AI-Based Noise-Reduction Filter for Whole-Body Planar Bone Scintigraphy Reliably Improves Low-Count Images
<b>Background/Objectives</b>: Artificial intelligence (AI) is a promising tool for the enhancement of physician workflow and serves to further improve the efficiency of their diagnostic evaluations. This study aimed to assess the performance of an AI-based bone scan noise-reduction filte...
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MDPI AG
2024-11-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/14/23/2686 |
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| author | Csaba Csikos Sándor Barna Ákos Kovács Péter Czina Ádám Budai Melinda Szoliková Iván Gábor Nagy Borbála Husztik Gábor Kiszler Ildikó Garai |
| author_facet | Csaba Csikos Sándor Barna Ákos Kovács Péter Czina Ádám Budai Melinda Szoliková Iván Gábor Nagy Borbála Husztik Gábor Kiszler Ildikó Garai |
| author_sort | Csaba Csikos |
| collection | DOAJ |
| description | <b>Background/Objectives</b>: Artificial intelligence (AI) is a promising tool for the enhancement of physician workflow and serves to further improve the efficiency of their diagnostic evaluations. This study aimed to assess the performance of an AI-based bone scan noise-reduction filter on noisy, low-count images in a routine clinical environment. <b>Methods</b>: The performance of the AI bone-scan filter (BS-AI filter) in question was retrospectively evaluated on 47 different patients’ <sup>99m</sup>Tc-MDP bone scintigraphy image pairs (anterior- and posterior-view images), which were obtained in such a manner as to represent the diverse characteristics of the general patient population. The BS-AI filter was tested on artificially degraded noisy images—75, 50, and 25% of total counts—which were generated by binominal sampling. The AI-filtered and unfiltered images were concurrently appraised for image quality and contrast by three nuclear medicine physicians. It was also determined whether there was any difference between the lesions seen on the unfiltered and filtered images. For quantitative analysis, an automatic lesion detector (BS-AI annotator) was utilized as a segmentation algorithm. The total number of lesions and their locations as detected by the BS-AI annotator in the BS-AI-filtered low-count images was compared to the total-count filtered images. The total number of pixels labeled as lesions in the filtered low-count images in relation to the number of pixels in the total-count filtered images was also compared to ensure the filtering process did not change lesion sizes significantly. The comparison of pixel numbers was performed using the reduced-count filtered images that contained only those lesions that were detected in the total-count images. <b>Results</b>: Based on visual assessment, observers agreed that image contrast and quality were better in the BS-AI-filtered images, increasing their diagnostic confidence. Similarities in lesion numbers and sites detected by the BS-AI annotator compared to filtered total-count images were 89%, 83%, and 75% for images degraded to counts of 75%, 50%, and 25%, respectively. No significant difference was found in the number of annotated pixels between filtered images with different counts (<i>p</i> > 0.05). <b>Conclusions</b>: Our findings indicate that the BS-AI noise-reduction filter enhances image quality and contrast without loss of vital information. The implementation of this filter in routine diagnostic procedures reliably improves diagnostic confidence in low-count images and elicits a reduction in the administered dose or acquisition time by a minimum of 50% relative to the original dose or acquisition time. |
| format | Article |
| id | doaj-art-3b7d2f999ede43309bbdfe001906c0ba |
| institution | DOAJ |
| issn | 2075-4418 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-3b7d2f999ede43309bbdfe001906c0ba2025-08-20T02:50:32ZengMDPI AGDiagnostics2075-44182024-11-011423268610.3390/diagnostics14232686AI-Based Noise-Reduction Filter for Whole-Body Planar Bone Scintigraphy Reliably Improves Low-Count ImagesCsaba Csikos0Sándor Barna1Ákos Kovács2Péter Czina3Ádám Budai4Melinda Szoliková5Iván Gábor Nagy6Borbála Husztik7Gábor Kiszler8Ildikó Garai9Division of Nuclear Medicine and Translational Imaging, Department of Medical Imaging, Faculty of Medicine, University of Debrecen, H-4032 Debrecen, HungaryDivision of Nuclear Medicine and Translational Imaging, Department of Medical Imaging, Faculty of Medicine, University of Debrecen, H-4032 Debrecen, HungaryMediso Ltd., H-1037 Budapest, HungaryDivision of Nuclear Medicine and Translational Imaging, Department of Medical Imaging, Faculty of Medicine, University of Debrecen, H-4032 Debrecen, HungaryScanomed Ltd., H-4032 Debrecen, HungaryMediso Ltd., H-1037 Budapest, HungaryDivision of Nuclear Medicine and Translational Imaging, Department of Medical Imaging, Faculty of Medicine, University of Debrecen, H-4032 Debrecen, HungaryMediso Ltd., H-1037 Budapest, HungaryMediso Ltd., H-1037 Budapest, HungaryDivision of Nuclear Medicine and Translational Imaging, Department of Medical Imaging, Faculty of Medicine, University of Debrecen, H-4032 Debrecen, Hungary<b>Background/Objectives</b>: Artificial intelligence (AI) is a promising tool for the enhancement of physician workflow and serves to further improve the efficiency of their diagnostic evaluations. This study aimed to assess the performance of an AI-based bone scan noise-reduction filter on noisy, low-count images in a routine clinical environment. <b>Methods</b>: The performance of the AI bone-scan filter (BS-AI filter) in question was retrospectively evaluated on 47 different patients’ <sup>99m</sup>Tc-MDP bone scintigraphy image pairs (anterior- and posterior-view images), which were obtained in such a manner as to represent the diverse characteristics of the general patient population. The BS-AI filter was tested on artificially degraded noisy images—75, 50, and 25% of total counts—which were generated by binominal sampling. The AI-filtered and unfiltered images were concurrently appraised for image quality and contrast by three nuclear medicine physicians. It was also determined whether there was any difference between the lesions seen on the unfiltered and filtered images. For quantitative analysis, an automatic lesion detector (BS-AI annotator) was utilized as a segmentation algorithm. The total number of lesions and their locations as detected by the BS-AI annotator in the BS-AI-filtered low-count images was compared to the total-count filtered images. The total number of pixels labeled as lesions in the filtered low-count images in relation to the number of pixels in the total-count filtered images was also compared to ensure the filtering process did not change lesion sizes significantly. The comparison of pixel numbers was performed using the reduced-count filtered images that contained only those lesions that were detected in the total-count images. <b>Results</b>: Based on visual assessment, observers agreed that image contrast and quality were better in the BS-AI-filtered images, increasing their diagnostic confidence. Similarities in lesion numbers and sites detected by the BS-AI annotator compared to filtered total-count images were 89%, 83%, and 75% for images degraded to counts of 75%, 50%, and 25%, respectively. No significant difference was found in the number of annotated pixels between filtered images with different counts (<i>p</i> > 0.05). <b>Conclusions</b>: Our findings indicate that the BS-AI noise-reduction filter enhances image quality and contrast without loss of vital information. The implementation of this filter in routine diagnostic procedures reliably improves diagnostic confidence in low-count images and elicits a reduction in the administered dose or acquisition time by a minimum of 50% relative to the original dose or acquisition time.https://www.mdpi.com/2075-4418/14/23/2686bone scannuclear medicineimagingnoise reduction filterartificial intelligence |
| spellingShingle | Csaba Csikos Sándor Barna Ákos Kovács Péter Czina Ádám Budai Melinda Szoliková Iván Gábor Nagy Borbála Husztik Gábor Kiszler Ildikó Garai AI-Based Noise-Reduction Filter for Whole-Body Planar Bone Scintigraphy Reliably Improves Low-Count Images Diagnostics bone scan nuclear medicine imaging noise reduction filter artificial intelligence |
| title | AI-Based Noise-Reduction Filter for Whole-Body Planar Bone Scintigraphy Reliably Improves Low-Count Images |
| title_full | AI-Based Noise-Reduction Filter for Whole-Body Planar Bone Scintigraphy Reliably Improves Low-Count Images |
| title_fullStr | AI-Based Noise-Reduction Filter for Whole-Body Planar Bone Scintigraphy Reliably Improves Low-Count Images |
| title_full_unstemmed | AI-Based Noise-Reduction Filter for Whole-Body Planar Bone Scintigraphy Reliably Improves Low-Count Images |
| title_short | AI-Based Noise-Reduction Filter for Whole-Body Planar Bone Scintigraphy Reliably Improves Low-Count Images |
| title_sort | ai based noise reduction filter for whole body planar bone scintigraphy reliably improves low count images |
| topic | bone scan nuclear medicine imaging noise reduction filter artificial intelligence |
| url | https://www.mdpi.com/2075-4418/14/23/2686 |
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