lesions, including small ones (<4 mm), from 2- to 4-fold-accelerated PET acquisition (30-60 s/bp). This study offers a promising avenue for accurate and high-quality image reconstruction with broad clinical value.
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Affiliation(s)
- Shu Liao
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Zhanhao Mo
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun 130033, China
| | - Mengsu Zeng
- Department of Radiology, Shanghai Institute of Medical Imaging, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Jiaojiao Wu
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Yuning Gu
- School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China
| | - Guobin Li
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai 201800, China
| | - Guotao Quan
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai 201800, China
| | - Yang Lv
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai 201800, China
| | - Lin Liu
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun 130033, China
| | - Chun Yang
- Department of Radiology, Shanghai Institute of Medical Imaging, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Xinglie Wang
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Xiaoqian Huang
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Yang Zhang
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Wenjing Cao
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai 201800, China
| | - Yun Dong
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai 201800, China
| | - Ying Wei
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Qing Zhou
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Yongqin Xiao
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Yiqiang Zhan
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Xiang Sean Zhou
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China.
| | - Dinggang Shen
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China; School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China; Shanghai Clinical Research and Trial Center, Shanghai 200122, China.
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Potočnik J, Foley S, Thomas E. Current and potential applications of artificial intelligence in medical imaging practice: A narrative review. J Med Imaging Radiat Sci 2023; 54:376-385. [PMID: 37062603 DOI: 10.1016/j.jmir.2023.03.033] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 03/25/2023] [Accepted: 03/28/2023] [Indexed: 04/18/2023]
Abstract
BACKGROUND AND PURPOSE Artificial intelligence (AI) is present in many areas of our lives. Much of the digital data generated in health care can be used for building automated systems to bring improvements to existing workflows and create a more personalised healthcare experience for patients. This review outlines select current and potential AI applications in medical imaging practice and provides a view of how diagnostic imaging suites will operate in the future. Challenges associated with potential applications will be discussed and healthcare staff considerations necessary to benefit from AI-enabled solutions will be outlined. METHODS Several electronic databases, including PubMed, ScienceDirect, Google Scholar, and University College Dublin Library Database, were used to identify relevant articles with a Boolean search strategy. Textbooks, government sources and vendor websites were also considered. RESULTS/DISCUSSION Many AI-enabled solutions in radiographic practice are available with more automation on the horizon. Traditional workflow will become faster, more effective, and more user friendly. AI can handle administrative or technical types of work, meaning it is applicable across all aspects of medical imaging practice. CONCLUSION AI offers significant potential to automate most of the manual tasks, ensure service consistency, and improve patient care. Radiographers, radiation therapists, and clinicians should ensure they have adequate understanding of the technology to enable ethical oversight of its implementation.
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Affiliation(s)
- Jaka Potočnik
- University College Dublin School of Medicine, Radiography & Diagnostic Imaging, Room A223, Belfield, Dublin 4, Ireland.
| | - Shane Foley
- University College Dublin School of Medicine, Radiography & Diagnostic Imaging, Room A223, Belfield, Dublin 4, Ireland
| | - Edel Thomas
- University College Dublin School of Medicine, Radiography & Diagnostic Imaging, Room A223, Belfield, Dublin 4, Ireland
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Koetzier LR, Mastrodicasa D, Szczykutowicz TP, van der Werf NR, Wang AS, Sandfort V, van der Molen AJ, Fleischmann D, Willemink MJ. Deep Learning Image Reconstruction for CT: Technical Principles and Clinical Prospects. Radiology 2023; 306:e221257. [PMID: 36719287 PMCID: PMC9968777 DOI: 10.1148/radiol.221257] [Citation(s) in RCA: 49] [Impact Index Per Article: 49.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 09/26/2022] [Accepted: 10/13/2022] [Indexed: 02/01/2023]
Abstract
Filtered back projection (FBP) has been the standard CT image reconstruction method for 4 decades. A simple, fast, and reliable technique, FBP has delivered high-quality images in several clinical applications. However, with faster and more advanced CT scanners, FBP has become increasingly obsolete. Higher image noise and more artifacts are especially noticeable in lower-dose CT imaging using FBP. This performance gap was partly addressed by model-based iterative reconstruction (MBIR). Yet, its "plastic" image appearance and long reconstruction times have limited widespread application. Hybrid iterative reconstruction partially addressed these limitations by blending FBP with MBIR and is currently the state-of-the-art reconstruction technique. In the past 5 years, deep learning reconstruction (DLR) techniques have become increasingly popular. DLR uses artificial intelligence to reconstruct high-quality images from lower-dose CT faster than MBIR. However, the performance of DLR algorithms relies on the quality of data used for model training. Higher-quality training data will become available with photon-counting CT scanners. At the same time, spectral data would greatly benefit from the computational abilities of DLR. This review presents an overview of the principles, technical approaches, and clinical applications of DLR, including metal artifact reduction algorithms. In addition, emerging applications and prospects are discussed.
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Affiliation(s)
| | | | - Timothy P. Szczykutowicz
- From the Department of Radiology (L.R.K., D.M., A.S.W., V.S., D.F.,
M.J.W.) and Stanford Cardiovascular Institute (D.M., D.F., M.J.W.), Stanford
University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305-5105;
Department of Radiology, University of Wisconsin–Madison, School of
Medicine and Public Health, Madison, Wis (T.P.S.); Department of Radiology,
Erasmus Medical Center, Rotterdam, the Netherlands (N.R.v.d.W.); Clinical
Science Western Europe, Philips Healthcare, Best, the Netherlands (N.R.v.d.W.);
and Department of Radiology, Leiden University Medical Center, Leiden, the
Netherlands (A.J.v.d.M.)
| | - Niels R. van der Werf
- From the Department of Radiology (L.R.K., D.M., A.S.W., V.S., D.F.,
M.J.W.) and Stanford Cardiovascular Institute (D.M., D.F., M.J.W.), Stanford
University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305-5105;
Department of Radiology, University of Wisconsin–Madison, School of
Medicine and Public Health, Madison, Wis (T.P.S.); Department of Radiology,
Erasmus Medical Center, Rotterdam, the Netherlands (N.R.v.d.W.); Clinical
Science Western Europe, Philips Healthcare, Best, the Netherlands (N.R.v.d.W.);
and Department of Radiology, Leiden University Medical Center, Leiden, the
Netherlands (A.J.v.d.M.)
| | - Adam S. Wang
- From the Department of Radiology (L.R.K., D.M., A.S.W., V.S., D.F.,
M.J.W.) and Stanford Cardiovascular Institute (D.M., D.F., M.J.W.), Stanford
University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305-5105;
Department of Radiology, University of Wisconsin–Madison, School of
Medicine and Public Health, Madison, Wis (T.P.S.); Department of Radiology,
Erasmus Medical Center, Rotterdam, the Netherlands (N.R.v.d.W.); Clinical
Science Western Europe, Philips Healthcare, Best, the Netherlands (N.R.v.d.W.);
and Department of Radiology, Leiden University Medical Center, Leiden, the
Netherlands (A.J.v.d.M.)
| | - Veit Sandfort
- From the Department of Radiology (L.R.K., D.M., A.S.W., V.S., D.F.,
M.J.W.) and Stanford Cardiovascular Institute (D.M., D.F., M.J.W.), Stanford
University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305-5105;
Department of Radiology, University of Wisconsin–Madison, School of
Medicine and Public Health, Madison, Wis (T.P.S.); Department of Radiology,
Erasmus Medical Center, Rotterdam, the Netherlands (N.R.v.d.W.); Clinical
Science Western Europe, Philips Healthcare, Best, the Netherlands (N.R.v.d.W.);
and Department of Radiology, Leiden University Medical Center, Leiden, the
Netherlands (A.J.v.d.M.)
| | - Aart J. van der Molen
- From the Department of Radiology (L.R.K., D.M., A.S.W., V.S., D.F.,
M.J.W.) and Stanford Cardiovascular Institute (D.M., D.F., M.J.W.), Stanford
University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305-5105;
Department of Radiology, University of Wisconsin–Madison, School of
Medicine and Public Health, Madison, Wis (T.P.S.); Department of Radiology,
Erasmus Medical Center, Rotterdam, the Netherlands (N.R.v.d.W.); Clinical
Science Western Europe, Philips Healthcare, Best, the Netherlands (N.R.v.d.W.);
and Department of Radiology, Leiden University Medical Center, Leiden, the
Netherlands (A.J.v.d.M.)
| | - Dominik Fleischmann
- From the Department of Radiology (L.R.K., D.M., A.S.W., V.S., D.F.,
M.J.W.) and Stanford Cardiovascular Institute (D.M., D.F., M.J.W.), Stanford
University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305-5105;
Department of Radiology, University of Wisconsin–Madison, School of
Medicine and Public Health, Madison, Wis (T.P.S.); Department of Radiology,
Erasmus Medical Center, Rotterdam, the Netherlands (N.R.v.d.W.); Clinical
Science Western Europe, Philips Healthcare, Best, the Netherlands (N.R.v.d.W.);
and Department of Radiology, Leiden University Medical Center, Leiden, the
Netherlands (A.J.v.d.M.)
| | - Martin J. Willemink
- From the Department of Radiology (L.R.K., D.M., A.S.W., V.S., D.F.,
M.J.W.) and Stanford Cardiovascular Institute (D.M., D.F., M.J.W.), Stanford
University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305-5105;
Department of Radiology, University of Wisconsin–Madison, School of
Medicine and Public Health, Madison, Wis (T.P.S.); Department of Radiology,
Erasmus Medical Center, Rotterdam, the Netherlands (N.R.v.d.W.); Clinical
Science Western Europe, Philips Healthcare, Best, the Netherlands (N.R.v.d.W.);
and Department of Radiology, Leiden University Medical Center, Leiden, the
Netherlands (A.J.v.d.M.)
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Agostini A, Borgheresi A, Mariotti F, Ottaviani L, Carotti M, Valenti M, Giovagnoni A. New frontiers in oncological imaging with Computed Tomography: from morphology to function. Semin Ultrasound CT MR 2023; 44:214-227. [DOI: 10.1053/j.sult.2023.03.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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19
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Lyu P, Liu N, Harrawood B, Solomon J, Wang H, Chen Y, Rigiroli F, Ding Y, Schwartz FR, Jiang H, Lowry C, Wang L, Samei E, Gao J, Marin D. Is it possible to use low-dose deep learning reconstruction for the detection of liver metastases on CT routinely? Eur Radiol 2023; 33:1629-1640. [PMID: 36323984 DOI: 10.1007/s00330-022-09206-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 08/28/2022] [Accepted: 09/26/2022] [Indexed: 11/06/2022]
Abstract
OBJECTIVES To compare the image quality and hepatic metastasis detection of low-dose deep learning image reconstruction (DLIR) with full-dose filtered back projection (FBP)/iterative reconstruction (IR). METHODS A contrast-detail phantom consisting of low-contrast objects was scanned at five CT dose index levels (10, 6, 3, 2, and 1 mGy). A total of 154 participants with 305 hepatic lesions who underwent abdominal CT were enrolled in a prospective non-inferiority trial with a three-arm design based on phantom results. Data sets with full dosage (13.6 mGy) and low dosages (9.5, 6.8, or 4.1 mGy) were acquired from two consecutive portal venous acquisitions, respectively. All images were reconstructed with FBP (reference), IR (control), and DLIR (test). Eleven readers evaluated phantom data sets for object detectability using a two-alternative forced-choice approach. Non-inferiority analyses were performed to interpret the differences in image quality and metastasis detection of low-dose DLIR relative to full-dose FBP/IR. RESULTS The phantom experiment showed the dose reduction potential from DLIR was up to 57% based on the reference FBP dose index. Radiation decreases of 30% and 50% resulted in non-inferior image quality and hepatic metastasis detection with DLIR compared to full-dose FBP/IR. Radiation reduction of 70% by DLIR performed inferiorly in detecting small metastases (< 1 cm) compared to full-dose FBP (difference: -0.112; 95% confidence interval [CI]: -0.178 to 0.047) and full-dose IR (difference: -0.123; 95% CI: -0.182 to 0.053) (p < 0.001). CONCLUSION DLIR enables a 50% dose reduction for detecting low-contrast hepatic metastases while maintaining comparable image quality to full-dose FBP and IR. KEY POINTS • Non-inferiority study showed that deep learning image reconstruction (DLIR) can reduce the dose to oncological patients with low-contrast lesions without compromising the diagnostic information. • Radiation dose levels for DLIR can be reduced to 50% of full-dose FBP and IR for detecting low-contrast hepatic metastases, while maintaining comparable image quality. • The reduction of radiation by 70% by DLIR is clinically acceptable but insufficient for detecting small low-contrast hepatic metastases (< 1 cm).
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Affiliation(s)
- Peijie Lyu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1 East Jianshe Road, Zhengzhou, 450052, Henan Province, China.,Department of Radiology, Duke University Medical Center, 2301 Erwin Rd, Box 3808, Durham, NC, 27710, USA
| | - Nana Liu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1 East Jianshe Road, Zhengzhou, 450052, Henan Province, China
| | - Brian Harrawood
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, 2424 Erwin Road, Suite 302, Durham, NC, 27705, USA
| | - Justin Solomon
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, 2424 Erwin Road, Suite 302, Durham, NC, 27705, USA
| | - Huixia Wang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1 East Jianshe Road, Zhengzhou, 450052, Henan Province, China
| | - Yan Chen
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1 East Jianshe Road, Zhengzhou, 450052, Henan Province, China
| | - Francesca Rigiroli
- Beth Israel Deaconess Medical Center Department of Radiology, Harvard Medical School, 1 Deaconess Rd, 330 Brookline Ave, Boston, MA, 02215, USA
| | - Yuqin Ding
- Department of Radiology, Duke University Medical Center, 2301 Erwin Rd, Box 3808, Durham, NC, 27710, USA.,Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 20032, China
| | - Fides Regina Schwartz
- Department of Radiology, Duke University Medical Center, 2301 Erwin Rd, Box 3808, Durham, NC, 27710, USA
| | - Hanyu Jiang
- Department of Radiology, Duke University Medical Center, 2301 Erwin Rd, Box 3808, Durham, NC, 27710, USA.,Department of Radiology, West China Hospital of Sichuan University, 37 Guoxue Alley, Chengdu, 610041, Sichuan Province, China
| | - Carolyn Lowry
- Clinical Imaging Physics Group, Duke University Health System, 2424 Erwin Rd, Ste. 302, Durham, NC, 27705, USA
| | - Luotong Wang
- CT Imaging Research Center, GE Healthcare China, No.1 Tongji South Road, Beijing, 100176, China
| | - Ehsan Samei
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, 2424 Erwin Road, Suite 302, Durham, NC, 27705, USA
| | - Jianbo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1 East Jianshe Road, Zhengzhou, 450052, Henan Province, China.
| | - Daniele Marin
- Department of Radiology, Duke University Medical Center, 2301 Erwin Rd, Box 3808, Durham, NC, 27710, USA
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Cao L, Liu X, Qu T, Cheng Y, Li J, Li Y, Chen L, Niu X, Tian Q, Guo J. Improving spatial resolution and diagnostic confidence with thinner slice and deep learning image reconstruction in contrast-enhanced abdominal CT. Eur Radiol 2023; 33:1603-1611. [PMID: 36190531 DOI: 10.1007/s00330-022-09146-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 08/22/2022] [Accepted: 09/05/2022] [Indexed: 12/01/2022]
Abstract
OBJECTIVE To evaluate image quality and diagnostic confidence improvement using a thin slice and a deep learning image reconstruction (DLIR) in contrast-enhanced abdominal CT. METHODS Forty patients with hepatic lesions in enhanced abdominal CT were retrospectively analyzed. Images in the portal phase were reconstructed at 5 mm and 1.25 mm slice thickness using the 50% adaptive statistical iterative reconstruction (ASIR-V) (ASIR-V50%) and at 1.25 mm using DLIR at medium (DLIR-M) and high (DLIR-H) settings. CT number and standard deviation of the hepatic parenchyma, spleen, portal vein, and subcutaneous fat were measured, and contrast-to-noise ratio (CNR) was calculated. Edge-rise-slope (ERS) was measured on the portal vein to reflect spatial resolution and the CT number skewness on liver parenchyma was calculated to reflect image texture. Two radiologists blindly assessed the overall image quality including subjective noise, image contrast, visibility of small structures using a 5-point scale, and object sharpness and lesion contour using a 4-point scale. RESULTS For the 1.25-mm images, DLIR significantly reduced image noise, improved CNR and overall subjective image quality compared to ASIR-V50%. Compared to the 5-mm ASIR-V50% images, DLIR images had significantly higher scores in the visibility and contour for small structures and lesions; as well as significantly higher ERS and lower CT number skewness. At a quarter of the signal strength, the 1.25-mm DLIR-H images had a similar subjective noise score as the 5-mm ASIR-V50% images. CONCLUSION DLIR significantly reduces image noise and maintains a more natural image texture; image spatial resolution and diagnostic confidence can be improved using thin slice images and DLIR in abdominal CT. KEY POINTS • DLIR further reduces image noise compared with ASIR-V while maintaining favorable image texture. • In abdominal CT, thinner slice images improve image spatial resolution and small object visualization but suffer from higher image noise. • Thinner slice images combined with DLIR in abdominal CT significantly suppress image noise for detecting low-density lesions while significantly improving image spatial resolution and overall image quality.
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Affiliation(s)
- Le Cao
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi Province, People's Republic of China
| | - Xiang Liu
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi Province, People's Republic of China
| | - Tingting Qu
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi Province, People's Republic of China
| | - Yannan Cheng
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi Province, People's Republic of China
| | - Jianying Li
- CT Research Center, GE Healthcare China, Beijing, 100176, China
| | - Yanan Li
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi Province, People's Republic of China
| | - Lihong Chen
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi Province, People's Republic of China
| | - Xinyi Niu
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi Province, People's Republic of China
| | - Qian Tian
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi Province, People's Republic of China
| | - Jianxin Guo
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi Province, People's Republic of China.
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Yang L, Liu H, Han J, Xu S, Zhang G, Wang Q, Du Y, Yang F, Zhao X, Shi G. Ultra-low-dose CT lung screening with artificial intelligence iterative reconstruction: evaluation via automatic nodule-detection software. Clin Radiol 2023:S0009-9260(23)00031-4. [PMID: 36948944 DOI: 10.1016/j.crad.2023.01.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 01/04/2023] [Accepted: 01/15/2023] [Indexed: 02/05/2023]
Abstract
AIM To test the feasibility of ultra-low-dose (ULD) computed tomography (CT) combined with an artificial intelligence iterative reconstruction (AIIR) algorithm for screening pulmonary nodules using computer-assisted diagnosis (CAD). MATERIALS AND METHODS A chest phantom with artificial pulmonary nodules was first scanned using the routine protocol and the ULD protocol (3.28 versus 0.18 mSv) to compare the image quality and to test the acceptability of the ULD CT protocol. Next, 147 lung-screening patients were enrolled prospectively, undergoing an additional ULD CT immediately after their routine CT examination for clinical validation. Images were reconstructed with filtered back-projection (FBP), hybrid iterative reconstruction (HIR), the AIIR, and were imported to the CAD software for preliminary nodule detection. Subjective image quality on the phantom was scored using a five-point scale and compared using the Mann-Whitney U-test. Nodule detection using CAD was evaluated for ULD HIR and AIIR images using the routine dose image as reference. RESULTS Higher image quality was scored for AIIR than for FBP and HIR at ULD (p<0.001). As reported by CAD, 107 patients were presented with more than five nodules on routine dose images and were chosen to represent the challenging cases at an early stage of pulmonary disease. Among such, the performance of nodule detection by CAD on ULD HIR and AIIR images was 75.2% and 92.2% of the routine dose image, respectively. CONCLUSION Combined with AIIR, it was feasible to use an ULD CT protocol with 95% dose reduction for CAD-based screening of pulmonary nodules.
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Affiliation(s)
- L Yang
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - H Liu
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - J Han
- United Imaging Healthcare, Shanghai, China
| | - S Xu
- United Imaging Healthcare, Shanghai, China
| | - G Zhang
- United Imaging Healthcare, Shanghai, China
| | - Q Wang
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Y Du
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - F Yang
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - X Zhao
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - G Shi
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
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Zhao K, Jiang B, Zhang S, Zhang L, Zhang L, Feng Y, Li J, Zhang Y, Xie X. Measurement Accuracy and Repeatability of RECIST-Defined Pulmonary Lesions and Lymph Nodes in Ultra-Low-Dose CT Based on Deep Learning Image Reconstruction. Cancers (Basel) 2022; 14:5016. [PMID: 36291800 PMCID: PMC9599467 DOI: 10.3390/cancers14205016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 10/11/2022] [Indexed: 08/16/2023] Open
Abstract
BACKGROUND Deep learning image reconstruction (DLIR) improves image quality. We aimed to compare the measured diameter of pulmonary lesions and lymph nodes between DLIR-based ultra-low-dose CT (ULDCT) and contrast-enhanced CT. METHODS The consecutive adult patients with noncontrast chest ULDCT (0.07-0.14 mSv) and contrast-enhanced CT (2.38 mSv) were prospectively enrolled. Patients with poor image quality and body mass index ≥ 30 kg/m2 were excluded. The diameter of pulmonary target lesions and lymph nodes defined by Response Evaluation Criteria in Solid Tumors (RECIST) was measured. The measurement variability between ULDCT and enhanced CT was evaluated by Bland-Altman analysis. RESULTS The 141 enrolled patients (62 ± 12 years) had 89 RECIST-defined measurable pulmonary target lesions (including 30 malignant lesions, mainly adenocarcinomas) and 45 measurable mediastinal lymph nodes (12 malignant). The measurement variation of pulmonary lesions between high-strength DLIR (DLIR-H) images of ULDCT and contrast-enhanced CT was 2.2% (95% CI: 1.7% to 2.6%) and the variation of lymph nodes was 1.4% (1.0% to 1.9%). CONCLUSIONS The measured diameters of pulmonary lesions and lymph nodes in DLIR-H images of ULDCT are highly close to those of contrast-enhanced CT. DLIR-based ULDCT may facilitate evaluating target lesions with greatly reduced radiation exposure in tumor evaluation and lung cancer screening.
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Affiliation(s)
- Keke Zhao
- Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai 200080, China
| | - Beibei Jiang
- Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai 200080, China
| | - Shuai Zhang
- CT Imaging Research Center, GE Healthcare China, Shanghai 201203, China
| | - Lu Zhang
- Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai 200080, China
| | - Lin Zhang
- Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai 200080, China
| | - Yan Feng
- Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai 200080, China
| | - Jianying Li
- CT Imaging Research Center, GE Healthcare China, Shanghai 201203, China
| | - Yaping Zhang
- Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai 200080, China
| | - Xueqian Xie
- Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai 200080, China
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Xu JJ, Lönn L, Budtz-Jørgensen E, Hansen KL, Ulriksen PS. Quantitative and qualitative assessments of deep learning image reconstruction in low-keV virtual monoenergetic dual-energy CT. Eur Radiol 2022; 32:7098-7107. [PMID: 35895120 DOI: 10.1007/s00330-022-09018-5] [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: 12/23/2021] [Revised: 06/01/2022] [Accepted: 07/04/2022] [Indexed: 11/28/2022]
Abstract
OBJECTIVES To evaluate a novel deep learning image reconstruction (DLIR) technique for dual-energy CT (DECT) derived virtual monoenergetic (VM) images compared to adaptive statistical iterative reconstruction (ASIR-V) in low kiloelectron volt (keV) images. METHODS We analyzed 30 venous phase acute abdominal DECT (80/140 kVp) scans. Data were reconstructed to ASIR-V and DLIR-High at four different keV levels (40, 50, 74, and 100) with 1- and 3-mm slice thickness. Quantitative Hounsfield unit (HU) and noise assessment were measured within the liver, aorta, fat, and muscle. Subjective assessment of image noise, sharpness, texture, and overall quality was performed by two board-certified radiologists. RESULTS DLIR reduced image noise by 19.9-35.5% (p < 0.001) compared to ASIR-V in all reconstructions at identical keV levels. Contrast-to-noise ratio (CNR) increased by 49.2-53.2% (p < 0.001) in DLIR 40-keV images compared to ASIR-V 50 keV, while no significant difference in noise was identified except for 1 and 3 mm in aorta and for 1-mm liver measurements, where ASIR-V 50 keV showed 5.5-6.8% (p < 0.002) lower noise levels. Qualitative assessment demonstrated significant improvement particularly in 1-mm reconstructions (p < 0.001). Lastly, DLIR 40 keV demonstrated comparable or improved image quality ratings when compared to ASIR-V 50 keV (p < 0.001 to 0.22). CONCLUSION DLIR significantly reduced image noise compared to ASIR-V. Qualitative assessment showed that DLIR significantly improved image quality particularly in thin sliced images. DLIR may facilitate 40 keV as a new standard for routine low-keV VM reconstruction in contrast-enhanced abdominal DECT. KEY POINTS • DLIR enables 40 keV as the routine low-keV VM reconstruction. • DLIR significantly reduced image noise compared to ASIR-V, across a wide range of keV levels in VM DECT images. • In low-keV VM reconstructions, improvements in image quality using DLIR were most evident and consistent in 1-mm sliced images.
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Affiliation(s)
- Jack Junchi Xu
- Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark. .,Department of Clinical Medicine, University of Copenhagen, 2100, Copenhagen, Denmark.
| | - Lars Lönn
- Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, 2100, Copenhagen, Denmark
| | - Esben Budtz-Jørgensen
- Section of Biostatistics, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Kristoffer L Hansen
- Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, 2100, Copenhagen, Denmark
| | - Peter S Ulriksen
- Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark
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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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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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Tamura A, Mukaida E, Ota Y, Nakamura I, Arakita K, Yoshioka K. Deep learning reconstruction allows low-dose imaging while maintaining image quality: comparison of deep learning reconstruction and hybrid iterative reconstruction in contrast-enhanced abdominal CT. Quant Imaging Med Surg 2022; 12:2977-2984. [PMID: 35502368 PMCID: PMC9014148 DOI: 10.21037/qims-21-1216] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 02/09/2022] [Indexed: 09/19/2023]
Abstract
We aimed to compare the radiation dose and image quality of a low-dose abdominal computed tomography (CT) protocol reconstructed with deep learning reconstruction (DLR) with those of a routine-dose protocol reconstructed with hybrid-iterative reconstruction. This retrospective study enrolled 71 patients [61 men; average age, 71.9 years; mean body mass index (BMI), 24.3 kg/m2] who underwent both low-dose abdominal CT with DLR [advanced intelligent clear-IQ engine (AiCE)] and routine-dose abdominal CT with hybrid-iterative reconstruction [adaptive iterative dose reduction 3D (AIDR 3D)]. Radiation dose parameters included volume CT dose index (CTDIvol), effective dose (ED), and size-specific dose estimate (SSDE). Mean image noise and contrast-to-noise ratio (CNR) were calculated. Image noise was measured in the hepatic parenchyma and bilateral erector spinae muscles. Moreover, subjective assessment of perceived image quality and diagnostic acceptability was performed. The low-dose protocol helped reduce the CTDIvol by 44.3%, ED by 43.7%, and SSDE by 44.9%. Moreover, the noise was significantly lower and CNR significantly higher with the low-dose protocol than with the normal-dose protocol (P<0.001). In the subjective assessment of image quality, there was no significant difference between the protocols with regard to image noise. Overall, AiCE was superior to AIDR 3D in terms of diagnostic acceptability (P=0.001). The use of AiCE can reduce overall radiation dose by more than 40% without loss of image quality compared to routine-dose abdominal CT with AIDR 3D.
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Affiliation(s)
- Akio Tamura
- Department of Radiology, Iwate Medical University School of Medicine, Iwate, Japan
| | - Eisuke Mukaida
- Department of Radiology, Iwate Medical University School of Medicine, Iwate, Japan
| | - Yoshitaka Ota
- Division of Central Radiology, Iwate Medical University Hospital, Iwate, Japan
| | - Iku Nakamura
- Iwate Medical University School of Medicine, Iwate, Japan
| | - Kazumasa Arakita
- Healthcare IT Development Center, Canon Medical Systems Corporation, Otawara, Japan
| | - Kunihiro Yoshioka
- Department of Radiology, Iwate Medical University School of Medicine, Iwate, Japan
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Jensen CT, Gupta S, Saleh MM, Liu X, Wong VK, Salem U, Qiao W, Samei E, Wagner-Bartak NA. Reduced-Dose Deep Learning Reconstruction for Abdominal CT of Liver Metastases. Radiology 2022; 303:90-98. [PMID: 35014900 PMCID: PMC8962777 DOI: 10.1148/radiol.211838] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 10/19/2021] [Accepted: 10/28/2021] [Indexed: 12/22/2022]
Abstract
Background Assessment of liver lesions is constrained as CT radiation doses are lowered; evidence suggests deep learning reconstructions mitigate such effects. Purpose To evaluate liver metastases and image quality between reduced-dose deep learning image reconstruction (DLIR) and standard-dose filtered back projection (FBP) contrast-enhanced abdominal CT. Materials and Methods In this prospective Health Insurance Portability and Accountability Act-compliant study (September 2019 through April 2021), participants with biopsy-proven colorectal cancer and liver metastases at baseline CT underwent standard-dose and reduced-dose portal venous abdominal CT in the same breath hold. Three radiologists detected and characterized lesions at standard-dose FBP and reduced-dose DLIR, reported confidence, and scored image quality. Contrast-to-noise ratios for liver metastases were recorded. Summary statistics were reported, and a generalized linear mixed model was used. Results Fifty-one participants (mean age ± standard deviation, 57 years ± 13; 31 men) were evaluated. The mean volume CT dose index was 65.1% lower with reduced-dose CT (12.2 mGy) than with standard-dose CT (34.9 mGy). A total of 161 lesions (127 metastases, 34 benign lesions) with a mean size of 0.7 cm ± 0.3 were identified. Subjective image quality of reduced-dose DLIR was superior to that of standard-dose FBP (P < .001). The mean contrast-to-noise ratio for liver metastases of reduced-dose DLIR (3.9 ± 1.7) was higher than that of standard-dose FBP (3.5 ± 1.4) (P < .001). Differences in detection were identified only for lesions 0.5 cm or smaller: 63 of 65 lesions detected with standard-dose FBP (96.9%; 95% CI: 89.3, 99.6) and 47 lesions with reduced-dose DLIR (72.3%; 95% CI: 59.8, 82.7). Lesion accuracy with standard-dose FBP and reduced-dose DLIR was 80.1% (95% CI: 73.1, 86.0; 129 of 161 lesions) and 67.1% (95% CI: 59.3, 74.3; 108 of 161 lesions), respectively (P = .01). Lower lesion confidence was reported with a reduced dose (P < .001). Conclusion Deep learning image reconstruction (DLIR) improved CT image quality at 65% radiation dose reduction while preserving detection of liver lesions larger than 0.5 cm. Reduced-dose DLIR demonstrated overall inferior characterization of liver lesions and reader confidence. Clinical trial registration no. NCT03151564 © RSNA, 2022 Online supplemental material is available for this article.
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Affiliation(s)
- Corey T. Jensen
- From the Departments of Abdominal Imaging (C.T.J., S.G., M.M.S.,
V.K.W., U.S., N.A.W.B.), Physics (X.L.), and Biostatistics (W.Q.), the
University of Texas MD Anderson Cancer Center, 1400 Pressler St, Unit 1473,
Houston, TX 77030-4009; and Center for Virtual Imaging Trials, Carl E. Ravin
Advanced Imaging Laboratories, Clinical Imaging Physics Group, Medical Physics
Graduate Program, Departments of Radiology, Physics, Biomedical Engineering, and
Electrical and Computer Engineering, Duke University Medical Center, Durham, NC
(E.S.)
| | - Shiva Gupta
- From the Departments of Abdominal Imaging (C.T.J., S.G., M.M.S.,
V.K.W., U.S., N.A.W.B.), Physics (X.L.), and Biostatistics (W.Q.), the
University of Texas MD Anderson Cancer Center, 1400 Pressler St, Unit 1473,
Houston, TX 77030-4009; and Center for Virtual Imaging Trials, Carl E. Ravin
Advanced Imaging Laboratories, Clinical Imaging Physics Group, Medical Physics
Graduate Program, Departments of Radiology, Physics, Biomedical Engineering, and
Electrical and Computer Engineering, Duke University Medical Center, Durham, NC
(E.S.)
| | - Mohammed M. Saleh
- From the Departments of Abdominal Imaging (C.T.J., S.G., M.M.S.,
V.K.W., U.S., N.A.W.B.), Physics (X.L.), and Biostatistics (W.Q.), the
University of Texas MD Anderson Cancer Center, 1400 Pressler St, Unit 1473,
Houston, TX 77030-4009; and Center for Virtual Imaging Trials, Carl E. Ravin
Advanced Imaging Laboratories, Clinical Imaging Physics Group, Medical Physics
Graduate Program, Departments of Radiology, Physics, Biomedical Engineering, and
Electrical and Computer Engineering, Duke University Medical Center, Durham, NC
(E.S.)
| | - Xinming Liu
- From the Departments of Abdominal Imaging (C.T.J., S.G., M.M.S.,
V.K.W., U.S., N.A.W.B.), Physics (X.L.), and Biostatistics (W.Q.), the
University of Texas MD Anderson Cancer Center, 1400 Pressler St, Unit 1473,
Houston, TX 77030-4009; and Center for Virtual Imaging Trials, Carl E. Ravin
Advanced Imaging Laboratories, Clinical Imaging Physics Group, Medical Physics
Graduate Program, Departments of Radiology, Physics, Biomedical Engineering, and
Electrical and Computer Engineering, Duke University Medical Center, Durham, NC
(E.S.)
| | - Vincenzo K. Wong
- From the Departments of Abdominal Imaging (C.T.J., S.G., M.M.S.,
V.K.W., U.S., N.A.W.B.), Physics (X.L.), and Biostatistics (W.Q.), the
University of Texas MD Anderson Cancer Center, 1400 Pressler St, Unit 1473,
Houston, TX 77030-4009; and Center for Virtual Imaging Trials, Carl E. Ravin
Advanced Imaging Laboratories, Clinical Imaging Physics Group, Medical Physics
Graduate Program, Departments of Radiology, Physics, Biomedical Engineering, and
Electrical and Computer Engineering, Duke University Medical Center, Durham, NC
(E.S.)
| | - Usama Salem
- From the Departments of Abdominal Imaging (C.T.J., S.G., M.M.S.,
V.K.W., U.S., N.A.W.B.), Physics (X.L.), and Biostatistics (W.Q.), the
University of Texas MD Anderson Cancer Center, 1400 Pressler St, Unit 1473,
Houston, TX 77030-4009; and Center for Virtual Imaging Trials, Carl E. Ravin
Advanced Imaging Laboratories, Clinical Imaging Physics Group, Medical Physics
Graduate Program, Departments of Radiology, Physics, Biomedical Engineering, and
Electrical and Computer Engineering, Duke University Medical Center, Durham, NC
(E.S.)
| | - Wei Qiao
- From the Departments of Abdominal Imaging (C.T.J., S.G., M.M.S.,
V.K.W., U.S., N.A.W.B.), Physics (X.L.), and Biostatistics (W.Q.), the
University of Texas MD Anderson Cancer Center, 1400 Pressler St, Unit 1473,
Houston, TX 77030-4009; and Center for Virtual Imaging Trials, Carl E. Ravin
Advanced Imaging Laboratories, Clinical Imaging Physics Group, Medical Physics
Graduate Program, Departments of Radiology, Physics, Biomedical Engineering, and
Electrical and Computer Engineering, Duke University Medical Center, Durham, NC
(E.S.)
| | - Ehsan Samei
- From the Departments of Abdominal Imaging (C.T.J., S.G., M.M.S.,
V.K.W., U.S., N.A.W.B.), Physics (X.L.), and Biostatistics (W.Q.), the
University of Texas MD Anderson Cancer Center, 1400 Pressler St, Unit 1473,
Houston, TX 77030-4009; and Center for Virtual Imaging Trials, Carl E. Ravin
Advanced Imaging Laboratories, Clinical Imaging Physics Group, Medical Physics
Graduate Program, Departments of Radiology, Physics, Biomedical Engineering, and
Electrical and Computer Engineering, Duke University Medical Center, Durham, NC
(E.S.)
| | - Nicolaus A. Wagner-Bartak
- From the Departments of Abdominal Imaging (C.T.J., S.G., M.M.S.,
V.K.W., U.S., N.A.W.B.), Physics (X.L.), and Biostatistics (W.Q.), the
University of Texas MD Anderson Cancer Center, 1400 Pressler St, Unit 1473,
Houston, TX 77030-4009; and Center for Virtual Imaging Trials, Carl E. Ravin
Advanced Imaging Laboratories, Clinical Imaging Physics Group, Medical Physics
Graduate Program, Departments of Radiology, Physics, Biomedical Engineering, and
Electrical and Computer Engineering, Duke University Medical Center, Durham, NC
(E.S.)
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Jungblut L, Blüthgen C, Polacin M, Messerli M, Schmidt B, Euler A, Alkadhi H, Frauenfelder T, Martini K. First Performance Evaluation of an Artificial Intelligence-Based Computer-Aided Detection System for Pulmonary Nodule Evaluation in Dual-Source Photon-Counting Detector CT at Different Low-Dose Levels. Invest Radiol 2022; 57:108-114. [PMID: 34324462 DOI: 10.1097/rli.0000000000000814] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE The aim of this study was to evaluate the image quality (IQ) and performance of an artificial intelligence (AI)-based computer-aided detection (CAD) system in photon-counting detector computed tomography (PCD-CT) for pulmonary nodule evaluation at different low-dose levels. MATERIALS AND METHODS An anthropomorphic chest-phantom containing 14 pulmonary nodules of different sizes (range, 3-12 mm) was imaged on a PCD-CT and on a conventional energy-integrating detector CT (EID-CT). Scans were performed with each of the 3 vendor-specific scanning modes (QuantumPlus [Q+], Quantum [Q], and High Resolution [HR]) at decreasing matched radiation dose levels (volume computed tomography dose index ranging from 1.79 to 0.31 mGy) by adapting IQ levels from 30 to 5. Image noise was measured manually in the chest wall at 8 different locations. Subjective IQ was evaluated by 2 readers in consensus. Nodule detection and volumetry were performed using a commercially available AI-CAD system. RESULTS Subjective IQ was superior in PCD-CT compared with EID-CT (P < 0.001), and objective image noise was similar in the Q+ and Q-mode (P > 0.05) and superior in the HR-mode (PCD 55.8 ± 11.7 HU vs EID 74.8 ± 5.4 HU; P = 0.01). High resolution showed the lowest image noise values among PCD modes (P = 0.01). Overall, the AI-CAD system delivered comparable results for lung nodule detection and volumetry between PCD- and dose-matched EID-CT (P = 0.08-1.00), with a mean sensitivity of 95% for PCD-CT and of 86% for dose-matched EID-CT in the lowest evaluated dose level (IQ5). Q+ and Q-mode showed higher false-positive rates than EID-CT at lower-dose levels (IQ10 and IQ5). The HR-mode showed a sensitivity of 100% with a false-positive rate of 1 even at the lowest evaluated dose level (IQ5; CDTIvol, 0.41 mGy). CONCLUSIONS Photon-counting detector CT was superior to dose-matched EID-CT in subjective IQ while showing comparable to lower objective image noise. Fully automatized AI-aided nodule detection and volumetry are feasible in PCD-CT, but attention has to be paid to false-positive findings.
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Affiliation(s)
- Lisa Jungblut
- From the Institute of Diagnostic and Interventional Radiology
| | | | | | - Michael Messerli
- Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | | | - Andre Euler
- From the Institute of Diagnostic and Interventional Radiology
| | - Hatem Alkadhi
- From the Institute of Diagnostic and Interventional Radiology
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Li Y, Jiang Y, Yu X, Ren B, Wang C, Chen S, Ma D, Su D, Liu H, Ren X, Yang X, Gao J, Wu Y. Deep-learning image reconstruction for image quality evaluation and accurate bone mineral density measurement on quantitative CT: A phantom-patient study. Front Endocrinol (Lausanne) 2022; 13:884306. [PMID: 36034436 PMCID: PMC9403270 DOI: 10.3389/fendo.2022.884306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Accepted: 05/31/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND AND PURPOSE To investigate the image quality and accurate bone mineral density (BMD) on quantitative CT (QCT) for osteoporosis screening by deep-learning image reconstruction (DLIR) based on a multi-phantom and patient study. MATERIALS AND METHODS High-contrast spatial resolution, low-contrast detectability, modulation function test (MTF), noise power spectrum (NPS), and image noise were evaluated for physical image quality on Caphan 500 phantom. Three calcium hydroxyapatite (HA) inserts were used for accurate BMD measurement on European Spine Phantom (ESP). CT images were reconstructed with filtered back projection (FBP), adaptive statistical iterative reconstruction-veo 50% (ASiR-V50%), and three levels of DLIR(L/M/H). Subjective evaluation of the image high-contrast spatial resolution and low-contrast detectability were compared visually by qualified radiologists, whilst the statistical difference in the objective evaluation of the image high-contrast spatial resolution and low-contrast detectability, image noise, and relative measurement error were compared using one-way analysis of variance (ANOVA). Cohen's kappa coefficient (k) was performed to determine the interobserver agreement in qualitative evaluation between two radiologists. RESULTS Overall, for three levels of DLIR, 50% MTF was about 4.50 (lp/cm), better than FBP (4.12 lp/cm) and ASiR-V50% (4.00 lp/cm); the 2 mm low-contrast object was clearly resolved at a 0.5% contrast level, while 3mm at FBP and ASiR-V50%. As the strength level decreased and radiation dose increased, DLIR at three levels showed a higher NPS peak frequency and lower noise level, leading to leftward and rightward shifts, respectively. Measured L1, L2, and L3 were slightly lower than that of nominal HA inserts (44.8, 95.9, 194.9 versus 50.2, 100.6, 199.2mg/cm3) with a relative measurement error of 9.84%, 4.08%, and 2.60%. Coefficients of variance for the L1, L2, and L3 HA inserts were 1.51%, 1.41%, and 1.18%. DLIR-M and DLIR-H scored significantly better than ASiR-V50% in image noise (4.83 ± 0.34, 4.50 ± 0.50 versus 4.17 ± 0.37), image contrast (4.67 ± 0.73, 4.50 ± 0.70 versus 3.80 ± 0.99), small structure visibility (4.83 ± 0.70, 4.17 ± 0.73 versus 3.83 ± 1.05), image sharpness (3.83 ± 1.12, 3.53 ± 0.90 versus 3.27 ± 1.16), and artifacts (3.83 ± 0.90, 3.42 ± 0.37 versus 3.10 ± 0.83). The CT value, image noise, contrast noise ratio, and image artifacts in DLIR-M and DLIR-H outperformed ASiR-V50% and FBP (P<0.001), whilst it showed no statistically significant between DLIR-L and ASiR-V50% (P>0.05). The prevalence of osteoporosis was 74 (24.67%) in women and 49 (11.79%) in men, whilst the osteoporotic vertebral fracture rate was 26 (8.67%) in women and (5.29%) in men. CONCLUSION Image quality with DLIR was high-qualified without affecting the accuracy of BMD measurement. It has a potential clinical utility in osteoporosis screening.
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Park J, Shin J, Min IK, Bae H, Kim YE, Chung YE. Image Quality and Lesion Detectability of Lower-Dose Abdominopelvic CT Obtained Using Deep Learning Image Reconstruction. Korean J Radiol 2022; 23:402-412. [PMID: 35289146 PMCID: PMC8961013 DOI: 10.3348/kjr.2021.0683] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 10/22/2021] [Accepted: 10/31/2021] [Indexed: 11/15/2022] Open
Affiliation(s)
- June Park
- Department of Radiology, Seoul Medical Center, Seoul, Korea
| | - Jaeseung Shin
- Department of Radiology, Yonsei University College of Medicine, Seoul, Korea
| | - In Kyung Min
- Biostatistics Collaboration Unit, Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Korea
| | - Heejin Bae
- Department of Radiology, Yonsei University College of Medicine, Seoul, Korea
| | - Yeo-Eun Kim
- Department of Radiology, Seoul Medical Center, Seoul, Korea
| | - Yong Eun Chung
- Department of Radiology, Yonsei University College of Medicine, Seoul, Korea
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Benz DC, Ersözlü S, Mojon FLA, Messerli M, Mitulla AK, Ciancone D, Kenkel D, Schaab JA, Gebhard C, Pazhenkottil AP, Kaufmann PA, Buechel RR. Radiation dose reduction with deep-learning image reconstruction for coronary computed tomography angiography. Eur Radiol 2021; 32:2620-2628. [PMID: 34792635 PMCID: PMC8921160 DOI: 10.1007/s00330-021-08367-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 09/20/2021] [Accepted: 09/28/2021] [Indexed: 11/29/2022]
Abstract
Objectives Deep-learning image reconstruction (DLIR) offers unique opportunities for reducing image noise without degrading image quality or diagnostic accuracy in coronary CT angiography (CCTA). The present study aimed at exploiting the capabilities of DLIR to reduce radiation dose and assess its impact on stenosis severity, plaque composition analysis, and plaque volume quantification. Methods This prospective study includes 50 patients who underwent two sequential CCTA scans at normal-dose (ND) and lower-dose (LD). ND scans were reconstructed with Adaptive Statistical Iterative Reconstruction-Veo (ASiR-V) 100%, and LD scans with DLIR. Image noise (in Hounsfield units, HU) and quantitative plaque volumes (in mm3) were assessed quantitatively. Stenosis severity was visually categorized into no stenosis (0%), stenosis (< 20%, 20–50%, 51–70%, 71–90%, 91–99%), and occlusion (100%). Plaque composition was classified as calcified, non-calcified, or mixed. Results Reduction of radiation dose from ND scans with ASiR-V 100% to LD scans with DLIR at the highest level (DLIR-H; 1.4 mSv vs. 0.8 mSv, p < 0.001) had no impact on image noise (28 vs. 27 HU, p = 0.598). Reliability of stenosis severity and plaque composition was excellent between ND scans with ASiR-V 100% and LD scans with DLIR-H (intraclass correlation coefficients of 0.995 and 0.974, respectively). Comparison of plaque volumes using Bland–Altman analysis revealed a mean difference of − 0.8 mm3 (± 2.5 mm3) and limits of agreement between − 5.8 and + 4.1 mm3. Conclusion DLIR enables a reduction in radiation dose from CCTA by 43% without significant impact on image noise, stenosis severity, plaque composition, and quantitative plaque volume. Key Points •Deep-learning image reconstruction (DLIR) enables radiation dose reduction by over 40% for coronary computed tomography angiography (CCTA). •Image noise remains unchanged between a normal-dose CCTA reconstructed by ASiR-V and a lower-dose CCTA reconstructed by DLIR. •There is no impact on the assessment of stenosis severity, plaque composition, and quantitative plaque volume between the two scans. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-021-08367-x.
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Affiliation(s)
- Dominik C Benz
- Department of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Ramistrasse 100, CH-8091, Zurich, Switzerland
| | - Sara Ersözlü
- Department of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Ramistrasse 100, CH-8091, Zurich, Switzerland
| | - François L A Mojon
- Department of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Ramistrasse 100, CH-8091, Zurich, Switzerland
| | - Michael Messerli
- Department of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Ramistrasse 100, CH-8091, Zurich, Switzerland
| | - Anna K Mitulla
- Department of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Ramistrasse 100, CH-8091, Zurich, Switzerland
| | - Domenico Ciancone
- Department of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Ramistrasse 100, CH-8091, Zurich, Switzerland
| | - David Kenkel
- Department of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Ramistrasse 100, CH-8091, Zurich, Switzerland
| | - Jan A Schaab
- Department of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Ramistrasse 100, CH-8091, Zurich, Switzerland
| | - Catherine Gebhard
- Department of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Ramistrasse 100, CH-8091, Zurich, Switzerland
| | - Aju P Pazhenkottil
- Department of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Ramistrasse 100, CH-8091, Zurich, Switzerland
| | - Philipp A Kaufmann
- Department of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Ramistrasse 100, CH-8091, Zurich, Switzerland
| | - Ronny R Buechel
- Department of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Ramistrasse 100, CH-8091, Zurich, Switzerland.
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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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Wang X, Zheng F, Xiao R, Liu Z, Li Y, Li J, Zhang X, Hao X, Zhang X, Guo J, Zhang Y, Xue H, Jin Z. Comparison of image quality and lesion diagnosis in abdominopelvic unenhanced CT between reduced-dose CT using deep learning post-processing and standard-dose CT using iterative reconstruction: A prospective study. Eur J Radiol 2021; 139:109735. [PMID: 33932717 DOI: 10.1016/j.ejrad.2021.109735] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Revised: 04/06/2021] [Accepted: 04/19/2021] [Indexed: 11/24/2022]
Abstract
PURPOSE To compare image quality and lesion diagnosis between reduced-dose abdominopelvic unenhanced computed tomography (CT) using deep learning (DL) post-processing and standard-dose CT using iterative reconstruction (IR). METHOD Totally 251 patients underwent two consecutive abdominopelvic unenhanced CT scans of the same range, including standard and reduced doses, respectively. In group A, standard-dose data were reconstructed by (blend 30 %) IR. In group B, reduced-dose data were reconstructed by filtered back projection reconstruction to obtain group B1 images, and post-processed using the DL algorithm (NeuAI denosing, Neusoft medical, Shenyang, China) with 50 % and 100 % weights to obtain group B2 and B3 images, respectively. Then, CT values of the liver, the second lumbar vertebral centrum, the erector spinae and abdominal subcutaneous fat were measured. CT values, noise levels, signal-to-noise ratios (SNRs), contrast-to-noise ratios (CNRs), radiation doses and subjective scores of image quality were compared. Subjective evaluations of low-density liver lesions were compared by diagnostic results from enhanced CT or Magnetic Resonance Imaging. RESULTS Groups B3 and B1 showed the lowest and highest noise levels, respectively (P < 0.001). The SNR and CNR in group B3 were highest (P < 0.001). The radiation dose in group B was reduced by 71.5 % on average compared to group A. Subjective scores in groups A and B2 were highest (P < 0.001). Diagnostic sensitivity and confidence for liver metastases in groups A and B2 were highest (P < 0.001). CONCLUSIONS Reduced-dose abdominopelvic unenhanced CT combined with DL post-processing could ensure image quality and satisfy diagnostic needs.
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Affiliation(s)
- Xiao Wang
- From the Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Fuling Zheng
- From the Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Ran Xiao
- From the Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Zhuoheng Liu
- From CT Business Unit, Neusoft Medical System Company, Shenyang, China
| | - Yutong Li
- From CT Business Unit, Neusoft Medical System Company, Shenyang, China
| | - Juan Li
- From the Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Xi Zhang
- From the Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Xuemin Hao
- From the Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Xinhu Zhang
- From the Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Jiawu Guo
- From the Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Yan Zhang
- From the Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Huadan Xue
- From the Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
| | - Zhengyu Jin
- From the Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
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