Bayesian statistical modeling to predict observer-specific optimal windowing parameters in magnetic resonance imaging
Magnetic resonance (MR) images require a process known as windowing for optimizing the display conditions. However, the conventional windowing process often fails to achieve the preferred display conditions for observers due to various factors. This study proposes a novel framework for predicting th...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-08-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023062461 |
| _version_ | 1827856090021232640 |
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| author | Kohei Sugimoto Masataka Oita Masahiro Kuroda |
| author_facet | Kohei Sugimoto Masataka Oita Masahiro Kuroda |
| author_sort | Kohei Sugimoto |
| collection | DOAJ |
| description | Magnetic resonance (MR) images require a process known as windowing for optimizing the display conditions. However, the conventional windowing process often fails to achieve the preferred display conditions for observers due to various factors. This study proposes a novel framework for predicting the preferred windowing parameters for each observer using Bayesian statistical modeling. MR images obtained from 1000 patients were divided into training and test sets at a 7:3 ratio. The image intensity and windowing parameters were standardized using previously reported methods. Bayesian statistical modeling was utilized to predict the windowing parameters preferred by three MR imaging (MRI) operators. The performance of the proposed framework was evaluated by assessing the mean relative error (MRE), mean absolute error (MAE), and Pearson's correlation coefficient (ρ) of the test set. In addition, the naive method, which presumes that the average value of the windowing parameters for each acquisition sequence and body region in the training set is optimal, was also used for comparison. Three MRI operators and three radiologists conducted visual assessments. The mean MRE, MAE, and ρ values for the window level and width (WL/WW) in the proposed framework were 12.6 and 13.9, 42.9 and 85.4, and 0.98 and 0.98, respectively. These results outperformed those obtained using the naive method. The visual assessments revealed no significant differences between the original and predicted display conditions, indicating that the proposed framework accurately predicts individualized windowing parameters with the additional advantages of robustness and ease of use. Thus, the proposed framework can effectively predict the windowing parameters preferred by each observer. |
| first_indexed | 2024-03-12T12:20:41Z |
| format | Article |
| id | doaj.art-808f45e62ab54ebc94cfca68fe041971 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-12T12:20:41Z |
| publishDate | 2023-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-808f45e62ab54ebc94cfca68fe0419712023-08-30T05:53:31ZengElsevierHeliyon2405-84402023-08-0198e19038Bayesian statistical modeling to predict observer-specific optimal windowing parameters in magnetic resonance imagingKohei Sugimoto0Masataka Oita1Masahiro Kuroda2Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University, 5-1 Shikata-cho, 2-chome, Kita-ku, Okayama, Okayama, 700-8558, Japan; Division of Imaging Technology, Okayama Diagnostic Imaging Center, 3-25, Daiku, 2-chome, Kita-ku, Okayama, Okayama, 700-0913, JapanFaculty of Interdisciplinary Science and Engineering in Health Systems, Okayama University, 5-1 Shikata-cho, 2-chome, Kita-ku, Okayama, Okayama, 700-8558, Japan; Corresponding author.Department of Radiological Technology, Faculty of Health Sciences, Okayama University, 5-1 Shikata-cho, 2-chome, Kita-ku, Okayama, Okayama, 700-8558, JapanMagnetic resonance (MR) images require a process known as windowing for optimizing the display conditions. However, the conventional windowing process often fails to achieve the preferred display conditions for observers due to various factors. This study proposes a novel framework for predicting the preferred windowing parameters for each observer using Bayesian statistical modeling. MR images obtained from 1000 patients were divided into training and test sets at a 7:3 ratio. The image intensity and windowing parameters were standardized using previously reported methods. Bayesian statistical modeling was utilized to predict the windowing parameters preferred by three MR imaging (MRI) operators. The performance of the proposed framework was evaluated by assessing the mean relative error (MRE), mean absolute error (MAE), and Pearson's correlation coefficient (ρ) of the test set. In addition, the naive method, which presumes that the average value of the windowing parameters for each acquisition sequence and body region in the training set is optimal, was also used for comparison. Three MRI operators and three radiologists conducted visual assessments. The mean MRE, MAE, and ρ values for the window level and width (WL/WW) in the proposed framework were 12.6 and 13.9, 42.9 and 85.4, and 0.98 and 0.98, respectively. These results outperformed those obtained using the naive method. The visual assessments revealed no significant differences between the original and predicted display conditions, indicating that the proposed framework accurately predicts individualized windowing parameters with the additional advantages of robustness and ease of use. Thus, the proposed framework can effectively predict the windowing parameters preferred by each observer.http://www.sciencedirect.com/science/article/pii/S2405844023062461MR imageImage intensity standardizationWindowingPredictionBayesian statistical modeling |
| spellingShingle | Kohei Sugimoto Masataka Oita Masahiro Kuroda Bayesian statistical modeling to predict observer-specific optimal windowing parameters in magnetic resonance imaging Heliyon MR image Image intensity standardization Windowing Prediction Bayesian statistical modeling |
| title | Bayesian statistical modeling to predict observer-specific optimal windowing parameters in magnetic resonance imaging |
| title_full | Bayesian statistical modeling to predict observer-specific optimal windowing parameters in magnetic resonance imaging |
| title_fullStr | Bayesian statistical modeling to predict observer-specific optimal windowing parameters in magnetic resonance imaging |
| title_full_unstemmed | Bayesian statistical modeling to predict observer-specific optimal windowing parameters in magnetic resonance imaging |
| title_short | Bayesian statistical modeling to predict observer-specific optimal windowing parameters in magnetic resonance imaging |
| title_sort | bayesian statistical modeling to predict observer specific optimal windowing parameters in magnetic resonance imaging |
| topic | MR image Image intensity standardization Windowing Prediction Bayesian statistical modeling |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023062461 |
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