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Kataria B, Woisetschläger M, Nilsson Althén J, Sandborg M, Smedby Ö. Image quality assessments in abdominal CT: Relative importance of dose, iterative reconstruction strength and slice thickness. Radiography (Lond) 2024; 30:1563-1571. [PMID: 39378665 DOI: 10.1016/j.radi.2024.09.060] [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: 05/23/2024] [Revised: 08/20/2024] [Accepted: 09/23/2024] [Indexed: 10/10/2024]
Abstract
INTRODUCTION Low contrast resolution in abdominal computed tomography (CT) may be negatively affected by attempts to lower patient doses. Iterative reconstruction (IR) algorithms play a key role in mitigating this problem. The reconstructed slice thickness also influences image quality. The aim was to assess the interaction and influence of patient dose, slice thickness, and IR strength on image quality in abdominal CT. METHOD With a simultaneous acquisition, images at 42 and 98 mAs were obtained in 25 patients. Multiplanar images with slice thicknesses of 1, 2, and 3 mm and advanced modeled iterative reconstruction (ADMIRE) strengths of 3 (AD3) and 5 (AD5) were reconstructed. Four radiologists evaluated the images in a pairwise manner based on five image criteria. Ordinal logistic regression with mixed effects was used to evaluate the effect of tube load, ADMIRE strength, and slice thickness using the visual grading regression technique. RESULTS For all assessed image criteria, the regression analysis showed significantly (p < 0.001) higher image quality for AD5, but lower for tube load 42 mAs, and slice thicknesses of 1 mm and 2 mm, compared to the reference categories of AD3, 98 mAs, and 3 mm, respectively. AD5 at 2 mm was superior to AD3 at 3 mm for all image criteria studied. AD5 1 mm produced inferior image quality for liver parenchyma and overall image quality compared to AD3 3 mm. Interobserver agreement (ICC) ranged from 0.874 to 0.920. CONCLUSION ADMIRE 5 at 2 mm slice thickness may allow for further dose reductions due to its superiority when compared to ADMIRE 3 at 3 mm slice thickness. IMPLICATIONS FOR PRACTICE Combination of thinner slices and higher ADMIRE strength facilitates imaging at low dose.
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Affiliation(s)
- B Kataria
- Department of Radiology, Linköping University, Linköping, Sweden; Department of Health, Medicine & Caring Sciences, Linköping University, Linköping, Sweden; Center for Medical Image Science & Visualization (CMIV), Linköping University, Linköping, Sweden.
| | - M Woisetschläger
- Department of Radiology, Linköping University, Linköping, Sweden; Department of Health, Medicine & Caring Sciences, Linköping University, Linköping, Sweden; Center for Medical Image Science & Visualization (CMIV), Linköping University, Linköping, Sweden.
| | - J Nilsson Althén
- Department of Health, Medicine & Caring Sciences, Linköping University, Linköping, Sweden; Department of Medical Physics, Linköping University, Linköping, Sweden.
| | - M Sandborg
- Department of Health, Medicine & Caring Sciences, Linköping University, Linköping, Sweden; Center for Medical Image Science & Visualization (CMIV), Linköping University, Linköping, Sweden; Department of Medical Physics, Linköping University, Linköping, Sweden.
| | - Ö Smedby
- Department of Biomedical Engineering and Health Systems (MTH), KTH Royal Institute of Technology, Stockholm, Sweden.
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Jaruvongvanich V, Muangsomboon K, Teerasamit W, Suvannarerg V, Komoltri C, Thammakittiphan S, Lornimitdee W, Ritsamrej W, Chaisue P, Pongnapang N, Apisarnthanarak P. Optimizing computed tomography image reconstruction for focal hepatic lesions: Deep learning image reconstruction vs iterative reconstruction. Heliyon 2024; 10:e34847. [PMID: 39170325 PMCID: PMC11336302 DOI: 10.1016/j.heliyon.2024.e34847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 05/27/2024] [Accepted: 07/17/2024] [Indexed: 08/23/2024] Open
Abstract
Background Deep learning image reconstruction (DLIR) is a novel computed tomography (CT) reconstruction technique that minimizes image noise, enhances image quality, and enables radiation dose reduction. This study aims to compare the diagnostic performance of DLIR and iterative reconstruction (IR) in the evaluation of focal hepatic lesions. Methods We conducted a retrospective study of 216 focal hepatic lesions in 109 adult participants who underwent abdominal CT scanning at our institution. We used DLIR (low, medium, and high strength) and IR (0 %, 10 %, 20 %, and 30 %) techniques for image reconstruction. Four experienced abdominal radiologists independently evaluated focal hepatic lesions based on five qualitative aspects (lesion detectability, lesion border, diagnostic confidence level, image artifact, and overall image quality). Quantitatively, we measured and compared the level of image noise for each technique at the liver and aorta. Results There were significant differences (p < 0.001) among the seven reconstruction techniques in terms of lesion borders, image artifacts, and overall image quality. Low-strength DLIR (DLIR-L) exhibited the best overall image quality. Although high-strength DLIR (DLIR-H) had the least image noise and fewest artifacts, it also had the lowest scores for lesion borders and overall image quality. Image noise showed a weak to moderate positive correlation with participants' body mass index and waist circumference. Conclusions The optimal-strength DLIR significantly improved overall image quality for evaluating focal hepatic lesions compared to the IR technique. DLIR-L achieved the best overall image quality while maintaining acceptable levels of image noise and quality of lesion borders.
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Affiliation(s)
- Varin Jaruvongvanich
- Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Kobkun Muangsomboon
- Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Wanwarang Teerasamit
- Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Voraparee Suvannarerg
- Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Chulaluk Komoltri
- Division of Research and Development, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Sastrawut Thammakittiphan
- Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Wimonrat Lornimitdee
- Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Witchuda Ritsamrej
- Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Parinya Chaisue
- Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Napapong Pongnapang
- Department of Radiological Technology, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand
| | - Piyaporn Apisarnthanarak
- Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
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Park M, Lim S, Kim H, Kim JY, Lee Y. Optimization of smoothing parameter for block matching and 3D filtering algorithm in low-dose chest and abdominal computed tomography images. Appl Radiat Isot 2024; 210:111374. [PMID: 38805985 DOI: 10.1016/j.apradiso.2024.111374] [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: 11/24/2023] [Revised: 04/29/2024] [Accepted: 05/24/2024] [Indexed: 05/30/2024]
Abstract
Computed tomography (CT), known for its exceptionally high accuracy, is associated with a substantial dose of ionizing radiation. Low-dose protocols have been devised to address this issue; however, a reduction in the radiation dose can lead to a deficiency in the number of photons, resulting in quantum noise. Thus, the aim of this study was to optimize the smoothing parameter (σ-value) of the block matching and 3D filtering (BM3D) algorithm to effectively reduce noise in low-dose chest and abdominal CT images. Acquired images were subsequently analyze using quantitative evaluation metrics, including contrast to noise ratio (CNR), coefficient of variation (CV), and naturalness image quality evaluator (NIQE). Quantitative evaluation results demonstrated that the optimal σ-value for CNR, CV, and NIQE were 0.10, 0.11, and 0.09 in low-dose chest CT images respectively, whereas those in abdominal images were 0.12, 0.11, and 0.09, respectively. The average of the optimal σ-values, which produced the most improved results, was 0.10, considering both visual and quantitative evaluations. In conclusion, we demonstrated that the optimized BM3D algorithm with σ-value is effective for noise reduction in low-dose chest and abdominal CT images indicating its feasibility of in the clinical field.
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Affiliation(s)
- Minji Park
- Department of Health Science, General Graduate School of Gachon University, Incheon, Republic of Korea
| | - Sewon Lim
- Department of Health Science, General Graduate School of Gachon University, Incheon, Republic of Korea
| | - Hajin Kim
- Department of Health Science, General Graduate School of Gachon University, Incheon, Republic of Korea
| | - Jae-Young Kim
- Department of Biochemistry, School of Dentistry, IHBR, Kyungpook National University, Daegu, Republic of Korea
| | - Youngjin Lee
- Department of Radiological Science, Gachon University, Incheon, Republic of Korea.
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Han Y, Liu X, Zhang N, Wang Y, Ju M, Ding Y. LDCT image denoising algorithm based on two-dimensional variational mode decomposition and dictionary learning. Sci Rep 2024; 14:17487. [PMID: 39080367 PMCID: PMC11289268 DOI: 10.1038/s41598-024-68668-1] [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: 12/27/2023] [Accepted: 07/26/2024] [Indexed: 08/02/2024] Open
Abstract
Low-dose X-CT scanning method effectively reduces radiation hazards, however, reducing the radiation dose will introduce noise and artifacts during the projection process, resulting in a decrease in the quality of the reconstructed image. To address this problem, we combined 2D variational modal decomposition and dictionary learning. We proposed a low-dose CT (LDCT) image denoising algorithm based on an improved K-SVD algorithm with image decomposition. The dictionary obtained by K-SVD training lacks consideration of image structure information. To address this problem, we employ the two-dimensional variational mode decomposition (2D-VMD) method to decompose the image into distinct modal components. Through the adaptive learning of dictionaries based on the characteristics of each modal component, independent denoising processing is applied to each component, avoiding the loss of structural and detailed information in the image. In addition, we introduce the regularized orthogonal matching pursuit algorithm (ROMP) and dictionary atom optimization method to improve the sparse representation ability of the dictionary and reduce the impact of noise atoms on denoising performance. The experiments show that the proposed method outperforms other denoising methods regarding peak signal-to-noise ratio and structural similarity. The proposed method maintains the denoised image details and structural information while removing LDCT image noise and artifacts. The image quality after denoising is significantly improved and facilitates more accurate detection and analysis of lesion areas.
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Affiliation(s)
- Yu Han
- School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, 130022, China
| | - Xuan Liu
- School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, 130022, China
| | - Nan Zhang
- School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, 130022, China
| | - Yingzhi Wang
- School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, 130022, China.
| | - Mingchi Ju
- School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, 130022, China
| | - Yan Ding
- School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, 130022, China
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Igarashi K, Imai K, Matsushima S, Yamauchi-Kawaura C, Fujii K. Development and validation of the effective CNR analysis method for evaluating the contrast resolution of CT images. Phys Eng Sci Med 2024; 47:717-727. [PMID: 38451464 PMCID: PMC11166862 DOI: 10.1007/s13246-024-01400-5] [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: 09/27/2023] [Accepted: 02/04/2024] [Indexed: 03/08/2024]
Abstract
Contrast resolution is an important index for evaluating the signal detectability of computed tomographic (CT) images. Recently, various noise reduction algorithms, such as iterative reconstruction (IR) and deep learning reconstruction (DLR), have been proposed to reduce the image noise in CT images. However, these algorithms cause changes in the image noise texture and blurred image signals in CT images. Furthermore, the contrast-to-noise ratio (CNR) cannot be accurately evaluated in CT images reconstructed using noise reduction methods. Therefore, in this study, we devised a new method, namely, "effective CNR analysis," for evaluating the contrast resolution of CT images. We verified whether the proposed algorithm could evaluate the effective contrast resolution based on the signal detectability of CT images. The findings showed that the effective CNR values obtained using the proposed method correlated well with the subjective visual impressions of CT images. To investigate whether signal detectability was appropriately evaluated using effective CNR analysis, the conventional CNR analysis method was compared with the proposed method. The CNRs of the IR and DLR images calculated using conventional CNR analysis were 13.2 and 10.7, respectively. By contrast, those calculated using the effective CNR analysis were estimated to be 0.7 and 1.1, respectively. Considering that the signal visibility of DLR images was superior to that of IR images, our proposed effective CNR analysis was shown to be appropriate for evaluating the contrast resolution of CT images.
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Affiliation(s)
- Kengo Igarashi
- Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, 1-1-20, Daiko-Minami, Higashi-ku, Nagoya, Aichi, 461-8673, Japan.
| | - Kuniharu Imai
- Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, 1-1-20, Daiko-Minami, Higashi-ku, Nagoya, Aichi, 461-8673, Japan
| | - Shigeru Matsushima
- Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, 1-1-20, Daiko-Minami, Higashi-ku, Nagoya, Aichi, 461-8673, Japan
| | - Chiyo Yamauchi-Kawaura
- Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, 1-1-20, Daiko-Minami, Higashi-ku, Nagoya, Aichi, 461-8673, Japan
| | - Keisuke Fujii
- Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, 1-1-20, Daiko-Minami, Higashi-ku, Nagoya, Aichi, 461-8673, Japan
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Im JY, Halliburton SS, Mei K, Perkins AE, Wong E, Roshkovan L, Sandvold OF, Liu LP, Gang GJ, Noël PB. Patient-derived PixelPrint phantoms for evaluating clinical imaging performance of a deep learning CT reconstruction algorithm. Phys Med Biol 2024; 69:115009. [PMID: 38604190 PMCID: PMC11097966 DOI: 10.1088/1361-6560/ad3dba] [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: 12/18/2023] [Revised: 03/22/2024] [Accepted: 04/11/2024] [Indexed: 04/13/2024]
Abstract
Objective. Deep learning reconstruction (DLR) algorithms exhibit object-dependent resolution and noise performance. Thus, traditional geometric CT phantoms cannot fully capture the clinical imaging performance of DLR. This study uses a patient-derived 3D-printed PixelPrint lung phantom to evaluate a commercial DLR algorithm across a wide range of radiation dose levels.Method. The lung phantom used in this study is based on a patient chest CT scan containing ground glass opacities and was fabricated using PixelPrint 3D-printing technology. The phantom was placed inside two different size extension rings to mimic a small- and medium-sized patient and was scanned on a conventional CT scanner at exposures between 0.5 and 20 mGy. Each scan was reconstructed using filtered back projection (FBP), iterative reconstruction, and DLR at five levels of denoising. Image noise, contrast to noise ratio (CNR), root mean squared error, structural similarity index (SSIM), and multi-scale SSIM (MS SSIM) were calculated for each image.Results.DLR demonstrated superior performance compared to FBP and iterative reconstruction for all measured metrics in both phantom sizes, with better performance for more aggressive denoising levels. DLR was estimated to reduce dose by 25%-83% in the small phantom and by 50%-83% in the medium phantom without decreasing image quality for any of the metrics measured in this study. These dose reduction estimates are more conservative compared to the estimates obtained when only considering noise and CNR.Conclusion. DLR has the capability of producing diagnostic image quality at up to 83% lower radiation dose, which can improve the clinical utility and viability of lower dose CT scans. Furthermore, the PixelPrint phantom used in this study offers an improved testing environment with more realistic tissue structures compared to traditional CT phantoms, allowing for structure-based image quality evaluation beyond noise and contrast-based assessments.
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Affiliation(s)
- Jessica Y Im
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States of America
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States of America
| | | | - Kai Mei
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Amy E Perkins
- Philips Healthcare, Cleveland, OH, United States of America
| | - Eddy Wong
- Philips Healthcare, Cleveland, OH, United States of America
| | - Leonid Roshkovan
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Olivia F Sandvold
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States of America
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Leening P Liu
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States of America
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Grace J Gang
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Peter B Noël
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States of America
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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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Cha BK, Lee KH, Lee Y, Kim K. Optimization Method to Predict Optimal Noise Reduction Parameters for the Non-Local Means Algorithm Based on the Scintillator Thickness in Radiography. SENSORS (BASEL, SWITZERLAND) 2023; 23:9803. [PMID: 38139649 PMCID: PMC10747373 DOI: 10.3390/s23249803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 12/09/2023] [Accepted: 12/12/2023] [Indexed: 12/24/2023]
Abstract
The resulting image obtained from an X-ray imaging system depends significantly on the characteristics of the detector. In particular, when an X-ray image is acquired by thinning the detector, a relatively large amount of noise inevitably occurs. In addition, when a thick detector is used to reduce noise in X-ray images, blurring increases and the ability to distinguish target areas deteriorates. In this study, we aimed to derive the optimal X-ray image quality by deriving the optimal noise reduction parameters based on the non-local means (NLM) algorithm. The detectors used were of two thicknesses (96 and 140 μm), and images were acquired based on the IEC 62220-1-1:2015 RQA-5 protocol. The optimal parameters were derived by calculating the edge preservation index and signal-to-noise ratio according to the sigma value of the NLM algorithm. As a result, a sigma value of the optimized NLM algorithm (0.01) was derived, and this algorithm was applied to a relatively thin X-ray detector system to obtain appropriate noise level and spatial resolution data. The no-reference-based blind/referenceless image spatial quality evaluator value, which analyzes the overall image quality, was best when using the proposed method. In conclusion, we propose an optimized NLM algorithm based on a new method that can overcome the noise amplification problem in thin X-ray detector systems and is expected to be applied in various photon imaging fields in the future.
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Affiliation(s)
- Bo Kyung Cha
- Precision Medical Device Research Center, Korea Electrotechnology Research Institute (KERI), 111 Hanggaul-ro, Sangnok-gu, Ansan-si 15588, Republic of Korea; (B.K.C.); (K.-H.L.)
| | - Kyeong-Hee Lee
- Precision Medical Device Research Center, Korea Electrotechnology Research Institute (KERI), 111 Hanggaul-ro, Sangnok-gu, Ansan-si 15588, Republic of Korea; (B.K.C.); (K.-H.L.)
| | - Youngjin Lee
- Department of Radiological Science, Gachon University, 191 Hambangmoe-ro, Yeonsu-gu, Incheon 21936, Republic of Korea
| | - Kyuseok Kim
- Department of Biomedical Engineering, Eulji University, 553 Sanseong-daero, Sujeong-gu, Seongnam-si 13135, Republic of Korea
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Skawran S, Sartoretti T, Gennari AG, Schwyzer M, Sartoretti E, Treyer V, Maurer A, Huellner MW, Waelti S, Messerli M. Evolution of CT radiation dose in pediatric patients undergoing hybrid 2-[ 18F]FDG PET/CT between 2007 and 2021. Br J Radiol 2023; 96:20220482. [PMID: 37751216 PMCID: PMC10646648 DOI: 10.1259/bjr.20220482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 09/16/2023] [Accepted: 09/20/2023] [Indexed: 09/27/2023] Open
Abstract
OBJECTIVES To evaluate the evolution of CT radiation dose in pediatric patients undergoing hybrid 2-[18F]fluoro-2-deoxy-D-glucose (2-[18F]FDG) PET/CT between 2007 and 2021. METHODS AND MATERIALS Data from all pediatric patients aged 0-18 years who underwent hybrid 2-[18F]FDG PET/CT of the body between January 2007 and May 2021 were reviewed. Demographic and imaging parameters were collected. A board-certified radiologist reviewed all CT scans and measured image noise in the brain, liver, and adductor muscles. RESULTS 294 scans from 167 children (72 females (43%); median age: 14 (IQR 10-15) years; BMI: median 17.5 (IQR 15-20.4) kg/m2) were included. CT dose index-volume (CTDIvol) and dose length product (DLP) both decreased significantly from 2007 to 2021 (both p < 0.001, Spearman's rho coefficients -0.46 and -0.35, respectively). Specifically, from 2007 to 2009 to 2019-2021 CTDIvol and DLP decreased from 2.94 (2.14-2.99) mGy and 309 (230-371) mGy*cm, respectively, to 0.855 (0.568-1.11) mGy and 108 (65.6-207) mGy*cm, respectively. From 2007 to 2021, image noise in the brain and liver remained constant (p = 0.26 and p = 0.06), while it decreased in the adductor muscles (p = 0.007). Peak tube voltage selection (in kilovolt, kV) of CT scans shifted from high kV imaging (140 or 120kVp) to low kV imaging (100 or 80kVp) (p < 0.001) from 2007 to 2021. CONCLUSION CT radiation dose in pediatric patients undergoing hybrid 2-[18F]FDG PET/CT has decreased in recent years equaling approximately one-third of the initial amount. ADVANCES IN KNOWLEDGE Over the past 15 years, CT radiation dose decreased considerably in pediatric patients undergoing hybrid imaging, while objective image quality may not have been compromised.
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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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Hirairi T, Ichikawa K, Urikura A, Kawashima H, Tabata T, Matsunami T. Improvement of diagnostic performance of hyperacute ischemic stroke in head CT using an image-based noise reduction technique with non-black-boxed process. Phys Med 2023; 112:102646. [PMID: 37549457 DOI: 10.1016/j.ejmp.2023.102646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 06/05/2023] [Accepted: 07/28/2023] [Indexed: 08/09/2023] Open
Abstract
PURPOSE This study aims to investigate whether an image-based noise reduction (INR) technique with a conventional rule-based algorithm involving no black-boxed processes can outperform an existing hybrid-type iterative reconstruction (HIR) technique, when applied to brain CT images for diagnosis of early CT signs, which generally exhibit low-contrast lesions that are difficult to detect. METHODS The subjects comprised 27 patients having infarctions within 4.5 h of onset and 27 patients with no change in brain parenchyma. Images with thicknesses of 5 mm and 0.625 mm were reconstructed by HIR. Images with a thickness of 0.625 mm reconstructed by filter back projection (FBP) were processed by INR. The contrast-to-noise ratios (CNRs) were calculated between gray and white matters; lentiform nucleus and internal capsule; infarcted and non-infarcted areas. Two radiologists subjectively evaluated the presence of hyperdense artery signs (HASs) and infarctions and visually scored three properties regarding image quality (0.625-mm HIR images were excluded because of their notably worse noise appearances). RESULTS The CNRs of INR were significantly better than those of HIR with P < 0.001 for all the indicators. INR yielded significantly higher areas under the curve for both infarction and HAS detections than HIR (P < 0.001). Also, INR significantly improved the visual scores of all the three indicators. CONCLUSION The INR incorporating a simple and reproducible algorithm was more effective than HIR in detecting early CT signs and can be potentially applied to CT images from a large variety of CT systems.
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Affiliation(s)
- Tetsuya Hirairi
- Department of Radiological Technology, Juntendo University Shizuoka Hospital, 1129 Nagaoka, Izunokuni, Shizuoka, 410-2295, Japan.
| | - Katsuhiro Ichikawa
- Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa, Ishikawa, 920-0942, Japan.
| | - Atsushi Urikura
- Department of Radiological Technology, Radiological Diagnosis, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuuouku, Tokyo, 104-0045, Japan.
| | - Hiroki Kawashima
- Faculty of Health Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa 920-0942, Japan.
| | - Takasumi Tabata
- Department of Radiology, Juntendo University Shizuoka Hospital, 1129 Nagaoka, Izunokuni, Shizuoka, 410-2295, Japan.
| | - Tamaki Matsunami
- Department of Radiology, Juntendo University Shizuoka Hospital, 1129 Nagaoka, Izunokuni, Shizuoka, 410-2295, Japan.
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12
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Nikolau EP, Toia GV, Nett B, Tang J, Szczykutowicz TP. A Characterization of Deep Learning Reconstruction Applied to Dual-Energy Computed Tomography Monochromatic and Material Basis Images. J Comput Assist Tomogr 2023; 47:437-444. [PMID: 36944100 DOI: 10.1097/rct.0000000000001442] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
Abstract
OBJECTIVE Advancements in computed tomography (CT) reconstruction have enabled image quality improvements and dose reductions. Previous advancements have included iterative and model-based reconstruction. The latest image reconstruction advancement uses deep learning, which has been evaluated for polychromatic imaging only. This article characterizes a commercially available deep learning imaging reconstruction applied to dual-energy CT. METHODS Monochromatic, iodine basis, and water basis images were reconstructed with filtered back projection (FBP), iterative (ASiR-V), and deep learning (DLIR) methods in a phantom experiment. Slice thickness, contrast-to-noise ratio, modulation transfer function, and noise power spectrum metrics were used to characterize ASiR-V and DLIR relative to FBP over a range of dose levels, phantom sizes, and iodine concentrations. RESULTS Slice thicknesses for ASiR-V and DLIR demonstrated no statistically significant difference relative to FBP for all measurement conditions. Contrast-to-noise ratio performance for DLIR-high and ASiR-V 40% at 2 mg I/mL on 40-keV images were 162% and 30% higher than FBP, respectively. Task-based modulation transfer function measurements demonstrated no clinically significant change between FBP and ASiR-V and DLIR on monochromatic or iodine basis images. CONCLUSIONS Deep learning image reconstruction enabled better image quality at lower monochromatic energies and on iodine basis images where image contrast is maximized relative to polychromatic or high-energy monochromatic images. Deep learning image reconstruction did not demonstrate thicker slices, decreased spatial resolution, or poor noise texture (ie, "plastic") relative to FBP.
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Affiliation(s)
| | - Giuseppe V Toia
- Radiology University of Wisconsin Madison School of Medicine and Public Health
| | - Brian Nett
- GE Healthcare, Waukesha Wisconsin, Waukesha; and
| | - Jie Tang
- GE Healthcare, Waukesha Wisconsin, Waukesha; and
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Katsuyama Y, Kojima T, Shirasaka T, Kondo M, Kato T. Characteristics of the deep learning-based virtual monochromatic image with fast kilovolt-switching CT: a phantom study. Radiol Phys Technol 2023; 16:77-84. [PMID: 36583827 DOI: 10.1007/s12194-022-00695-x] [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: 09/15/2022] [Revised: 12/19/2022] [Accepted: 12/23/2022] [Indexed: 12/31/2022]
Abstract
PURPOSE We assessed the physical properties of virtual monochromatic images (VMIs) obtained with different energy levels in various contrast settings and radiation doses using deep learning-based spectral computed tomography (DL-Spectral CT) and compared the results with those from single-energy CT (SECT) imaging. MATERIALS AND METHODS A Catphan® 600 phantom was scanned by DL-Spectral CT at various radiation doses. We reconstructed the VMIs obtained at 50, 70, and 100 keV. SECT (120 kVp) images were acquired at the same radiation doses. The standard deviations of the CT number and noise power spectrum (NPS) were calculated for noise characterization. We evaluated the spatial resolution by determining the 10% task-based transfer function (TTF) level, and we assessed the task-based detectability index (d'). RESULTS Regardless of the radiation dose, the noise was the lowest at 70 keV VMI. The NPS showed that the noise amplitude at all spatial frequencies was the lowest among other VMI and 120 kVp images. The spatial resolution was higher for 70 keV VMI compared to the other VMIs, except for high-contrast objects. The d' of 70 keV VMI was the highest among the VMI and 120 kVp images at all radiation doses and contrast settings. The d' of the 70 keV VMIs at the minimum dose was higher than that at the maximum dose in any other image. CONCLUSION The physical properties of the DL-Spectral CT VMIs varied with the energy level. The 70 keV VMI had the highest detectability by far among the VMI and 120-kVp images. DL-Spectral CT may be useful to reduce radiation doses.
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Affiliation(s)
- Yuna Katsuyama
- Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Fukuoka, Japan.
| | - Tsukasa Kojima
- Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Fukuoka, Japan.,Department of Health Sciences, Graduate school of Medical Sciences, Kyushu University, Fukuoka, Fukuoka, Japan
| | - Takashi Shirasaka
- Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Fukuoka, Japan
| | - Masatoshi Kondo
- Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Fukuoka, Japan
| | - Toyoyuki Kato
- Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Fukuoka, Japan
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14
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Lee HJ, Kim JS, Lee JK, Lee HA, Pak S. Ultra-low-dose hepatic multiphase CT using deep learning-based image reconstruction algorithm focused on arterial phase in chronic liver disease: A non-inferiority study. Eur J Radiol 2023; 159:110659. [PMID: 36584563 DOI: 10.1016/j.ejrad.2022.110659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 12/07/2022] [Accepted: 12/14/2022] [Indexed: 12/24/2022]
Abstract
PURPOSE This study determined whether image quality and detectability of ultralow-dose hepatic multiphase CT (ULDCT, 33.3% dose) using a vendor-agnostic deep learning model(DLM) are noninferior to those of standard-dose CT (SDCT, 100% dose) using model-based iterative reconstruction(MBIR) in patients with chronic liver disease focusing on arterial phase. METHODS Sixty-seven patients underwent hepatic multiphase CT using a dual-source scanner to obtain two different radiation dose CT scans (100%, SDCT and 33.3%, ULDCT). ULDCT using DLM and SDCT using MBIR were compared. A margin of -0.5 for the difference between the two protocols was pre-defined as noninferiority of the overall image quality of the arterial phase image. Quantitative image analysis (signal to noise ratio[SNR] and contrast to noise ratio[CNR]) was also conducted. The detectability of hepatic arterial focal lesions was compared using the Jackknife free-response receiver operating characteristic analysis. Non-inferiority was satisfied if the margin of the lower limit of 95%CI of the difference in figure-of-merit was less than -0.1. RESULTS Mean overall arterial phase image quality scores with ULDCT using DLM and SDCT using MBIR were 4.35 ± 0.57 and 4.08 ± 0.58, showing noninferiority (difference: -0.269; 95 %CI, -0.374 to -0.164). ULDCT using DLM showed a significantly superior contrast-to-noise ratio of arterial enhancing lesion (p < 0.05). Figure-of-merit for detectability of arterial hepatic focal lesion was 0.986 for ULDCT using DLM and 0.963 for SDCT using MBIR, showing noninferiority (difference: -0.023, 95 %CI: -0.016 to 0.063). CONCLUSION ULDCT using DLM with 66.7% dose reduction showed non-inferior overall image quality and detectability of arterial focal hepatic lesion compared to SDCT using MBIR.
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Affiliation(s)
- Hyun Joo Lee
- Department of Radiology, College of Medicine, Ewha Womans University, Seoul, Republic of Korea
| | - Jin Sil Kim
- Department of Radiology, College of Medicine, Ewha Womans University, Seoul, Republic of Korea.
| | - Jeong Kyong Lee
- Department of Radiology, College of Medicine, Ewha Womans University, Seoul, Republic of Korea
| | - Hye Ah Lee
- Clinical Trial Center, Mokdong Hospital, Ewha Womans University, Seoul, Republic of Korea
| | - Seongyong Pak
- Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology,Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
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15
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Inoue A, Voss BA, Lee NJ, Takahashi H, Kozaka K, Heiken JP, Ehman EC, Vasconcelos R, Fidler JL, Lee YS, Mileto A, Johnson MP, Baer-Beck M, Weber NM, Michalak GJ, Halaweish A, Carter RE, McCollough CH, Fletcher JG. Diagnostic Performance in Low- and High-Contrast Tasks of an Image-Based Denoising Algorithm Applied to Radiation Dose-Reduced Multiphase Abdominal CT Examinations. AJR Am J Roentgenol 2023; 220:73-85. [PMID: 35731096 DOI: 10.2214/ajr.22.27806] [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] [Indexed: 02/07/2023]
Abstract
BACKGROUND. Anatomic redundancy between phases can be used to achieve denoising of multiphase CT examinations. A limitation of iterative reconstruction (IR) techniques is that they generally require use of CT projection data. A frequency-split multi-band-filtration algorithm applies denoising to the multiphase CT images themselves. This method does not require knowledge of the acquisition process or integration into the reconstruction system of the scanner, and it can be implemented as a supplement to commercially available IR algorithms. OBJECTIVE. The purpose of the present study is to compare radiologists' performance for low-contrast and high-contrast diagnostic tasks (i.e., tasks for which differences in CT attenuation between the imaging target and its anatomic background are subtle or large, respectively) evaluated on multiphase abdominal CT between routine-dose images and radiation dose-reduced images processed by a frequency-split multiband-filtration denoising algorithm. METHODS. This retrospective single-center study included 47 patients who underwent multiphase contrast-enhanced CT for known or suspected liver metastases (a low-contrast task) and 45 patients who underwent multiphase contrast-enhanced CT for pancreatic cancer staging (a high-contrast task). Radiation dose-reduced images corresponding to dose reduction of 50% or more were created using a validated noise insertion technique and then underwent denoising using the frequency-split multi-band-filtration algorithm. Images were independently evaluated in multiple sessions by different groups of abdominal radiologists for each task (three readers in the low-contrast arm and four readers in the high-contrast arm). The noninferiority of denoised radiation dose-reduced images to routine-dose images was assessed using the jackknife alternative free-response ROC (JAFROC) figure-of-merit (FOM; limit of noninferiority, -0.10) for liver metastases detection and using the Cohen kappa statistic and reader confidence scores (100-point scale) for pancreatic cancer vascular invasion. RESULTS. For liver metastases detection, the JAFROC FOM for denoised radiation dose-reduced images was 0.644 (95% CI, 0.510-0.778), and that for routine-dose images was 0.668 (95% CI, 0.543-0.792; estimated difference, -0.024 [95% CI, -0.084 to 0.037]). Intraobserver agreement for pancreatic cancer vascular invasion was substantial to near perfect when the two image sets were compared (κ = 0.53-1.00); the 95% CIs of all differences in confidence scores between image sets contained zero. CONCLUSION. Multiphase contrast-enhanced abdominal CT images with a radiation dose reduction of 50% or greater that undergo denoising by a frequency-split multiband-filtration algorithm yield performance similar to that of routine-dose images for detection of liver metastases and vascular staging of pancreatic cancer. CLINICAL IMPACT. The image-based denoising algorithm facilitates radiation dose reduction of multiphase examinations for both low- and high-contrast diagnostic tasks without requiring manufacturer-specific hardware or software.
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Affiliation(s)
- Akitoshi Inoue
- Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905
- Present affiliation: Department of Radiology, Shiga University of Medical Science, Shiga, Japan
| | - Benjamin A Voss
- Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905
- Present affiliation: Department of Radiology, Medical College of Wisconsin, Milwaukee, WI
| | - Nam Ju Lee
- Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905
| | - Hiroaki Takahashi
- Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905
| | - Kazuto Kozaka
- Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905
- Present affiliation: Department of Radiology, Kanazawa University Graduate School of Medicine, Kanazawa, Japan
| | - Jay P Heiken
- Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905
| | - Eric C Ehman
- Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905
| | | | - Jeff L Fidler
- Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905
| | - Yong S Lee
- Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905
| | - Achille Mileto
- Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905
| | - Matthew P Johnson
- Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN
| | | | - Nikkole M Weber
- Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905
| | - Gregory J Michalak
- Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905
| | - Ahmed Halaweish
- Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905
- Siemens Medical Solutions USA, Malvern, PA
| | - Rickey E Carter
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL
| | | | - Joel G Fletcher
- Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905
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16
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Wu TH, Ting YL, Hwang YS, Chui CS, Lin CKJ. Validation of Virtual Monochromatic Images and Effect of Body Size Obtained Using a Rapid kVp-switching Dual-energy Computed Tomography System: A Phantom Study. HEALTH PHYSICS 2022; 123:287-294. [PMID: 35951348 DOI: 10.1097/hp.0000000000001596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The objective of this paper is to validate virtual monochromatic computed tomography (CT) numbers and the effect of the body size of insert materials in phantoms on the findings of a dual-energy CT scanner. The material inserted in the phantom simulates human organs. This study investigated the effect of different body sizes on CT numbers to understand the accuracy of dual-energy CT. The effect of body size on virtual monochromatic CT numbers was investigated using a QRM phantom. The true monochromatic CT numbers of insert materials were calculated from coefficients obtained using NIST XCOM. The true Z eff values were supplied by phantom manufacturers or computed using Mayneord's equation. The virtual monochromatic CT numbers of insert materials in both the phantoms varied with energy. The CT numbers of materials with a Z eff of >7.42 (water Z eff ) and <7.42 decreased and increased with energy, respectively. The CT numbers were affected by phantom size as a function of energy. For water, tissues, and air, the CT numbers in the XL phantom were considerably larger than those in other phantom sizes at 40 keV. Body size affected the CT numbers, particularly for the XL size and at low energies. For all materials, the magnitude of difference between the measured and true CT numbers was related to the Z eff of the materials, potentially because the photoelectric effect is more prominent at low energies for materials with a higher Z eff . The difference in CT numbers appeared to be dependent on position. The true and measured Z eff agreed to within 6% for all the materials except the SR2 brain, for which the discrepancy was 25%.
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Affiliation(s)
- Tung-Hsin Wu
- Service Unit, Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan, R.O.C
| | | | - Yi-Shuan Hwang
- Service Unit, Department of Medical Imaging and Intervention, New Taipei Municipal, TuCheng Hospital, New Taipei City, Taiwan, R.O.C
| | - Chen-Shou Chui
- Service Unit, Department of Radiology, Koo Foundation Sun Yat-Sen Cancer Center, Taipei, Taiwan, R.O.C
| | - Cristopher K J Lin
- Service Unit, Department of Radiology, Koo Foundation Sun Yat-Sen Cancer Center, Taipei, Taiwan, R.O.C
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A Review of Deep Learning CT Reconstruction: Concepts, Limitations, and Promise in Clinical Practice. CURRENT RADIOLOGY REPORTS 2022. [DOI: 10.1007/s40134-022-00399-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
Abstract
Abstract
Purpose of Review
Deep Learning reconstruction (DLR) is the current state-of-the-art method for CT image formation. Comparisons to existing filter back-projection, iterative, and model-based reconstructions are now available in the literature. This review summarizes the prior reconstruction methods, introduces DLR, and then reviews recent findings from DLR from a physics and clinical perspective.
Recent Findings
DLR has been shown to allow for noise magnitude reductions relative to filtered back-projection without suffering from “plastic” or “blotchy” noise texture that was found objectionable with most iterative and model-based solutions. Clinically, early reader studies have reported increases in subjective quality scores and studies have successfully implemented DLR-enabled dose reductions.
Summary
The future of CT image reconstruction is bright; deep learning methods have only started to tackle problems in this space via addressing noise reduction. Artifact mitigation and spectral applications likely be future candidates for DLR applications.
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Kawashima H, Ichikawa K, Takata T, Seto I. Comparative Assessment of Noise Properties for Two Deep Learning CT Image Reconstruction Techniques and Filtered Back Projection. Med Phys 2022; 49:6359-6367. [PMID: 36047991 DOI: 10.1002/mp.15918] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 07/25/2022] [Accepted: 08/03/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Two deep-learning image reconstruction (DLIR) techniques from two different CT vendors have recently been introduced into clinical practice. PURPOSE To characterize the noise properties of two DLIR techniques with different training methods, using a phantom containing a simple uniform and a complex non-uniform region. METHODS A water-bath phantom with a diameter of 300 mm was used as a base phantom. A textured phantom with a diameter of 128 mm, which was made of two materials, one equivalent to water and the other being 12 mg/mL diluted iodine, irregularly mixed to create a complex texture (non-uniform region), was placed in the base phantom. Thirty repeated phantom scans were performed using two CT scanners (GE, Revolution CT with Apex Edition; Canon, Aquilion One PRISM Edition) at two dose levels (CTDI: 5 and 15 mGy). Images were reconstructed with each CT system's filtered back projection (FBP) and DLIR [GE, TrueFidelity (TF); Canon, Advanced intelligent Clear-IQ Engine Body Sharp (AC)] for three process strengths. For basic characteristics of noise, the standard deviation (SD) and noise power spectrum (NPS) were measured for the uniform (water) region. A noise magnitude map was generated by calculating the inter-image SD at each pixel position across the 30 images. Then, a noise reduction map (NRM), which visualizes the relative differences in noise magnitude between FBP and DLIR, was calculated. The NRM values ranged from 0.0 to 1.0. A low NRM value represents a less aggressive noise reduction. The histograms of the NRM value were analyzed for the uniform and non-uniform regions. RESULTS The reduction in noise magnitude compared with FBP tended to be greater with AC (45%-85%) than with TF (32%-65%). The average NPS frequencies of TF and AC were almost comparable to those of FBP, except for the low-dose condition and the high noise reduction strength for AC. The NRM values of TF and AC were higher in the uniform region than in the non-uniform region. In the non-uniform region, TF's average NRM values (0.21-0.48) tended to be lower than AC's (0.39-0.78). The histograms for TF showed a small overlap between the uniform and the non-uniform regions; in contrast, those for AC showed a greater overlap. This difference seems to indicate that TF processes the uniform and non-uniform regions more differently than AC does. CONCLUSION This study has revealed a distinct difference in characteristics between the two DLIR techniques: TF tends to offer less aggressive noise reduction in non-uniform regions and preserve the original signals, whereas AC tends to prioritize noise filtering over edge-preservation, especially at the low-dose condition and with the high noise reduction strength. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Hiroki Kawashima
- Faculty of Health Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University
| | - Katsuhiro Ichikawa
- Faculty of Health Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University
| | | | - Issei Seto
- Department of Radiological Technology, Mitsubishi Kyoto Hospital
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19
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Chun M, Choi JH, Kim S, Ahn C, Kim JH. Fully automated image quality evaluation on patient CT: Multi-vendor and multi-reconstruction study. PLoS One 2022; 17:e0271724. [PMID: 35857804 PMCID: PMC9299323 DOI: 10.1371/journal.pone.0271724] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 07/06/2022] [Indexed: 12/21/2022] Open
Abstract
While the recent advancements of computed tomography (CT) technology have contributed in reducing radiation dose and image noise, an objective evaluation of image quality in patient scans has not yet been established. In this study, we present a patient-specific CT image quality evaluation method that includes fully automated measurements of noise level, structure sharpness, and alteration of structure. This study used the CT images of 120 patients from four different CT scanners reconstructed with three types of algorithm: filtered back projection (FBP), vendor-specific iterative reconstruction (IR), and a vendor-agnostic deep learning model (DLM, ClariCT.AI, ClariPi Inc.). The structure coherence feature (SCF) was used to divide an image into the homogeneous (RH) and structure edge (RS) regions, which in turn were used to localize the regions of interests (ROIs) for subsequent analysis of image quality indices. The noise level was calculated by averaging the standard deviations from five randomly selected ROIs on RH, and the mean SCFs on RS was used to estimate the structure sharpness. The structure alteration was defined by the standard deviation ratio between RS and RH on the subtraction image between FBP and IR or DLM, in which lower structure alterations indicate successful noise reduction without degradation of structure details. The estimated structure sharpness showed a high correlation of 0.793 with manually measured edge slopes. Compared to FBP, IR and DLM showed 34.38% and 51.30% noise reduction, 2.87% and 0.59% lower structure sharpness, and 2.20% and -12.03% structure alteration, respectively, on an average. DLM showed statistically superior performance to IR in all three image quality metrics. This study is expected to contribute to enhance the CT protocol optimization process by allowing a high throughput and quantitative image quality evaluation during the introduction or adjustment of lower-dose CT protocol into routine practice.
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Affiliation(s)
- Minsoo Chun
- Department of Radiation Oncology, Chung-Ang University Gwang Myeong Hospital, Gyeonggi-do, Republic of Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
| | - Jin Hwa Choi
- Department of Radiation Oncology, Chung-Ang University College of Medicine, Seoul, Republic of Korea
| | - Sihwan Kim
- Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea
| | - Chulkyun Ahn
- Department of Transdisciplinary Studies, Program in Biomedical Radiation Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea
- ClariPi Research, Seoul, Republic of Korea
| | - Jong Hyo Kim
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
- Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea
- Department of Transdisciplinary Studies, Program in Biomedical Radiation Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea
- ClariPi Research, Seoul, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Center for Medical-IT Convergence Technology Research, Advanced Institutes of Convergence Technology, Suwon, Republic of Korea
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20
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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: 15] [Impact Index Per Article: 7.5] [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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21
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Evaluation of Abdominal CT Obtained Using a Deep Learning-Based Image Reconstruction Engine Compared with CT Using Adaptive Statistical Iterative Reconstruction. J Belg Soc Radiol 2022; 106:15. [PMID: 35480337 PMCID: PMC8992765 DOI: 10.5334/jbsr.2638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 03/24/2022] [Indexed: 11/20/2022] Open
Abstract
Purpose: Materials and Methods: Results: Conclusions:
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22
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Li Q, Li S, Li R, Wu W, Dong Y, Zhao J, Qiang Y, Aftab R. Low-dose computed tomography image reconstruction via a multistage convolutional neural network with autoencoder perceptual loss network. Quant Imaging Med Surg 2022; 12:1929-1957. [PMID: 35284282 PMCID: PMC8899925 DOI: 10.21037/qims-21-465] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 12/01/2021] [Indexed: 07/05/2024]
Abstract
BACKGROUND Computed tomography (CT) is widely used in medical diagnoses due to its ability to non-invasively detect the internal structures of the human body. However, CT scans with normal radiation doses can cause irreversible damage to patients. The radiation exposure is reduced with low-dose CT (LDCT), although considerable speckle noise and streak artifacts in CT images and even structural deformation may result, significantly undermining its diagnostic capability. METHODS This paper proposes a multistage network framework which gradually divides the entire process into 2-staged sub-networks to complete the task of image reconstruction. Specifically, a dilated residual convolutional neural network (DRCNN) was used to denoise the LDCT image. Then, the learned context information was combined with the channel attention subnet, which retains local information, to preserve the structural details and features of the image and textural information. To obtain recognizable characteristic details, we introduced a novel self-calibration module (SCM) between the 2 stages to reweight the local features, which realizes the complementation of information at different stages while refining feature information. In addition, we also designed an autoencoder neural network, using a self-supervised learning scheme to train a perceptual loss neural network specifically for CT images. RESULTS We evaluated the diagnostic quality of the results and performed ablation experiments on the loss function and network structure modules to verify each module's effectiveness in the network. Our proposed network architecture obtained high peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and visual information fidelity (VIF) values in terms of quantitative evaluation. In the analysis of qualitative results, our network structure maintained a better balance between eliminating image noise and preserving image details. Experimental results showed that our proposed network structure obtained better metrics and visual evaluation. CONCLUSIONS This study proposed a new LDCT image reconstruction method by combining autoencoder perceptual loss networks with multistage convolutional neural networks (MSCNN). Experimental results showed that the newly proposed method has performance than other methods.
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Affiliation(s)
- Qing Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Saize Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Runrui Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Wei Wu
- Department of Clinical Laboratory, Affiliated People’s Hospital of Shanxi Medical University, Shanxi Provincial People’s Hospital, Taiyuan, China
| | - Yunyun Dong
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Juanjuan Zhao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Yan Qiang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Rukhma Aftab
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
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23
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Matsuura K, Ichikawa K, Kawashima H. Task-specific spatial resolution properties of iterative and deep learning-based reconstructions in computed tomography: Comparison using tasks assuming small and large enhanced vessels. Phys Med 2022; 95:64-72. [PMID: 35123172 DOI: 10.1016/j.ejmp.2022.01.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Revised: 12/15/2021] [Accepted: 01/26/2022] [Indexed: 10/19/2022] Open
Abstract
PURPOSE The present study aims to evaluate TTFs of deep-learning-based image reconstruction (DLIR) and iterative reconstruction (IR) in computed tomography (CT) using a conventional task with a rod object with a diameter of 30 mm and a newly-proposed task with a wire of 1 mm in diameter, simulating large and small enhanced vessels, respectively. METHODS The rod or wire phantom made of a material equivalent to diluted iodine that exhibits about 270 Hounsfield unit (HU) was placed inside a 30-cm water phantom. In-plane and z-directional TTFs were measured for the rod using the circular edge (CE) and plane edge (PE) methods, respectively. By using the wire (iodine wire: IW), in-plane and z-directional TTFs were measured using Fourier transform (IW method). TTFs of filtered back projection (FBP), IR, and DLIR of a 256-row CT system and FBP and IR of a 64-row CT system were evaluated with CT dose indices of 10 and 5 mGy. RESULTS For DLIR and IR, TTFs measured using the IW method were notably lower than those using the CE (or PE) method; moreover, they were also lower than those of corresponding FBP, indicating that the small enhanced vessels with a diameter of about 1 mm would be blurred with both DLIR and IR. CONCLUSIONS The proposed IW method has turned out to be effective to evaluate TTFs for small enhanced vessels, which have not been properly evaluated by the CE or PE method conventionally recommended.
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Affiliation(s)
- Kanae Matsuura
- Dept of Radiological Technology, Faculty of Health Science, Suzuka University of Medical Science, 1001-1 Kishioka-cho, Suzuka 510-0293, Japan; Division of Health Sciences, Graduate School of Medical Sciences, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa, 920-0942, Japan.
| | - Katsuhiro Ichikawa
- Faculty of Health Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa, 920-0942, Japan.
| | - Hiroki Kawashima
- Faculty of Health Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa, 920-0942, Japan.
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24
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Mikayama R, Shirasaka T, Kojima T, Sakai Y, Yabuuchi H, Kondo M, Kato T. Deep-learning reconstruction for ultra-low-dose lung CT: Volumetric measurement accuracy and reproducibility of artificial ground-glass nodules in a phantom study. Br J Radiol 2022; 95:20210915. [PMID: 34908478 PMCID: PMC8822562 DOI: 10.1259/bjr.20210915] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
OBJECTIVES The lung nodule volume determined by CT is used for nodule diagnoses and monitoring tumor responses to therapy. Increased image noise on low-dose CT degrades the measurement accuracy of the lung nodule volume. We compared the volumetric accuracy among deep-learning reconstruction (DLR), model-based iterative reconstruction (MBIR), and hybrid iterative reconstruction (HIR) at an ultra-low-dose setting. METHODS Artificial ground-glass nodules (6 mm and 10 mm diameters, -660 HU) placed at the lung-apex and the middle-lung field in chest phantom were scanned by 320-row CT with the ultra-low-dose setting of 6.3 mAs. Each scan data set was reconstructed by DLR, MBIR, and HIR. The volumes of nodules were measured semi-automatically, and the absolute percent volumetric error (APEvol) was calculated. The APEvol provided by each reconstruction were compared by the Tukey-Kramer method. Inter- and intraobserver variabilities were evaluated by a Bland-Altman analysis with limits of agreements. RESULTS DLR provided a lower APEvol compared to MBIR and HIR. The APEvol of DLR (1.36%) was significantly lower than those of the HIR (8.01%, p = 0.0022) and MBIR (7.30%, p = 0.0053) on a 10-mm-diameter middle-lung nodule. DLR showed narrower limits of agreement compared to MBIR and HIR in the inter- and intraobserver agreement of the volumetric measurement. CONCLUSIONS DLR showed higher accuracy compared to MBIR and HIR for the volumetric measurement of artificial ground-glass nodules by ultra-low-dose CT. ADVANCES IN KNOWLEDGE DLR with ultra-low-dose setting allows a reduction of dose exposure, maintaining accuracy for the volumetry of lung nodule, especially in patients which deserve a long-term follow-up.
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Affiliation(s)
- Ryoji Mikayama
- Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Japan
| | - Takashi Shirasaka
- Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Japan
| | | | - Yuki Sakai
- Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Japan
| | - Hidetake Yabuuchi
- Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Masatoshi Kondo
- Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Japan
| | - Toyoyuki Kato
- Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Japan
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Evaluation of Apparent Noise on CT Images Using Moving Average Filters. J Digit Imaging 2022; 35:77-85. [PMID: 34761322 PMCID: PMC8854541 DOI: 10.1007/s10278-021-00531-5] [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: 04/06/2021] [Revised: 09/15/2021] [Accepted: 10/26/2021] [Indexed: 02/03/2023] Open
Abstract
This study aims to devise a simple method for evaluating the magnitude of texture noise (apparent noise) observed on computed tomography (CT) images scanned at a low radiation dose and reconstructed using iterative reconstruction (IR) and deep learning reconstruction (DLR) algorithms, and to evaluate the apparent noise in CT images reconstructed using the filtered back projection (FBP), IR, and two types of DLR (AiCE Body and AiCE Body Sharp) algorithms. We set a square region of interest (ROI) on CT images of standard- and obese-sized low-contrast phantoms, slid different-sized moving average filters in the ROI vertically and horizontally in steps of 1 pixel, and calculated the standard deviation (SD) of the mean CT values for each filter size. The SD of the mean CT values was fitted with a curve inversely proportional to the filter size, and an apparent noise index was determined from the curve-fitting formula. The apparent noise index of AiCE Body Sharp images for a given mAs value was approximately 58, 23, and 18% lower than that of the FBP, AIDR 3D, and AiCE Body images, respectively. The apparent noise index was considered to reflect noise power spectrum values at lower spatial frequency. Moreover, the apparent noise index was inversely proportional to the square roots of the mAs values. Thus, the apparent noise index could be a useful indicator to quantify and compare texture noise on CT images obtained with different scan parameters and reconstruction algorithms.
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Zhao XY, Li LL, Song J, Chen J, Xu J, Liu B, Wu XW. Effects of Adaptive Statistical Iterative Reconstruction-V Technology on the Image Quality and Radiation Dose of Unenhanced and Enhanced CT Scans of the Piglet Abdomen. Radiat Res 2022; 197:157-165. [PMID: 34644380 DOI: 10.1667/rade-20-00244.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Accepted: 09/16/2021] [Indexed: 11/03/2022]
Abstract
To investigate the optimal pre- and post-adaptive statistical iterative reconstruction-V (ASiR-V) levels in pediatric abdominal computed tomography (CT) to minimize radiation exposure and maintain image quality using an animal model. A total of 10 standard piglets were selected and scanned to obtain unenhanced and enhanced images under different pre-ASiR-V conditions. The corresponding images were obtained using ASiR-V algorithm at different post-ASiR-V levels. CT value, signal-to-noise ratio (SNR), contrast noise ratio (CNR) of abdominal tissues, subjective image score, and radiation dose of unenhanced and enhanced scans were analyzed. With the increase of pre-ASiR-V level, the radiation dose in piglets gradually decreased (P < 0.05). Within the same group of pre-ASiR-V, the image noise was decreased (P < 0.05) by increasing post-ASiR-V level. There was no statistical difference between SNR and CNR values. In unenhanced CT, the subjective score of the images with the combination of 40% pre- and 60% post-ASiR-V levels had no statistical difference compared to the combination of 0% pre- and 60% post-ASiR-V levels, while the radiation dose decreased by 31.6%. In the enhanced CT, the subjective image score with the 60% pre- and 60% post-ASiR-V combination had no statistical difference compared to the 0% pre- and 60% post-ASiR-V combination, while the radiation dose was reduced by 48.9%. The combined use of pre- and post-ASiR-V maintains image quality at the reduced radiation dose. The optimal level for unenhanced CT is 40% pre-combined with 60% post-ASiR-V, while that for enhanced CT is 60% pre-combined with 60% post-ASiR-V in pediatric abdominal CT.
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Affiliation(s)
- Xiao-Ying Zhao
- First Affiliated Hospital of Anhui Medical University, Department of Radiology, Hefei 230022, China
| | - Lu-Lu Li
- First Affiliated Hospital of Anhui Medical University, Department of Radiology, Hefei 230022, China
| | - Jian Song
- First Affiliated Hospital of Anhui Medical University, Department of Radiology, Hefei 230022, China
| | - Jing Chen
- First Affiliated Hospital of Anhui Medical University, Department of Radiology, Hefei 230022, China
| | - Ji Xu
- Huangshan People's Hospital, Department of Radiology, Huangshan, 242700, China
| | - Bin Liu
- First Affiliated Hospital of Anhui Medical University, Department of Radiology, Hefei 230022, China
| | - Xing-Wang Wu
- First Affiliated Hospital of Anhui Medical University, Department of Radiology, Hefei 230022, China
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Park HS, Jeon K, Lee J, You SK. Denoising of pediatric low dose abdominal CT using deep learning based algorithm. PLoS One 2022; 17:e0260369. [PMID: 35061701 PMCID: PMC8782418 DOI: 10.1371/journal.pone.0260369] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 11/08/2021] [Indexed: 11/25/2022] Open
Abstract
OBJECTIVES To evaluate standard dose-like computed tomography (CT) images generated by a deep learning method, trained using unpaired low-dose CT (LDCT) and standard-dose CT (SDCT) images. MATERIALS AND METHODS LDCT (80 kVp, 100 mAs, n = 83) and SDCT (120 kVp, 200 mAs, n = 42) images were divided into training (42 LDCT and 42 SDCT) and validation (41 LDCT) sets. A generative adversarial network framework was used to train unpaired datasets. The trained deep learning method generated virtual SDCT images (VIs) from the original LDCT images (OIs). To test the proposed method, LDCT images (80 kVp, 262 mAs, n = 33) were collected from another CT scanner using iterative reconstruction (IR). Image analyses were performed to evaluate the qualities of VIs in the validation set and to compare the performance of deep learning and IR in the test set. RESULTS The noise of the VIs was the lowest in both validation and test sets (all p<0.001). The mean CT number of the VIs for the portal vein and liver was lower than that of OIs in both validation and test sets (all p<0.001) and was similar to those of SDCT. The contrast-to-noise ratio of portal vein and the signal-to-noise ratio (SNR) of portal vein and liver of VIs were higher than those of SDCT (all p<0.05). The SNR of VIs in test sets was the highest among three images. CONCLUSION The deep learning method trained by unpaired datasets could reduce noise of LDCT images and showed similar performance to SAFIRE. It can be applied to LDCT images of older CT scanners without IR.
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Affiliation(s)
- Hyoung Suk Park
- National Institute for Mathematical Sciences, Daejeon, Republic of Korea
| | - Kiwan Jeon
- National Institute for Mathematical Sciences, Daejeon, Republic of Korea
| | - JeongEun Lee
- Department of Radiology, Chungnam National University College of Medicine, Daejeon, Republic of Korea
- Department of Radiology, Chungnam National University Hospital, Daejeon, Republic of Korea
| | - Sun Kyoung You
- Department of Radiology, Chungnam National University College of Medicine, Daejeon, Republic of Korea
- Department of Radiology, Chungnam National University Hospital, Daejeon, Republic of Korea
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Muramatsu S, Sato K, Yamashiro T, Doi K. Quantitative measurements of emphysema in ultra-high resolution computed tomography using model-based iterative reconstruction in comparison to that using hybrid iterative reconstruction. Phys Eng Sci Med 2022; 45:115-124. [PMID: 35023075 DOI: 10.1007/s13246-021-01091-2] [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: 07/01/2021] [Accepted: 12/07/2021] [Indexed: 10/19/2022]
Abstract
The percentage of low attenuation volume ratio (LAVR), which is measured using computed tomography (CT), is an index of the severity of emphysema. For LAVR evaluation, ultra-high-resolution (U-HR) CT images are useful. To improve the image quality of U-HRCT, iterative reconstruction is used. There are two types of iterative reconstruction: hybrid iterative reconstruction (HIR) and model-based iterative reconstruction (MBIR). In this study, we physically and clinically evaluated U-HR images reconstructed with HIR and MBIR, and demonstrated the usefulness of U-HR images with MBIR for quantitative measurements of emphysema. Both images were reconstructed with a slice thickness of 0.25 mm and an image matrix size of 1024 × 1024 pixels. For physical evaluation, the modulation transfer function (MTF) and noise power spectrum (NPS) of HIR and MBIR were compared. For clinical evaluation, LAVR calculated from HIR and MBIR were compared using the Wilcoxon matched-pairs signed-rank test. In addition, the correlation between LAVR and forced expiratory volume in one second (FEV1%) was evaluated using the Spearman rank correlation test. The MTFs of HIR and MBIR were comparable. The NPS of MBIR was lower than that of HIR. The mean LAVR values calculated from HIR and MBIR were 19.5 ± 12.6% and 20.4 ± 11.7%, respectively (p = 0.84). The correlation coefficients between LAVR and FEV1% that were taken from HIR and MBIR were 0.64 and 0.74, respectively (p < 0.01). MBIR is more useful than HIR for the quantitative measurements of emphysema with U-HR images.
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Affiliation(s)
- Shun Muramatsu
- Department of Radiology, Ohara General Hospital, 6-1 Ue-machi, Fukushima-shi, Fukushima, 960-8611, Japan.
| | - Kazuhiro Sato
- Health Sciences, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan
| | - Tsuneo Yamashiro
- Department of Diagnostic Radiology, Yokohama City University, 3-9 Fukuura, Kanazawa-ku, Yokohama, Kanagawa, 236-0004, Japan
| | - Kunio Doi
- Department of Radiology, University of Chicago, 5841 Maryland Av, Chicago, IL, 60637, USA.,Gunma Prefectural College of Health Sciences, 323-1, Kamioki-machi, Maebashi-shi, Gunma-ken, 371-0052, Japan
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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: 17] [Impact Index Per Article: 8.5] [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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Li Y, Jiang Y, Liu H, Yu X, Chen S, Ma D, Gao J, Wu Y. A phantom study comparing low-dose CT physical image quality from five different CT scanners. Quant Imaging Med Surg 2022; 12:766-780. [PMID: 34993117 PMCID: PMC8666789 DOI: 10.21037/qims-21-245] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Accepted: 07/29/2021] [Indexed: 12/26/2022]
Abstract
BACKGROUND To systematically evaluate the physical image quality of low-dose computed tomography (LDCT) on CT scanners from 5 different manufacturers using a phantom model. METHODS CT images derived from a Catphan 500 phantom were acquired using manufacturer-specific iterative reconstruction (IR) algorithms and deep learning image reconstruction (DLIR) on CT scanners from 5 different manufacturers and compared using filtered back projection with 2 radiation doses of 0.25 and 0.75 mGy. Image high-contrast spatial resolution and image noise were objectively characterized by modulation transfer function (MTF) and noise power spectrum (NPS). Image high-contrast spatial resolution and image low-contrast detectability were compared directly by visual evaluation. CT number linearity and image uniformity were compared with intergroup differences using one-way analysis of variance (ANOVA). RESULTS The CT number linearity of 4 insert materials were as follows: acrylic (95% CI: 120.35 to 121.27; P=0.134), low-density polyethylene (95% CI: -98.43 to -97.43; P=0.070), air (95% CI: -996.16 to -994.51; P=0.018), and Teflon (95% CI: 984.40 to 986.87; P=0.883). The image uniformity values of GE Healthcare (95% CI: 3.24 to 3.83; P=0.138), Philips (95% CI: 2.62 to 3.70; P=0.299), Siemens (95% CI: 2.10 to 3.59; P=0.054), Minfound (95% CI: 2.35 to 3.65; P=0.589), and Neusoft (95% CI: 2.63 to 3.37; P=0.900) were evaluated and found to be within ±4 Hounsfield units (HU), with a range of 0.99-2.76 HU for standard deviations. There was no statistically significant difference in CT number linearity and image uniformity across the 5 CT scanners under different radiation doses with IR and DLIR algorithms (P>0.05). The resolution level at 10% MTF was 6.98 line-pairs-per-centimeter (lp/cm) on average, which was similar to the subjective evaluation results (mostly up to 7 lp/cm). DLIR at all 3 levels had the highest 50% MTF values among all reconstruction algorithms. For image low-contrast detectability, the minimum diameter of distinguishable contrast holes reached 4 mm at a 0.5% resolution. Increasing the radiation dose and IR strength reduced the image noise and NPS curve peak frequency while improving image low-contrast detectability. CONCLUSIONS This study demonstrated that the image quality of CT scanners from 5 different manufacturers in LDCT is comparable and that the CT number linearity is unbiased and can contribute to accurate bone mineral density quantification.
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Affiliation(s)
- Yali Li
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yaojun Jiang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Huilong Liu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xi Yu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Sihui Chen
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Duoshan Ma
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jianbo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yan Wu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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Hasegawa A, Ishihara T, Thomas MA, Pan T. Noise reduction profile: A new method for evaluation of noise reduction techniques in CT. Med Phys 2021; 49:186-200. [PMID: 34837717 PMCID: PMC9300212 DOI: 10.1002/mp.15382] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Revised: 10/26/2021] [Accepted: 11/22/2021] [Indexed: 12/19/2022] Open
Abstract
Purpose Noise power spectrum (NPS) is a commonly used performance metric to evaluate noise‐reduction techniques (NRT) in imaging systems. The images reconstructed with and without an NRT can be compared via their NPS to better understand the NRT's effects on image noise. However, when comparing NPSs, simple visual assessments or a comparison of NPS peaks or medians are often used. These assessments make it difficult to objectively evaluate the effect of noise reduction across all spatial frequencies. In this work, we propose a new noise reduction profile (NRP) to facilitate a more complete and objective evaluation of NPSs for a range of NRTs used specifically in computed tomography (CT). Methods and materials The homogeneous section of the ACR or Catphan phantoms was scanned on different CT scanners equipped with the following NRTs: AIDR3D, AiCE, ASiR, ASiR‐V, TrueFidelity, iDose, SAFIRE, and ADMIRE. The images were then reconstructed with all strengths of each NRT in reference to the baseline filtered back projection (FBP) images. One set of the baseline FBP images was also processed with PixelShine, an NRT based on artificial intelligence. The NPSs of the images before and after noise reduction were calculated in both the xy‐plane and along the z‐direction. The difference in the logarithmic scale between each NPS (baseline FBP and NRT) was then calculated and deemed the NRP. Furthermore, the relationship between the NRP and NPS peak positions was mathematically analyzed. Results Each NRT has its own unique NRP. By comparing the NPS and NRP for each NRT, it was found that NRP is related to the peak shift of NPS. Additionally, under the assumption that the NPS has one peak and is differentiable, a relationship was mathematically derived between the slope of the NRP at the peak position of the NPS before noise reduction and the shift of the NPS peak position after noise reduction. Conclusions A new metric, NRP, was proposed based on NPS to objectively evaluate and compare methods for noise reduction in CT. The NRP can be used to compare the effects of various NRTs on image noise in both the xy‐plane and z‐direction. It also enables unbiased assessment of the detailed noise reduction properties of each NRT over all relevant spatial frequencies.
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Affiliation(s)
- Akira Hasegawa
- Department of Radiological Technology, National Cancer Center Japan, Tokyo, Japan.,AlgoMedica, Inc., Sunnyvale, California, USA
| | - Toshihiro Ishihara
- Department of Radiological Technology, National Cancer Center Japan, Tokyo, Japan
| | - M Allan Thomas
- Department of Imaging Physics, M.D. Anderson Cancer Center, University of Texas, Houston, Texas, USA
| | - Tinsu Pan
- Department of Imaging Physics, M.D. Anderson Cancer Center, University of Texas, Houston, Texas, USA
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Nagayama Y, Sakabe D, Goto M, Emoto T, Oda S, Nakaura T, Kidoh M, Uetani H, Funama Y, Hirai T. Deep Learning-based Reconstruction for Lower-Dose Pediatric CT: Technical Principles, Image Characteristics, and Clinical Implementations. Radiographics 2021; 41:1936-1953. [PMID: 34597178 DOI: 10.1148/rg.2021210105] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Optimizing the CT acquisition parameters to obtain diagnostic image quality at the lowest possible radiation dose is crucial in the radiosensitive pediatric population. The image quality of low-dose CT can be severely degraded by increased image noise with filtered back projection (FBP) reconstruction. Iterative reconstruction (IR) techniques partially resolve the trade-off relationship between noise and radiation dose but still suffer from degraded noise texture and low-contrast detectability at considerably low-dose settings. Furthermore, sophisticated model-based IR usually requires a long reconstruction time, which restricts its clinical usability. With recent advances in artificial intelligence technology, deep learning-based reconstruction (DLR) has been introduced to overcome the limitations of the FBP and IR approaches and is currently available clinically. DLR incorporates convolutional neural networks-which comprise multiple layers of mathematical equations-into the image reconstruction process to reduce image noise, improve spatial resolution, and preserve preferable noise texture in the CT images. For DLR development, numerous network parameters are iteratively optimized through an extensive learning process to discriminate true attenuation from noise by using low-dose training and high-dose teaching image data. After rigorous validations of network generalizability, the DLR engine can be used to generate high-quality images from low-dose projection data in a short reconstruction time in a clinical environment. Application of the DLR technique allows substantial dose reduction in pediatric CT performed for various clinical indications while preserving the diagnostic image quality. The authors present an overview of the basic concept, technical principles, and image characteristics of DLR and its clinical feasibility for low-dose pediatric CT. ©RSNA, 2021.
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Affiliation(s)
- Yasunori Nagayama
- From the Department of Diagnostic Radiology, Graduate School of Medical Sciences (Y.N., S.O., T.N., M.K., H.U., T.H.), and Department of Medical Radiation Sciences, Faculty of Life Sciences (Y.F.), Kumamoto University, 1-1-1 Honjo, Chuo-ku, Kumamoto 860-8556, Japan; and Department of Central Radiology, Kumamoto University Hospital, Chuo-ku, Kumamoto, Japan (D.S., M.G., T.E.)
| | - Daisuke Sakabe
- From the Department of Diagnostic Radiology, Graduate School of Medical Sciences (Y.N., S.O., T.N., M.K., H.U., T.H.), and Department of Medical Radiation Sciences, Faculty of Life Sciences (Y.F.), Kumamoto University, 1-1-1 Honjo, Chuo-ku, Kumamoto 860-8556, Japan; and Department of Central Radiology, Kumamoto University Hospital, Chuo-ku, Kumamoto, Japan (D.S., M.G., T.E.)
| | - Makoto Goto
- From the Department of Diagnostic Radiology, Graduate School of Medical Sciences (Y.N., S.O., T.N., M.K., H.U., T.H.), and Department of Medical Radiation Sciences, Faculty of Life Sciences (Y.F.), Kumamoto University, 1-1-1 Honjo, Chuo-ku, Kumamoto 860-8556, Japan; and Department of Central Radiology, Kumamoto University Hospital, Chuo-ku, Kumamoto, Japan (D.S., M.G., T.E.)
| | - Takafumi Emoto
- From the Department of Diagnostic Radiology, Graduate School of Medical Sciences (Y.N., S.O., T.N., M.K., H.U., T.H.), and Department of Medical Radiation Sciences, Faculty of Life Sciences (Y.F.), Kumamoto University, 1-1-1 Honjo, Chuo-ku, Kumamoto 860-8556, Japan; and Department of Central Radiology, Kumamoto University Hospital, Chuo-ku, Kumamoto, Japan (D.S., M.G., T.E.)
| | - Seitaro Oda
- From the Department of Diagnostic Radiology, Graduate School of Medical Sciences (Y.N., S.O., T.N., M.K., H.U., T.H.), and Department of Medical Radiation Sciences, Faculty of Life Sciences (Y.F.), Kumamoto University, 1-1-1 Honjo, Chuo-ku, Kumamoto 860-8556, Japan; and Department of Central Radiology, Kumamoto University Hospital, Chuo-ku, Kumamoto, Japan (D.S., M.G., T.E.)
| | - Takeshi Nakaura
- From the Department of Diagnostic Radiology, Graduate School of Medical Sciences (Y.N., S.O., T.N., M.K., H.U., T.H.), and Department of Medical Radiation Sciences, Faculty of Life Sciences (Y.F.), Kumamoto University, 1-1-1 Honjo, Chuo-ku, Kumamoto 860-8556, Japan; and Department of Central Radiology, Kumamoto University Hospital, Chuo-ku, Kumamoto, Japan (D.S., M.G., T.E.)
| | - Masafumi Kidoh
- From the Department of Diagnostic Radiology, Graduate School of Medical Sciences (Y.N., S.O., T.N., M.K., H.U., T.H.), and Department of Medical Radiation Sciences, Faculty of Life Sciences (Y.F.), Kumamoto University, 1-1-1 Honjo, Chuo-ku, Kumamoto 860-8556, Japan; and Department of Central Radiology, Kumamoto University Hospital, Chuo-ku, Kumamoto, Japan (D.S., M.G., T.E.)
| | - Hiroyuki Uetani
- From the Department of Diagnostic Radiology, Graduate School of Medical Sciences (Y.N., S.O., T.N., M.K., H.U., T.H.), and Department of Medical Radiation Sciences, Faculty of Life Sciences (Y.F.), Kumamoto University, 1-1-1 Honjo, Chuo-ku, Kumamoto 860-8556, Japan; and Department of Central Radiology, Kumamoto University Hospital, Chuo-ku, Kumamoto, Japan (D.S., M.G., T.E.)
| | - Yoshinori Funama
- From the Department of Diagnostic Radiology, Graduate School of Medical Sciences (Y.N., S.O., T.N., M.K., H.U., T.H.), and Department of Medical Radiation Sciences, Faculty of Life Sciences (Y.F.), Kumamoto University, 1-1-1 Honjo, Chuo-ku, Kumamoto 860-8556, Japan; and Department of Central Radiology, Kumamoto University Hospital, Chuo-ku, Kumamoto, Japan (D.S., M.G., T.E.)
| | - Toshinori Hirai
- From the Department of Diagnostic Radiology, Graduate School of Medical Sciences (Y.N., S.O., T.N., M.K., H.U., T.H.), and Department of Medical Radiation Sciences, Faculty of Life Sciences (Y.F.), Kumamoto University, 1-1-1 Honjo, Chuo-ku, Kumamoto 860-8556, Japan; and Department of Central Radiology, Kumamoto University Hospital, Chuo-ku, Kumamoto, Japan (D.S., M.G., T.E.)
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Park SB. Advances in deep learning for computed tomography denoising. World J Clin Cases 2021; 9:7614-7619. [PMID: 34621813 PMCID: PMC8462260 DOI: 10.12998/wjcc.v9.i26.7614] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 05/12/2021] [Accepted: 08/17/2021] [Indexed: 02/06/2023] Open
Abstract
Computed tomography (CT) has seen a rapid increase in use in recent years. Radiation from CT accounts for a significant proportion of total medical radiation. However, given the known harmful impact of radiation exposure to the human body, the excessive use of CT in medical environments raises concerns. Concerns over increasing CT use and its associated radiation burden have prompted efforts to reduce radiation dose during the procedure. Therefore, low-dose CT has attracted major attention in the radiology, since CT-associated x-ray radiation carries health risks for patients. The reduction of the CT radiation dose, however, compromises the signal-to-noise ratio, which affects image quality and diagnostic performance. Therefore, several denoising methods have been developed and applied to image processing technologies with the goal of reducing image noise. Recently, deep learning applications that improve image quality by reducing the noise and artifacts have become commercially available for diagnostic imaging. Deep learning image reconstruction shows great potential as an advanced reconstruction method to improve the quality of clinical CT images. These improvements can provide significant benefit to patients regardless of their disease, and further advances are expected in the near future.
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Affiliation(s)
- Sung Bin Park
- Department of Radiology, Chung-Ang University Hospital, Seoul 06973, South Korea
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Mohammadinejad P, Mileto A, Yu L, Leng S, Guimaraes LS, Missert AD, Jensen CT, Gong H, McCollough CH, Fletcher JG. CT Noise-Reduction Methods for Lower-Dose Scanning: Strengths and Weaknesses of Iterative Reconstruction Algorithms and New Techniques. Radiographics 2021; 41:1493-1508. [PMID: 34469209 DOI: 10.1148/rg.2021200196] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Iterative reconstruction (IR) algorithms are the most widely used CT noise-reduction method to improve image quality and have greatly facilitated radiation dose reduction within the radiology community. Various IR methods have different strengths and limitations. Because IR algorithms are typically nonlinear, they can modify spatial resolution and image noise texture in different regions of the CT image; hence traditional image-quality metrics are not appropriate to assess the ability of IR to preserve diagnostic accuracy, especially for low-contrast diagnostic tasks. In this review, the authors highlight emerging IR algorithms and CT noise-reduction techniques and summarize how these techniques can be evaluated to help determine the appropriate radiation dose levels for different diagnostic tasks in CT. In addition to advanced IR techniques, we describe novel CT noise-reduction methods based on convolutional neural networks (CNNs). CNN-based noise-reduction techniques may offer the ability to reduce image noise while maintaining high levels of image detail but may have unique drawbacks. Other novel CT noise-reduction methods are being developed to leverage spatial and/or spectral redundancy in multiphase or multienergy CT. Radiologists and medical physicists should be familiar with these different alternatives to adapt available CT technology for different diagnostic tasks. The scope of this article is (a) to review the clinical applications of IR algorithms as well as their strengths, weaknesses, and methods of assessment and (b) to explore new CT image reconstruction and noise-reduction techniques that promise to facilitate radiation dose reduction. ©RSNA, 2021.
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Affiliation(s)
- Payam Mohammadinejad
- From the Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905 (P.M., L.Y., S.L., A.D.M., H.G., C.H.M., J.G.F.); Department of Radiology, Harborview Medical Center, Seattle, Wash (A.M.); Department of Medical Imaging, Toronto General Hospital, Toronto, ON, Canada (L.S.G.); and Department of Abdominal Imaging, University of Texas MD Anderson Cancer Center, Houston, Tex (C.T.J.)
| | - Achille Mileto
- From the Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905 (P.M., L.Y., S.L., A.D.M., H.G., C.H.M., J.G.F.); Department of Radiology, Harborview Medical Center, Seattle, Wash (A.M.); Department of Medical Imaging, Toronto General Hospital, Toronto, ON, Canada (L.S.G.); and Department of Abdominal Imaging, University of Texas MD Anderson Cancer Center, Houston, Tex (C.T.J.)
| | - Lifeng Yu
- From the Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905 (P.M., L.Y., S.L., A.D.M., H.G., C.H.M., J.G.F.); Department of Radiology, Harborview Medical Center, Seattle, Wash (A.M.); Department of Medical Imaging, Toronto General Hospital, Toronto, ON, Canada (L.S.G.); and Department of Abdominal Imaging, University of Texas MD Anderson Cancer Center, Houston, Tex (C.T.J.)
| | - Shuai Leng
- From the Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905 (P.M., L.Y., S.L., A.D.M., H.G., C.H.M., J.G.F.); Department of Radiology, Harborview Medical Center, Seattle, Wash (A.M.); Department of Medical Imaging, Toronto General Hospital, Toronto, ON, Canada (L.S.G.); and Department of Abdominal Imaging, University of Texas MD Anderson Cancer Center, Houston, Tex (C.T.J.)
| | - Luis S Guimaraes
- From the Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905 (P.M., L.Y., S.L., A.D.M., H.G., C.H.M., J.G.F.); Department of Radiology, Harborview Medical Center, Seattle, Wash (A.M.); Department of Medical Imaging, Toronto General Hospital, Toronto, ON, Canada (L.S.G.); and Department of Abdominal Imaging, University of Texas MD Anderson Cancer Center, Houston, Tex (C.T.J.)
| | - Andrew D Missert
- From the Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905 (P.M., L.Y., S.L., A.D.M., H.G., C.H.M., J.G.F.); Department of Radiology, Harborview Medical Center, Seattle, Wash (A.M.); Department of Medical Imaging, Toronto General Hospital, Toronto, ON, Canada (L.S.G.); and Department of Abdominal Imaging, University of Texas MD Anderson Cancer Center, Houston, Tex (C.T.J.)
| | - Corey T Jensen
- From the Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905 (P.M., L.Y., S.L., A.D.M., H.G., C.H.M., J.G.F.); Department of Radiology, Harborview Medical Center, Seattle, Wash (A.M.); Department of Medical Imaging, Toronto General Hospital, Toronto, ON, Canada (L.S.G.); and Department of Abdominal Imaging, University of Texas MD Anderson Cancer Center, Houston, Tex (C.T.J.)
| | - Hao Gong
- From the Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905 (P.M., L.Y., S.L., A.D.M., H.G., C.H.M., J.G.F.); Department of Radiology, Harborview Medical Center, Seattle, Wash (A.M.); Department of Medical Imaging, Toronto General Hospital, Toronto, ON, Canada (L.S.G.); and Department of Abdominal Imaging, University of Texas MD Anderson Cancer Center, Houston, Tex (C.T.J.)
| | - Cynthia H McCollough
- From the Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905 (P.M., L.Y., S.L., A.D.M., H.G., C.H.M., J.G.F.); Department of Radiology, Harborview Medical Center, Seattle, Wash (A.M.); Department of Medical Imaging, Toronto General Hospital, Toronto, ON, Canada (L.S.G.); and Department of Abdominal Imaging, University of Texas MD Anderson Cancer Center, Houston, Tex (C.T.J.)
| | - Joel G Fletcher
- From the Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905 (P.M., L.Y., S.L., A.D.M., H.G., C.H.M., J.G.F.); Department of Radiology, Harborview Medical Center, Seattle, Wash (A.M.); Department of Medical Imaging, Toronto General Hospital, Toronto, ON, Canada (L.S.G.); and Department of Abdominal Imaging, University of Texas MD Anderson Cancer Center, Houston, Tex (C.T.J.)
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Deep Learning for Low-Dose CT Denoising Using Perceptual Loss and Edge Detection Layer. J Digit Imaging 2021; 33:504-515. [PMID: 31515756 DOI: 10.1007/s10278-019-00274-4] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
Low-dose CT denoising is a challenging task that has been studied by many researchers. Some studies have used deep neural networks to improve the quality of low-dose CT images and achieved fruitful results. In this paper, we propose a deep neural network that uses dilated convolutions with different dilation rates instead of standard convolution helping to capture more contextual information in fewer layers. Also, we have employed residual learning by creating shortcut connections to transmit image information from the early layers to later ones. To further improve the performance of the network, we have introduced a non-trainable edge detection layer that extracts edges in horizontal, vertical, and diagonal directions. Finally, we demonstrate that optimizing the network by a combination of mean-square error loss and perceptual loss preserves many structural details in the CT image. This objective function does not suffer from over smoothing and blurring effects causing by per-pixel loss and grid-like artifacts resulting from perceptual loss. The experiments show that each modification to the network improves the outcome while changing the complexity of the network, minimally.
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Hasegawa A, Ishihara T, Allan Thomas M, Pan T. Scanner dependence of adaptive statistical iterative reconstruction with 3D noise power spectrum central frequency and noise magnitude ratios. Med Phys 2021; 48:4993-5003. [PMID: 34287936 DOI: 10.1002/mp.15104] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 06/27/2021] [Accepted: 06/27/2021] [Indexed: 11/09/2022] Open
Abstract
PURPOSE In this study, the noise reduction properties of the adaptive statistical iterative reconstruction (IR) on two different CT scanners of 64 and 256-slice were compared and their differences were assessed. METHODS AND MATERIALS The homogeneous module of the ACR CT phantom was scanned on the 64 and 256 slices CT scanners from the same vendor in the range of 15-40 mA. On each scanner, the data were reconstructed using filtered back projection (FBP) and at all strengths of IR with the STANDARD kernel. For each reconstruction, a 3D noise power spectrum (NPS) was calculated and the central frequency ratio in the xy plane (CFRxy ), CFR in the z-direction (CFRz ), and noise magnitude ratio (NMR) were derived. CFR is the central frequency ratio of NPS between the denoised image and the FBP image, and NMR is the ratio of the areas under the NPS curves. Ideally, both CFRxy and CFRz should be near 1, indicating minimal texture changes in both xy and z directions, while NMR should be as close to 0 as possible, indicating more noise reduction. RESULTS When comparing strengths with equivalent impact on noise texture, IR on the 64-slice reduced the noise magnitude in the xy plane more than that on the 256-slice. In the z-direction, the IR on the 256-slice produced a central frequency shift on the 256-slice but not on the 64-slice. In addition, the noise reduction effects of the IR on the 256-slice were affected when radiation exposure was below 2.0 mGy, but there was no observable dose-dependence on the 64-slice. CONCLUSIONS Our noise property analysis revealed that iterative reconstructions on different scanner platforms from the same vendor can be distinct, with unique effects on the noise texture and magnitude in CT images. The IR on a 64-slice scanner provides slightly enhanced noise reduction and maintains a noise reduction rate independent of dose, unlike the one on a 256-slice scanner. Notably, the IR on the 64-slice scanner was a 2D noise reduction technique (NRT), while the one on the 256-slice was a 3D NRT. These observations showcase the impact of different NRTs on clinical CT images, even when comparing the same NRT on different scanners.
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Affiliation(s)
- Akira Hasegawa
- Department of Radiological Technology, National Cancer Center Japan, Tokyo, Japan.,AlgoMedica, Inc., Sunnyvale, California, USA
| | - Toshihiro Ishihara
- Department of Radiological Technology, National Cancer Center Japan, Tokyo, Japan
| | - Matthew Allan Thomas
- Department of Imaging Physics, M.D. Anderson Cancer Center, University of Texas, Houston, Texas, USA
| | - Tinsu Pan
- Department of Imaging Physics, M.D. Anderson Cancer Center, University of Texas, Houston, Texas, USA
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Usefulness of Model-Based Iterative Reconstruction in Brain CT as Compared With Hybrid Iterative Reconstruction. J Comput Assist Tomogr 2021; 45:600-605. [PMID: 34176874 DOI: 10.1097/rct.0000000000001171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVE The aim of this study was to compare the contrast of gray to white matter between forward-projected model-based iterative reconstruction solution (FIRST) and hybrid iterative reconstruction (IR) by measuring computed tomography value of brain parenchyma. METHODS Computed tomography values of the gray and white matter in 15 areas of 21 patients (7 males, 14 females; average age, 49.5 ± 10.7 years) were measured and compared between FIRST and hybrid IR with filtered back projection (FBP) using 2 different reconstruction kernels FC21 and FC26. RESULTS The ratio of gray to white matter obtained using FIRST (1.25 ± 0.08) was significantly higher than that obtained using FBP with both kernel FC21 (1.13 ± 0.03) and kernel FC26 (1.22 ± 0.06). CONCLUSIONS FIRST increases the contrast between the gray and white matter, and decreases noise in brain computed tomography compared with FBP with hybrid IR.
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Niwa S, Ichikawa K, Kawashima H, Takata T, Minami S, Mitsui W. Reduction of streak artifacts caused by low photon counts utilizing an image-based forward projection in computed tomography. Comput Biol Med 2021; 135:104583. [PMID: 34216891 DOI: 10.1016/j.compbiomed.2021.104583] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 06/02/2021] [Accepted: 06/11/2021] [Indexed: 11/19/2022]
Abstract
BACKGROUND The streak artifacts in computed tomography (CT) images caused by low photon counts are known to be effectively suppressed by raw-data-based techniques. This study aims to propose a technique to reduce the streak artifact without accessing the raw data. METHODS The proposed streak artifact reduction (SAR) technique consists of three steps: numerical forward projection to a CT image, adaptive filtering of the generated sinogram, and image reconstruction from the processed sinogram. The authors have expanded the two-dimensional method (2D-SAR) to three dimensions (3D-SAR) by using consecutive CT images. The modulation transfer function (MTF), the image noise (standard deviation), and the visibility of comb-shaped objects were evaluated at a low dose of 5 mGy. Using anthropomorphic abdominal and chest phantoms, CT images and the artifact index (AI) were compared between 3D-SAR and two types of iterative reconstruction (IR). RESULTS Sufficient artifact reductions associated with 54% and 61% reduction of noise for 2D- and 3D-SAR, respectively, were obtained in the phantom images, although the 50%MTF decreased by 28%. The visibility of the combs was improved with both the 2D- and 3D-SAR methods. The AI results of 3D-SAR were better than one type of IR and almost equal to the other type of IR, which was consistent with observed artifacts. CONCLUSION Both 2D-SAR and 3D-SAR have turned out to be effective in reducing streak artifacts. The proposed technique will be an effective tool since it needs no raw data, and thus can be applied to any CT images produced by a wide variety of CT systems.
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Affiliation(s)
- Shinji Niwa
- Department of Medical Technology, Nakatsugawa Municipal General Hospital, 1522-1 Komanba, Nakatsugawa, Gifu, 508-0011, Japan; Division of Health Sciences, Graduate School of Medical Science, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa, 920-0942, Japan.
| | - Katsuhiro Ichikawa
- Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa, Ishikawa, 920-0942, Japan.
| | - Hiroki Kawashima
- Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa, Ishikawa, 920-0942, Japan.
| | - Tadanori Takata
- Radiology Division, Kanazawa University Hospital, 13-1 Takara-machi, Kanazawa, 920-8641, Japan.
| | - Shuhei Minami
- Radiology Division, Kanazawa University Hospital, 13-1 Takara-machi, Kanazawa, 920-8641, Japan.
| | - Wataru Mitsui
- Radiology Division, Kanazawa University Hospital, 13-1 Takara-machi, Kanazawa, 920-8641, Japan.
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Protocol Optimization Considerations for Implementing Deep Learning CT Reconstruction. AJR Am J Roentgenol 2021; 216:1668-1677. [PMID: 33852337 DOI: 10.2214/ajr.20.23397] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
OBJECTIVE. Previous advances over filtered back projection (FBP) have incorporated model-based iterative reconstruction. The purpose of this study was to characterize the latest advance in image reconstruction, that is, deep learning. The focus was on applying characterization results of a deep learning approach to decisions about clinical CT protocols. MATERIALS AND METHODS. A proprietary deep learning image reconstruction (DLIR) method was characterized against an existing advanced adaptive statistical iterative reconstruction method (ASIR-V) and FBP from the same vendor. The metrics used were contrast-to-noise ratio, spatial resolution as a function of contrast level, noise texture (i.e., noise power spectra [NPS]), noise scaling as a function of slice thickness, and CT number consistency. The American College of Radiology accreditation phantom and a uniform water phantom were used at a range of doses and slice thicknesses for both axial and helical acquisition modes. RESULTS. ASIR-V and DLIR were associated with improved contrast-to-noise ratio over FBP for all doses and slice thicknesses. No dose or contrast dependencies of spatial resolution were observed for ASIR-V or DLIR. NPS results showed DLIR maintained an FBP-like noise texture whereas ASIR-V shifted the NPS to lower frequencies. Noise changed with dose and slice thickness in the same manner for ASIR-V and FBP. DLIR slice thickness noise scaling differed from FBP, exhibiting less noise penalty with decreasing slice thickness. No clinically significant changes were observed in CT numbers for any measurement condition. CONCLUSION. In a phantom model, DLIR does not suffer from the concerns over reduction in spatial resolution and introduction of poor noise texture associated with previous methods.
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Njølstad T, Schulz A, Godt JC, Brøgger HM, Johansen CK, Andersen HK, Martinsen ACT. Improved image quality in abdominal computed tomography reconstructed with a novel Deep Learning Image Reconstruction technique - initial clinical experience. Acta Radiol Open 2021; 10:20584601211008391. [PMID: 33889427 PMCID: PMC8040588 DOI: 10.1177/20584601211008391] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Accepted: 03/19/2021] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND A novel Deep Learning Image Reconstruction (DLIR) technique for computed tomography has recently received clinical approval. PURPOSE To assess image quality in abdominal computed tomography reconstructed with DLIR, and compare with standardly applied iterative reconstruction. MATERIAL AND METHODS Ten abdominal computed tomography scans were reconstructed with iterative reconstruction and DLIR of medium and high strength, with 0.625 mm and 2.5 mm slice thickness. Image quality was assessed using eight visual grading criteria in a side-by-side comparative setting. All series were presented twice to evaluate intraobserver agreement. Reader scores were compared using univariate logistic regression. Image noise and contrast-to-noise ratio were calculated for quantitative analyses. RESULTS For 2.5 mm slice thickness, DLIR images were more frequently perceived as equal or better than iterative reconstruction across all visual grading criteria (for both DLIR of medium and high strength, p < 0.001). Correspondingly, DLIR images were more frequently perceived as better (as opposed to equal or in favor of iterative reconstruction) for visual reproduction of liver parenchyma, intrahepatic vascular structures as well as overall impression of image noise and texture (p < 0.001). This improved image quality was also observed for 0.625 mm slice images reconstructed with DLIR of high strength when directly comparing to traditional iterative reconstruction in 2.5 mm slices. Image noise was significantly lower and contrast-to-noise ratio measurements significantly higher for images reconstructed with DLIR compared to iterative reconstruction (p < 0.01). CONCLUSIONS Abdominal computed tomography images reconstructed using a DLIR technique shows improved image quality when compared to standardly applied iterative reconstruction across a variety of clinical image quality criteria.
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Affiliation(s)
- Tormund Njølstad
- Department of Radiology and Nuclear Medicine, Oslo University Hospital Ullevål, Oslo, Norway
- Department of Diagnostic Physics, Oslo University Hospital, Oslo, Norway
- Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Anselm Schulz
- Department of Radiology and Nuclear Medicine, Oslo University Hospital Ullevål, Oslo, Norway
- Department of Diagnostic Physics, Oslo University Hospital, Oslo, Norway
| | - Johannes C Godt
- Department of Radiology and Nuclear Medicine, Oslo University Hospital Ullevål, Oslo, Norway
| | - Helga M Brøgger
- Department of Radiology and Nuclear Medicine, Oslo University Hospital Ullevål, Oslo, Norway
| | - Cathrine K Johansen
- Department of Radiology and Nuclear Medicine, Oslo University Hospital Ullevål, Oslo, Norway
| | - Hilde K Andersen
- Department of Diagnostic Physics, Oslo University Hospital, Oslo, Norway
| | - Anne Catrine T Martinsen
- Department of Diagnostic Physics, Oslo University Hospital, Oslo, Norway
- Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway
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Addressing signal alterations induced in CT images by deep learning processing: A preliminary phantom study. Phys Med 2021; 83:88-100. [DOI: 10.1016/j.ejmp.2021.02.022] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 02/19/2021] [Accepted: 02/23/2021] [Indexed: 12/13/2022] Open
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Papadakis AE, Damilakis J. Technical Note: Quality assessment of virtual monochromatic spectral images on a dual energy CT scanner. Phys Med 2021; 82:114-121. [DOI: 10.1016/j.ejmp.2021.01.079] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 01/26/2021] [Accepted: 01/29/2021] [Indexed: 12/11/2022] Open
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Ichikawa Y, Kanii Y, Yamazaki A, Nagasawa N, Nagata M, Ishida M, Kitagawa K, Sakuma H. Deep learning image reconstruction for improvement of image quality of abdominal computed tomography: comparison with hybrid iterative reconstruction. Jpn J Radiol 2021; 39:598-604. [PMID: 33449305 DOI: 10.1007/s11604-021-01089-6] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2020] [Accepted: 01/02/2021] [Indexed: 12/13/2022]
Abstract
PURPOSE To evaluate the usefulness of the deep learning image reconstruction (DLIR) to enhance the image quality of abdominal CT, compared to iterative reconstruction technique. METHOD Pre and post-contrast abdominal CT images in 50 patients were reconstructed with 2 different algorithms: hybrid iterative reconstruction (hybrid IR: ASiR-V 50%) and DLIR (TrueFidelity). Standard deviation of attenuation in normal liver parenchyma was measured as the image noise on pre and post-contrast CT. The contrast-to-noise ratio (CNR) for the aorta, and the signal-to-noise ratio (SNR) of the liver were calculated on post-contrast CT. The overall image quality was graded on a 5-point scale ranging from 1 (poor) to 5 (excellent). RESULTS The image noise was significantly decreased by DLIR compared to hybrid-IR [hybrid IR, median 8.3 Hounsfield unit (HU) (interquartile range (IQR) 7.6-9.2 HU); DLIR, median 5.2 HU (IQR 4.6-5.8), P < 0.0001 for post-contrast CT]. The CNR and SNR were significantly improved by DLIR [CNR, median 4.5 (IQR 3.8-5.6) vs 7.3 (IQR 6.2-8.8), P < 0.0001; SNR, median 9.4 (IQR 8.3-10.1) vs 15.0 (IQR 13.2-16.4), P < 0.0001]. The overall image quality score was also higher for DLIR compared to hybrid-IR (hybrid IR 3.1 ± 0.6 vs DLIR 4.6 ± 0.5, P < 0.0001 for post-contrast CT). CONCLUSIONS Image noise, overall image quality, CNR and SNR for abdominal CT images are improved with DLIR compared to hybrid IR.
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Affiliation(s)
- Yasutaka Ichikawa
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan.
| | - Yoshinori Kanii
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan
| | - Akio Yamazaki
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan
| | - Naoki Nagasawa
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan
| | - Motonori Nagata
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan
| | - Masaki Ishida
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan
| | - Kakuya Kitagawa
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan
| | - Hajime Sakuma
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan
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Kawashima H, Ichikawa K, Takata T, Mitsui W, Ueta H, Yoneda N, Kobayashi S. Performance of clinically available deep learning image reconstruction in computed tomography: a phantom study. J Med Imaging (Bellingham) 2020; 7:063503. [PMID: 33344672 DOI: 10.1117/1.jmi.7.6.063503] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Accepted: 12/01/2020] [Indexed: 11/14/2022] Open
Abstract
Purpose: To assess the physical performance of deep learning image reconstruction (DLIR) compared with those of filtered back projection (FBP) and iterative reconstruction (IR) and to estimate the dose reduction potential of the technique. Approach: A cylindrical water bath phantom with a diameter of 300 mm including two rods composed of acrylic and soft tissue-equivalent material was scanned using a clinical computed tomography (CT) scanner at four dose levels (CT dose index of 20, 15, 10, and 5 mGy). Phantom images were reconstructed using FBP, DLIR, and IR. The in-plane and z axis task transfer functions (TTFs) and in-plane noise power spectrum (NPS) were measured. The dose reduction potential was estimated by evaluating the system performance function calculated from TTF and NPS. The visibilities of a bar pattern phantom placed in the same water bath phantom were compared. Results: The use of DLIR resulted in a notable decrease in noise magnitude. The shift in peak NPS frequency was reduced compared with IR. Preservation of in-plane TTF was superior using DLIR than using IR. The estimated dose reduction potentials of DLIR and IR were 39% to 54% and 19% to 29%, respectively. However, the z axis resolution was decreased with DLIR by 6% to 21% compared with FBP. The bar pattern visibilities were approximately consistent with the TTF results in both planes. Conclusions: The in-plane edge-preserving noise reduction performance of DLIR is superior to that of IR. Moreover, DLIR enables approximately half-dose acquisitions with no deterioration in noise texture in cases that permit some z axis resolution reduction.
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Affiliation(s)
- Hiroki Kawashima
- Kanazawa University, Institute of Medical, Pharmaceutical, and Health Sciences, Faculty of Health Sciences, Kanazawa, Japan
| | - Katsuhiro Ichikawa
- Kanazawa University, Institute of Medical, Pharmaceutical, and Health Sciences, Faculty of Health Sciences, Kanazawa, Japan
| | - Tadanori Takata
- Kanazawa University Hospital, Radiology Division, Kanazawa, Japan
| | - Wataru Mitsui
- Kanazawa University Hospital, Radiology Division, Kanazawa, Japan
| | - Hiroshi Ueta
- Kanazawa University Hospital, Radiology Division, Kanazawa, Japan
| | - Norihide Yoneda
- Kanazawa University Graduate School of Medical Science, Department of Radiology, Kanazawa, Japan
| | - Satoshi Kobayashi
- Kanazawa University, Institute of Medical, Pharmaceutical, and Health Sciences, Faculty of Health Sciences, Kanazawa, Japan
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Shin YJ, Chang W, Ye JC, Kang E, Oh DY, Lee YJ, Park JH, Kim YH. Low-Dose Abdominal CT Using a Deep Learning-Based Denoising Algorithm: A Comparison with CT Reconstructed with Filtered Back Projection or Iterative Reconstruction Algorithm. Korean J Radiol 2020; 21:356-364. [PMID: 32090528 PMCID: PMC7039719 DOI: 10.3348/kjr.2019.0413] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Accepted: 11/07/2019] [Indexed: 12/30/2022] Open
Abstract
Objective To compare the image quality of low-dose (LD) computed tomography (CT) obtained using a deep learning-based denoising algorithm (DLA) with LD CT images reconstructed with a filtered back projection (FBP) and advanced modeled iterative reconstruction (ADMIRE). Materials and Methods One hundred routine-dose (RD) abdominal CT studies reconstructed using FBP were used to train the DLA. Simulated CT images were made at dose levels of 13%, 25%, and 50% of the RD (DLA-1, -2, and -3) and reconstructed using FBP. We trained DLAs using the simulated CT images as input data and the RD CT images as ground truth. To test the DLA, the American College of Radiology CT phantom was used together with 18 patients who underwent abdominal LD CT. LD CT images of the phantom and patients were processed using FBP, ADMIRE, and DLAs (LD-FBP, LD-ADMIRE, and LD-DLA images, respectively). To compare the image quality, we measured the noise power spectrum and modulation transfer function (MTF) of phantom images. For patient data, we measured the mean image noise and performed qualitative image analysis. We evaluated the presence of additional artifacts in the LD-DLA images. Results LD-DLAs achieved lower noise levels than LD-FBP and LD-ADMIRE for both phantom and patient data (all p < 0.001). LD-DLAs trained with a lower radiation dose showed less image noise. However, the MTFs of the LD-DLAs were lower than those of LD-ADMIRE and LD-FBP (all p < 0.001) and decreased with decreasing training image dose. In the qualitative image analysis, the overall image quality of LD-DLAs was best for DLA-3 (50% simulated radiation dose) and not significantly different from LD-ADMIRE. There were no additional artifacts in LD-DLA images. Conclusion DLAs achieved less noise than FBP and ADMIRE in LD CT images, but did not maintain spatial resolution. The DLA trained with 50% simulated radiation dose showed the best overall image quality.
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Affiliation(s)
- Yoon Joo Shin
- Department of Radiology, Konkuk University Medical Center, Seoul, Korea
| | - Won Chang
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Korea.
| | - Jong Chul Ye
- Bio Imaging and Signal Processing Lab, Department of Bio and Brain Engineering, KAIST, Daejeon, Korea
| | - Eunhee Kang
- Bio Imaging and Signal Processing Lab, Department of Bio and Brain Engineering, KAIST, Daejeon, Korea
| | - Dong Yul Oh
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, Korea
| | - Yoon Jin Lee
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Ji Hoon Park
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Young Hoon Kim
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Korea
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Tao S, Rajendran K, Zhou W, Fletcher JG, McCollough CH, Leng S. Noise reduction in CT image using prior knowledge aware iterative denoising. Phys Med Biol 2020; 65. [PMID: 33065559 DOI: 10.1088/1361-6560/abc231] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Accepted: 10/16/2020] [Indexed: 11/11/2022]
Abstract
The clinical demand for low image noise often limits the slice thickness used in many CT applications. However, a thick-slice image is more susceptible to longitudinal partial volume effects, which can blur key anatomic structures and pathologies of interest. In this work, we develop a prior-knowledge-aware iterative denoising (PKAID) framework that utilizes spatial data redundancy in the slice increment direction to generate low-noise, thin-slice images, and demonstrate its application in non-contrast head CT exams. The proposed technique takes advantage of the low-noise of thicker images and exploits the structural similarity between the thick- and thin-slice images to reduce noise in the thin-slice image. Phantom data and patient cases (n=3) of head CT were used to assess performance of this method. Images were reconstructed at clinically-utilized slice thickness (5 mm) and thinner slice thickness (2 mm). PKAID was used to reduce image noise in 2 mm images using the 5 mm images as low-noise prior. Noise amplitude, noise power spectra (NPS), modulation transfer function (MTF), and slice sensitivity profiles (SSP) of images before/after denoising were analyzed. The NPS and MTF analysis showed that PKAID preserved noise texture and resolution of the original thin-slice image, while reducing noise to the level of thick-slice image. The SSP analysis showed that the slice thickness of the original thin-slice image was retained. Patient examples demonstrated that PKAID-processed, thin-slice images better delineated brain structures and key pathologies such as subdural hematoma compared to the clinical 5 mm images, while additionally reducing image noise. To test an alternative PKAID utilization for dose reduction, a head exam with 40% dose reduction was simulated using projection-domain noise insertion. The image of 5 mm slice thickness was then denoised using PKAID. The results showed that the PKAID-processed reduced-dose images maintained similar noise and image quality compared to the full-dose images.
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Affiliation(s)
- Shengzhen Tao
- Radiology, Mayo Clinic Minnesota, 200 First St SW, Rochester, Rochester, Minnesota, 55905-0002, UNITED STATES
| | - Kishore Rajendran
- Radiology, Mayo Clinic , 200 First street SW, Rochester, Minnesota, 55905, UNITED STATES
| | - Wei Zhou
- Radiology, University of Colorado Denver, Denver, Colorado, UNITED STATES
| | - Joel G Fletcher
- Radiology, Mayo Clinic , Rochester, Minnesota, UNITED STATES
| | - Cynthia H McCollough
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA, Rochester, Minnesota, UNITED STATES
| | - Shuai Leng
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, Minnesota 55905, USA, Rochester, UNITED STATES
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Jensen K, Hagemo G, Tingberg A, Steinfeldt-Reisse C, Mynarek GK, Rivero RJ, Fosse E, Martinsen AC. Evaluation of Image Quality for 7 Iterative Reconstruction Algorithms in Chest Computed Tomography Imaging: A Phantom Study. J Comput Assist Tomogr 2020; 44:673-680. [PMID: 32936576 DOI: 10.1097/rct.0000000000001037] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
OBJECTIVES This study aimed to evaluate the image quality of 7 iterative reconstruction (IR) algorithms in comparison to filtered back-projection (FBP) algorithm. METHODS An anthropomorphic chest phantom was scanned on 4 computed tomography scanners and reconstructed with FBP and IR algorithms. Image quality of anatomical details-large/medium-sized pulmonary vessels, small pulmonary vessels, thoracic wall, and small and large lesions-was scored. Furthermore, general impression of noise, image contrast, and artifacts were evaluated. Visual grading regression was used to analyze the data. Standard deviations were measured, and the noise power spectrum was calculated. RESULTS Iterative reconstruction algorithms showed significantly better results when compared with FBP for these criteria (regression coefficients/P values in parentheses): vessels (FIRST: -1.8/0.05, AIDR Enhanced: <-2.3/0.01, Veo: <-0.1/0.03, ADMIRE: <-2.1/0.04), lesions (FIRST: <-2.6/0.01, AIDR Enhanced: <-1.9/0.03, IMR1: <-2.7/0.01, Veo: <-2.4/0.02, ADMIRE: -2.3/0.02), image noise (FIRST: <-3.2/0.004, AIDR Enhanced: <-3.5/0.002, IMR1: <-6.1/0.001, iDose: <-2.3/0.02, Veo: <-3.4/0.002, ADMIRE: <-3.5/0.02), image contrast (FIRST: -2.3/0.01, AIDR Enhanced: -2.5/0.01, IMR1: -3.7/0.001, iDose: -2.1/0.02), and artifacts (FIRST: <-3.8/0.004, AIDR Enhanced: <-2.7/0.02, IMR1: <-2.6/0.02, iDose: -2.1/0.04, Veo: -2.6/0.02). The iDose algorithm was the only IR algorithm that maintained the noise frequencies. CONCLUSIONS Iterative reconstruction algorithms performed differently on all evaluated criteria, showing the importance of careful implementation of algorithms for diagnostic purposes.
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Affiliation(s)
| | - Guro Hagemo
- Department of Radiology and Nuclear Medicine, Radiumhospitalet, Oslo University Hospital, Oslo, Norway
| | - Anders Tingberg
- Department of Medical Radiation Physics, Lund University, Skåne University Hospital, Malmö, Sweden
| | | | - Georg Karl Mynarek
- Department of Radiology and Nuclear Medicine, Rikshospitalet, Oslo University Hospital
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Size and volume of kidney stones in computed tomography: Influence of acquisition techniques and image reconstruction parameters. Eur J Radiol 2020; 132:109267. [PMID: 32949914 DOI: 10.1016/j.ejrad.2020.109267] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Revised: 08/26/2020] [Accepted: 08/28/2020] [Indexed: 12/31/2022]
Abstract
PURPOSE Computed tomography (CT) is routinely used to assess suspected urolithiasis. Information obtained from CT include presence, location and size of stones, with the latter frequently determining treatment strategy. While there is consensus regarding measurements procedures of kidney stones, influence of radiation dose and reconstruction techniques on stone measurements are unknown. The purpose of this study was to systematically evaluate the influence of these technical determinants on kidney stone size measurements. METHOD 47 kidney stones of different composition were scanned using a 64-row-multi-detector CT in a 3D-printed, semi-anthropomorphic phantom. Reference stone sizes were measured manually with a digital caliper (Man-M). Stones were imaged with 2 and 10 mGy CTDI. Images were reconstructed using filtered-back-projection, hybrid-iterative and model-based-iterative reconstruction algorithms (FBP, HIR, MBIR) in combination with different kernels and denoising levels. All stones underwent semi-automatic, threshold-based segmentation for computation of maximum diameter and volume. Statistics were conducted using ANOVA ± correction for multiple comparisons. RESULTS Overall stone size as compared to manual measurements was overestimated in CT (10.0 ± 3.1 vs. 8.8 ± 2.9 mm, p < 0.05) yet showing a good correlation (R2 = 0.66). Radiation dose and denoising levels did not significantly influence measurements (p > 0.05). MBIR and sharp kernels showed closest agreement with Man-M (9.3 ± 3.1 vs. 8.8 ± 2.9 mm, p < 0.05). Differences within single stones were as high as 40 % (e.g. Man-M: 5.9 mm, CT: 7.3-12.0 mm). CONCLUSIONS CT-based measurements of kidney stone size appear unaffected by radiation dose and denoising technique, whereas reconstruction algorithms and kernels demonstrate a relevant impact on size measurements. Smallest differences were found using MBIR with a sharp kernel.
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Chu JS, Wang ZJ. Protocol Optimization for Renal Mass Detection and Characterization. Radiol Clin North Am 2020; 58:851-873. [PMID: 32792119 DOI: 10.1016/j.rcl.2020.05.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
Renal masses increasingly are found incidentally, largely due to the frequent use of medical imaging. Computed tomography (CT) and MR imaging are mainstays for renal mass characterization, presurgical planning of renal tumors, and surveillance after surgery or systemic therapy for advanced renal cell carcinomas. CT protocols should be tailored to different clinical indications, balancing diagnostic accuracy and radiation exposure. MR imaging protocols should take advantage of the improved soft tissue contrast for renal tumor diagnosis and staging. Optimized imaging protocols enable analysis of imaging features that help narrow the differential diagnoses and guide management in patients with renal masses.
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Affiliation(s)
- Jason S Chu
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 505 Parnassus Avenue, Box 0628, San Francisco, CA 94143, USA
| | - Zhen J Wang
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 505 Parnassus Avenue, Box 0628, San Francisco, CA 94143, USA.
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