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Fujita N, Yasaka K, Hatano S, Sakamoto N, Kurokawa R, Abe O. Deep learning reconstruction for high-resolution computed tomography images of the temporal bone: comparison with hybrid iterative reconstruction. Neuroradiology 2024; 66:1105-1112. [PMID: 38514472 DOI: 10.1007/s00234-024-03330-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 03/04/2024] [Indexed: 03/23/2024]
Abstract
PURPOSE We investigated whether the quality of high-resolution computed tomography (CT) images of the temporal bone improves with deep learning reconstruction (DLR) compared with hybrid iterative reconstruction (HIR). METHODS This retrospective study enrolled 36 patients (15 men, 21 women; age, 53.9 ± 19.5 years) who had undergone high-resolution CT of the temporal bone. Axial and coronal images were reconstructed using DLR, HIR, and filtered back projection (FBP). In qualitative image analyses, two radiologists independently compared the DLR and HIR images with FBP in terms of depiction of structures, image noise, and overall quality, using a 5-point scale (5 = better than FBP, 1 = poorer than FBP) to evaluate image quality. The other two radiologists placed regions of interest on the tympanic cavity and measured the standard deviation of CT attenuation (i.e., quantitative image noise). Scores from the qualitative and quantitative analyses of the DLR and HIR images were compared using, respectively, the Wilcoxon signed-rank test and the paired t-test. RESULTS Qualitative and quantitative image noise was significantly reduced in DLR images compared with HIR images (all comparisons, p ≤ 0.016). Depiction of the otic capsule, auditory ossicles, and tympanic membrane was significantly improved in DLR images compared with HIR images (both readers, p ≤ 0.003). Overall image quality was significantly superior in DLR images compared with HIR images (both readers, p < 0.001). CONCLUSION Compared with HIR, DLR provided significantly better-quality high-resolution CT images of the temporal bone.
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Affiliation(s)
- Nana Fujita
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan
| | - Koichiro Yasaka
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan.
| | - Sosuke Hatano
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan
| | - Naoya Sakamoto
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan
| | - Ryo Kurokawa
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan
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Nakaura T, Ito R, Ueda D, Nozaki T, Fushimi Y, Matsui Y, Yanagawa M, Yamada A, Tsuboyama T, Fujima N, Tatsugami F, Hirata K, Fujita S, Kamagata K, Fujioka T, Kawamura M, Naganawa S. The impact of large language models on radiology: a guide for radiologists on the latest innovations in AI. Jpn J Radiol 2024; 42:685-696. [PMID: 38551772 PMCID: PMC11217134 DOI: 10.1007/s11604-024-01552-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 02/21/2024] [Indexed: 07/03/2024]
Abstract
The advent of Deep Learning (DL) has significantly propelled the field of diagnostic radiology forward by enhancing image analysis and interpretation. The introduction of the Transformer architecture, followed by the development of Large Language Models (LLMs), has further revolutionized this domain. LLMs now possess the potential to automate and refine the radiology workflow, extending from report generation to assistance in diagnostics and patient care. The integration of multimodal technology with LLMs could potentially leapfrog these applications to unprecedented levels.However, LLMs come with unresolved challenges such as information hallucinations and biases, which can affect clinical reliability. Despite these issues, the legislative and guideline frameworks have yet to catch up with technological advancements. Radiologists must acquire a thorough understanding of these technologies to leverage LLMs' potential to the fullest while maintaining medical safety and ethics. This review aims to aid in that endeavor.
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Affiliation(s)
- Takeshi Nakaura
- Department of Central Radiology, Kumamoto University Hospital, Honjo 1-1-1, Kumamoto, 860-8556, Japan.
| | - Rintaro Ito
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Daiju Ueda
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, 1‑4‑3 Asahi‑Machi, Abeno‑ku, Osaka, 545‑8585, Japan
| | - Taiki Nozaki
- Department of Radiology, Keio University School of Medicine, Shinjuku‑ku, Tokyo, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Sakyoku, Kyoto, Japan
| | - Yusuke Matsui
- Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Kita‑ku, Okayama, Japan
| | - Masahiro Yanagawa
- Department of Radiology, Osaka University Graduate School of Medicine, Suita City, Osaka, Japan
| | - Akira Yamada
- Department of Radiology, Shinshu University School of Medicine, Matsumoto, Nagano, Japan
| | - Takahiro Tsuboyama
- Department of Radiology, Osaka University Graduate School of Medicine, Suita City, Osaka, Japan
| | - Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Japan
| | - Fuminari Tatsugami
- Department of Diagnostic Radiology, Hiroshima University, Minami‑ku, Hiroshima, Japan
| | - Kenji Hirata
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Kita‑ku, Sapporo, Hokkaido, Japan
| | - Shohei Fujita
- Department of Radiology, University of Tokyo, Bunkyo‑ku, Tokyo, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Bunkyo‑ku, Tokyo, Japan
| | - Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Bunkyo‑ku, Tokyo, Japan
| | - Mariko Kawamura
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
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Winfree T, McCollough C, Yu L. Development and validation of a noise insertion algorithm for photon-counting-detector CT. Med Phys 2024. [PMID: 38923526 DOI: 10.1002/mp.17263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 05/30/2024] [Accepted: 05/31/2024] [Indexed: 06/28/2024] Open
Abstract
BACKGROUND Inserting noise into existing patient projection data to simulate lower-radiation-dose exams has been frequently used in traditional energy-integrating-detector (EID)-CT to optimize radiation dose in clinical protocols and to generate paired images for training deep-learning-based reconstruction and noise reduction methods. Recent introduction of photon counting detector CT (PCD-CT) also requires such a method to accomplish these tasks. However, clinical PCD-CT scanners often restrict the users access to the raw count data, exporting only the preprocessed, log-normalized sinogram. Therefore, it remains a challenge to employ projection domain noise insertion algorithms on PCD-CT. PURPOSE To develop and validate a projection domain noise insertion algorithm for PCD-CT that does not require access to the raw count data. MATERIALS AND METHODS A projection-domain noise model developed originally for EID-CT was adapted for PCD-CT. This model requires, as input, a map of the incident number of photons at each detector pixel when no object is in the beam. To obtain the map of incident number of photons, air scans were acquired on a PCD-CT scanner, then the noise equivalent photon number (NEPN) was calculated from the variance in the log normalized projection data of each scan. Additional air scans were acquired at various mA settings to investigate the impact of pulse pileup on the linearity of NEPN measurement. To validate the noise insertion algorithm, Noise Power Spectra (NPS) were generated from a 30 cm water tank scan and used to compare the noise texture and noise level of measured and simulated half dose and quarter dose images. An anthropomorphic thorax phantom was scanned with automatic exposure control, and noise levels at different slice locations were compared between simulated and measured half dose and quarter dose images. Spectral correlation between energy thresholds T1 and T2, and energy bins, B1 and B2, was compared between simulated and measured data across a wide range of tube current. Additionally, noise insertion was performed on a clinical patient case for qualitative assessment. RESULTS The NPS generated from simulated low dose water tank images showed similar shape and amplitude to that generated from the measured low dose images, differing by a maximum of 5.0% for half dose (HD) T1 images, 6.3% for HD T2 images, 4.1% for quarter dose (QD) T1 images, and 6.1% for QD T2 images. Noise versus slice measurements of the lung phantom showed comparable results between measured and simulated low dose images, with root mean square percent errors of 5.9%, 5.4%, 5.0%, and 4.6% for QD T1, HD T1, QD T2, and HD T2, respectively. NEPN measurements in air were linear up until 112 mA, after which pulse pileup effects significantly distort the air scan NEPN profile. Spectral correlation between T1 and T2 in simulation agreed well with that in the measured data in typical dose ranges. CONCLUSIONS A projection-domain noise insertion algorithm was developed and validated for PCD-CT to synthesize low-dose images from existing scans. It can be used for optimizing scanning protocols and generating paired images for training deep-learning-based methods.
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Affiliation(s)
- Timothy Winfree
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Lifeng Yu
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
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Okimoto N, Yasaka K, Cho S, Koshino S, Kanzawa J, Asari Y, Fujita N, Kubo T, Suzuki Y, Abe O. New liver window width in detecting hepatocellular carcinoma on dynamic contrast-enhanced computed tomography with deep learning reconstruction. Radiol Phys Technol 2024:10.1007/s12194-024-00817-7. [PMID: 38837119 DOI: 10.1007/s12194-024-00817-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 05/12/2024] [Accepted: 05/30/2024] [Indexed: 06/06/2024]
Abstract
Changing a window width (WW) alters appearance of noise and contrast of CT images. The aim of this study was to investigate the impact of adjusted WW for deep learning reconstruction (DLR) in detecting hepatocellular carcinomas (HCCs) on CT with DLR. This retrospective study included thirty-five patients who underwent abdominal dynamic contrast-enhanced CT. DLR was used to reconstruct arterial, portal, and delayed phase images. The investigation of the optimal WW involved two blinded readers. Then, five other blinded readers independently read the image sets for detection of HCCs and evaluation of image quality with optimal or conventional liver WW. The optimal WW for detection of HCC was 119 (rounded to 120 in the subsequent analyses) Hounsfield unit (HU), which was the average of adjusted WW in the arterial, portal, and delayed phases. The average figures of merit for the readers for the jackknife alternative free-response receiver operating characteristic analysis to detect HCC were 0.809 (reader 1/2/3/4/5, 0.765/0.798/0.892/0.764/0.827) in the optimal WW (120 HU) and 0.765 (reader 1/2/3/4/5, 0.707/0.769/0.838/0.720/0.791) in the conventional WW (150 HU), and statistically significant difference was observed between them (p < 0.001). Image quality in the optimal WW was superior to those in the conventional WW, and significant difference was seen for some readers (p < 0.041). The optimal WW for detection of HCC was narrower than conventional WW on dynamic contrast-enhanced CT with DLR. Compared with the conventional liver WW, optimal liver WW significantly improved detection performance of HCC.
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Affiliation(s)
- Naomasa Okimoto
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Koichiro Yasaka
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
| | - Shinichi Cho
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Saori Koshino
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Jun Kanzawa
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Yusuke Asari
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Nana Fujita
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Takatoshi Kubo
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Yuichi Suzuki
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
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Akai H, Yasaka K, Sugawara H, Furuta T, Tajima T, Kato S, Yamaguchi H, Ohtomo K, Abe O, Kiryu S. Faster acquisition of magnetic resonance imaging sequences of the knee via deep learning reconstruction: a volunteer study. Clin Radiol 2024; 79:453-459. [PMID: 38614869 DOI: 10.1016/j.crad.2024.03.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 12/29/2023] [Accepted: 03/02/2024] [Indexed: 04/15/2024]
Abstract
AIM To evaluate whether deep learning reconstruction (DLR) can accelerate the acquisition of magnetic resonance imaging (MRI) sequences of the knee for clinical use. MATERIALS AND METHODS Using a 1.5-T MRI scanner, sagittal fat-suppressed T2-weighted imaging (fs-T2WI), coronal proton density-weighted imaging (PDWI), and coronal T1-weighted imaging (T1WI) were performed. DLR was applied to images with a number of signal averages (NSA) of 1 to obtain 1DLR images. Then 1NSA, 1DLR, and 4NSA images were compared subjectively, and by noise (standard deviation of intra-articular water or medial meniscus) and contrast-to-noise ratio between two anatomical structures or between an anatomical structure and intra-articular water. RESULTS Twenty-seven healthy volunteers (age: 40.6 ± 11.9 years) were enrolled. Three 1DLR image sequences were obtained within 200 s (approximately 12 minutes for 4NSA image). According to objective evaluations, PDWI 1DLR images showed the smallest noise and significantly higher contrast than 1NSA and 4NSA images. For fs-T2WI, smaller noise and higher contrast were observed in the order of 4NSA, 1DLR, and 1NSA images. According to the subjective analysis, structure visibility, image noise, and overall image quality were significantly better for PDWI 1DLR than 1NSA images; moreover, the visibility of the meniscus and bone, image noise, and overall image quality were significantly better for 1DLR than 4NSA images. Fs-T2WI and T1WI 1DLR images showed no difference between 1DLR and 4NSA images. CONCLUSION Compared to PDWI 4NSA images, PDWI 1DLR images were of higher quality, while the quality of fs-T2WI and T1WI 1DLR images was similar to that of 4NSA images.
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Affiliation(s)
- H Akai
- Department of Radiology, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan; Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita, Chiba, 286-0124, Japan
| | - K Yasaka
- Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita, Chiba, 286-0124, Japan; Department of Radiology, Graduate School of Medicine, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - H Sugawara
- Department of Diagnostic Radiology, McGill University, 1650 Cedar Avenue, Montreal, Quebec, H3G 1A4, Canada
| | - T Furuta
- Department of Radiology, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan
| | - T Tajima
- Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita, Chiba, 286-0124, Japan; Department of Radiology, International University of Health and Welfare Mita Hospital, 1-4-3 Mita, Minato-ku, Tokyo, 108-8329, Japan
| | - S Kato
- Department of Radiology, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan
| | - H Yamaguchi
- Department of Radiology, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan
| | - K Ohtomo
- International University of Health and Welfare, 2600-1 Kiakanemaru, Ohtawara, Tochigi, 324-8501, Japan
| | - O Abe
- Department of Radiology, Graduate School of Medicine, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - S Kiryu
- Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita, Chiba, 286-0124, Japan.
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Higaki T, Tatsugami F, Ohana M, Nakamura Y, Kawashita I, Awai K. Super resolution deep learning reconstruction for coronary CT angiography: A structured phantom study. Eur J Radiol Open 2024; 12:100570. [PMID: 38828096 PMCID: PMC11140562 DOI: 10.1016/j.ejro.2024.100570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 05/14/2024] [Accepted: 05/15/2024] [Indexed: 06/05/2024] Open
Abstract
Purpose Super-resolution deep-learning-based reconstruction: SR-DLR is a newly developed and clinically available deep-learning-based image reconstruction method that can improve the spatial resolution of CT images. The image quality of the output from non-linear image reconstructions, such as DLR, is known to vary depending on the structure of the object being scanned, and a simple phantom cannot explicitly evaluate the clinical performance of SR-DLR. This study aims to accurately investigate the quality of the images reconstructed by SR-DLR by utilizing a structured phantom that simulates the human anatomy in coronary CT angiography. Methods The structural phantom had ribs and vertebrae made of plaster, a left ventricle filled with dilute contrast medium, a coronary artery with simulated stenosis, and an implanted stent graft. By scanning the structured phantom, we evaluated noise and spatial resolution on the images reconstructed with SR-DLR and conventional reconstructions. Results The spatial resolution of SR-DLR was higher than conventional reconstructions; the 10 % modulation transfer function of hybrid IR (HIR), DLR, and SR-DLR were 0.792-, 0.976-, and 1.379 cycle/mm, respectively. At the same time, image noise was lowest (HIR: 21.1-, DLR: 19.0-, and SR-DLR: 13.1 HU). SR-DLR could accurately assess coronary artery stenosis and the lumen of the implanted stent graft. Conclusions SR-DLR can obtain CT images with high spatial resolution and lower noise without special CT equipments, and will help diagnose coronary artery disease in CCTA and other CT examinations that require high spatial resolution.
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Affiliation(s)
- Toru Higaki
- Graduate School of Advanced Science and Engineering, Hiroshima University, Japan
| | - Fuminari Tatsugami
- Graduate School of Biomedical and Health Sciences, Hiroshima University, Japan
| | | | - Yuko Nakamura
- Graduate School of Biomedical and Health Sciences, Hiroshima University, Japan
| | - Ikuo Kawashita
- Graduate School of Biomedical and Health Sciences, Hiroshima University, Japan
| | - Kazuo Awai
- Graduate School of Biomedical and Health Sciences, Hiroshima University, Japan
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Fujima N, Nakagawa J, Ikebe Y, Kameda H, Harada T, Shimizu Y, Tsushima N, Kano S, Homma A, Kwon J, Yoneyama M, Kudo K. Improved image quality in contrast-enhanced 3D-T1 weighted sequence by compressed sensing-based deep-learning reconstruction for the evaluation of head and neck. Magn Reson Imaging 2024; 108:111-115. [PMID: 38340971 DOI: 10.1016/j.mri.2024.02.006] [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: 07/18/2023] [Revised: 02/07/2024] [Accepted: 02/07/2024] [Indexed: 02/12/2024]
Abstract
PURPOSE To assess the utility of deep learning (DL)-based image reconstruction with the combination of compressed sensing (CS) denoising cycle by comparing images reconstructed by conventional CS-based method without DL in fat-suppressed (Fs)-contrast enhanced (CE) three-dimensional (3D) T1-weighted images (T1WIs) of the head and neck. MATERIALS AND METHODS We retrospectively analyzed the cases of 39 patients who had undergone head and neck Fs-CE 3D T1WI applying reconstructions based on conventional CS and CS augmented by DL, respectively. In the qualitative assessment, we evaluated overall image quality, visualization of anatomical structures, degree of artifacts, lesion conspicuity, and lesion edge sharpness based on a five-point system. In the quantitative assessment, we calculated the signal-to-noise ratios (SNRs) of the lesion and the posterior neck muscle and the contrast-to-noise ratio (CNR) between the lesion and the adjacent muscle. RESULTS For all items of the qualitative analysis, significantly higher scores were awarded to images with DL-based reconstruction (p < 0.001). In the quantitative analysis, DL-based reconstruction resulted in significantly higher values for both the SNR of lesions (p < 0.001) and posterior neck muscles (p < 0.001). Significantly higher CNRs were also observed in images with DL-based reconstruction (p < 0.001). CONCLUSION DL-based image reconstruction integrating into the CS-based denoising cycle offered superior image quality compared to the conventional CS method. This technique will be useful for the assessment of patients with head and neck disease.
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Affiliation(s)
- Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo 0608638, Japan.
| | - Junichi Nakagawa
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo 0608638, Japan
| | - Yohei Ikebe
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, N15 W7, Kita-Ku, Sapporo, Hokkaido 060-8638, Japan; Center for Cause of Death investigation, Faculty of Medicine, Hokkaido University, N15 W7, Kita-Ku, Sapporo, Hokkaido 060-8638, Japan
| | - Hiroyuki Kameda
- Faculty of Dental Medicine Department of Radiology Hokkaido University, N13 W7, Kita-ku, Sapporo, Hokkaido 060-8586, Japan
| | - Taisuke Harada
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo 0608638, Japan
| | - Yukie Shimizu
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo 0608638, Japan
| | - Nayuta Tsushima
- Department of Otolaryngology-Head and Neck Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15 W7, Kita ku, Sapporo 060-8638, Japan
| | - Satoshi Kano
- Department of Otolaryngology-Head and Neck Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15 W7, Kita ku, Sapporo 060-8638, Japan
| | - Akihiro Homma
- Department of Otolaryngology-Head and Neck Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15 W7, Kita ku, Sapporo 060-8638, Japan
| | - Jihun Kwon
- Philips Japan, 3-37 Kohnan 2-chome, Minato-ku, Tokyo 108-8507, Japan
| | - Masami Yoneyama
- Philips Japan, 3-37 Kohnan 2-chome, Minato-ku, Tokyo 108-8507, Japan
| | - Kohsuke Kudo
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo 0608638, Japan; Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, N15 W7, Kita-Ku, Sapporo, Hokkaido 060-8638, Japan; Clinical AI Human Resources Development Program, Faculty of Medicine, Hokkaido University, N15 W7, Kita-Ku, Sapporo, Hokkaido 060-8638, Japan; Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University, N14 W5, Kita-Ku, Sapporo, Hokkaido 060-8638, Japan
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Yunaga H, Miyoshi H, Ochiai R, Gonda T, Sakoh T, Noma H, Fujii S. Image Quality and Lesion Detection of Multiplanar Reconstruction Images Using Deep Learning: Comparison with Hybrid Iterative Reconstruction. Yonago Acta Med 2024; 67:100-107. [PMID: 38803592 PMCID: PMC11128077 DOI: 10.33160/yam.2024.05.001] [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: 11/08/2023] [Accepted: 02/16/2024] [Indexed: 05/29/2024]
Abstract
Background We assessed and compared the image quality of normal and pathologic structures as well as the image noise in chest computed tomography images using "adaptive statistical iterative reconstruction-V" (ASiR-V) or deep learning reconstruction "TrueFidelity". Methods Forty consecutive patients with suspected lung disease were evaluated. The 1.25-mm axial images and 2.0-mm coronal multiplanar images were reconstructed under the following three conditions: (i) ASiR-V, lung kernel with 60% of ASiR-V; (ii) TF-M, standard kernel, image filter (Lung) with TrueFidelity at medium strength; and (iii) TF-H, standard kernel, image filter (Lung) with TrueFidelity at high strength. Two radiologists (readers) independently evaluated the image quality of anatomic structures using a scale ranging from 1 (best) to 5 (worst). In addition, readers ranked their image preference. Objective image noise was measured using a circular region of interest in the lung parenchyma. Subjective image quality scores, total scores for normal and abnormal structures, and lesion detection were compared using Wilcoxon's signed-rank test. Objective image quality was compared using Student's paired t-test and Wilcoxon's signed-rank test. The Bonferroni correction was applied to the P value, and significance was assumed only for values of P < 0.016. Results Both readers rated TF-M and TF-H images significantly better than ASiR-V images in terms of visualization of the centrilobular region in axial images. The preference score of TF-M and TF-H images for reader 1 were better than that of ASiR-V images, and the preference score of TF-H images for reader 2 were significantly better than that of ASiR-V and TF-M images. TF-M images showed significantly lower objective image noise than ASiR-V or TF-H images. Conclusion TrueFidelity showed better image quality, especially in the centrilobular region, than ASiR-V in subjective and objective evaluations. In addition, the image texture preference for TrueFidelity was better than that for ASiR-V.
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Affiliation(s)
- Hiroto Yunaga
- Division of Radiology, Department of Multidisciplinary Internal Medicine, School of Medicine, Faculty of Medicine, Tottori University, Yonago 683-8503, Japan
| | - Hidenao Miyoshi
- Division of Radiology, Department of Multidisciplinary Internal Medicine, School of Medicine, Faculty of Medicine, Tottori University, Yonago 683-8503, Japan
| | - Ryoya Ochiai
- Division of Radiology, Department of Multidisciplinary Internal Medicine, School of Medicine, Faculty of Medicine, Tottori University, Yonago 683-8503, Japan
| | - Takuro Gonda
- Division of Radiology, Department of Multidisciplinary Internal Medicine, School of Medicine, Faculty of Medicine, Tottori University, Yonago 683-8503, Japan
| | - Toshio Sakoh
- Division of Clinical Radiology, School of Medicine, Faculty of Medicine, Tottori University, Yonago 683-8503, Japan
| | - Hisashi Noma
- Department of Data Science, The Institute of Statistical Mathematics, Tachikawa 190-8562, Japan
| | - Shinya Fujii
- Division of Radiology, Department of Multidisciplinary Internal Medicine, School of Medicine, Faculty of Medicine, Tottori University, Yonago 683-8503, Japan
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9
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Ikeda H, Ohno Y, Yamamoto K, Murayama K, Ikedo M, Yui M, Kumazawa Y, Shimamura Y, Takagi Y, Nakagaki Y, Hanamatsu S, Obama Y, Ueda T, Nagata H, Ozawa Y, Iwase A, Toyama H. Deep Learning Reconstruction for DWIs by EPI and FASE Sequences for Head and Neck Tumors. Cancers (Basel) 2024; 16:1714. [PMID: 38730665 PMCID: PMC11083776 DOI: 10.3390/cancers16091714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 04/25/2024] [Accepted: 04/26/2024] [Indexed: 05/13/2024] Open
Abstract
BACKGROUND Diffusion-weighted images (DWI) obtained by echo-planar imaging (EPI) are frequently degraded by susceptibility artifacts. It has been suggested that DWI obtained by fast advanced spin-echo (FASE) or reconstructed with deep learning reconstruction (DLR) could be useful for image quality improvements. The purpose of this investigation using in vitro and in vivo studies was to determine the influence of sequence difference and of DLR for DWI on image quality, apparent diffusion coefficient (ADC) evaluation, and differentiation of malignant from benign head and neck tumors. METHODS For the in vitro study, a DWI phantom was scanned by FASE and EPI sequences and reconstructed with and without DLR. Each ADC within the phantom for each DWI was then assessed and correlated for each measured ADC and standard value by Spearman's rank correlation analysis. For the in vivo study, DWIs obtained by EPI and FASE sequences were also obtained for head and neck tumor patients. Signal-to-noise ratio (SNR) and ADC were then determined based on ROI measurements, while SNR of tumors and ADC were compared between all DWI data sets by means of Tukey's Honest Significant Difference test. RESULTS For the in vitro study, all correlations between measured ADC and standard reference were significant and excellent (0.92 ≤ ρ ≤ 0.99, p < 0.0001). For the in vivo study, the SNR of FASE with DLR was significantly higher than that of FASE without DLR (p = 0.02), while ADC values for benign and malignant tumors showed significant differences between each sequence with and without DLR (p < 0.05). CONCLUSION In comparison with EPI sequence, FASE sequence and DLR can improve image quality and distortion of DWIs without significantly influencing ADC measurements or differentiation capability of malignant from benign head and neck tumors.
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Affiliation(s)
- Hirotaka Ikeda
- Department of Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan
| | - Yoshiharu Ohno
- Department of Diagnostic Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan
- Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan
| | - Kaori Yamamoto
- Canon Medical Systems Corporation, Otawara 324-8550, Tochigi, Japan
| | - Kazuhiro Murayama
- Department of Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan
| | - Masato Ikedo
- Canon Medical Systems Corporation, Otawara 324-8550, Tochigi, Japan
| | - Masao Yui
- Canon Medical Systems Corporation, Otawara 324-8550, Tochigi, Japan
| | - Yunosuke Kumazawa
- Department of Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan
| | - Yurika Shimamura
- Department of Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan
| | - Yui Takagi
- Department of Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan
| | - Yuhei Nakagaki
- Department of Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan
| | - Satomu Hanamatsu
- Department of Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan
| | - Yuki Obama
- Department of Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan
| | - Takahiro Ueda
- Department of Diagnostic Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan
| | - Hiroyuki Nagata
- Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan
| | - Yoshiyuki Ozawa
- Department of Diagnostic Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan
| | - Akiyoshi Iwase
- Department of Radiology, Fujita Health University Hospital, Toyoake 470-1192, Aichi, Japan
| | - Hiroshi Toyama
- Department of Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan
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10
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Yasaka K, Uehara S, Kato S, Watanabe Y, Tajima T, Akai H, Yoshioka N, Akahane M, Ohtomo K, Abe O, Kiryu S. Super-resolution Deep Learning Reconstruction Cervical Spine 1.5T MRI: Improved Interobserver Agreement in Evaluations of Neuroforaminal Stenosis Compared to Conventional Deep Learning Reconstruction. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01112-y. [PMID: 38671337 DOI: 10.1007/s10278-024-01112-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 03/28/2024] [Accepted: 04/01/2024] [Indexed: 04/28/2024]
Abstract
The aim of this study was to investigate whether super-resolution deep learning reconstruction (SR-DLR) is superior to conventional deep learning reconstruction (DLR) with respect to interobserver agreement in the evaluation of neuroforaminal stenosis using 1.5T cervical spine MRI. This retrospective study included 39 patients who underwent 1.5T cervical spine MRI. T2-weighted sagittal images were reconstructed with SR-DLR and DLR. Three blinded radiologists independently evaluated the images in terms of the degree of neuroforaminal stenosis, depictions of the vertebrae, spinal cord and neural foramina, sharpness, noise, artefacts and diagnostic acceptability. In quantitative image analyses, a fourth radiologist evaluated the signal-to-noise ratio (SNR) by placing a circular or ovoid region of interest on the spinal cord, and the edge slope based on a linear region of interest placed across the surface of the spinal cord. Interobserver agreement in the evaluations of neuroforaminal stenosis using SR-DLR and DLR was 0.422-0.571 and 0.410-0.542, respectively. The kappa values between reader 1 vs. reader 2 and reader 2 vs. reader 3 significantly differed. Two of the three readers rated depictions of the spinal cord, sharpness, and diagnostic acceptability as significantly better with SR-DLR than with DLR. Both SNR and edge slope (/mm) were also significantly better with SR-DLR (12.9 and 6031, respectively) than with DLR (11.5 and 3741, respectively) (p < 0.001 for both). In conclusion, compared to DLR, SR-DLR improved interobserver agreement in the evaluations of neuroforaminal stenosis using 1.5T cervical spine MRI.
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Affiliation(s)
- Koichiro Yasaka
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
- Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita, Chiba, 286-0124, Japan
| | - Shunichi Uehara
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Shimpei Kato
- Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan
| | - Yusuke Watanabe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Taku Tajima
- Department of Radiology, International University of Health and Welfare Mita Hospital, 1-4-3 Mita, Minato-ku, Tokyo, 108-8329, Japan
| | - Hiroyuki Akai
- Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita, Chiba, 286-0124, Japan
- Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan
| | - Naoki Yoshioka
- Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita, Chiba, 286-0124, Japan
| | - Masaaki Akahane
- Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita, Chiba, 286-0124, Japan
| | - Kuni Ohtomo
- International University of Health and Welfare, 2600-1 Ktiakanemaru, Ohtawara, Tochigi, 324-8501, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Shigeru Kiryu
- Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita, Chiba, 286-0124, Japan.
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11
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Mizuki M, Yasaka K, Miyo R, Ohtake Y, Hamada A, Hosoi R, Abe O. Deep Learning Reconstruction Plus Single-Energy Metal Artifact Reduction for Supra Hyoid Neck CT in Patients With Dental Metals. Can Assoc Radiol J 2024; 75:74-81. [PMID: 37387607 DOI: 10.1177/08465371231182904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2023] Open
Abstract
Purpose: We investigated the effect of deep learning reconstruction (DLR) plus single-energy metal artifact reduction (SEMAR) on neck CT in patients with dental metals, comparing it with DLR and with hybrid iterative reconstruction (Hybrid IR)-SEMAR. Methods: In this retrospective study, 32 patients (25 men, 7 women; mean age: 63 ± 15 years) with dental metals underwent contrast-enhanced CT of the oral and oropharyngeal regions. Axial images were reconstructed using DLR, Hybrid IR-SEMAR, and DLR-SEMAR. In quantitative analyses, degrees of image noise and artifacts were evaluated. In one-by-one qualitative analyses, 2 radiologists evaluated metal artifacts, the depiction of structures, and noise on five-point scales. In side-by-side qualitative analyses, artifacts and overall image quality were evaluated by comparing Hybrid IR-SEMAR with DLR-SEMAR. Results: Artifacts were significantly less with DLR-SEMAR than with DLR in quantitative (P < .001) and one-by-one qualitative (P < .001) analyses, which resulted in significantly better depiction of most structures (P < .004). Artifacts in side-by-side analysis and image noise in quantitative and one-by-one qualitative analyses (P < .001) were significantly less with DLR-SEMAR than with Hybrid IR-SEMAR, resulting in significantly better overall quality of DLR-SEMAR. Conclusions: Compared with DLR and Hybrid IR-SEMAR, DLR-SEMAR provided significantly better supra hyoid neck CT images in patients with dental metals.
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Affiliation(s)
- Masumi Mizuki
- Department of Radiology, The University of Tokyo Hospital, Bunkyo-ku, Tokyo, Japan
| | - Koichiro Yasaka
- Department of Radiology, The University of Tokyo Hospital, Bunkyo-ku, Tokyo, Japan
| | - Rintaro Miyo
- Department of Radiology, The University of Tokyo Hospital, Bunkyo-ku, Tokyo, Japan
| | - Yuta Ohtake
- Department of Radiology, The University of Tokyo Hospital, Bunkyo-ku, Tokyo, Japan
| | - Akiyoshi Hamada
- Department of Radiology, The University of Tokyo Hospital, Bunkyo-ku, Tokyo, Japan
| | - Reina Hosoi
- Department of Radiology, The University of Tokyo Hospital, Bunkyo-ku, Tokyo, Japan
| | - Osamu Abe
- Department of Radiology, The University of Tokyo Hospital, Bunkyo-ku, Tokyo, Japan
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12
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Hokamura M, Uetani H, Nakaura T, Matsuo K, Morita K, Nagayama Y, Kidoh M, Yamashita Y, Ueda M, Mukasa A, Hirai T. Exploring the impact of super-resolution deep learning on MR angiography image quality. Neuroradiology 2024; 66:217-226. [PMID: 38148334 DOI: 10.1007/s00234-023-03271-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 12/14/2023] [Indexed: 12/28/2023]
Abstract
PURPOSE The aim of this study is to assess the effect of super-resolution deep learning-based reconstruction (SR-DLR), which uses k-space properties, on image quality of intracranial time-of-flight (TOF) magnetic resonance angiography (MRA) at 3 T. METHODS This retrospective study involved 35 patients who underwent intracranial TOF-MRA using a 3-T MRI system with SR-DLR based on k-space properties in October and November 2022. We reconstructed MRA with SR-DLR (matrix = 1008 × 1008) and MRA without SR-DLR (matrix = 336 × 336). We measured the signal-to-noise ratio (SNR), contrast, and contrast-to-noise ratio (CNR) in the basilar artery (BA) and the anterior cerebral artery (ACA) and the sharpness of the posterior cerebral artery (PCA) using the slope of the signal intensity profile curve at the half-peak points. Two radiologists evaluated image noise, artifacts, contrast, sharpness, and overall image quality of the two image types using a 4-point scale. We compared quantitative and qualitative scores between images with and without SR-DLR using the Wilcoxon signed-rank test. RESULTS The SNRs, contrasts, and CNRs were all significantly higher in images with SR-DLR than those without SR-DLR (p < 0.001). The slope was significantly greater in images with SR-DLR than those without SR-DLR (p < 0.001). The qualitative scores in MRAs with SR-DLR were all significantly higher than MRAs without SR-DLR (p < 0.001). CONCLUSION SR-DLR with k-space properties can offer the benefits of increased spatial resolution without the associated drawbacks of longer scan times and reduced SNR and CNR in intracranial MRA.
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Affiliation(s)
- Masamichi Hokamura
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Chuo-ku, Kumamoto-shi, Kumamoto, 860-8556, Japan
| | - Hiroyuki Uetani
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Chuo-ku, Kumamoto-shi, Kumamoto, 860-8556, Japan
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Chuo-ku, Kumamoto-shi, Kumamoto, 860-8556, Japan.
| | - Kensei Matsuo
- Department of Central Radiology, Kumamoto University Hospital, Honjo 1-1-1, Kumamoto, 860-8556, Japan
| | - Kosuke Morita
- Department of Central Radiology, Kumamoto University Hospital, Honjo 1-1-1, Kumamoto, 860-8556, Japan
| | - Yasunori Nagayama
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Chuo-ku, Kumamoto-shi, Kumamoto, 860-8556, Japan
| | - Masafumi Kidoh
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Chuo-ku, Kumamoto-shi, Kumamoto, 860-8556, Japan
| | - Yuichi Yamashita
- Canon Medical Systems Corporation, 70-1, Yanagi-cho, Saiwai-ku, Kawasaki-shi, Kanagawa, 212-0015, Japan
| | - Mitsuharu Ueda
- Department of Neurology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Kumamoto, 860-8556, Japan
| | - Akitake Mukasa
- Department of Neurosurgery, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Kumamoto, 860-8556, Japan
| | - Toshinori Hirai
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Chuo-ku, Kumamoto-shi, Kumamoto, 860-8556, Japan
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13
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Hamada A, Yasaka K, Hatano S, Kurokawa M, Inui S, Kubo T, Watanabe Y, Abe O. Deep-Learning Reconstruction of High-Resolution CT Improves Interobserver Agreement for the Evaluation of Pulmonary Fibrosis. Can Assoc Radiol J 2024:8465371241228468. [PMID: 38293802 DOI: 10.1177/08465371241228468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2024] Open
Abstract
Objective: This study aimed to investigate whether deep-learning reconstruction (DLR) improves interobserver agreement in the evaluation of honeycombing for patients with interstitial lung disease (ILD) who underwent high-resolution computed tomography (CT) compared with hybrid iterative reconstruction (HIR). Methods: In this retrospective study, 35 consecutive patients suspected of ILD who underwent CT including the chest region were included. High-resolution CT images of the unilateral lung with DLR and HIR were reconstructed for the right and left lungs. A radiologist placed regions of interest on the lung and measured standard deviation of CT attenuation (i.e., quantitative image noise). In the qualitative image analyses, 5 blinded readers assessed the presence of honeycombing and reticulation, qualitative image noise, artifacts, and overall image quality using a 5-point scale (except for artifacts which was evaluated using a 3-point scale). Results: The quantitative and qualitative image noise in DLR was remarkably reduced compared to that in HIR (P < .001). Artifacts and overall DLR quality were significantly improved compared to those of HIR (P < .001 for 4 out of 5 readers). Interobserver agreement in the evaluations of honeycombing and reticulation for DLR (0.557 [0.450-0.693] and 0.525 [0.470-0.541], respectively) were higher than those for HIR (0.321 [0.211-0.520] and 0.470 [0.354-0.533], respectively). A statistically significant difference was found for honeycombing (P = .014). Conclusions: DLR improved interobserver agreement in the evaluation of honeycombing in patients with ILD on CT compared to HIR.
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Affiliation(s)
- Akiyoshi Hamada
- Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
| | - Koichiro Yasaka
- Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
| | - Sosuke Hatano
- Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
| | - Mariko Kurokawa
- Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
| | - Shohei Inui
- Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
| | - Takatoshi Kubo
- Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
| | - Yusuke Watanabe
- Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
| | - Osamu Abe
- Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
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14
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Higaki T. [[CT] 5. Various CT Image Reconstruction Methods Applying Deep Learning]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2024; 80:112-117. [PMID: 38246633 DOI: 10.6009/jjrt.2024-2309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2024]
Affiliation(s)
- Toru Higaki
- Graduate School of Advanced Science and Engineering, Hiroshima University
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15
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Okimoto N, Yasaka K, Fujita N, Watanabe Y, Kanzawa J, Abe O. Deep learning reconstruction for improving the visualization of acute brain infarct on computed tomography. Neuroradiology 2024; 66:63-71. [PMID: 37991522 PMCID: PMC10761512 DOI: 10.1007/s00234-023-03251-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 11/13/2023] [Indexed: 11/23/2023]
Abstract
PURPOSE This study aimed to investigate the impact of deep learning reconstruction (DLR) on acute infarct depiction compared with hybrid iterative reconstruction (Hybrid IR). METHODS This retrospective study included 29 (75.8 ± 13.2 years, 20 males) and 26 (64.4 ± 12.4 years, 18 males) patients with and without acute infarction, respectively. Unenhanced head CT images were reconstructed with DLR and Hybrid IR. In qualitative analyses, three readers evaluated the conspicuity of lesions based on five regions and image quality. A radiologist placed regions of interest on the lateral ventricle, putamen, and white matter in quantitative analyses, and the standard deviation of CT attenuation (i.e., quantitative image noise) was recorded. RESULTS Conspicuity of acute infarct in DLR was superior to that in Hybrid IR, and a statistically significant difference was observed for two readers (p ≤ 0.038). Conspicuity of acute infarct with time from onset to CT imaging at < 24 h in DLR was significantly improved compared with Hybrid IR for all readers (p ≤ 0.020). Image noise in DLR was significantly reduced compared with Hybrid IR with both the qualitative and quantitative analyses (p < 0.001 for all). CONCLUSION DLR in head CT helped improve acute infarct depiction, especially those with time from onset to CT imaging at < 24 h.
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Affiliation(s)
- Naomasa Okimoto
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan
- Department of Radiology, Tokyo Metropolitan Bokutoh Hospital, 4-23-15 Kotobashi, Sumida-Ku, Tokyo, 130-8575, Japan
| | - Koichiro Yasaka
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan.
| | - Nana Fujita
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan
| | - Yusuke Watanabe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan
| | - Jun Kanzawa
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan
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16
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Hamabuchi N, Ohno Y, Kimata H, Ito Y, Fujii K, Akino N, Takenaka D, Yoshikawa T, Oshima Y, Matsuyama T, Nagata H, Ueda T, Ikeda H, Ozawa Y, Toyama H. Effectiveness of deep learning reconstruction on standard to ultra-low-dose high-definition chest CT images. Jpn J Radiol 2023; 41:1373-1388. [PMID: 37498483 PMCID: PMC10687108 DOI: 10.1007/s11604-023-01470-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 07/09/2023] [Indexed: 07/28/2023]
Abstract
PURPOSE Deep learning reconstruction (DLR) has been introduced by major vendors, tested for CT examinations of a variety of organs, and compared with other reconstruction methods. The purpose of this study was to compare the capabilities of DLR for image quality improvement and lung texture evaluation with those of hybrid-type iterative reconstruction (IR) for standard-, reduced- and ultra-low-dose CTs (SDCT, RDCT and ULDCT) obtained with high-definition CT (HDCT) and reconstructed at 0.25-mm, 0.5-mm and 1-mm section thicknesses with 512 × 512 or 1024 × 1024 matrixes for patients with various pulmonary diseases. MATERIALS AND METHODS Forty age-, gender- and body mass index-matched patients with various pulmonary diseases underwent SDCT (CT dose index volume : mean ± standard deviation, 9.0 ± 1.8 mGy), RDCT (CTDIvol: 1.7 ± 0.2 mGy) and ULDCT (CTDIvol: 0.8 ± 0.1 mGy) at a HDCT. All CT data set were then reconstructed with 512 × 512 or 1024 × 1024 matrixes by means of hybrid-type IR and DLR. SNR of lung parenchyma and probabilities of all lung textures were assessed for each CT data set. SNR and detection performance of each lung texture reconstructed with DLR and hybrid-type IR were then compared by means of paired t tests and ROC analyses for all CT data at each section thickness. RESULTS Data for each radiation dose showed DLR attained significantly higher SNR than hybrid-type IR for each of the CT data (p < 0.0001). On assessments of all findings except consolidation and nodules or masses, areas under the curve (AUCs) for ULDCT with hybrid-type IR for each section thickness (0.91 ≤ AUC ≤ 0.97) were significantly smaller than those with DLR (0.97 ≤ AUC ≤ 1, p < 0.05) and the standard protocol (0.98 ≤ AUC ≤ 1, p < 0.05). CONCLUSION DLR is potentially more effective for image quality improvement and lung texture evaluation than hybrid-type IR on all radiation dose CTs obtained at HDCT and reconstructed with each section thickness with both matrixes for patients with a variety of pulmonary diseases.
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Affiliation(s)
- Nayu Hamabuchi
- Department of Radiology, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan
| | - Yoshiharu Ohno
- Department of Diagnostic Radiology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan.
- Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine, Toyoake, Aichi, Japan.
| | - Hirona Kimata
- Canon Medical Systems Corporation, Otawara, Tochigi, Japan
| | - Yuya Ito
- Canon Medical Systems Corporation, Otawara, Tochigi, Japan
| | - Kenji Fujii
- Canon Medical Systems Corporation, Otawara, Tochigi, Japan
| | - Naruomi Akino
- Canon Medical Systems Corporation, Otawara, Tochigi, Japan
| | - Daisuke Takenaka
- Department of Radiology, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan
- Department of Diagnostic Radiology, Hyogo Cancer Center, Akashi, Hyogo, Japan
| | - Takeshi Yoshikawa
- Department of Radiology, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan
- Department of Diagnostic Radiology, Hyogo Cancer Center, Akashi, Hyogo, Japan
| | - Yuka Oshima
- Department of Radiology, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan
| | - Takahiro Matsuyama
- Department of Radiology, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan
| | - Hiroyuki Nagata
- Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
| | - Takahiro Ueda
- Department of Radiology, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan
| | - Hirotaka Ikeda
- Department of Radiology, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan
| | - Yoshiyuki Ozawa
- Department of Radiology, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan
| | - Hiroshi Toyama
- Department of Radiology, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan
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17
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Nagayama Y, Emoto T, Kato Y, Kidoh M, Oda S, Sakabe D, Funama Y, Nakaura T, Hayashi H, Takada S, Uchimura R, Hatemura M, Tsujita K, Hirai T. Improving image quality with super-resolution deep-learning-based reconstruction in coronary CT angiography. Eur Radiol 2023; 33:8488-8500. [PMID: 37432405 DOI: 10.1007/s00330-023-09888-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 03/22/2023] [Accepted: 04/23/2023] [Indexed: 07/12/2023]
Abstract
OBJECTIVES To evaluate the effect of super-resolution deep-learning-based reconstruction (SR-DLR) on the image quality of coronary CT angiography (CCTA). METHODS Forty-one patients who underwent CCTA using a 320-row scanner were retrospectively included. Images were reconstructed with hybrid (HIR), model-based iterative reconstruction (MBIR), normal-resolution deep-learning-based reconstruction (NR-DLR), and SR-DLR algorithms. For each image series, image noise, and contrast-to-noise ratio (CNR) at the left main trunk, right coronary artery, left anterior descending artery, and left circumflex artery were quantified. Blooming artifacts from calcified plaques were measured. Image sharpness, noise magnitude, noise texture, edge smoothness, overall quality, and delineation of the coronary wall, calcified and noncalcified plaques, cardiac muscle, and valves were subjectively ranked on a 4-point scale (1, worst; 4, best). The quantitative parameters and subjective scores were compared among the four reconstructions. Task-based image quality was assessed with a physical evaluation phantom. The detectability index for the objects simulating the coronary lumen, calcified plaques, and noncalcified plaques was calculated from the noise power spectrum (NPS) and task-based transfer function (TTF). RESULTS SR-DLR yielded significantly lower image noise and blooming artifacts with higher CNR than HIR, MBIR, and NR-DLR (all p < 0.001). The best subjective scores for all the evaluation criteria were attained with SR-DLR, with significant differences from all other reconstructions (p < 0.001). In the phantom study, SR-DLR provided the highest NPS average frequency, TTF50%, and detectability for all task objects. CONCLUSION SR-DLR considerably improved the subjective and objective image qualities and object detectability of CCTA relative to HIR, MBIR, and NR-DLR algorithms. CLINICAL RELEVANCE STATEMENT The novel SR-DLR algorithm has the potential to facilitate accurate assessment of coronary artery disease on CCTA by providing excellent image quality in terms of spatial resolution, noise characteristics, and object detectability. KEY POINTS • SR-DLR designed for CCTA improved image sharpness, noise property, and delineation of cardiac structures with reduced blooming artifacts from calcified plaques relative to HIR, MBIR, and NR-DLR. • In the task-based image-quality assessments, SR-DLR yielded better spatial resolution, noise property, and detectability for objects simulating the coronary lumen, coronary calcifications, and noncalcified plaques than other reconstruction techniques. • The image reconstruction times of SR-DLR were shorter than those of MBIR, potentially serving as a novel standard-of-care reconstruction technique for CCTA performed on a 320-row CT scanner.
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Affiliation(s)
- Yasunori Nagayama
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan.
| | - Takafumi Emoto
- Department of Central Radiology, Kumamoto University Hospital, Kumamoto, Japan
| | - Yuki Kato
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Masafumi Kidoh
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Seitaro Oda
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Daisuke Sakabe
- Department of Central Radiology, Kumamoto University Hospital, Kumamoto, Japan
| | - Yoshinori Funama
- Department of Medical Radiation Sciences, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Hidetaka Hayashi
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Sentaro Takada
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Ryutaro Uchimura
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Masahiro Hatemura
- Department of Central Radiology, Kumamoto University Hospital, Kumamoto, Japan
| | - Kenichi Tsujita
- Department of Cardiovascular Medicine, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Toshinori Hirai
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
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Fujita N, Yasaka K, Katayama A, Ohtake Y, Konishiike M, Abe O. Assessing the Effects of Deep Learning Reconstruction on Abdominal CT Without Arm Elevation. Can Assoc Radiol J 2023; 74:688-694. [PMID: 37041699 DOI: 10.1177/08465371231169672] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/13/2023] Open
Abstract
Purpose: To evaluate the effects of deep learning reconstruction (DLR) on image quality of abdominal computed tomography (CT) in patients without arm elevation compared with hybrid-iterative reconstruction (Hybrid-IR) and filtered back projection (FBP). Methods: In this retrospective study, axial images of 26 patients who underwent CT without arm elevation were reconstructed using DLR, Hybrid-IR, and FBP. Streak artifact index (SAI) was calculated by dividing the standard deviation of CT attenuation in the liver or spleen by that in fat. Two other blinded radiologists evaluated streak artifacts on images (in the liver, spleen, and kidney), depiction of liver vessels, subjective image noise, and overall quality. They were also asked to detect space-occupying lesions other than cysts in the liver, spleen, and kidney. Results: The SAI (liver/spleen) in DLR images was significantly reduced compared with Hybrid-IR and FBP. Regarding qualitative image analysis, streak artifacts in the 3 organs, qualitative image noise, and overall quality in DLR images were rated by both readers as significantly improved compared with Hybrid-IR (P ≤ .012) and FBP (P < .001). Both blinded readers detected more lesions on DLR images than on Hybrid-IR and FBP ones. Conclusion: DLR resulted in significantly better-quality abdominal CT images in patients scanned without elevating their arms with reducing streak artifacts compared with Hybrid-IR and FBP.
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Affiliation(s)
- Nana Fujita
- Department of Radiology, The University of Tokyo Hospital, Bunkyo-ku, Tokyo, Japan
| | - Koichiro Yasaka
- Department of Radiology, The University of Tokyo Hospital, Bunkyo-ku, Tokyo, Japan
| | - Akira Katayama
- Department of Radiology, The University of Tokyo Hospital, Bunkyo-ku, Tokyo, Japan
- Department of Radiology, Tokyo-Kita Medical Centre, Kita-ku, Tokyo, Japan
| | - Yuta Ohtake
- Department of Radiology, The University of Tokyo Hospital, Bunkyo-ku, Tokyo, Japan
| | - Mao Konishiike
- Department of Radiology, The University of Tokyo Hospital, Bunkyo-ku, Tokyo, Japan
| | - Osamu Abe
- Department of Radiology, The University of Tokyo Hospital, Bunkyo-ku, Tokyo, Japan
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Matsuo K, Nakaura T, Morita K, Uetani H, Nagayama Y, Kidoh M, Hokamura M, Yamashita Y, Shinoda K, Ueda M, Mukasa A, Hirai T. Feasibility study of super-resolution deep learning-based reconstruction using k-space data in brain diffusion-weighted images. Neuroradiology 2023; 65:1619-1629. [PMID: 37673835 DOI: 10.1007/s00234-023-03212-y] [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: 04/23/2023] [Accepted: 08/08/2023] [Indexed: 09/08/2023]
Abstract
PURPOSE The purpose of this study is to evaluate the influence of super-resolution deep learning-based reconstruction (SR-DLR), which utilizes k-space data, on the quality of images and the quantitation of the apparent diffusion coefficient (ADC) for diffusion-weighted images (DWI) in brain magnetic resonance imaging (MRI). METHODS A retrospective analysis was performed on 34 patients who had undergone DWI using a 3 T MRI system with SR-DLR reconstruction based on k-space data in August 2022. DWI was reconstructed with SR-DLR (Matrix = 684 × 684) and without SR-DLR (Matrix = 228 × 228). Measurements were made of the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) in white matter (WM) and grey matter (GM), and the full width at half maximum (FWHM) of the septum pellucidum. Two radiologists assessed image noise, contrast, artifacts, blur, and the overall quality of three image types using a four-point scale. Quantitative and qualitative scores between images with and without SR-DLR were compared using the Wilcoxon signed-rank test. RESULTS Images with SR-DLR showed significantly higher SNRs and CNRs than those without SR-DLR (p < 0.001). No statistically significant variances were found in the apparent diffusion coefficients (ADCs) in WM and GM between images with and without SR-DLR (ADC in WM, p = 0.945; ADC in GM, p = 0.235). Moreover, the FWHM without SR-DLR was notably lower compared to that with SR-DLR (p < 0.001). CONCLUSION SR-DLR has the potential to augment the quality of DWI in DL MRI scans without significantly impacting ADC quantitation.
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Affiliation(s)
- Kensei Matsuo
- Department of Central Radiology, Kumamoto University Hospital, Honjo 1-1-1, Kumamoto, 860-8556, Japan
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Chuo, Kumamoto, 860-8556, Japan.
| | - Kosuke Morita
- Department of Central Radiology, Kumamoto University Hospital, Honjo 1-1-1, Kumamoto, 860-8556, Japan
| | - Hiroyuki Uetani
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Chuo, Kumamoto, 860-8556, Japan
| | - Yasunori Nagayama
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Chuo, Kumamoto, 860-8556, Japan
| | - Masafumi Kidoh
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Chuo, Kumamoto, 860-8556, Japan
| | - Masamichi Hokamura
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Chuo, Kumamoto, 860-8556, Japan
| | - Yuichi Yamashita
- Canon Medical Systems Corporation, 70-1, Yanagi, Saiwai, Kawasaki, Kanagawa, 212-0015, Japan
| | - Kensuke Shinoda
- MRI Systems Division, Canon Medical Systems Corporation, 1385 Shimoishigami, Otawara, Tochigi, 324-8550, Japan
| | - Mitsuharu Ueda
- Department of Neurology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Chuo, Kumamoto, 860-8556, Japan
| | - Akitake Mukasa
- Department of Neurosurgery, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Chuo, Kumamoto, Japan
| | - Toshinori Hirai
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Chuo, Kumamoto, 860-8556, Japan
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20
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Ohtake Y, Yasaka K, Hamada A, Fujita N, Abe O. Effect of Deep Learning Reconstruction on Evaluating Cervical Spinal Canal Stenosis With Computed Tomography. J Comput Assist Tomogr 2023; 47:996-1001. [PMID: 37948377 DOI: 10.1097/rct.0000000000001490] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
OBJECTIVE Magnetic resonance imaging (MRI) is commonly used to evaluate cervical spinal canal stenosis; however, some patients are ineligible for MRI. We aimed to assess the effect of deep learning reconstruction (DLR) in evaluating cervical spinal canal stenosis using computed tomography (CT) compared with hybrid iterative reconstruction (hybrid IR). METHODS This retrospective study included 33 patients (16 male patients; mean age, 57.7 ± 18.4 years) who underwent cervical spine CT. Images were reconstructed using DLR and hybrid IR. In the quantitative analyses, noise was recorded by placing the regions of interest on the trapezius muscle. In the qualitative analyses, 2 radiologists evaluated the depiction of structures, image noise, overall image quality, and degree of cervical canal stenosis. We additionally evaluated the agreement between MRI and CT in 15 patients for whom preoperative cervical MRI was available. RESULTS Image noise was less with DLR than hybrid IR in the quantitative ( P ≤ 0.0395) and subjective analyses ( P ≤ 0.0023), and the depiction of most structures was improved ( P ≤ 0.0052), which resulted in better overall quality ( P ≤ 0.0118). Interobserver agreement in the assessment of spinal canal stenosis with DLR (0.7390; 95% confidence interval [CI], 0.7189-0.7592) was superior to that with hybrid IR (0.7038; 96% CI, 0.6846-0.7229). As for the agreement between MRI and CT, significant improvement was observed for 1 reader with DLR (0.7910; 96% CI, 0.7762-0.8057) than hybrid IR (0.7536; 96% CI, 0.7383-0.7688). CONCLUSIONS Deep learning reconstruction provided better quality cervical spine CT images in the evaluation of cervical spinal stenosis than hybrid IR.
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Affiliation(s)
- Yuta Ohtake
- From the Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
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21
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Chen Q, Fang S, Yuchen Y, Li R, Deng R, Chen Y, Ma D, Lin H, Yan F. Clinical feasibility of deep learning reconstruction in liver diffusion-weighted imaging: Improvement of image quality and impact on apparent diffusion coefficient value. Eur J Radiol 2023; 168:111149. [PMID: 37862927 DOI: 10.1016/j.ejrad.2023.111149] [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: 06/05/2023] [Revised: 09/26/2023] [Accepted: 10/10/2023] [Indexed: 10/22/2023]
Abstract
PURPOSE Diffusion-weighted imaging (DWI) of the liver suffers from low resolution, noise, and artifacts. This study aimed to investigate the effect of deep learning reconstruction (DLR) on image quality and apparent diffusion coefficient (ADC) quantification of liver DWI at 3 Tesla. METHOD In this prospective study, images of the liver obtained at DWI with b-values of 0 (DWI0), 50 (DWI50) and 800 s/mm2 (DWI800) from consecutive patients with liver lesions from February 2022 to February 2023 were reconstructed with and without DLR (non-DLR). Image quality was assessed qualitatively using Likert scoring system and quantitatively using signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and liver/parenchyma boundary sharpness from region-of-interest (ROI) analysis. ADC value of lesion were measured. Phantom experiment was also performed to investigate the factors that determine the effect of DLR on ADC value. Qualitative score, SNR, CNR, boundary sharpness, and apparent diffusion coefficients (ADCs) for DWI were compared using paired t-test and Wilcoxon signed rank test. P < 0.05 was considered statistically significant. RESULTS A total of 85 patients with 170 lesions were included. DLR group showed a higher qualitative score than the non-DLR group. for example, with DWI800 the score was 4.77 ± 0.52 versus 4.30 ± 0.63 (P < 0.001). DLR group also showed higher SNRs, CNRs and boundary sharpness than the non-DLR group. DLR reduced the ADC of malignant tumors (1.105[0.904, 1.340] versus 1.114[0.904, 1.320]) (P < 0.001), but there was no significant difference in the diagnostic value of malignancy for DLR and non-DLR groups (P = 57.3). The phantom study confirmed a reduction of ADC in images with low resolution, and a stronger reduction of ADC in heterogeneous structures than in homogeneous ones (P < 0.001). CONCLUSIONS DLR improved image quality of liver DWI. DLR reduced the ADC value of lesions, but did not affect the diagnostic performance of ADC in distinguishing malignant tumors on a 3.0-T MRI system.
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Affiliation(s)
- Qian Chen
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, NO. 197 Ruijin Er Road, Shanghai 200025, China; Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Huan-Hu-Xi Road, Ti-Yuan-Bei, He Xi District, Tianjin 300060, China
| | - Shu Fang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, NO. 197 Ruijin Er Road, Shanghai 200025, China
| | - Yang Yuchen
- Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School Of Medicine, NO. 197 Ruijin Er Road, Shanghai 200025, China
| | - Ruokun Li
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, NO. 197 Ruijin Er Road, Shanghai 200025, China
| | - Rong Deng
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, NO. 197 Ruijin Er Road, Shanghai 200025, China
| | - Yongjun Chen
- Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School Of Medicine, NO. 197 Ruijin Er Road, Shanghai 200025, China
| | - Di Ma
- Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School Of Medicine, NO. 197 Ruijin Er Road, Shanghai 200025, China
| | - Huimin Lin
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, NO. 197 Ruijin Er Road, Shanghai 200025, China.
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, NO. 197 Ruijin Er Road, Shanghai 200025, China; College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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22
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Wang X, Guo Y. The intelligent football players' motion recognition system based on convolutional neural network and big data. Heliyon 2023; 9:e22316. [PMID: 38053884 PMCID: PMC10694318 DOI: 10.1016/j.heliyon.2023.e22316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 11/08/2023] [Accepted: 11/09/2023] [Indexed: 12/07/2023] Open
Abstract
This article focuses on evaluating the efficacy of intelligent image processing techniques using deep learning algorithms in the context of football, to present pragmatic solutions for enhancing the functional strength training of football players. The article commences by delving into the prevailing research landscape concerning image recognition in football. It then embarks on a comprehensive examination of the prevailing landscape in soccer image recognition research. Subsequently, a novel soccer image classification model is meticulously crafted through the fusion of Space-Time Graph Neural Network (STGNN) and Bi-directional Long Short-Term Memory (BiLSTM). The devised model introduces the potency of STGNN to extract spatial features from sequences of images, adeptly harnessing spatial information through judiciously integrated graph convolutional layers. These layers are further bolstered by the infusion of graph attention modules and channel attention modules, working in tandem to amplify salient information within distinct channels. Concurrently, the temporal dimension is adroitly addressed by the incorporation of BiLSTM, effectively capturing the temporal dynamics inherent in image sequences. Rigorous simulation analyses are conducted to gauge the prowess of this model. The empirical outcomes resoundingly affirm the potency of the proposed deep hybrid attention network model in the realm of soccer image processing tasks. In the arena of action recognition and classification, this model emerges as a paragon of performance enhancement. Impressively, the model notched an accuracy of 94.34 %, precision of 92.35 %, recall of 90.44 %, and F1-score of 89.22 %. Further scrutiny of the model's image recognition capabilities unveils its proficiency in extracting comprehensive features and maintaining stable recognition performance when applied to football images. Consequently, the football intelligent image processing model based on deep hybrid attention networks, as formulated within this article, attains high recognition accuracy and demonstrates consistent recognition performance. These findings offer invaluable insights for injury prevention and personalized skill enhancement in the training of football players.
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Affiliation(s)
- Xin Wang
- College of Physical Education and Music,Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250353, China
| | - Yingqing Guo
- China Institute of Sport Science, Beijing, 100061, China
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23
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Zou L, Cai Z, Qiu Y, Gui L, Mao L, Yang X. CTG-Net: an efficient cascaded framework driven by terminal guidance mechanism for dilated pancreatic duct segmentation. Phys Med Biol 2023; 68:215006. [PMID: 37586389 DOI: 10.1088/1361-6560/acf110] [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: 05/24/2023] [Accepted: 08/16/2023] [Indexed: 08/18/2023]
Abstract
Pancreatic duct dilation indicates a high risk of various pancreatic diseases. Segmentation for dilated pancreatic duct (DPD) on computed tomography (CT) image shows the potential to assist the early diagnosis, surgical planning and prognosis. Because of the DPD's tiny size, slender tubular structure and the surrounding distractions, most current researches on DPD segmentation achieve low accuracy and always have segmentation errors on the terminal DPD regions. To address these problems, we propose a cascaded terminal guidance network to efficiently improve the DPD segmentation performance. Firstly, a basic cascaded segmentation architecture is established to get the pancreas and coarse DPD segmentation, a DPD graph structure is build on the coarse DPD segmentation to locate the terminal DPD regions. Then, a terminal anatomy attention module is introduced for jointly learning the local intensity from the CT images, feature cues from the coarse DPD segmentation and global anatomy information from the designed pancreas anatomy-aware maps. Finally, a terminal distraction attention module which explicitly learns the distribution of the terminal distraction regions is proposed to reduce the false positive and false negative predictions. We also propose a new metric called tDice to measure the terminal segmentation accuracy for targets with tubular structures and two other metrics for segmentation error evaluation. We collect our dilated pancreatic duct segmentation dataset with 150 CT scans from patients with five types of pancreatic tumors. Experimental results on our dataset show that our proposed approach boosts DPD segmentation accuracy by nearly 20% compared with the existing results, and achieves more than 9% improvement for the terminal segmentation accuracy compared with the state-of-the-art methods.
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Affiliation(s)
- Liwen Zou
- Department of Mathematics, Nanjing University, Nanjing, 210093, People's Republic of China
| | - Zhenghua Cai
- Medical School, Nanjing University, Nanjing, 210007, People's Republic of China
| | - Yudong Qiu
- Department of General Surgery, Nanjing Drum Tower Hospital, Nanjing, 210008, People's Republic of China
| | - Luying Gui
- School of Mathematics and Statistics, Nanjing University of Science and Technology, Nanjing, 210094, People's Republic of China
| | - Liang Mao
- Department of General Surgery, Nanjing Drum Tower Hospital, Nanjing, 210008, People's Republic of China
| | - Xiaoping Yang
- Department of Mathematics, Nanjing University, Nanjing, 210093, People's Republic of China
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Fujima N, Kamagata K, Ueda D, Fujita S, Fushimi Y, Yanagawa M, Ito R, Tsuboyama T, Kawamura M, Nakaura T, Yamada A, Nozaki T, Fujioka T, Matsui Y, Hirata K, Tatsugami F, Naganawa S. Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging. Magn Reson Med Sci 2023; 22:401-414. [PMID: 37532584 PMCID: PMC10552661 DOI: 10.2463/mrms.rev.2023-0047] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 07/09/2023] [Indexed: 08/04/2023] Open
Abstract
Due primarily to the excellent soft tissue contrast depictions provided by MRI, the widespread application of head and neck MRI in clinical practice serves to assess various diseases. Artificial intelligence (AI)-based methodologies, particularly deep learning analyses using convolutional neural networks, have recently gained global recognition and have been extensively investigated in clinical research for their applicability across a range of categories within medical imaging, including head and neck MRI. Analytical approaches using AI have shown potential for addressing the clinical limitations associated with head and neck MRI. In this review, we focus primarily on the technical advancements in deep-learning-based methodologies and their clinical utility within the field of head and neck MRI, encompassing aspects such as image acquisition and reconstruction, lesion segmentation, disease classification and diagnosis, and prognostic prediction for patients presenting with head and neck diseases. We then discuss the limitations of current deep-learning-based approaches and offer insights regarding future challenges in this field.
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Affiliation(s)
- Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Hokkaido, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Daiju Ueda
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Osaka, Japan
| | - Shohei Fujita
- Department of Radiology, University of Tokyo, Tokyo, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Kyoto, Japan
| | - Masahiro Yanagawa
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Rintaro Ito
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Takahiro Tsuboyama
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Mariko Kawamura
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Kumamoto University Graduate School of Medicine, Kumamoto, Kumamoto, Japan
| | - Akira Yamada
- Department of Radiology, Shinshu University School of Medicine, Matsumoto, Nagano, Japan
| | - Taiki Nozaki
- Department of Radiology, Keio University School of Medicine, Tokyo, Japan
| | - Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Yusuke Matsui
- Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Okayama, Japan
| | - Kenji Hirata
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Fuminari Tatsugami
- Department of Diagnostic Radiology, Hiroshima University, Hiroshima, Hiroshima, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
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Ahmed R, Al Shehhi A, Hassan B, Werghi N, Seghier ML. An appraisal of the performance of AI tools for chronic stroke lesion segmentation. Comput Biol Med 2023; 164:107302. [PMID: 37572443 DOI: 10.1016/j.compbiomed.2023.107302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 07/18/2023] [Accepted: 07/28/2023] [Indexed: 08/14/2023]
Abstract
Automated demarcation of stoke lesions from monospectral magnetic resonance imaging scans is extremely useful for diverse research and clinical applications, including lesion-symptom mapping to explain deficits and predict recovery. There is a significant surge of interest in the development of supervised artificial intelligence (AI) methods for that purpose, including deep learning, with a performance comparable to trained experts. Such AI-based methods, however, require copious amounts of data. Thanks to the availability of large datasets, the development of AI-based methods for lesion segmentation has immensely accelerated in the last decade. One of these datasets is the Anatomical Tracings of Lesions After Stroke (ATLAS) dataset which includes T1-weighted images from hundreds of chronic stroke survivors with their manually traced lesions. This systematic review offers an appraisal of the impact of the ATLAS dataset in promoting the development of AI-based segmentation of stroke lesions. An examination of all published studies, that used the ATLAS dataset to both train and test their methods, highlighted an overall moderate performance (median Dice index = 59.40%) and a huge variability across studies in terms of data preprocessing, data augmentation, AI architecture, and the mode of operation (two-dimensional versus three-dimensional methods). Perhaps most importantly, almost all AI tools were borrowed from existing AI architectures in computer vision, as 90% of all selected studies relied on conventional convolutional neural network-based architectures. Overall, current research has not led to the development of robust AI architectures than can handle spatially heterogenous lesion patterns. This review also highlights the difficulty of gauging the performance of AI tools in the presence of uncertainties in the definition of the ground truth.
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Affiliation(s)
- Ramsha Ahmed
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Aamna Al Shehhi
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates; Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Bilal Hassan
- Department of Electrical Engineering and Computer Science, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Naoufel Werghi
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates; Department of Electrical Engineering and Computer Science, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Mohamed L Seghier
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates; Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.
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Quint R, Vaussy A, Stemmer A, Hautefort C, Houdart E, Eliezer M. Iterative Denoising Accelerated 3D FLAIR Sequence for Hydrops MR Imaging at 3T. AJNR Am J Neuroradiol 2023; 44:1064-1069. [PMID: 37536733 PMCID: PMC10494947 DOI: 10.3174/ajnr.a7953] [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: 02/13/2023] [Accepted: 06/27/2023] [Indexed: 08/05/2023]
Abstract
BACKGROUND AND PURPOSE 3D FLAIR sequences have become the criterion standard for identifying endolymphatic hydrops, but scan time remains an important limitation to their widespread use. Our purpose was to evaluate the diagnostic performance and image quality of an accelerated 3D FLAIR sequence combined with an iterative denoising algorithm. MATERIALS AND METHODS This was a retrospective study performed on 30 patients with clinical suspicion of endolymphatic hydrops who underwent 3T MR imaging 4 hours after gadolinium injection using two 3D FLAIR sequences. The first (conventional FLAIR) was accelerated with a conventional turbo factor of 187. The second was accelerated with an increased turbo factor of 263, resulting in a 33% scan time reduction (5 minutes 36 seconds versus 8 minutes 15 seconds, respectively). A sequence was reconstructed in-line immediately after the accelerated 3D FLAIR acquisition from the same raw data with iterative denoising (accelerated-FLAIR iterative denoising). The signal intensity ratio image quality score and endolymphatic hydrops diagnosis were evaluated. RESULTS The mean signal intensity ratio for symptomatic and asymptomatic ears of accelerated-FLAIR iterative denoising was significantly higher than the mean SNR of conventional FLAIR (29.5 versus 19 and 25.9 versus 16.3, P < .001). Compared with the conventional FLAIR sequence, the image-quality score was higher with accelerated-FLAIR iterative denoising (mean image-quality score, 3.8 [SD, 0.4] versus 3.3 [SD, 0.6] for accelerated-FLAIR iterative denoising and conventional FLAIR, respectively, P = .003). There was no significant difference in the diagnosis of endolymphatic hydrops between the 2 sequences. Interreader agreement was good-to-excellent. CONCLUSIONS The iterative denoising algorithm applied to an accelerated 3D FLAIR sequence for exploration of endolymphatic hydrops enabled significantly reducing the scan time without compromising image quality and diagnostic performance.
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Affiliation(s)
- R Quint
- From the Department of Neuroradiology (R.Q., E.H., M.E.), Lariboisière University Hospital, Paris, France
| | - A Vaussy
- Siemens Healthineers France (A.V.), Saint-Denis, France
| | - A Stemmer
- Siemens Healthineers (A.S.), Erlangen, Germany
| | - C Hautefort
- Department of Head and Neck Surgery (C.H.), Lariboisière University Hospital, Paris, France
| | - E Houdart
- From the Department of Neuroradiology (R.Q., E.H., M.E.), Lariboisière University Hospital, Paris, France
- Faculté de Médecine (E.H.), Université de Paris, Paris, France
| | - M Eliezer
- From the Department of Neuroradiology (R.Q., E.H., M.E.), Lariboisière University Hospital, Paris, France
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Oshima Y, Ohno Y, Takenaka D, Ito Y, Kimata H, Fujii K, Akino N, Hamabuchi N, Matsuyama T, Nagata H, Ueda T, Ikeda H, Ozawa Y, Yoshikawa T, Toyama H. Capability for dose reduction while maintaining nodule detection: Comparison of silver and copper X-ray spectrum modulation filters for chest CT using a phantom study with different reconstruction methods. Eur J Radiol 2023; 166:110969. [PMID: 37454556 DOI: 10.1016/j.ejrad.2023.110969] [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: 02/06/2023] [Revised: 07/03/2023] [Accepted: 07/10/2023] [Indexed: 07/18/2023]
Abstract
PURPOSE To compare the capability of CTs obtained with a silver or copper x-ray beam spectral modulation filter (Ag filter and Cu filter) and reconstructed with FBP, hybrid-type IR and deep learning reconstruction (DLR) for radiation dose reduction for lung nodule detection using a chest phantom study. MATERIALS AND METHODS A chest CT phantom was scanned with a 320-detector row CT with Ag filter at 0.6, 1.6 and 2.5 mGy and Cu filters at 0.6, 1.6, 2.5 and 9.6 mGy, and reconstructed with the aforementioned methods. To compare image quality of all the CT data, SNRs and CNRs for any nodule were calculated for all protocols. To compare nodule detection capability among all protocols, the probability of detection of any nodule was assessed with a 5-point visual scoring system. Then, ROC analyses were performed to compare nodule detection capability of Ag and Cu filters for each radiation dose data with the same method and of the three methods for any radiation dose data and obtained with either filter. RESULTS At any of the doses, SNR, CNR and area under the curve for the Ag filter were significantly higher or larger than those for the Cu filter (p < 0.05). Moreover, with DLR, those values were significantly higher or larger than all the others for CTs obtained with any of the radiation doses and either filter (p < 0.05). CONCLUSION The Ag filter and DLR can significantly improve image quality and nodule detection capability compared with the Cu filter and other reconstruction methods at each of radiation doses used.
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Affiliation(s)
- Yuka Oshima
- Department of Radiology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
| | - Yoshiharu Ohno
- Department of Diagnostic Radiology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan; Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine, Toyoake, Aichi, Japan.
| | - Daisuke Takenaka
- Department of Radiology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan; Department of Diagnostic Radiology, Hyogo Cancer Center, Akashi, Hyogo, Japan
| | - Yuya Ito
- Canon Medical Systems Corporation, Otawara, Tochigi, Japan
| | - Hirona Kimata
- Canon Medical Systems Corporation, Otawara, Tochigi, Japan
| | - Kenji Fujii
- Canon Medical Systems Corporation, Otawara, Tochigi, Japan
| | - Naruomi Akino
- Canon Medical Systems Corporation, Otawara, Tochigi, Japan
| | - Nayu Hamabuchi
- Department of Radiology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
| | - Takahiro Matsuyama
- Department of Radiology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
| | - Hiroyuki Nagata
- Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
| | - Takahiro Ueda
- Department of Radiology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
| | - Hirotaka Ikeda
- Department of Radiology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
| | - Yoshiyuki Ozawa
- Department of Radiology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
| | - Takeshi Yoshikawa
- Department of Radiology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan; Canon Medical Systems Corporation, Otawara, Tochigi, Japan
| | - Hiroshi Toyama
- Department of Radiology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
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Li T, Wang J, Yang Y, Glide-Hurst CK, Wen N, Cai J. Multi-parametric MRI for radiotherapy simulation. Med Phys 2023; 50:5273-5293. [PMID: 36710376 PMCID: PMC10382603 DOI: 10.1002/mp.16256] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 09/10/2022] [Accepted: 12/06/2022] [Indexed: 01/31/2023] Open
Abstract
Magnetic resonance imaging (MRI) has become an important imaging modality in the field of radiotherapy (RT) in the past decade, especially with the development of various novel MRI and image-guidance techniques. In this review article, we will describe recent developments and discuss the applications of multi-parametric MRI (mpMRI) in RT simulation. In this review, mpMRI refers to a general and loose definition which includes various multi-contrast MRI techniques. Specifically, we will focus on the implementation, challenges, and future directions of mpMRI techniques for RT simulation.
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Affiliation(s)
- Tian Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Jihong Wang
- Department of Radiation Physics, Division of Radiation Oncology, MD Anderson Cancer Center, Houston, Texas, USA
| | - Yingli Yang
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong Univeristy School of Medicine, Shanghai, China
- SJTU-Ruijing-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Carri K Glide-Hurst
- Department of Radiation Oncology, University of Wisconsin, Madison, Wisconsin, USA
| | - Ning Wen
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong Univeristy School of Medicine, Shanghai, China
- SJTU-Ruijing-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
- The Global Institute of Future Technology, Shanghai Jiaotong University, Shanghai, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
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29
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Ohno Y, Ozawa Y, Nagata H, Bando S, Cong S, Takahashi T, Oshima Y, Hamabuchi N, Matsuyama T, Ueda T, Yoshikawa T, Takenaka D, Toyama H. Area-Detector Computed Tomography for Pulmonary Functional Imaging. Diagnostics (Basel) 2023; 13:2518. [PMID: 37568881 PMCID: PMC10416899 DOI: 10.3390/diagnostics13152518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 07/22/2023] [Accepted: 07/25/2023] [Indexed: 08/13/2023] Open
Abstract
An area-detector CT (ADCT) has a 320-detector row and can obtain isotropic volume data without helical scanning within an area of nearly 160 mm. The actual-perfusion CT data within this area can, thus, be obtained by means of continuous dynamic scanning for the qualitative or quantitative evaluation of regional perfusion within nodules, lymph nodes, or tumors. Moreover, this system can obtain CT data with not only helical but also step-and-shoot or wide-volume scanning for body CT imaging. ADCT also has the potential to use dual-energy CT and subtraction CT to enable contrast-enhanced visualization by means of not only iodine but also xenon or krypton for functional evaluations. Therefore, systems using ADCT may be able to function as a pulmonary functional imaging tool. This review is intended to help the reader understand, with study results published during the last a few decades, the basic or clinical evidence about (1) newly applied reconstruction methods for radiation dose reduction for functional ADCT, (2) morphology-based pulmonary functional imaging, (3) pulmonary perfusion evaluation, (4) ventilation assessment, and (5) biomechanical evaluation.
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Affiliation(s)
- Yoshiharu Ohno
- Department of Diagnostic Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan
- Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan;
| | - Yoshiyuki Ozawa
- Department of Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan; (Y.O.)
| | - Hiroyuki Nagata
- Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan;
| | - Shuji Bando
- Department of Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan; (Y.O.)
| | - Shang Cong
- Department of Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan; (Y.O.)
| | - Tomoki Takahashi
- Department of Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan; (Y.O.)
| | - Yuka Oshima
- Department of Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan; (Y.O.)
| | - Nayu Hamabuchi
- Department of Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan; (Y.O.)
| | - Takahiro Matsuyama
- Department of Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan; (Y.O.)
| | - Takahiro Ueda
- Department of Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan; (Y.O.)
| | - Takeshi Yoshikawa
- Department of Diagnostic Radiology, Hyogo Cancer Center, Akashi 673-0021, Hyogo, Japan
| | - Daisuke Takenaka
- Department of Diagnostic Radiology, Hyogo Cancer Center, Akashi 673-0021, Hyogo, Japan
| | - Hiroshi Toyama
- Department of Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan; (Y.O.)
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Zhan G, Wang D, Cabezas M, Bai L, Kyle K, Ouyang W, Barnett M, Wang C. Learning from pseudo-labels: deep networks improve consistency in longitudinal brain volume estimation. Front Neurosci 2023; 17:1196087. [PMID: 37483345 PMCID: PMC10358358 DOI: 10.3389/fnins.2023.1196087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 06/16/2023] [Indexed: 07/25/2023] Open
Abstract
Introduction Brain atrophy is a critical biomarker of disease progression and treatment response in neurodegenerative diseases such as multiple sclerosis (MS). Confounding factors such as inconsistent imaging acquisitions hamper the accurate measurement of brain atrophy in the clinic. This study aims to develop and validate a robust deep learning model to overcome these challenges; and to evaluate its impact on the measurement of disease progression. Methods Voxel-wise pseudo-atrophy labels were generated using SIENA, a widely adopted tool for the measurement of brain atrophy in MS. Deformation maps were produced for 195 pairs of longitudinal 3D T1 scans from patients with MS. A 3D U-Net, namely DeepBVC, was specifically developed overcome common variances in resolution, signal-to-noise ratio and contrast ratio between baseline and follow up scans. The performance of DeepBVC was compared against SIENA using McLaren test-retest dataset and 233 in-house MS subjects with MRI from multiple time points. Clinical evaluation included disability assessment with the Expanded Disability Status Scale (EDSS) and traditional imaging metrics such as lesion burden. Results For 3 subjects in test-retest experiments, the median percent brain volume change (PBVC) for DeepBVC and SIENA was 0.105 vs. 0.198% (subject 1), 0.061 vs. 0.084% (subject 2), 0.104 vs. 0.408% (subject 3). For testing consistency across multiple time points in individual MS subjects, the mean (± standard deviation) PBVC difference of DeepBVC and SIENA were 0.028% (± 0.145%) and 0.031% (±0.154%), respectively. The linear correlation with baseline T2 lesion volume were r = -0.288 (p < 0.05) and r = -0.249 (p < 0.05) for DeepBVC and SIENA, respectively. There was no significant correlation of disability progression with PBVC as estimated by either method (p = 0.86, p = 0.84). Discussion DeepBVC is a deep learning powered brain volume change estimation method for assessing brain atrophy used T1-weighted images. Compared to SIENA, DeepBVC demonstrates superior performance in reproducibility and in the context of common clinical scan variances such as imaging contrast, voxel resolution, random bias field, and signal-to-noise ratio. Enhanced measurement robustness, automation, and processing speed of DeepBVC indicate its potential for utilisation in both research and clinical environments for monitoring disease progression and, potentially, evaluating treatment effectiveness.
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Affiliation(s)
- Geng Zhan
- Brain and Mind Center, The University of Sydney, Sydney, NSW, Australia
- Sydney Neuroimaging Analysis Center, Sydney, NSW, Australia
| | - Dongang Wang
- Brain and Mind Center, The University of Sydney, Sydney, NSW, Australia
- Sydney Neuroimaging Analysis Center, Sydney, NSW, Australia
| | - Mariano Cabezas
- Brain and Mind Center, The University of Sydney, Sydney, NSW, Australia
| | - Lei Bai
- Shanghai AI Laboratory, Shanghai, China
| | - Kain Kyle
- Brain and Mind Center, The University of Sydney, Sydney, NSW, Australia
- Sydney Neuroimaging Analysis Center, Sydney, NSW, Australia
| | | | - Michael Barnett
- Brain and Mind Center, The University of Sydney, Sydney, NSW, Australia
- Sydney Neuroimaging Analysis Center, Sydney, NSW, Australia
| | - Chenyu Wang
- Brain and Mind Center, The University of Sydney, Sydney, NSW, Australia
- Sydney Neuroimaging Analysis Center, Sydney, NSW, Australia
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31
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Miyo R, Yasaka K, Hamada A, Sakamoto N, Hosoi R, Mizuki M, Abe O. Deep-learning reconstruction for the evaluation of lumbar spinal stenosis in computed tomography. Medicine (Baltimore) 2023; 102:e33910. [PMID: 37335676 DOI: 10.1097/md.0000000000033910] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/21/2023] Open
Abstract
To compare the quality and interobserver agreement in the evaluation of lumbar spinal stenosis (LSS) on computed tomography (CT) images between deep-learning reconstruction (DLR) and hybrid iterative reconstruction (hybrid IR). This retrospective study included 30 patients (age, 71.5 ± 12.5 years; 20 men) who underwent unenhanced lumbar CT. Axial and sagittal CT images were reconstructed using hybrid IR and DLR. In the quantitative analysis, a radiologist placed regions of interest within the aorta and recorded the standard deviation of the CT attenuation (i.e., quantitative image noise). In the qualitative analysis, 2 other blinded radiologists evaluated the subjective image noise, depictions of structures, overall image quality, and degree of LSS. The quantitative image noise in DLR (14.8 ± 1.9/14.2 ± 1.8 in axial/sagittal images) was significantly lower than that in hybrid IR (21.4 ± 4.4/20.6 ± 4.0) (P < .0001 for both, paired t test). Subjective image noise, depictions of structures, and overall image quality were significantly better with DLR than with hybrid IR (P < .006, Wilcoxon signed-rank test). Interobserver agreements in the evaluation of LSS (with 95% confidence interval) were 0.732 (0.712-0.751) and 0.794 (0.781-0.807) for hybrid IR and DLR, respectively. DLR provided images with improved quality and higher interobserver agreement in the evaluation of LSS in lumbar CT than hybrid IR.
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Affiliation(s)
- Rintaro Miyo
- Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
- Department of Radiology, International University of Health and Welfare Narita Hospital, Chiba, Japan
| | - Koichiro Yasaka
- Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
| | - Akiyoshi Hamada
- Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
| | - Naoya Sakamoto
- Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
- Department of Radiology, Toranomon Hospital, Tokyo, Japan
| | - Reina Hosoi
- Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
- Department of Radiology, Juntendo University Urayasu Hospital, Chiba, Japan
| | - Masumi Mizuki
- Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
- Department of Radiology, Nerimahikarigaoka Hospital, Tokyo, Japan
| | - Osamu Abe
- Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
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Sarvari AVP, Sridevi K. An optimized EBRSA-Bi LSTM model for highly undersampled rapid CT image reconstruction. Biomed Signal Process Control 2023; 83:104637. [PMID: 36776947 PMCID: PMC9904992 DOI: 10.1016/j.bspc.2023.104637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 12/22/2022] [Accepted: 01/25/2023] [Indexed: 02/10/2023]
Abstract
COVID-19 has spread all over the world, causing serious panic around the globe. Chest computed tomography (CT) images are integral in confirming COVID positive patients. Several investigations were conducted to improve or maintain the image reconstruction quality for the sample image reconstruction. Deep learning (DL) methods have recently been proposed to achieve fast reconstruction, but many have focused on a single domain, such as the image domain of k-space. In this research, the highly under-sampled enhanced battle royale self-attention based bi-directional long short-term (EBRSA-bi LSTM) CT image reconstruction model is proposed to reconstruct the image from the under-sampled data. The research is adapted with two phases, namely, pre-processing and reconstruction. The extended cascaded filter (ECF) is proposed for image pre-processing and tends to suppress the noise and enhance the reconstruction accuracy. In the reconstruction model, the battle royale optimization (BrO) is intended to diminish the loss function of the reconstruction network model and weight updation. The proposed model is tested with two datasets, COVID-CT- and SARS-CoV-2 CT. The reconstruction accuracy of the proposed model with two datasets is 93.5 % and 97.7 %, respectively. Also, the image quality assessment parameters such as Peak-Signal to Noise Ratio (PSNR), Root Mean Square Error (RMSE) and Structural Similarity Index metric (SSIM) are evaluated, and it yields an outcome of (45 and 46 dB), (0.0026 and 0.0022) and (0.992, 0.996) with two datasets.
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Affiliation(s)
- A V P Sarvari
- Department of Electronics and Communication Engineering, GITAM Deemed to be University, Andhra Pradesh 530045, India
| | - K Sridevi
- Department of Electronics and Communication Engineering, GITAM Deemed to be University, Andhra Pradesh 530045, India
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Okimoto N, Yasaka K, Kaiume M, Kanemaru N, Suzuki Y, Abe O. Improving detection performance of hepatocellular carcinoma and interobserver agreement for liver imaging reporting and data system on CT using deep learning reconstruction. Abdom Radiol (NY) 2023; 48:1280-1289. [PMID: 36757454 PMCID: PMC10115733 DOI: 10.1007/s00261-023-03834-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 01/23/2023] [Accepted: 01/24/2023] [Indexed: 02/10/2023]
Abstract
PURPOSE This study aimed to compare the hepatocellular carcinoma (HCC) detection performance, interobserver agreement for Liver Imaging Reporting and Data System (LI-RADS) categories, and image quality between deep learning reconstruction (DLR) and conventional hybrid iterative reconstruction (Hybrid IR) in CT. METHODS This retrospective study included patients who underwent abdominal dynamic contrast-enhanced CT between October 2021 and March 2022. Arterial, portal, and delayed phase images were reconstructed using DLR and Hybrid IR. Two blinded readers independently read the image sets with detecting HCCs, scoring LI-RADS, and evaluating image quality. RESULTS A total of 26 patients with HCC (mean age, 73 years ± 12.3) and 23 patients without HCC (mean age, 66 years ± 14.7) were included. The figures of merit (FOM) for the jackknife alternative free-response receiver operating characteristic analysis in detecting HCC averaged for the readers were 0.925 (reader 1, 0.937; reader 2, 0.913) in DLR and 0.878 (reader 1, 0.904; reader 2, 0.851) in Hybrid IR, and the FOM in DLR were significantly higher than that in Hybrid IR (p = 0.038). The interobserver agreement (Cohen's weighted kappa statistics) for LI-RADS categories was moderate for DLR (0.595; 95% CI, 0.585-0.605) and significantly superior to Hybrid IR (0.568; 95% CI, 0.553-0.582). According to both readers, DLR was significantly superior to Hybrid IR in terms of image quality (p ≤ 0.021). CONCLUSION DLR improved HCC detection, interobserver agreement for LI-RADS categories, and image quality in evaluations of HCC compared to Hybrid IR in abdominal dynamic contrast-enhanced CT.
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Affiliation(s)
- Naomasa Okimoto
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan
| | - Koichiro Yasaka
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan.
| | - Masafumi Kaiume
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan
| | - Noriko Kanemaru
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan
| | - Yuichi Suzuki
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan
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Surianarayanan C, Lawrence JJ, Chelliah PR, Prakash E, Hewage C. Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders-A Scoping Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:3062. [PMID: 36991773 PMCID: PMC10053494 DOI: 10.3390/s23063062] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 03/09/2023] [Accepted: 03/09/2023] [Indexed: 06/19/2023]
Abstract
Artificial intelligence (AI) is a field of computer science that deals with the simulation of human intelligence using machines so that such machines gain problem-solving and decision-making capabilities similar to that of the human brain. Neuroscience is the scientific study of the struczture and cognitive functions of the brain. Neuroscience and AI are mutually interrelated. These two fields help each other in their advancements. The theory of neuroscience has brought many distinct improvisations into the AI field. The biological neural network has led to the realization of complex deep neural network architectures that are used to develop versatile applications, such as text processing, speech recognition, object detection, etc. Additionally, neuroscience helps to validate the existing AI-based models. Reinforcement learning in humans and animals has inspired computer scientists to develop algorithms for reinforcement learning in artificial systems, which enables those systems to learn complex strategies without explicit instruction. Such learning helps in building complex applications, like robot-based surgery, autonomous vehicles, gaming applications, etc. In turn, with its ability to intelligently analyze complex data and extract hidden patterns, AI fits as a perfect choice for analyzing neuroscience data that are very complex. Large-scale AI-based simulations help neuroscientists test their hypotheses. Through an interface with the brain, an AI-based system can extract the brain signals and commands that are generated according to the signals. These commands are fed into devices, such as a robotic arm, which helps in the movement of paralyzed muscles or other human parts. AI has several use cases in analyzing neuroimaging data and reducing the workload of radiologists. The study of neuroscience helps in the early detection and diagnosis of neurological disorders. In the same way, AI can effectively be applied to the prediction and detection of neurological disorders. Thus, in this paper, a scoping review has been carried out on the mutual relationship between AI and neuroscience, emphasizing the convergence between AI and neuroscience in order to detect and predict various neurological disorders.
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Affiliation(s)
| | | | | | - Edmond Prakash
- Research Center for Creative Arts, University for the Creative Arts (UCA), Farnham GU9 7DS, UK
| | - Chaminda Hewage
- Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff CF5 2YB, UK
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Comparison of Deep-Learning Image Reconstruction With Hybrid Iterative Reconstruction for Evaluating Lung Nodules With High-Resolution Computed Tomography. J Comput Assist Tomogr 2023:00004728-990000000-00154. [PMID: 36877787 DOI: 10.1097/rct.0000000000001460] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2023]
Abstract
OBJECTIVE This study aimed to investigate the impact of deep-learning reconstruction (DLR) on the detailed evaluation of solitary lung nodule using high-resolution computed tomography (HRCT) compared with hybrid iterative reconstruction (hybrid IR). METHODS This retrospective study was approved by our institutional review board and included 68 consecutive patients (mean ± SD age, 70.1 ± 12.0 years; 37 men and 31 women) who underwent computed tomography between November 2021 and February 2022. High-resolution computed tomography images with a targeted field of view of the unilateral lung were reconstructed using filtered back projection, hybrid IR, and DLR, which is commercially available. Objective image noise was measured by placing the regions of interest on the skeletal muscle and recording the SD of the computed tomography attenuation. Subjective image analyses were performed by 2 blinded radiologists taking into consideration the subjective noise, artifacts, depictions of small structures and nodule rims, and the overall image quality. In subjective analyses, filtered back projection images were used as controls. Data were compared between DLR and hybrid IR using the paired t test and Wilcoxon signed-rank sum test. RESULTS Objective image noise in DLR (32.7 ± 4.2) was significantly reduced compared with hybrid IR (35.3 ± 4.4) (P < 0.0001). According to both readers, significant improvements in subjective image noise, artifacts, depictions of small structures and nodule rims, and overall image quality were observed in images derived from DLR compared with those from hybrid IR (P < 0.0001 for all). CONCLUSIONS Deep-learning reconstruction provides a better high-resolution computed tomography image with improved quality compared with hybrid IR.
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Deep learning reconstruction with single-energy metal artifact reduction in pelvic computed tomography for patients with metal hip prostheses. Jpn J Radiol 2023:10.1007/s11604-023-01402-5. [PMID: 36862290 PMCID: PMC10366278 DOI: 10.1007/s11604-023-01402-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 02/16/2023] [Indexed: 03/03/2023]
Abstract
PURPOSE The aim of this study was to assess the impact of the deep learning reconstruction (DLR) with single-energy metal artifact reduction (SEMAR) (DLR-S) technique in pelvic helical computed tomography (CT) images for patients with metal hip prostheses and compare it with DLR and hybrid iterative reconstruction (IR) with SEMAR (IR-S). MATERIALS AND METHODS This retrospective study included 26 patients (mean age 68.6 ± 16.6 years, with 9 males and 17 females) with metal hip prostheses who underwent a CT examination including the pelvis. Axial pelvic CT images were reconstructed using DLR-S, DLR, and IR-S. In one-by-one qualitative analyses, two radiologists evaluated the degree of metal artifacts, noise, and pelvic structure depiction. In side-by-side qualitative analyses (DLR-S vs. IR-S), the two radiologists evaluated metal artifacts and overall quality. By placing regions of interest on the bladder and psoas muscle, the standard deviations of their CT attenuation were recorded, and the artifact index was calculated based on them. Results were compared between DLR-S vs. DLR and DLR vs. IR-S using the Wilcoxon signed-rank test. RESULTS In one-by-one qualitative analyses, metal artifacts and structure depiction in DLR-S were significantly better than those in DLR; however, between DLR-S and IR-S, significant differences were noted only for reader 1. Image noise in DLR-S was rated as significantly reduced compared with that in IR-S by both readers. In side-by-side analyses, both readers rated that the DLR-S images are significantly better than IR-S images regarding overall image quality and metal artifacts. The median (interquartile range) of the artifact index for DLR-S was 10.1 (4.4-16.0) and was significantly better than those for DLR (23.1, 6.5-36.1) and IR-S (11.4, 7.8-17.9). CONCLUSION DLR-S provided better pelvic CT images in patients with metal hip prostheses than IR-S and DLR.
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Huang X, Zhao W, Wang G, Wang Y, Li J, Li Y, Zeng Q, Guo J. Improving image quality with deep learning image reconstruction in double-low-dose head CT angiography compared with standard dose and adaptive statistical iterative reconstruction. Br J Radiol 2023; 96:20220625. [PMID: 36606518 PMCID: PMC9975360 DOI: 10.1259/bjr.20220625] [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: 06/22/2022] [Revised: 12/01/2022] [Accepted: 12/02/2022] [Indexed: 01/07/2023] Open
Abstract
OBJECTIVE To demonstrate similar image quality with deep learning image reconstruction (DLIR) in reduced contrast medium (CM) and radiation dose (double-low-dose) head CT angiography (CTA), in comparison with standard-dose and adaptive statistical iterative reconstruction-Veo (ASIR-V). METHODS A prospective study was performed in 63 patients who under head CTA using 256-slice CT. Patients were randomized into either the standard-dose group (n = 38) with 40 ml of Iopromide (370 mgI ml-1 at 4.5 ml s-1); or a double-low-dose group (n = 25) with CM of 25 ml at 3.0 ml s-1. For image reconstruction, the double-low-dose group used DLIR-M and DLIR-H strength, and the standard-dose group used ASIR-V with 50% strength. The CT value and standard deviation, signal-to-noise ratio and contrast-to-noise ratio of posterior fossa, neck muscles, carotid, vertebral and middle cerebral arteries were measured. The image noise, vessel edge and structure blurring and overall image quality were assessed by using a 5-grade method. The double-low-dose group reduced CM dose by 37.5% and CT dose index by 41% compared with the standard-dose group. DLIR further reduced the standard deviation value of the middle cerebral artery and posterior fossa and provided better overall subjective image quality (p < 0.05). CONCLUSION DLIR significantly reduces image noise and provides higher overall image quality in the double-low-dose CTA. ADVANCES IN KNOWLEDGE It is feasible to reduce CM dose by 37.5% and volume CT dose index by 41% with the combination of 80 kVp and DLIR in head CTA. Compared with ASIR-V, DLIR further reduces image noise and achieves better image quality with reduced contrast and radiation dose.
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Affiliation(s)
- Xin Huang
- Department of Radiology, the First Affiliated Hospital of Xi’an Jiaotong University, Shaanxi province, China
| | - Wenzhe Zhao
- Department of Radiology, the First Affiliated Hospital of Xi’an Jiaotong University, Shaanxi province, China
| | - Geliang Wang
- Department of Radiology, the First Affiliated Hospital of Xi’an Jiaotong University, Shaanxi province, China
| | - Yiming Wang
- Department of Radiology, the First Affiliated Hospital of Xi’an Jiaotong University, Shaanxi province, China
| | - Jianying Li
- CT Research Center, GE Healthcare China, Beijing, China
| | - Yanshou Li
- Department of Radiology, the First Affiliated Hospital of Xi’an Jiaotong University, Shaanxi province, China
| | - Qiang Zeng
- Department of Radiology, the First Affiliated Hospital of Xi’an Jiaotong University, Shaanxi province, China
| | - Jianxin Guo
- Department of Radiology, the First Affiliated Hospital of Xi’an Jiaotong University, Shaanxi province, China
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Fu L, Lu B, Tian J, Hu Z. PSSGAN: Towards spectrum shift based perceptual quality enhancement for fluorescence imaging. Comput Med Imaging Graph 2023; 107:102216. [DOI: 10.1016/j.compmedimag.2023.102216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 03/08/2023] [Accepted: 03/09/2023] [Indexed: 03/19/2023]
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Mori M, Fujioka T, Hara M, Katsuta L, Yashima Y, Yamaga E, Yamagiwa K, Tsuchiya J, Hayashi K, Kumaki Y, Oda G, Nakagawa T, Onishi I, Kubota K, Tateishi U. Deep Learning-Based Image Quality Improvement in Digital Positron Emission Tomography for Breast Cancer. Diagnostics (Basel) 2023; 13:diagnostics13040794. [PMID: 36832283 PMCID: PMC9955555 DOI: 10.3390/diagnostics13040794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 02/14/2023] [Accepted: 02/18/2023] [Indexed: 02/22/2023] Open
Abstract
We investigated whether 18F-fluorodeoxyglucose positron emission tomography (PET)/computed tomography images restored via deep learning (DL) improved image quality and affected axillary lymph node (ALN) metastasis diagnosis in patients with breast cancer. Using a five-point scale, two readers compared the image quality of DL-PET and conventional PET (cPET) in 53 consecutive patients from September 2020 to October 2021. Visually analyzed ipsilateral ALNs were rated on a three-point scale. The standard uptake values SUVmax and SUVpeak were calculated for breast cancer regions of interest. For "depiction of primary lesion", reader 2 scored DL-PET significantly higher than cPET. For "noise", "clarity of mammary gland", and "overall image quality", both readers scored DL-PET significantly higher than cPET. The SUVmax and SUVpeak for primary lesions and normal breasts were significantly higher in DL-PET than in cPET (p < 0.001). Considering the ALN metastasis scores 1 and 2 as negative and 3 as positive, the McNemar test revealed no significant difference between cPET and DL-PET scores for either reader (p = 0.250, 0.625). DL-PET improved visual image quality for breast cancer compared with cPET. SUVmax and SUVpeak were significantly higher in DL-PET than in cPET. DL-PET and cPET exhibited comparable diagnostic abilities for ALN metastasis.
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Affiliation(s)
- Mio Mori
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
- Correspondence: ; Tel.: +81-3-5803-5311
| | - Mayumi Hara
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Leona Katsuta
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Yuka Yashima
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Emi Yamaga
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Ken Yamagiwa
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Junichi Tsuchiya
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Kumiko Hayashi
- Department of Surgery, Breast Surgery, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Yuichi Kumaki
- Department of Surgery, Breast Surgery, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Goshi Oda
- Department of Surgery, Breast Surgery, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Tsuyoshi Nakagawa
- Department of Surgery, Breast Surgery, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Iichiroh Onishi
- Department of Comprehensive Pathology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Kazunori Kubota
- Department of Radiology, Dokkyo Medical University Saitama Medical Center, 2-1-50 Minamiko-shigaya, Koshigaya 343-8555, Japan
| | - Ukihide Tateishi
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
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Barreiro-Ares A, Morales-Santiago A, Sendra-Portero F, Souto-Bayarri M. Impact of the Rise of Artificial Intelligence in Radiology: What Do Students Think? INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:1589. [PMID: 36674348 PMCID: PMC9867061 DOI: 10.3390/ijerph20021589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 01/12/2023] [Accepted: 01/13/2023] [Indexed: 06/17/2023]
Abstract
The rise of artificial intelligence (AI) in medicine, and particularly in radiology, is becoming increasingly prominent. Its impact will transform the way the specialty is practiced and the current and future education model. The aim of this study is to analyze the perception that undergraduate medical students have about the current situation of AI in medicine, especially in radiology. A survey with 17 items was distributed to medical students between 3 January to 31 March 2022. Two hundred and eighty-one students correctly responded the questionnaire; 79.3% of them claimed that they knew what AI is. However, their objective knowledge about AI was low but acceptable. Only 24.9% would choose radiology as a specialty, and only 40% of them as one of their first three options. The applications of this technology were valued positively by most students, who give it an important Support Role, without fear that the radiologist will be replaced by AI (79.7%). The majority (95.7%) agreed with the need to implement well-established ethical principles in AI, and 80% valued academic training in AI positively. Surveyed medical students have a basic understanding of AI and perceive it as a useful tool that will transform radiology.
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Affiliation(s)
- Andrés Barreiro-Ares
- Department of Radiology, School of Medicine, University of Santiago de Compostela/CHUS/IDIS (Instituto de Investigación Sanitaria de Santiago), 15782 Santiago de Compostela, Spain
| | - Annia Morales-Santiago
- Department of Radiology, School of Medicine, University of Santiago de Compostela/CHUS/IDIS (Instituto de Investigación Sanitaria de Santiago), 15782 Santiago de Compostela, Spain
| | - Francisco Sendra-Portero
- Department of Radiology and Physical Medicine, School of Medicine, University of Malaga, 29010 Málaga, Spain
| | - Miguel Souto-Bayarri
- Department of Radiology, School of Medicine, University of Santiago de Compostela/CHUS/IDIS (Instituto de Investigación Sanitaria de Santiago), 15782 Santiago de Compostela, Spain
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Isgut M, Gloster L, Choi K, Venugopalan J, Wang MD. Systematic Review of Advanced AI Methods for Improving Healthcare Data Quality in Post COVID-19 Era. IEEE Rev Biomed Eng 2023; 16:53-69. [PMID: 36269930 DOI: 10.1109/rbme.2022.3216531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
At the beginning of the COVID-19 pandemic, there was significant hype about the potential impact of artificial intelligence (AI) tools in combatting COVID-19 on diagnosis, prognosis, or surveillance. However, AI tools have not yet been widely successful. One of the key reason is the COVID-19 pandemic has demanded faster real-time development of AI-driven clinical and health support tools, including rapid data collection, algorithm development, validation, and deployment. However, there was not enough time for proper data quality control. Learning from the hard lessons in COVID-19, we summarize the important health data quality challenges during COVID-19 pandemic such as lack of data standardization, missing data, tabulation errors, and noise and artifact. Then we conduct a systematic investigation of computational methods that address these issues, including emerging novel advanced AI data quality control methods that achieve better data quality outcomes and, in some cases, simplify or automate the data cleaning process. We hope this article can assist healthcare community to improve health data quality going forward with novel AI development.
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Artificial Intelligence-Driven Ultra-Fast Superresolution MRI: 10-Fold Accelerated Musculoskeletal Turbo Spin Echo MRI Within Reach. Invest Radiol 2023; 58:28-42. [PMID: 36355637 DOI: 10.1097/rli.0000000000000928] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
ABSTRACT Magnetic resonance imaging (MRI) is the keystone of modern musculoskeletal imaging; however, long pulse sequence acquisition times may restrict patient tolerability and access. Advances in MRI scanners, coil technology, and innovative pulse sequence acceleration methods enable 4-fold turbo spin echo pulse sequence acceleration in clinical practice; however, at this speed, conventional image reconstruction approaches the signal-to-noise limits of temporal, spatial, and contrast resolution. Novel deep learning image reconstruction methods can minimize signal-to-noise interdependencies to better advantage than conventional image reconstruction, leading to unparalleled gains in image speed and quality when combined with parallel imaging and simultaneous multislice acquisition. The enormous potential of deep learning-based image reconstruction promises to facilitate the 10-fold acceleration of the turbo spin echo pulse sequence, equating to a total acquisition time of 2-3 minutes for entire MRI examinations of joints without sacrificing spatial resolution or image quality. Current investigations aim for a better understanding of stability and failure modes of image reconstruction networks, validation of network reconstruction performance with external data sets, determination of diagnostic performances with independent reference standards, establishing generalizability to other centers, scanners, field strengths, coils, and anatomy, and building publicly available benchmark data sets to compare methods and foster innovation and collaboration between the clinical and image processing community. In this article, we review basic concepts of deep learning-based acquisition and image reconstruction techniques for accelerating and improving the quality of musculoskeletal MRI, commercially available and developing deep learning-based MRI solutions, superresolution, denoising, generative adversarial networks, and combined strategies for deep learning-driven ultra-fast superresolution musculoskeletal MRI. This article aims to equip radiologists and imaging scientists with the necessary practical knowledge and enthusiasm to meet this exciting new era of musculoskeletal MRI.
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Pack JD, Xu M, Wang G, Baskaran L, Min J, De Man B. Cardiac CT blooming artifacts: clinical significance, root causes and potential solutions. Vis Comput Ind Biomed Art 2022; 5:29. [PMID: 36484886 PMCID: PMC9733770 DOI: 10.1186/s42492-022-00125-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 11/03/2022] [Indexed: 12/13/2022] Open
Abstract
This review paper aims to summarize cardiac CT blooming artifacts, how they present clinically and what their root causes and potential solutions are. A literature survey was performed covering any publications with a specific interest in calcium blooming and stent blooming in cardiac CT. The claims from literature are compared and interpreted, aiming at narrowing down the root causes and most promising solutions for blooming artifacts. More than 30 journal publications were identified with specific relevance to blooming artifacts. The main reported causes of blooming artifacts are the partial volume effect, motion artifacts and beam hardening. The proposed solutions are classified as high-resolution CT hardware, high-resolution CT reconstruction, subtraction techniques and post-processing techniques, with a special emphasis on deep learning (DL) techniques. The partial volume effect is the leading cause of blooming artifacts. The partial volume effect can be minimized by increasing the CT spatial resolution through higher-resolution CT hardware or advanced high-resolution CT reconstruction. In addition, DL techniques have shown great promise to correct for blooming artifacts. A combination of these techniques could avoid repeat scans for subtraction techniques.
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Affiliation(s)
- Jed D. Pack
- grid.418143.b0000 0001 0943 0267GE Research, Niskayuna, NY 12309 USA
| | - Mufeng Xu
- grid.33647.350000 0001 2160 9198Rensselaer Polytechnic Institute, Troy, NY 12180 USA
| | - Ge Wang
- grid.33647.350000 0001 2160 9198Rensselaer Polytechnic Institute, Troy, NY 12180 USA
| | - Lohendran Baskaran
- grid.5386.8000000041936877XWeill Cornell Medicine, New York, NY 10065 USA ,grid.419385.20000 0004 0620 9905National Heart Centre, Singapore, 169609 Singapore
| | - James Min
- grid.5386.8000000041936877XWeill Cornell Medicine, New York, NY 10065 USA ,Cleerly, New York, NY 10065 USA
| | - Bruno De Man
- grid.418143.b0000 0001 0943 0267GE Research, Niskayuna, NY 12309 USA
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Automated quality assessment of chest radiographs based on deep learning and linear regression cascade algorithms. Eur Radiol 2022; 32:7680-7690. [PMID: 35420306 DOI: 10.1007/s00330-022-08771-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 02/21/2022] [Accepted: 03/26/2022] [Indexed: 01/03/2023]
Abstract
OBJECTIVES Develop and evaluate the performance of deep learning and linear regression cascade algorithms for automated assessment of the image layout and position of chest radiographs. METHODS This retrospective study used 10 quantitative indices to capture subjective perceptions of radiologists regarding image layout and position of chest radiographs, including the chest edges, field of view (FOV), clavicles, rotation, scapulae, and symmetry. An automated assessment system was developed using a training dataset consisting of 1025 adult posterior-anterior chest radiographs. The evaluation steps included: (i) use of a CNN framework based on ResNet - 34 to obtain measurement parameters for quantitative indices and (ii) analysis of quantitative indices using a multiple linear regression model to obtain predicted scores for the layout and position of chest radiograph. In the testing dataset (n = 100), the performance of the automated system was evaluated using the intraclass correlation coefficient (ICC), Pearson correlation coefficient (r), mean absolute difference (MAD), and mean absolute percentage error (MAPE). RESULTS The stepwise regression showed a statistically significant relationship between the 10 quantitative indices and subjective scores (p < 0.05). The deep learning model showed high accuracy in predicting the quantitative indices (ICC = 0.82 to 0.99, r = 0.69 to 0.99, MAD = 0.01 to 2.75). The automatic system provided assessments similar to the mean opinion scores of radiologists regarding image layout (MAPE = 3.05%) and position (MAPE = 5.72%). CONCLUSIONS Ten quantitative indices correlated well with the subjective perceptions of radiologists regarding the image layout and position of chest radiographs. The automated system provided high performance in measuring quantitative indices and assessing image quality. KEY POINTS • Objective and reliable assessment for image quality of chest radiographs is important for improving image quality and diagnostic accuracy. • Deep learning can be used for automated measurements of quantitative indices from chest radiographs. • Linear regression can be used for interpretation-based quality assessment of chest radiographs.
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The Value of Deep Learning Image Reconstruction in Improving the Quality of Low-Dose Chest CT Images. Diagnostics (Basel) 2022; 12:diagnostics12102560. [PMID: 36292249 PMCID: PMC9601258 DOI: 10.3390/diagnostics12102560] [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: 08/28/2022] [Revised: 10/16/2022] [Accepted: 10/18/2022] [Indexed: 11/17/2022] Open
Abstract
This study aimed to evaluate the value of the deep learning image reconstruction (DLIR) algorithm (GE Healthcare’s TrueFidelity™) in improving the image quality of low-dose computed tomography (LDCT) of the chest. First, we retrospectively extracted raw data of chest LDCT from 50 patients and reconstructed them by using model-based adaptive statistical iterative reconstruction-Veo at 50% (ASIR-V 50%) and DLIR at medium and high strengths (DLIR-M and DLIR-H). Three sets of images were obtained. Next, two radiographers measured the mean CT value/image signal and standard deviation (SD) in Hounsfield units at the region of interest (ROI) and calculated the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Two radiologists subjectively evaluated the image quality using a 5-point Likert scale. The differences between the groups of data were analyzed through a repeated measures ANOVA or the Friedman test. Last, our result show that the three reconstructions did not differ significantly in signal (p > 0.05) but had significant differences in noise, SNR, and CNR (p < 0.001). The subjective scores significantly differed among the three reconstruction modalities in soft tissue (p < 0.001) but not in lung tissue (p > 0.05). DLIR-H had the best noise reduction ability and improved SNR and CNR without distorting the image texture, followed by DLIR-M and ASIR-V 50%. In summary, DLIR can provide a higher image quality at the same dose, enhancing the physicians’ diagnostic confidence and improving the diagnostic efficacy of LDCT for lung cancer screening.
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Gao W, Wang C, Li Q, Zhang X, Yuan J, Li D, Sun Y, Chen Z, Gu Z. Application of medical imaging methods and artificial intelligence in tissue engineering and organ-on-a-chip. Front Bioeng Biotechnol 2022; 10:985692. [PMID: 36172022 PMCID: PMC9511994 DOI: 10.3389/fbioe.2022.985692] [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: 07/04/2022] [Accepted: 08/08/2022] [Indexed: 12/02/2022] Open
Abstract
Organ-on-a-chip (OOC) is a new type of biochip technology. Various types of OOC systems have been developed rapidly in the past decade and found important applications in drug screening and precision medicine. However, due to the complexity in the structure of both the chip-body itself and the engineered-tissue inside, the imaging and analysis of OOC have still been a big challenge for biomedical researchers. Considering that medical imaging is moving towards higher spatial and temporal resolution and has more applications in tissue engineering, this paper aims to review medical imaging methods, including CT, micro-CT, MRI, small animal MRI, and OCT, and introduces the application of 3D printing in tissue engineering and OOC in which medical imaging plays an important role. The achievements of medical imaging assisted tissue engineering are reviewed, and the potential applications of medical imaging in organoids and OOC are discussed. Moreover, artificial intelligence - especially deep learning - has demonstrated its excellence in the analysis of medical imaging; we will also present the application of artificial intelligence in the image analysis of 3D tissues, especially for organoids developed in novel OOC systems.
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Affiliation(s)
- Wanying Gao
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Chunyan Wang
- State Key Laboratory of Space Medicine Fundamentals and Application, Chinese Astronaut Science Researching and Training Center, Beijing, China
| | - Qiwei Li
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Xijing Zhang
- Central Research Institute, United Imaging Group, Shanghai, China
| | - Jianmin Yuan
- Central Research Institute, United Imaging Group, Shanghai, China
| | - Dianfu Li
- The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yu Sun
- International Children’s Medical Imaging Research Laboratory, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Zaozao Chen
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Zhongze Gu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
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Zerunian M, Pucciarelli F, Caruso D, Polici M, Masci B, Guido G, De Santis D, Polverari D, Principessa D, Benvenga A, Iannicelli E, Laghi A. Artificial intelligence based image quality enhancement in liver MRI: a quantitative and qualitative evaluation. Radiol Med 2022; 127:1098-1105. [PMID: 36070066 PMCID: PMC9512724 DOI: 10.1007/s11547-022-01539-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Accepted: 08/07/2022] [Indexed: 11/25/2022]
Abstract
Purpose To compare liver MRI with AIR Recon Deep Learning™(ARDL) algorithm applied and turned-off (NON-DL) with conventional high-resolution acquisition (NAÏVE) sequences, in terms of quantitative and qualitative image analysis and scanning time. Material and methods This prospective study included fifty consecutive volunteers (31 female, mean age 55.5 ± 20 years) from September to November 2021. 1.5 T MRI was performed and included three sets of images: axial single-shot fast spin-echo (SSFSE) T2 images, diffusion-weighted images(DWI) and apparent diffusion coefficient(ADC) maps acquired with both ARDL and NAÏVE protocol; the NON-DL images, were also assessed. Two radiologists in consensus drew fixed regions of interest in liver parenchyma to calculate signal-to-noise-ratio (SNR) and contrast to-noise-ratio (CNR). Subjective image quality was assessed by two other radiologists independently with a five-point Likert scale. Acquisition time was recorded. Results SSFSE T2 objective analysis showed higher SNR and CNR for ARDL vs NAÏVE, ARDL vs NON-DL(all P < 0.013). Regarding DWI, no differences were found for SNR with ARDL vs NAÏVE and, ARDL vs NON-DL (all P > 0.2517).CNR was higher for ARDL vs NON-DL(P = 0.0170), whereas no differences were found between ARDL and NAÏVE(P = 1). No differences were observed for all three comparisons, in terms of SNR and CNR, for ADC maps (all P > 0.32). Qualitative analysis for all sequences showed better overall image quality for ARDL with lower truncation artifacts, higher sharpness and contrast (all P < 0.0070) with excellent inter-rater agreement (k ≥ 0.8143). Acquisition time was lower in ARDL sequences compared to NAÏVE (SSFSE T2 = 19.08 ± 2.5 s vs. 24.1 ± 2 s and DWI = 207.3 ± 54 s vs. 513.6 ± 98.6 s, all P < 0.0001). Conclusion ARDL applied on upper abdomen showed overall better image quality and reduced scanning time compared with NAÏVE protocol.
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Affiliation(s)
- Marta Zerunian
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Francesco Pucciarelli
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Damiano Caruso
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Michela Polici
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Benedetta Masci
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Gisella Guido
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Domenico De Santis
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Daniele Polverari
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Daniele Principessa
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Antonella Benvenga
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Elsa Iannicelli
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Andrea Laghi
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy.
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Kim G, Moon S, Choi JH. Deep Learning with Multimodal Integration for Predicting Recurrence in Patients with Non-Small Cell Lung Cancer. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22176594. [PMID: 36081053 PMCID: PMC9459700 DOI: 10.3390/s22176594] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Revised: 08/27/2022] [Accepted: 08/30/2022] [Indexed: 05/20/2023]
Abstract
Due to high recurrence rates in patients with non-small cell lung cancer (NSCLC), medical professionals need extremely accurate diagnostic methods to prevent bleak prognoses. However, even the most commonly used diagnostic method, the TNM staging system, which describes the tumor-size, nodal-involvement, and presence of metastasis, is often inaccurate in predicting NSCLC recurrence. These limitations make it difficult for clinicians to tailor treatments to individual patients. Here, we propose a novel approach, which applies deep learning to an ensemble-based method that exploits patient-derived, multi-modal data. This will aid clinicians in successfully identifying patients at high risk of recurrence and improve treatment planning.
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Affiliation(s)
- Gihyeon Kim
- Computational Medicine, Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul 03760, Korea
| | - Sehwa Moon
- Division of Mechanical and Biomedical Engineering, Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul 03760, Korea
| | - Jang-Hwan Choi
- Division of Mechanical and Biomedical Engineering, Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul 03760, Korea
- Department of Artificial Intelligence, Ewha Womans University, Seoul 03760, Korea
- Correspondence:
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Deep learning reconstruction for the evaluation of neuroforaminal stenosis using 1.5T cervical spine MRI: comparison with 3T MRI without deep learning reconstruction. Neuroradiology 2022; 64:2077-2083. [PMID: 35918450 DOI: 10.1007/s00234-022-03024-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 07/23/2022] [Indexed: 10/16/2022]
Abstract
PURPOSE To compare image quality and interobserver agreement in evaluations of neuroforaminal stenosis between 1.5T cervical spine magnetic resonance imaging (MRI) with deep learning reconstruction (DLR) and 3T MRI without DLR. METHODS In this prospective study, 21 volunteers (mean age: 42.4 ± 11.9 years; 17 males) underwent cervical spine T2-weighted sagittal 1.5T and 3T MRI on the same day. The 1.5T and 3T MRI data were used to reconstruct images with (1.5T-DLR) and without (3T-nonDLR) DLR, respectively. Regions of interest were marked on the spinal cord to calculate non-uniformity (NU; standard deviation/signal intensity × 100), as an indicator of image noise. Two blinded radiologists evaluated the images in terms of the depiction of structures, artifacts, noise, overall image quality, and neuroforaminal stenosis. The NU value and the subjective image quality scores were compared between 1.5T-DLR and 3T-nonDLR using the Wilcoxon signed-rank test. Interobserver agreement in evaluations of neuroforaminal stenosis for 1.5T-DLR and 3T-nonDLR was evaluated using Cohen's weighted kappa analysis. RESULTS The NU value for 1.5T-DLR was 8.4, which was significantly better than that for 3T-nonDLR (10.3; p < 0.001). Subjective image scores were significantly better for 1.5T-DLR than 3T-nonDLR images (p < 0.037). Interobserver agreement (95% confidence intervals) in the evaluations of neuroforaminal stenosis was significantly superior for 1.5T-DLR (0.920 [0.916-0.924]) than 3T-nonDLR (0.894 [0.889-0.898]). CONCLUSION By using DLR, image quality and interobserver agreement in evaluations of neuroforaminal stenosis on 1.5T cervical spine MRI could be improved compared to 3T MRI without DLR.
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Ohno Y, Akino N, Fujisawa Y, Kimata H, Ito Y, Fujii K, Kataoka Y, Ida Y, Oshima Y, Hamabuchi N, Shigemura C, Watanabe A, Obama Y, Hanamatsu S, Ueda T, Ikeda H, Murayama K, Toyama H. Comparison of lung CT number and airway dimension evaluation capabilities of ultra-high-resolution CT, using different scan modes and reconstruction methods including deep learning reconstruction, with those of multi-detector CT in a QIBA phantom study. Eur Radiol 2022; 33:368-379. [PMID: 35841417 DOI: 10.1007/s00330-022-08983-1] [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: 12/02/2021] [Revised: 06/05/2022] [Accepted: 06/22/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVE Ultra-high-resolution CT (UHR-CT), which can be applied normal resolution (NR), high-resolution (HR), and super-high-resolution (SHR) modes, has become available as in conjunction with multi-detector CT (MDCT). Moreover, deep learning reconstruction (DLR) method, as well as filtered back projection (FBP), hybrid-type iterative reconstruction (IR), and model-based IR methods, has been clinically used. The purpose of this study was to directly compare lung CT number and airway dimension evaluation capabilities of UHR-CT using different scan modes with those of MDCT with different reconstruction methods as investigated in a lung density and airway phantom design recommended by QIBA. MATERIALS AND METHODS Lung CT number, inner diameter (ID), inner area (IA), and wall thickness (WT) were measured, and mean differences between measured CT number, ID, IA, WT, and standard reference were compared by means of Tukey's HSD test between all UHR-CT data and MDCT reconstructed with FBP as 1.0-mm section thickness. RESULTS For each reconstruction method, mean differences in lung CT numbers and all airway parameters on 0.5-mm and 1-mm section thickness CTs obtained with SHR and HR modes showed significant differences with those obtained with the NR mode on UHR-CT and MDCT (p < 0.05). Moreover, the mean differences on all UHR-CTs obtained with SHR, HR, or NR modes were significantly different from those of 1.0-mm section thickness MDCTs reconstructed with FBP (p < 0.05). CONCLUSION Scan modes and reconstruction methods used for UHR-CT were found to significantly affect lung CT number and airway dimension evaluations as did reconstruction methods used for MDCT. KEY POINTS • Scan and reconstruction methods used for UHR-CT showed significantly higher CT numbers and smaller airway dimension evaluations as did those for MDCT in a QIBA phantom study (p < 0.05). • Mean differences in lung CT number for 0.25-mm, 0.5-mm, and 1.0-mm section thickness CT images obtained with SHR and HR modes were significantly larger than those for CT images at 1.0-mm section thickness obtained with MDCT and reconstructed with FBP (p < 0.05). • Mean differences in inner diameter (ID), inner area (IA), and wall thickness (WT) measured with SHR and HR modes on 0.5- and 1.0-mm section thickness CT images were significantly smaller than those obtained with NR mode on UHR-CT and MDCT (p < 0.05).
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Affiliation(s)
- Yoshiharu Ohno
- Department of Radiology, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan. .,Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine, Toyoake, Aichi, Japan.
| | - Naruomi Akino
- Canon Medical Systems Corporation, Otawara, Tochigi, Japan
| | | | - Hirona Kimata
- Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
| | - Yuya Ito
- Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
| | - Kenji Fujii
- Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
| | - Yumi Kataoka
- Department of Radiology, Fujita Health University Hospital, Toyoake, Aichi, Japan
| | - Yoshihiro Ida
- Department of Radiology, Fujita Health University Hospital, Toyoake, Aichi, Japan
| | - Yuka Oshima
- Department of Radiology, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan
| | - Nayu Hamabuchi
- Department of Radiology, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan
| | - Chika Shigemura
- Department of Radiology, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan
| | - Ayumi Watanabe
- Department of Radiology, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan
| | - Yuki Obama
- Department of Radiology, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan
| | - Satomu Hanamatsu
- Department of Radiology, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan
| | - Takahiro Ueda
- Department of Radiology, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan
| | - Hirotaka Ikeda
- Department of Radiology, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan
| | - Kazuhiro Murayama
- Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
| | - Hiroshi Toyama
- Department of Radiology, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan
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