1
|
Zhu B, Yang Y. Quality assessment of abdominal CT images: an improved ResNet algorithm with dual-attention mechanism. Am J Transl Res 2024; 16:3099-3107. [PMID: 39114678 PMCID: PMC11301486 DOI: 10.62347/wkns8633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Accepted: 05/19/2024] [Indexed: 08/10/2024]
Abstract
OBJECTIVES To enhance medical image classification using a Dual-attention ResNet model and investigate the impact of attention mechanisms on model performance in a clinical setting. METHODS We utilized a dataset of medical images and implemented a Dual-attention ResNet model, integrating self-attention and spatial attention mechanisms. The model was trained and evaluated using binary and five-level quality classification tasks, leveraging standard evaluation metrics. RESULTS Our findings demonstrated substantial performance improvements with the Dual-attention ResNet model in both classification tasks. In the binary classification task, the model achieved an accuracy of 0.940, outperforming the conventional ResNet model. Similarly, in the five-level quality classification task, the Dual-attention ResNet model attained an accuracy of 0.757, highlighting its efficacy in capturing nuanced distinctions in image quality. CONCLUSIONS The integration of attention mechanisms within the ResNet model resulted in significant performance enhancements, showcasing its potential for improving medical image classification tasks. These results underscore the promising role of attention mechanisms in facilitating more accurate and discriminative analysis of medical images, thus holding substantial promise for clinical applications in radiology and diagnostics.
Collapse
Affiliation(s)
- Boying Zhu
- Shanghai Institute of Technical Physics, Chinese Academy of SciencesShanghai 200083, China
- University of Chinese Academy of SciencesBeijing 100049, China
| | - Yuanyuan Yang
- Shanghai Institute of Technical Physics, Chinese Academy of SciencesShanghai 200083, China
- University of Chinese Academy of SciencesBeijing 100049, China
| |
Collapse
|
2
|
Sadia RT, Chen J, Zhang J. CT image denoising methods for image quality improvement and radiation dose reduction. J Appl Clin Med Phys 2024; 25:e14270. [PMID: 38240466 PMCID: PMC10860577 DOI: 10.1002/acm2.14270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 12/15/2023] [Accepted: 12/28/2023] [Indexed: 02/13/2024] Open
Abstract
With the ever-increasing use of computed tomography (CT), concerns about its radiation dose have become a significant public issue. To address the need for radiation dose reduction, CT denoising methods have been widely investigated and applied in low-dose CT images. Numerous noise reduction algorithms have emerged, such as iterative reconstruction and most recently, deep learning (DL)-based approaches. Given the rapid advancements in Artificial Intelligence techniques, we recognize the need for a comprehensive review that emphasizes the most recently developed methods. Hence, we have performed a thorough analysis of existing literature to provide such a review. Beyond directly comparing the performance, we focus on pivotal aspects, including model training, validation, testing, generalizability, vulnerability, and evaluation methods. This review is expected to raise awareness of the various facets involved in CT image denoising and the specific challenges in developing DL-based models.
Collapse
Affiliation(s)
- Rabeya Tus Sadia
- Department of Computer ScienceUniversity of KentuckyLexingtonKentuckyUSA
| | - Jin Chen
- Department of Medicine‐NephrologyUniversity of Alabama at BirminghamBirminghamAlabamaUSA
| | - Jie Zhang
- Department of RadiologyUniversity of KentuckyLexingtonKentuckyUSA
| |
Collapse
|
3
|
Cao J, Mroueh N, Pisuchpen N, Parakh A, Lennartz S, Pierce TT, Kambadakone AR. Can 1.25 mm thin-section images generated with Deep Learning Image Reconstruction technique replace standard-of-care 5 mm images in abdominal CT? Abdom Radiol (NY) 2023; 48:3253-3264. [PMID: 37369922 DOI: 10.1007/s00261-023-03992-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 06/14/2023] [Accepted: 06/15/2023] [Indexed: 06/29/2023]
Abstract
BACKGROUND CT image reconstruction has evolved from filtered back projection to hybrid- and model-based iterative reconstruction. Deep learning-based image reconstruction is a relatively new technique that uses deep convolutional neural networks to improve image quality. OBJECTIVE To evaluate and compare 1.25 mm thin-section abdominal CT images reconstructed with deep learning image reconstruction (DLIR) with 5 mm thick images reconstructed with adaptive statistical iterative reconstruction (ASIR-V). METHODS This retrospective study included 52 patients (31 F; 56.9±16.9 years) who underwent abdominal CT scans between August-October 2019. Image reconstruction was performed to generate 5 mm images at 40% ASIR-V and 1.25 mm DLIR images at three strengths (low [DLIR-L], medium [DLIR-M], and high [DLIR-H]). Qualitative assessment was performed to determine image noise, contrast, visibility of small structures, sharpness, and artifact based on a 5-point-scale. Image preference determination was based on a 3-point-scale. Quantitative assessment included measurement of attenuation, image noise, and contrast-to-noise ratios (CNR). RESULTS Thin-section images reconstructed with DLIR-M and DLIR-H yielded better image quality scores than 5 mm ASIR-V reconstructed images. Mean qualitative scores of DLIR-H for noise (1.77 ± 0.71), contrast (1.6 ± 0.68), small structure visibility (1.42 ± 0.66), sharpness (1.34 ± 0.55), and image preference (1.11 ± 0.34) were the best (p<0.05). DLIR-M yielded intermediate scores. All DLIR reconstructions showed superior ratings for artifacts compared to ASIR-V (p<0.05), whereas each DLIR group performed comparably (p>0.05, 0.405-0.763). In the quantitative assessment, there were no significant differences in attenuation values between all reconstructions (p>0.05). However, DLIR-H demonstrated the lowest noise (9.17 ± 3.11) and the highest CNR (CNRliver = 26.88 ± 6.54 and CNRportal vein = 7.92 ± 3.85) (all p<0.001). CONCLUSION DLIR allows generation of thin-section (1.25 mm) abdominal CT images, which provide improved image quality with higher inter-reader agreement compared to 5 mm thick images reconstructed with ASIR-V. CLINICAL IMPACT Improved image quality of thin-section CT images reconstructed with DLIR has several benefits in clinical practice, such as improved diagnostic performance without radiation dose penalties.
Collapse
Affiliation(s)
- Jinjin Cao
- Abdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, White 270, Boston, MA, 02114-2696, USA
| | - Nayla Mroueh
- Abdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, White 270, Boston, MA, 02114-2696, USA
| | - Nisanard Pisuchpen
- Abdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, White 270, Boston, MA, 02114-2696, USA
- Department of Radiology, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand
| | - Anushri Parakh
- Abdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, White 270, Boston, MA, 02114-2696, USA
| | - Simon Lennartz
- Abdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, White 270, Boston, MA, 02114-2696, USA
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Kerpener Straße 62, 50937, Cologne, Germany
| | - Theodore T Pierce
- Abdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, White 270, Boston, MA, 02114-2696, USA
| | - Avinash R Kambadakone
- Abdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, White 270, Boston, MA, 02114-2696, USA.
| |
Collapse
|
4
|
Abdulkareem NK, Hajee SI, Hassan FF, Ibrahim IK, Al-Khalidi REH, Abdulqader NA. Investigating the slice thickness effect on noise and diagnostic content of single-source multi-slice computerized axial tomography. J Med Life 2023; 16:862-867. [PMID: 37675166 PMCID: PMC10478658 DOI: 10.25122/jml-2022-0188] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 12/11/2022] [Indexed: 09/08/2023] Open
Abstract
High-quality and detailed CT scan images are crucial for accurate diagnosis. Factors such as image noise and slice thickness affect image quality. This study aimed to determine the optimal slice thickness that minimized image noise while maintaining sufficient diagnostic information using the single-source computed tomography head protocol. Single-source CT images were examined using the Linux Operating system Ge Revolution 64-slice CT scanner, and a combination of statical analysis and DICOM CT image analysis was employed. The single-source energy head CT protocol was used to investigate the effect of slice thickness on noise and visibility in images. Different values of slice thickness 0.625, 1.25, 2.5, 3.75, 5, 7.5, and 10 were prepared, and then quantitative analysis was performed. Thinner slice thickness decreased image noise, increased visibility, and improved detection. Therefore, the balance between changing the thickness of the slice with the diagnostic content and image noise must be considered. Maximum slice thickness enhances CT image detail and structure despite more noise. Based on the results, a slice thickness of 1.25mm was identified as the optimal choice for reducing image noise and achieving better and more accurate detection using the single-source computed tomography head protocol. The study revealed that image noise tends to increase with greater slice thickness according to the Linux operating system. These findings can serve as a valuable guide for quality control methods in CT centers, emphasizing the need to determine the appropriate slice thickness to ensure an accurate diagnosis.
Collapse
Affiliation(s)
- Nashwan Karkhi Abdulkareem
- Department of Pharmacology and Medical Physics and Clinical Biochemistry, College of Medicine, Hawler Medical University, Erbil, Iraq
| | - Shereen Ismail Hajee
- Department of Pharmacology and Medical Physics and Clinical Biochemistry, College of Medicine, Hawler Medical University, Erbil, Iraq
| | - Fatiheea Fatihalla Hassan
- Department of Pharmacology and Medical Physics and Clinical Biochemistry, College of Medicine, Hawler Medical University, Erbil, Iraq
| | - Ilham Khalid Ibrahim
- Department of Pharmacology and Medical Physics and Clinical Biochemistry, College of Medicine, Hawler Medical University, Erbil, Iraq
| | | | | |
Collapse
|
5
|
Image Quality Evaluation in Dual-Energy CT of the Chest, Abdomen, and Pelvis in Obese Patients With Deep Learning Image Reconstruction. J Comput Assist Tomogr 2022; 46:604-611. [PMID: 35483100 DOI: 10.1097/rct.0000000000001316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE The aim of this study was to evaluate image quality in vascular and oncologic dual-energy computed tomography (CT) imaging studies performed with a deep learning (DL)-based image reconstruction algorithm in patients with body mass index of ≥30. METHODS Vascular and multiphase oncologic staging dual-energy CT examinations were evaluated. Two image reconstruction algorithms were applied to the dual-energy CT data sets: standard of care Adaptive Statistical Iterative Reconstruction (ASiR-V) and TrueFidelity DL image reconstruction at 2 levels (medium and high). Subjective quality criteria were independently evaluated by 4 abdominal radiologists, and interreader agreement was assessed. Signal-to-noise ratio (SNR) and contrast-to-noise ratio were compared between image reconstruction methods. RESULTS Forty-eight patients were included in this study, and the mean patient body mass index was 39.5 (SD, 7.36). TrueFidelity-High (DL-High) and TrueFidelity-Medium (DL-Med) image reconstructions showed statistically significant higher Likert scores compared with ASiR-V across all subjective image quality criteria (P < 0.001 for DL-High vs ASiR-V; P < 0.05 for DL-Med vs ASiR-V), and SNRs for aorta and liver were significantly higher for DL-High versus ASiR-V (P < 0.001). Contrast-to-noise ratio for aorta and SNR for aorta and liver were significantly higher for DL-Med versus ASiR-V (P < 0.05). CONCLUSIONS TrueFidelity DL image reconstruction provides improved image quality compared with ASiR-V in dual-energy CTs obtained in obese patients.
Collapse
|
6
|
Brendlin AS, Estler A, Plajer D, Lutz A, Grözinger G, Bongers MN, Tsiflikas I, Afat S, Artzner CP. AI Denoising Significantly Enhances Image Quality and Diagnostic Confidence in Interventional Cone-Beam Computed Tomography. Tomography 2022; 8:933-947. [PMID: 35448709 PMCID: PMC9031402 DOI: 10.3390/tomography8020075] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 03/15/2022] [Accepted: 03/18/2022] [Indexed: 11/16/2022] Open
Abstract
(1) To investigate whether interventional cone-beam computed tomography (cbCT) could benefit from AI denoising, particularly with respect to patient body mass index (BMI); (2) From 1 January 2016 to 1 January 2022, 100 patients with liver-directed interventions and peri-procedural cbCT were included. The unenhanced mask run and the contrast-enhanced fill run of the cbCT were reconstructed using weighted filtered back projection. Additionally, each dataset was post-processed using a novel denoising software solution. Place-consistent regions of interest measured signal-to-noise ratio (SNR) per dataset. Corrected mixed-effects analysis with BMI subgroup analyses compared objective image quality. Multiple linear regression measured the contribution of “Radiation Dose”, “Body-Mass-Index”, and “Mode” to SNR. Two radiologists independently rated diagnostic confidence. Inter-rater agreement was measured using Spearman correlation (r); (3) SNR was significantly higher in the denoised datasets than in the regular datasets (p < 0.001). Furthermore, BMI subgroup analysis showed significant SNR deteriorations in the regular datasets for higher patient BMI (p < 0.001), but stable results for denoising (p > 0.999). In regression, only denoising contributed positively towards SNR (0.6191; 95%CI 0.6096 to 0.6286; p < 0.001). The denoised datasets received overall significantly higher diagnostic confidence grades (p = 0.010), with good inter-rater agreement (r ≥ 0.795, p < 0.001). In a subgroup analysis, diagnostic confidence deteriorated significantly for higher patient BMI (p < 0.001) in the regular datasets but was stable in the denoised datasets (p ≥ 0.103).; (4) AI denoising can significantly enhance image quality in interventional cone-beam CT and effectively mitigate diagnostic confidence deterioration for rising patient BMI.
Collapse
|
7
|
Yamamuro O, Tsukijima M. [Effects of Image-based Noise Reduction Software on Magnetic Resonance Imaging]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2021; 77:1416-1423. [PMID: 34924478 DOI: 10.6009/jjrt.2021_jsrt_77.12.1416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
In magnetic resonance imaging (MRI) examinations, a trade-off is noted among acquisition time, resolution, and signal-to-noise ratio (SNR). High-resolution images are expected to improve the detection of small lesions. However, to ensure a high SNR, the imaging time must be extended. If the number of additions is reduced to shorten the imaging time, a reduction in slice thickness and in-plane resolution is necessary to ensure an adequate SNR. A combination of acceleration and denoising using deep learning has been previously reported. However, although it may be useful as a noise reduction technique onboard device, it cannot be used for general purposes. We studied the effects of a recently developed general-purpose image-based noise reduction software on MRI by measuring SNR and other parameters such as contrast, resolution, and noise power spectrum (NPS). NPS was influenced by the difference in processing mode, whereas contrast remained uninfluenced. Regarding resolution, the edge information was retained and was found to be better in iNoir 3D than in iNoir 2D. However, owing to the increased intensity of noise-reduction processing, the slope of the edge in the low-contrast area was smoothed, presenting a visually blurred impression.
Collapse
Affiliation(s)
- Osamu Yamamuro
- Department of Imaging Technology, East Nagoya Imaging Diagnosis Center.,Department of Imaging Technology, Nagoya Kyoritsu Hospital
| | | |
Collapse
|
8
|
Tamura A, Mukaida E, Ota Y, Kamata M, Abe S, Yoshioka K. Superior objective and subjective image quality of deep learning reconstruction for low-dose abdominal CT imaging in comparison with model-based iterative reconstruction and filtered back projection. Br J Radiol 2021; 94:20201357. [PMID: 34142867 PMCID: PMC8248220 DOI: 10.1259/bjr.20201357] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Objective: This study aimed to conduct objective and subjective comparisons of image quality among abdominal computed tomography (CT) reconstructions with deep learning reconstruction (DLR) algorithms, model-based iterative reconstruction (MBIR), and filtered back projection (FBP). Methods: Datasets from consecutive patients who underwent low-dose liver CT were retrospectively identified. Images were reconstructed using DLR, MBIR, and FBP. Mean image noise and contrast-to-noise ratio (CNR) were calculated, and noise, artifacts, sharpness, and overall image quality were subjectively assessed. Dunnett’s test was used for statistical comparisons. Results: Ninety patients (67 ± 12.7 years; 63 males; mean body mass index [BMI], 25.5 kg/m2) were included. The mean noise in the abdominal aorta and hepatic parenchyma of DLR was lower than that in FBP and MBIR (p < .001). For FBP and MBIR, image noise was significantly higher for obese patients than for those with normal BMI. The CNR for the abdominal aorta and hepatic parenchyma was higher for DLR than for FBP and MBIR (p < .001). MBIR images were subjectively rated as superior to FBP images in terms of noise, artifacts, sharpness, and overall quality (p < .001). DLR images were rated as superior to MBIR images in terms of noise (p < .001) and overall quality (p = .03). Conclusions: Based on objective and subjective comparisons, the image quality of DLR was found to be superior to that of MBIR and FBP on low-dose abdominal CT. DLR was the only method for which image noise was not higher for obese patients than for those with a normal BMI. Advances in knowledge: This study provides previously unavailable information on the properties of DLR systems and their clinical utility.
Collapse
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
| | - Masayoshi Kamata
- Division of Central Radiology, Iwate Medical University Hospital, Iwate, Japan
| | - Shun Abe
- Division of Central Radiology, Iwate Medical University Hospital, Iwate, Japan
| | - Kunihiro Yoshioka
- Department of Radiology, Iwate Medical University School of Medicine, Iwate, Japan
| |
Collapse
|