Journal of Medical Imaging最新文献

{"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":"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":"https://doi.org/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":"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":"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":"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":"https://doi.org/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":1.9,"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}

{"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":"Weakly supervised pathological differentiation of primary central nervous system lymphoma and glioblastoma on multi-site whole slide images.","authors":"Liping Wang, Lin Chen, Kaixi Wei, Huiyu Zhou, Reyer Zwiggelaar, Weiwei Fu, Yingchao Liu","doi":"10.1117/1.JMI.12.1.017502","DOIUrl":"10.1117/1.JMI.12.1.017502","url":null,"abstract":"<p><strong>Purpose: </strong>Differentiating primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM) is crucial because their prognosis and treatment differ substantially. Manual examination of their histological characteristics is considered the golden standard in clinical diagnosis. However, this process is tedious and time-consuming and might lead to misdiagnosis caused by morphological similarity between their histology and tumor heterogeneity. Existing research focuses on radiological differentiation, which mostly uses multi-parametric magnetic resonance imaging. By contrast, we investigate the pathological differentiation between the two types of tumors using whole slide images (WSIs) of postoperative formalin-fixed paraffin-embedded samples.</p><p><strong>Approach: </strong>To learn the specific and intrinsic histological feature representations from the WSI patches, a self-supervised feature extractor is trained. Then, the patch representations are fused by feeding into a weakly supervised multiple-instance learning model for the WSI classification. We validate our approach on 134 PCNSL and 526 GBM cases collected from three hospitals. We also investigate the effect of feature extraction on the final prediction by comparing the performance of applying the feature extractors trained on the PCNSL/GBM slides from specific institutions, multi-site PCNSL/GBM slides, and large-scale histopathological images.</p><p><strong>Results: </strong>Different feature extractors perform comparably with the overall area under the receiver operating characteristic curve value exceeding 85% for each dataset and close to 95% for the combined multi-site dataset. Using the institution-specific feature extractors generally obtains the best overall prediction with both of the PCNSL and GBM classification accuracies reaching 80% for each dataset.</p><p><strong>Conclusions: </strong>The excellent classification performance suggests that our approach can be used as an assistant tool to reduce the pathologists' workload by providing an accurate and objective second diagnosis. Moreover, the discriminant regions indicated by the generated attention heatmap improve the model interpretability and provide additional diagnostic information.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 1","pages":"017502"},"PeriodicalIF":1.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11724367/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142972751","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":"Breast cancer classification in point-of-care ultrasound imaging-the impact of training data.","authors":"Jennie Karlsson, Ida Arvidsson, Freja Sahlin, Kalle Åström, Niels Christian Overgaard, Kristina Lång, Anders Heyden","doi":"10.1117/1.JMI.12.1.014502","DOIUrl":"10.1117/1.JMI.12.1.014502","url":null,"abstract":"<p><strong>Purpose: </strong>The survival rate of breast cancer for women in low- and middle-income countries is poor compared with that in high-income countries. Point-of-care ultrasound (POCUS) combined with deep learning could potentially be a suitable solution enabling early detection of breast cancer. We aim to improve a classification network dedicated to classifying POCUS images by comparing different techniques for increasing the amount of training data.</p><p><strong>Approach: </strong>Two data sets consisting of breast tissue images were collected, one captured with POCUS and another with standard ultrasound (US). The data sets were expanded by using different techniques, including augmentation, histogram matching, histogram equalization, and cycle-consistent adversarial networks (CycleGANs). A classification network was trained on different combinations of the original and expanded data sets. Different types of augmentation were investigated and two different CycleGAN approaches were implemented.</p><p><strong>Results: </strong>Almost all methods for expanding the data sets significantly improved the classification results compared with solely using POCUS images during the training of the classification network. When training the classification network on POCUS and CycleGAN-generated POCUS images, it was possible to achieve an area under the receiver operating characteristic curve of 95.3% (95% confidence interval 93.4% to 97.0%).</p><p><strong>Conclusions: </strong>Applying augmentation during training showed to be important and increased the performance of the classification network. Adding more data also increased the performance, but using standard US images or CycleGAN-generated POCUS images gave similar results.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 1","pages":"014502"},"PeriodicalIF":1.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11740782/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143014090","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":"Accurate volume image reconstruction for digital breast tomosynthesis with directional-gradient and pixel sparsity regularization.","authors":"Emil Y Sidky, Xiangyi Wu, Xiaoyu Duan, Hailiang Huang, Wei Zhao, Leo Y Zhang, John Paul Phillips, Zheng Zhang, Buxin Chen, Dan Xia, Ingrid S Reiser, Xiaochuan Pan","doi":"10.1117/1.JMI.12.S1.S13013","DOIUrl":"10.1117/1.JMI.12.S1.S13013","url":null,"abstract":"<p><strong>Purpose: </strong>We aim to develop accurate volumetric quantitative imaging of iodinated contrast agent (ICA) in contrast-enhanced digital breast tomosynthesis (DBT).</p><p><strong>Approach: </strong>The two main components of the approach are the use of a dual-energy DBT (DE-DBT) scan and the development of an optimization-based algorithm that can yield accurate images with isotropic resolution. The image reconstruction algorithm exploits sparsity in the subject's directional derivative magnitudes, and it also performs direct sparsity regularization to help confine the reconstruction to the true support of the subject. The algorithm is demonstrated with three sets of simulations in 2D and 3D, and a physical DE-DBT scan. The last of the three simulations employs an anthropomorphic phantom derived from the VICTRE project, testing quantitative tumor imaging with ICA.</p><p><strong>Results: </strong>The 2D simulations of the algorithm demonstrate accurate and stable image reconstruction. With the first 3D simulation, the proposed algorithm shows the ability to resolve overlapping objects, and with the anthropomorphic phantom, accurate recovery of the irregular ICA distribution in the shape of a tumor model is demonstrated. Applying the algorithm to DE-DBT transmission data of the CIRS BR3D phantom with solid ICA inserts yields images in which the depth-blurring is greatly reduced and the ICA distribution is accurately reconstructed.</p><p><strong>Conclusion: </strong>The results for the sparsity regularization algorithm applied to DE-DBT show promise, but as the algorithm performance is necessarily subject-dependent, further investigation using subjects with varying complexity in the ICA distribution is required.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 Suppl 1","pages":"S13013"},"PeriodicalIF":1.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11889465/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143587703","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":"Automatic detection of main pancreatic duct dilation and pancreatic parenchymal atrophy based on a shape feature in abdominal contrast-enhanced CT images.","authors":"Shintaro Ambo, Ryo Hirano, Chihiro Hattori","doi":"10.1117/1.JMI.12.1.014504","DOIUrl":"10.1117/1.JMI.12.1.014504","url":null,"abstract":"<p><strong>Purpose: </strong>The purpose of this study was to develop and evaluate an algorithm for calculating a shape feature to automatically detect both main pancreatic duct dilation (MPDD) and pancreatic parenchymal atrophy (PPA) in abdominal contrast-enhanced CT (CE-CT) images.</p><p><strong>Approach: </strong>The proposed algorithm for the automatic detection of MPDD and PPA is composed of five processes: coarse pancreas segmentation, fine pancreas segmentation, main pancreatic duct (MPD) segmentation, centerline estimation, and shape feature calculation. First, the pancreas region is segmented by a deep learning convolutional neural network (CNN). Then, the MPD region is segmented inside the pancreatic region by the deep learning CNN. Next, centerline estimation is performed using Dijkstra's rooting algorithm. Finally, in shape feature calculation, the cross-sectional area ratio of the pancreatic duct to the pancreatic parenchyma (DP ratio) is calculated in all cross sections perpendicular to the identified centerline, and the 90th percentile value of the DP ratio for all cross sections (90th DP ratio) is calculated. The detection performance of the 90th DP ratio for MPDD and PPA was evaluated using 56 abdominal CE-CT images available as public data.</p><p><strong>Results: </strong>The average of the 90th DP ratio was 0.059 in 48 cases with MPDD and 0.007 in eight cases without MPDD ( <math><mrow><mi>p</mi> <mo><</mo> <mn>0.001</mn></mrow> </math> ) and 0.074 in 31 cases with PPA and 0.023 in 25 cases without PPA ( <math><mrow><mi>p</mi> <mo><</mo> <mn>0.001</mn></mrow> </math> ).</p><p><strong>Conclusions: </strong>We have developed an algorithm for calculating an automatically measurable shape feature called the 90th DP ratio for the detection of MPDD and PPA.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 1","pages":"014504"},"PeriodicalIF":1.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11782102/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143081749","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}