Deep learning reconstruction for brain diffusion-weighted imaging: efficacy for image quality improvement, apparent diffusion coefficient assessment, and intravoxel incoherent motion evaluation in in vitro and in vivo studies
PURPOSEDeep learning reconstruction (DLR) to improve imaging quality has already been introduced, but no studies have evaluated the effect of DLR on diffusion-weighted imaging (DWI) or intravoxel incoherent motion (IVIM) in in vitro or in vivo studies. The purpose of this study was to determine the...
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| Format: | Article |
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Galenos Publishing House
2023-09-01
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| Series: | Diagnostic and Interventional Radiology |
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http://www.dirjournal.org/archives/archive-detail/article-preview/deep-learning-reconstruction-for-brain-diffusion-w/61479
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| _version_ | 1797691439262466048 |
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| author | Satomu Hanamatsu Kazuhiro Murayama Yoshiharu Ohno Kaori Yamamoto Masao Yui Hiroshi Toyama |
| author_facet | Satomu Hanamatsu Kazuhiro Murayama Yoshiharu Ohno Kaori Yamamoto Masao Yui Hiroshi Toyama |
| author_sort | Satomu Hanamatsu |
| collection | DOAJ |
| description | PURPOSEDeep learning reconstruction (DLR) to improve imaging quality has already been introduced, but no studies have evaluated the effect of DLR on diffusion-weighted imaging (DWI) or intravoxel incoherent motion (IVIM) in in vitro or in vivo studies. The purpose of this study was to determine the effect of DLR for magnetic resonance imaging (MRI) in terms of image quality improvement, apparent diffusion coefficient (ADC) assessment, and IVIM index evaluation on DWI through in vitro and in vivo studies.METHODSFor the in vitro study, a phantom recommended by the Quantitative Imaging Biomarkers Alliance was scanned and reconstructed with and without DLR, and 15 patients with brain tumors with normal-appearing gray and white matter examined using IVIM and reconstructed with and without DLR were included in the in vivo study. The ADCs of all phantoms for DWI with and without DLR, as well as the coefficient of variation percentage (CV%), and ADCs and IVIM indexes for each participant, were evaluated based on DWI with and without DLR by means of region-of-interest measurements. For the in vitro study, using the mean ADCs for all phantoms, a t-test was adopted to compare DWI with and without DLR. For the in vivo study, a Wilcoxon signed-rank test was used to compare the CV% between the two types of DWI. In addition, the Wilcoxon signed-rank test was used to compare the ADC, true diffusion coefficient (D), pseudodiffusion coefficient (D*), and percentage of water molecules in micro perfusion within 1 voxel (f) with and without DLR; the limits of agreement of each parameter were determined through a Bland–Altman analysis.RESULTSThe in vitro study identified no significant differences between the ADC values for DWI with and without DLR (P > 0.05), and the CV% was significantly different for DWI with and without DLR (P < 0.05) when b values ≥250 s/mm2 were used. The in vivo study revealed that D* and f with and without DLR were significantly different (P < 0.001). The limits of agreement of the ADC, D, and D* values for DWI with and without DLR were determined as 0.00 ± 0.51 × 10-3, 0.00 ± 0.06 × 10-3, and 1.13 ± 4.04 × 10-3 mm2/s, respectively. The limits of agreement of the f values for DWI with and without DLR were determined as −0.01 ± 0.07.CONCLUSIONDeep learning reconstruction for MRI has the potential to significantly improve DWI quality at higher b values. It has some effect on D* and f values in the IVIM index evaluation, but ADC and D values are less affected by DLR. |
| first_indexed | 2024-03-12T02:13:19Z |
| format | Article |
| id | doaj.art-970f1c72dbaa47d580ae0df0807a791c |
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| issn | 1305-3825 1305-3612 |
| language | English |
| last_indexed | 2024-03-12T02:13:19Z |
| publishDate | 2023-09-01 |
| publisher | Galenos Publishing House |
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| series | Diagnostic and Interventional Radiology |
| spelling | doaj.art-970f1c72dbaa47d580ae0df0807a791c2023-09-06T11:33:40ZengGalenos Publishing HouseDiagnostic and Interventional Radiology1305-38251305-36122023-09-0129566467310.4274/dir.2023.23214913049054Deep learning reconstruction for brain diffusion-weighted imaging: efficacy for image quality improvement, apparent diffusion coefficient assessment, and intravoxel incoherent motion evaluation in in vitro and in vivo studiesSatomu Hanamatsu0Kazuhiro Murayama1Yoshiharu Ohno2Kaori Yamamoto3Masao Yui4Hiroshi Toyama5 Department of Radiology, Fujita Health University School of Medicine, Toyoake, Japan Department of Radiology, Fujita Health University School of Medicine, Toyoake, Japan Department of Radiology, Fujita Health University School of Medicine, Toyoake, Japan Canon Medical Systems Corporation, Otawara, Japan Canon Medical Systems Corporation, Otawara, Japan Department of Radiology, Fujita Health University School of Medicine, Toyoake, Japan PURPOSEDeep learning reconstruction (DLR) to improve imaging quality has already been introduced, but no studies have evaluated the effect of DLR on diffusion-weighted imaging (DWI) or intravoxel incoherent motion (IVIM) in in vitro or in vivo studies. The purpose of this study was to determine the effect of DLR for magnetic resonance imaging (MRI) in terms of image quality improvement, apparent diffusion coefficient (ADC) assessment, and IVIM index evaluation on DWI through in vitro and in vivo studies.METHODSFor the in vitro study, a phantom recommended by the Quantitative Imaging Biomarkers Alliance was scanned and reconstructed with and without DLR, and 15 patients with brain tumors with normal-appearing gray and white matter examined using IVIM and reconstructed with and without DLR were included in the in vivo study. The ADCs of all phantoms for DWI with and without DLR, as well as the coefficient of variation percentage (CV%), and ADCs and IVIM indexes for each participant, were evaluated based on DWI with and without DLR by means of region-of-interest measurements. For the in vitro study, using the mean ADCs for all phantoms, a t-test was adopted to compare DWI with and without DLR. For the in vivo study, a Wilcoxon signed-rank test was used to compare the CV% between the two types of DWI. In addition, the Wilcoxon signed-rank test was used to compare the ADC, true diffusion coefficient (D), pseudodiffusion coefficient (D*), and percentage of water molecules in micro perfusion within 1 voxel (f) with and without DLR; the limits of agreement of each parameter were determined through a Bland–Altman analysis.RESULTSThe in vitro study identified no significant differences between the ADC values for DWI with and without DLR (P > 0.05), and the CV% was significantly different for DWI with and without DLR (P < 0.05) when b values ≥250 s/mm2 were used. The in vivo study revealed that D* and f with and without DLR were significantly different (P < 0.001). The limits of agreement of the ADC, D, and D* values for DWI with and without DLR were determined as 0.00 ± 0.51 × 10-3, 0.00 ± 0.06 × 10-3, and 1.13 ± 4.04 × 10-3 mm2/s, respectively. The limits of agreement of the f values for DWI with and without DLR were determined as −0.01 ± 0.07.CONCLUSIONDeep learning reconstruction for MRI has the potential to significantly improve DWI quality at higher b values. It has some effect on D* and f values in the IVIM index evaluation, but ADC and D values are less affected by DLR. http://www.dirjournal.org/archives/archive-detail/article-preview/deep-learning-reconstruction-for-brain-diffusion-w/61479 brainmagnetic resonance imagingdiffusionintravoxel incoherent motiondeep learning reconstruction |
| spellingShingle | Satomu Hanamatsu Kazuhiro Murayama Yoshiharu Ohno Kaori Yamamoto Masao Yui Hiroshi Toyama Deep learning reconstruction for brain diffusion-weighted imaging: efficacy for image quality improvement, apparent diffusion coefficient assessment, and intravoxel incoherent motion evaluation in in vitro and in vivo studies Diagnostic and Interventional Radiology brain magnetic resonance imaging diffusion intravoxel incoherent motion deep learning reconstruction |
| title | Deep learning reconstruction for brain diffusion-weighted imaging: efficacy for image quality improvement, apparent diffusion coefficient assessment, and intravoxel incoherent motion evaluation in in vitro and in vivo studies |
| title_full | Deep learning reconstruction for brain diffusion-weighted imaging: efficacy for image quality improvement, apparent diffusion coefficient assessment, and intravoxel incoherent motion evaluation in in vitro and in vivo studies |
| title_fullStr | Deep learning reconstruction for brain diffusion-weighted imaging: efficacy for image quality improvement, apparent diffusion coefficient assessment, and intravoxel incoherent motion evaluation in in vitro and in vivo studies |
| title_full_unstemmed | Deep learning reconstruction for brain diffusion-weighted imaging: efficacy for image quality improvement, apparent diffusion coefficient assessment, and intravoxel incoherent motion evaluation in in vitro and in vivo studies |
| title_short | Deep learning reconstruction for brain diffusion-weighted imaging: efficacy for image quality improvement, apparent diffusion coefficient assessment, and intravoxel incoherent motion evaluation in in vitro and in vivo studies |
| title_sort | deep learning reconstruction for brain diffusion weighted imaging efficacy for image quality improvement apparent diffusion coefficient assessment and intravoxel incoherent motion evaluation in in vitro and in vivo studies |
| topic | brain magnetic resonance imaging diffusion intravoxel incoherent motion deep learning reconstruction |
| url |
http://www.dirjournal.org/archives/archive-detail/article-preview/deep-learning-reconstruction-for-brain-diffusion-w/61479
|
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