Journal of Medical Imaging最新文献

{"title":"Toward Continued Growth for the JMI Community.","authors":"","doi":"10.1117/1.JMI.12.1.010101","DOIUrl":"https://doi.org/10.1117/1.JMI.12.1.010101","url":null,"abstract":"<p><p>JMI Editor in Chief Bennett Landman provides an overview of JMI Volume 12 Issue 1 and spotlights key aspects of JMI peer review, with an eye toward continued growth for the JMI community.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 1","pages":"010101"},"PeriodicalIF":1.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11817210/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143415687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparing synthetic mammograms based on wide-angle digital breast tomosynthesis with digital mammograms.","authors":"Magnus Dustler, Gustav Hellgren, Pontus Timberg","doi":"10.1117/1.JMI.12.S1.S13011","DOIUrl":"10.1117/1.JMI.12.S1.S13011","url":null,"abstract":"<p><strong>Purpose: </strong>We aim to investigate the characteristics and evaluate the performance of synthetic mammograms (SMs) based on wide-angle digital breast tomosynthesis (DBT) compared with digital mammography (DM).</p><p><strong>Approach: </strong>Fifty cases with both synthetic and digital mammograms were selected from the Malmö Breast Tomosynthesis Screening Trial. They were categorized into five groups consisting of normal cases and recalled cases with false-positive and true-positive findings from DM and DBT only. The DBT system used was a wide-angle (WA) system from Siemens, and the SM images were reconstructed from the DBT images. Visual grading, detection, and recall were evaluated by experienced breast radiologists in both SM and DM images.</p><p><strong>Results: </strong>Some image quality criteria of the SM images were rated as qualitatively inferior to DM images. However, reader-averaged diagnostic accuracy (0.57 versus 0.55), sensitivity (0.46 versus 0.50), and specificity (0.64 versus 0.58) were not significantly different between SM and DM, respectively.</p><p><strong>Conclusions: </strong>Synthetic mammography plays a promising role to complement or even replace DM. The study could not find any indications of substantial differences in the sensitivity or specificity of SM for WA DBT systems compared with DM. However, certain image quality criteria of SM fall slightly short compared with DM images. Next-generation DBT systems could address such limitations through improved reconstruction algorithms and system design, and their performance should be the focus of future research studies.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 Suppl 1","pages":"S13011"},"PeriodicalIF":1.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11745418/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143014059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vision transformer distillation for enhanced gastrointestinal abnormality recognition in wireless capsule endoscopy images.","authors":"Yassine Oukdach, Anass Garbaz, Zakaria Kerkaou, Mohamed El Ansari, Lahcen Koutti, Nikolaos Papachrysos, Ahmed Fouad El Ouafdi, Thomas de Lange, Cosimo Distante","doi":"10.1117/1.JMI.12.1.014505","DOIUrl":"10.1117/1.JMI.12.1.014505","url":null,"abstract":"<p><strong>Purpose: </strong>Wireless capsule endoscopy (WCE) is a non-invasive technology used for diagnosing gastrointestinal abnormalities. A single examination generates <math><mrow><mo>∼</mo> <mn>55,000</mn></mrow> </math> images, making manual review both time-consuming and costly for doctors. Therefore, the development of computer vision-assisted systems is highly desirable to aid in the diagnostic process.</p><p><strong>Approach: </strong>We presents a deep learning approach leveraging knowledge distillation (KD) from a convolutional neural network (CNN) teacher model to a vision transformer (ViT) student model for gastrointestinal abnormality recognition. The CNN teacher model utilizes attention mechanisms and depth-wise separable convolutions to extract features from WCE images, supervising the ViT in learning these representations.</p><p><strong>Results: </strong>The proposed method achieves accuracy of 97% and 96% on the Kvasir and KID datasets, respectively, demonstrating its effectiveness in distinguishing normal from abnormal regions and bleeding from non-bleeding cases. The proposed approach offers computational efficiency and generalization to unseen datasets, outperforming several state-of-the-art methods.</p><p><strong>Conclusions: </strong>We proposed a deep learning approach utilizing CNNs and a ViT with KD to effectively classify gastrointestinal diseases in WCE images. It demonstrates promising performance on public datasets, distinguishing normal from abnormal regions and bleeding from non-bleeding cases while offering optimal computational efficiency compared with existing methods, making it suitable for GI disease applications.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 1","pages":"014505"},"PeriodicalIF":1.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11796471/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143366556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"OPHash: learning of organ and pathology context-sensitive hashing for medical image retrieval.","authors":"Asim Manna, Rakshith Sathish, Ramanathan Sethuraman, Debdoot Sheet","doi":"10.1117/1.JMI.12.1.017503","DOIUrl":"10.1117/1.JMI.12.1.017503","url":null,"abstract":"<p><strong>Purpose: </strong>Retrieving images of organs and their associated pathologies is essential for evidence-based clinical diagnosis. Deep neural hashing (DNH) has demonstrated the ability to retrieve images fast on large datasets. Conventional pairwise DNH methods can focus on semantic similarity between either organs or pathology of an image pair but not on both simultaneously.</p><p><strong>Approach: </strong>We propose an organ and pathology contextual-supervised hashing approach (OPHash) learned using three types of samples (called bags) to learn accurate hash representation. Because only semantic similarity is inadequate to incorporate with these bags, we introduce relational similarity to generate identical hash codes from most similar image pairs. OPHash is trained by minimizing classification loss, two retrieval losses implemented using Cauchy cross-entropy and maximizing discriminator loss over training samples.</p><p><strong>Results: </strong>Experiments are performed with two radiology datasets derived from the publicly available datasets. OPHash achieves 24% higher mean average precision than the state-of-the-art for top-100 retrieval.</p><p><strong>Conclusion: </strong>OPHash retrieves images with semantic similarity of organs and their associated pathology. It is agnostic to image size as well. This method improves retrieval efficiency across diverse medical imaging datasets, accommodating multiple organs and pathologies. The code is available at https://github.com/asimmanna17/OPHash.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 1","pages":"017503"},"PeriodicalIF":1.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11838790/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143469590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lung nodule localization and size estimation on chest tomosynthesis.","authors":"Micael Oliveira Diniz, Mohammad Khalil, Erika Fagman, Jenny Vikgren, Faiz Haj, Angelica Svalkvist, Magnus Båth, Åse Allansdotter Johnsson","doi":"10.1117/1.JMI.12.S1.S13007","DOIUrl":"10.1117/1.JMI.12.S1.S13007","url":null,"abstract":"<p><strong>Purpose: </strong>We aim to investigate the localization, visibility, and measurement of lung nodules in digital chest tomosynthesis (DTS).</p><p><strong>Approach: </strong>Computed tomography (CT), maximum intensity projections (CT-MIP) (transaxial versus coronal orientation), and computer-aided detection (CAD) were used as location reference, and inter- and intra-observer agreement regarding lung nodule size was assessed. Five radiologists analyzed DTS and CT images from 24 participants with lung <math><mrow><mtext>nodules</mtext> <mo>≥</mo> <mn>100</mn> <mtext>  </mtext> <msup><mrow><mi>mm</mi></mrow> <mrow><mn>3</mn></mrow> </msup> </mrow> </math> , focusing on lung nodule localization, visibility, and measurement on DTS. Visual grading was used to compare if coronal or transaxial CT-MIP better facilitated the localization of lung nodules in DTS.</p><p><strong>Results: </strong>The majority of the lung nodules (79%) were rated as visible in DTS, although less clearly in comparison with CT. Coronal CT-MIP was the preferred orientation in the task of locating nodules on DTS. On DTS, area-based lung nodule size estimates resulted in significantly less measurement variability when compared with nodule size estimated based on mean diameter (mD) ( <math><mrow><mi>p</mi> <mo><</mo> <mn>0.05</mn></mrow> </math> ). Also, on DTS, area-based lung nodule size estimates were more accurate ( <math><mrow><mi>SEE</mi> <mo>=</mo> <mn>38.7</mn> <mtext>  </mtext> <msup><mi>mm</mi> <mn>3</mn></msup> </mrow> </math> ) than lung nodule size estimates based on mean diameter ( <math><mrow><mi>SEE</mi> <mo>=</mo> <mn>42.7</mn> <mtext>  </mtext> <msup><mi>mm</mi> <mn>3</mn></msup> </mrow> </math> ).</p><p><strong>Conclusions: </strong>Coronal CT-MIP images are superior to transaxial CT-MIP images in facilitating lung nodule localization in DTS. Most <math><mrow><mtext>nodules</mtext> <mo>≥</mo> <mn>100</mn> <mtext>  </mtext> <msup><mrow><mi>mm</mi></mrow> <mrow><mn>3</mn></mrow> </msup> </mrow> </math> found on CT can be visualized, correctly localized, and measured in DTS, and area-based measurement may be the key to more precise and less variable nodule measurements on DTS.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 Suppl 1","pages":"S13007"},"PeriodicalIF":1.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11514701/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142548312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evolution of tomosynthesis.","authors":"Mitchell M Goodsitt, Andrew D A Maidment","doi":"10.1117/1.JMI.12.S1.S13012","DOIUrl":"10.1117/1.JMI.12.S1.S13012","url":null,"abstract":"<p><strong>Purpose: </strong>Tomosynthesis is a limited-angle multi-projection method that was conceived to address a significant limitation of conventional single-projection x-ray imaging: the overlap of structures in an image. We trace the historical evolution of tomosynthesis.</p><p><strong>Approach: </strong>Relevant papers are discussed including descriptions of technical advances and clinical applications.</p><p><strong>Results: </strong>We start with the invention of tomosynthesis by Ziedses des Plantes in the Netherlands and Kaufman in the United States in the mid-1930s and end with our predictions of future technical advances. Some of the other topics that are covered include a respiratory-gated chest tomosynthesis system of the late 1930s, film-based systems of the 1960s and 1970s, coded aperture tomosynthesis, fluoroscopy tomosynthesis, digital detector-based tomosynthesis for imaging the breast and body, orthopedic, dental and radiotherapy applications, optimization of acquisition parameters for breast and body tomosynthesis, reconstruction methods, characteristics of present-day tomosynthesis systems, x-ray tubes, and promising new applications including contrast-enhanced and multimodal breast imaging systems.</p><p><strong>Conclusion: </strong>Tomosynthesis has had an exciting history that continues today. This should serve as a foundation for other papers in the special issue \"Celebrating Digital Tomosynthesis: Past, Present and Future\" in the <i>Journal of Medical Imaging</i>.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 Suppl 1","pages":"S13012"},"PeriodicalIF":1.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11817815/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143415683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dye amount quantification of Papanicolaou-stained cytological images by multispectral unmixing: spectral analysis of cytoplasmic mucin.","authors":"Saori Takeyama, Tomoaki Watanabe, Nanxin Gong, Masahiro Yamaguchi, Takumi Urata, Fumikazu Kimura, Keiko Ishii","doi":"10.1117/1.JMI.12.1.017501","DOIUrl":"10.1117/1.JMI.12.1.017501","url":null,"abstract":"<p><strong>Purpose: </strong>The color of Papanicolaou-stained specimens is a crucial feature in cytology diagnosis. However, the quantification of color using digital images is challenging due to the variations in the staining process and characteristics of imaging equipment. The dye amount estimation of stained specimens is helpful for quantitatively interpreting the color based on a physical model. It has been realized with color unmixing and applied to staining with three or fewer dyes. Nevertheless, the Papanicolaou stain comprises five dyes. Thus, we employ multispectral imaging with more channels for quantitative analysis of the Papanicolaou-stained cervical cytology samples.</p><p><strong>Approach: </strong>We estimate the dye amount map from a 14-band multispectral observation capturing a Papanicolaou-stained specimen using the actual measured spectral characteristics of the single-stained samples. The estimated dye amount maps were employed for the quantitative interpretation of the color of cytoplasmic mucin of lobular endocervical glandular hyperplasia (LEGH) and normal endocervical (EC) cells in a uterine cervical lesion.</p><p><strong>Results: </strong>We demonstrated the dye amount estimation performance of the proposed method using single-stain images and Papanicolaou-stain images. Moreover, the yellowish color in the LEGH cells is found to be interpreted with more orange G (OG) and less Eosin Y (EY) dye amounts. We also elucidated that LEGH and EC cells could be classified using linear classifiers from the dye amount.</p><p><strong>Conclusions: </strong>Multispectral imaging enables the quantitative analysis of dye amount maps of Papanicolaou-stained cytology specimens. The effectiveness is demonstrated in interpreting and classifying the cytoplasmic mucin of EC and LEGH cells in cervical cytology.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 1","pages":"017501"},"PeriodicalIF":1.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11681424/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142903877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dependence of observer task on conclusions drawn from <i>in silico</i> trials evaluating the performance of full-field digital mammography and digital breast tomosynthesis.","authors":"Dan Li, Andrey Makeev, Stephen J Glick","doi":"10.1117/1.JMI.12.S1.S13014","DOIUrl":"10.1117/1.JMI.12.S1.S13014","url":null,"abstract":"<p><strong>Purpose: </strong>We aim to refine the task-based evaluation of full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT) through <i>in silico</i> trials (ISTs). Previous ISTs mostly employ lesion detection tasks for task-based performance evaluation, which differ from clinical practice where the task normally involves the radiologists both detecting whether a suspicious lesion is present and rating how likely it is that the lesion is malignant. We hypothesize that differing conclusions may result from ISTs based on the defined task.</p><p><strong>Approach: </strong>The shape of the masses was employed as a surrogate indicator for malignancy, with spiculated masses representing malignant lesions and lobular masses representing benign lesions. A convolutional neural network (CNN) model observer was then trained to differentiate between spiculated and nonspiculated masses using Monte Carlo-simulated breast images. This approach leverages prior research demonstrating that CNN-based frameworks can approximate the performance of an ideal observer. We systematically evaluated the effects of varying dose levels, detector pixel size, and projection angular range on the CNN model observer's performance in both detection and classification tasks, assessing the performance of both FFDM and DBT systems.</p><p><strong>Results: </strong>Our findings demonstrate significant variations in conclusions drawn from IST models depending on whether the task is lesion detection or classification. Specifically, we observed that varying average glandular dose levels from 2.0 to 0.5 mGy had little effect on the detection of masses, whereas a small but significant decrease in performance with reduced dose was observed with the classification task across FFDM and DBT. Similarly, reduced spatial resolution resulted in a small but significant decrease in performance with the classification task for FFDM. For DBT ISTs, we also observed that the preferred angular range varies depending on whether the task is detection or classification.</p><p><strong>Conclusions: </strong>Integrating classification tasks into ISTs and potentially physical phantom studies can provide additional information in the evaluation of clinical breast imaging systems. This methodology can enhance the reliability of performance assessments for new breast imaging technologies. Depending on the study's objective, ISTs and physical phantom studies should aim to employ tasks that closely model actual clinical scenarios.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 Suppl 1","pages":"S13014"},"PeriodicalIF":1.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12087637/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144112305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving coronary artery segmentation with self-supervised learning and automated pericoronary adipose tissue segmentation: a multi-institutional study on coronary computed tomography angiography images.","authors":"Justin N Kim, Yingnan Song, Hao Wu, Ananya Subramaniam, Jihye Lee, Mohamed H E Makhlouf, Neda S Hassani, Sadeer Al-Kindi, David L Wilson, Juhwan Lee","doi":"10.1117/1.JMI.12.1.016002","DOIUrl":"10.1117/1.JMI.12.1.016002","url":null,"abstract":"<p><strong>Purpose: </strong>Coronary artery disease (CAD) is a leading cause of morbidity and mortality worldwide, with coronary computed tomography angiography (CCTA) playing a crucial role in its diagnosis. The mean Hounsfield unit (HU) of pericoronary adipose tissue (PCAT) is linked to cardiovascular risk. We utilized a self-supervised learning framework (SSL) to improve the accuracy and generalizability of coronary artery segmentation on CCTA volumes while addressing the limitations of small-annotated datasets.</p><p><strong>Approach: </strong>We utilized self-supervised pretraining followed by supervised fine-tuning to segment coronary arteries. To evaluate the data efficiency of SSL, we varied the number of CCTA volumes used during pretraining. In addition, we developed an automated PCAT segmentation algorithm utilizing centerline extraction, spatial-geometric coronary identification, and landmark detection. We evaluated our method on a multi-institutional dataset by assessing coronary artery and PCAT segmentation accuracy via Dice scores and comparing mean PCAT HU values with the ground truth.</p><p><strong>Results: </strong>Our approach significantly improved coronary artery segmentation, achieving Dice scores up to 0.787 after self-supervised pretraining. The automated PCAT segmentation achieved near-perfect performance, with <math><mrow><mi>R</mi></mrow> </math> -squared values of 0.9998 for both the left anterior descending artery and the right coronary artery indicating excellent agreement between predicted and actual mean PCAT HU values. Self-supervised pretraining notably enhanced model generalizability on external datasets, improving overall segmentation accuracy.</p><p><strong>Conclusions: </strong>We demonstrate the potential of SSL to advance CCTA image analysis, enabling more accurate CAD diagnostics. Our findings highlight the robustness of SSL for automated coronary artery and PCAT segmentation, offering promising advancements in cardiovascular care.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 1","pages":"016002"},"PeriodicalIF":1.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11831809/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143450598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Breathing motion compensation in chest tomosynthesis: evaluation of the effect on image quality and presence of artifacts.","authors":"Maral Mirzai, Jenny Nilsson, Patrik Sund, Rauni Rossi Norrlund, Micael Oliveira Diniz, Bengt Gottfridsson, Ida Häggström, Åse A Johnsson, Magnus Båth, Angelica Svalkvist","doi":"10.1117/1.JMI.12.S1.S13004","DOIUrl":"10.1117/1.JMI.12.S1.S13004","url":null,"abstract":"<p><strong>Purpose: </strong>Chest tomosynthesis (CTS) has a relatively longer acquisition time compared with chest X-ray, which may increase the risk of motion artifacts in the reconstructed images. Motion artifacts induced by breathing motion adversely impact the image quality. This study aims to reduce these artifacts by excluding projection images identified with breathing motion prior to the reconstruction of section images and to assess if motion compensation improves overall image quality.</p><p><strong>Approach: </strong>In this study, 2969 CTS examinations were analyzed to identify examinations where breathing motion has occurred using a method based on localizing the diaphragm border in each of the projection images. A trajectory over diaphragm positions was estimated from a second-order polynomial curve fit, and projection images where the diaphragm border deviated from the trajectory were removed before reconstruction. The image quality between motion-compensated and uncompensated examinations was evaluated using the image quality criteria for anatomical structures and image artifacts in a visual grading characteristic (VGC) study. The resulting rating data were statistically analyzed using the software VGC analyzer.</p><p><strong>Results: </strong>A total of 58 examinations were included in this study with breathing motion occurring either at the beginning or end ( <math><mrow><mi>n</mi> <mo>=</mo> <mn>17</mn></mrow> </math> ) or throughout the entire acquisition ( <math><mrow><mi>n</mi> <mo>=</mo> <mn>41</mn></mrow> </math> ). In general, no significant difference in image quality or presence of motion artifacts was shown between the motion-compensated and uncompensated examinations. However, motion compensation significantly improved the image quality and reduced the motion artifacts in cases where motion occurred at the beginning or end. In examinations where motion occurred throughout the acquisition, motion compensation led to a significant increase in ripple artifacts and noise.</p><p><strong>Conclusions: </strong>Compensation for respiratory motion in CTS by excluding projection images may improve the image quality if the motion occurs mainly at the beginning or end of the examination. However, the disadvantages of excluding projections may outweigh the benefits of motion compensation.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 Suppl 1","pages":"S13004"},"PeriodicalIF":1.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11399550/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142298677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}