Journal of Medical Imaging最新文献

{"title":"Impact of menopause and age on breast density and background parenchymal enhancement in dynamic contrast-enhanced magnetic resonance imaging.","authors":"Grey Kuling, Jennifer D Brooks, Belinda Curpen, Ellen Warner, Anne L Martel","doi":"10.1117/1.JMI.12.S2.S22002","DOIUrl":"10.1117/1.JMI.12.S2.S22002","url":null,"abstract":"<p><strong>Purpose: </strong>Breast density (BD) and background parenchymal enhancement (BPE) are important imaging biomarkers for breast cancer (BC) risk. We aim to evaluate longitudinal changes in quantitative BD and BPE in high-risk women undergoing dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), focusing on the effects of age and transition into menopause.</p><p><strong>Approach: </strong>A retrospective cohort study analyzed 834 high-risk women undergoing breast DCE-MRI for screening between 2005 and 2020. Quantitative BD and BPE were derived using deep-learning segmentation. Linear mixed-effects models assessed longitudinal changes and the effects of age, menopausal status, weeks since the last menstrual period (LMP-wks), body mass index (BMI), and hormone replacement therapy (HRT) on these imaging biomarkers.</p><p><strong>Results: </strong>BD decreased with age across all menopausal stages, whereas BPE declined with age in postmenopausal women but remained stable in premenopausal women. HRT elevated BPE in postmenopausal women. Perimenopausal women exhibited decreases in both BD and BPE during the menopausal transition, though cross-sectional age at menopause had no significant effect on either measure. Fibroglandular tissue was positively associated with BPE in perimenopausal women.</p><p><strong>Conclusions: </strong>We highlight the dynamic impact of menopause on BD and BPE and correlate well with the known relationship between risk and age at menopause. These findings advance the understanding of imaging biomarkers in high-risk populations and may contribute to the development of improved risk assessment leading to personalized chemoprevention and BC screening recommendations.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 Suppl 2","pages":"S22002"},"PeriodicalIF":1.9,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11894108/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143617600","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 tumor diagnosis via multimodal deep learning using ultrasound B-mode and Nakagami images.","authors":"Sabiq Muhtadi, Caterina M Gallippi","doi":"10.1117/1.JMI.12.S2.S22009","DOIUrl":"10.1117/1.JMI.12.S2.S22009","url":null,"abstract":"<p><strong>Purpose: </strong>We propose and evaluate multimodal deep learning (DL) approaches that combine ultrasound (US) B-mode and Nakagami parametric images for breast tumor classification. It is hypothesized that integrating tissue brightness information from B-mode images with scattering properties from Nakagami images will enhance diagnostic performance compared with single-input approaches.</p><p><strong>Approach: </strong>An EfficientNetV2B0 network was used to develop multimodal DL frameworks that took as input (i) numerical two-dimensional (2D) maps or (ii) rendered red-green-blue (RGB) representations of both B-mode and Nakagami data. The diagnostic performance of these frameworks was compared with single-input counterparts using 831 US acquisitions from 264 patients. In addition, gradient-weighted class activation mapping was applied to evaluate diagnostically relevant information utilized by the different networks.</p><p><strong>Results: </strong>The multimodal architectures demonstrated significantly higher area under the receiver operating characteristic curve (AUC) values ( <math><mrow><mi>p</mi> <mo><</mo> <mn>0.05</mn></mrow> </math> ) than their monomodal counterparts, achieving an average improvement of 10.75%. In addition, the multimodal networks incorporated, on average, 15.70% more diagnostically relevant tissue information. Among the multimodal models, those using RGB representations as input outperformed those that utilized 2D numerical data maps ( <math><mrow><mi>p</mi> <mo><</mo> <mn>0.05</mn></mrow> </math> ). The top-performing multimodal architecture achieved a mean AUC of 0.896 [95% confidence interval (CI): 0.813 to 0.959] when performance was assessed at the image level and 0.848 (95% CI: 0.755 to 0.903) when assessed at the lesion level.</p><p><strong>Conclusions: </strong>Incorporating B-mode and Nakagami information together in a multimodal DL framework improved classification outcomes and increased the amount of diagnostically relevant information accessed by networks, highlighting the potential for automating and standardizing US breast cancer diagnostics to enhance clinical outcomes.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 Suppl 2","pages":"S22009"},"PeriodicalIF":1.9,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12077846/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144081335","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":"Sureness of classification of breast cancers as pure ductal carcinoma <i>in situ</i> or with invasive components on dynamic contrast-enhanced magnetic resonance imaging: application of likelihood assurance metrics for computer-aided diagnosis.","authors":"Heather M Whitney, Karen Drukker, Alexandra Edwards, Maryellen L Giger","doi":"10.1117/1.JMI.12.S2.S22012","DOIUrl":"10.1117/1.JMI.12.S2.S22012","url":null,"abstract":"<p><strong>Purpose: </strong>Breast cancer may persist within milk ducts (ductal carcinoma <i>in situ</i>, DCIS) or advance into surrounding breast tissue (invasive ductal carcinoma, IDC). Occasionally, invasiveness in cancer may be underestimated during biopsy, leading to adjustments in the treatment plan based on unexpected surgical findings. Artificial intelligence/computer-aided diagnosis (AI/CADx) techniques in medical imaging may have the potential to predict whether a lesion is purely DCIS or exhibits a mixture of IDC and DCIS components, serving as a valuable supplement to biopsy findings. To enhance the evaluation of AI/CADx performance, assessing variability on a lesion-by-lesion basis via likelihood assurance measures could add value.</p><p><strong>Approach: </strong>We evaluated the performance in the task of distinguishing between pure DCIS and mixed IDC/DCIS breast cancers using computer-extracted radiomic features from dynamic contrast-enhanced magnetic resonance imaging using 0.632+ bootstrapping methods (2000 folds) on 550 lesions (135 pure DCIS, 415 mixed IDC/DCIS). Lesion-based likelihood assurance was measured using a sureness metric based on the 95% confidence interval of the classifier output for each lesion.</p><p><strong>Results: </strong>The median and 95% CI of the 0.632+-corrected area under the receiver operating characteristic curve for the task of classifying lesions as pure DCIS or mixed IDC/DCIS were 0.81 [0.75, 0.86]. The sureness metric varied across the dataset with a range of 0.0002 (low sureness) to 0.96 (high sureness), with combinations of high and low classifier output and high and low sureness for some lesions.</p><p><strong>Conclusions: </strong>Sureness metrics can provide additional insights into the ability of CADx algorithms to pre-operatively predict whether a lesion is invasive.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 Suppl 2","pages":"S22012"},"PeriodicalIF":1.9,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12175085/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144334195","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":"Semi-supervised semantic segmentation of cell nuclei with diffusion model and collaborative learning.","authors":"Zhuchen Shao, Sourya Sengupta, Mark A Anastasio, Hua Li","doi":"10.1117/1.JMI.12.6.061403","DOIUrl":"10.1117/1.JMI.12.6.061403","url":null,"abstract":"<p><strong>Purpose: </strong>Automated segmentation and classification of the cell nuclei in microscopic images is crucial for disease diagnosis and tissue microenvironment analysis. Given the difficulties in acquiring large labeled datasets for supervised learning, semi-supervised methods offer alternatives by utilizing unlabeled data alongside labeled data. Effective semi-supervised methods to address the challenges of extremely limited labeled data or diverse datasets with varying numbers and types of annotations remain under-explored.</p><p><strong>Approach: </strong>Unlike other semi-supervised learning methods that iteratively use labeled and unlabeled data for model training, we introduce a semi-supervised learning framework that combines a latent diffusion model (LDM) with a transformer-based decoder, allowing for independent usage of unlabeled data to optimize their contribution to model training. The model is trained based on a sequential training strategy. LDM is trained in an unsupervised manner on diverse datasets, independent of cell nuclei types, thereby expanding the training data and enhancing training performance. The pre-trained LDM serves as a powerful feature extractor to support the transformer-based decoder's supervised training on limited labeled data and improve final segmentation performance. In addition, the paper explores a collaborative learning strategy to enhance segmentation performance on out-of-distribution (OOD) data.</p><p><strong>Results: </strong>Extensive experiments conducted on four diverse datasets demonstrated that the proposed framework significantly outperformed other semi-supervised and supervised methods for both in-distribution and OOD cases. Through collaborative learning with supervised methods, diffusion model and transformer decoder-based segmentation (DTSeg) achieved consistent performance across varying cell types and different amounts of labeled data.</p><p><strong>Conclusions: </strong>The proposed DTSeg framework addresses cell nuclei segmentation under limited labeled data by integrating unsupervised LDM training on diverse unlabeled datasets. Collaborative learning demonstrated effectiveness in enhancing the generalization capability of DTSeg to achieve superior results across diverse datasets and cases. Furthermore, the method supports multi-channel inputs and demonstrates strong generalization to both in-distribution and OOD scenarios.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 6","pages":"061403"},"PeriodicalIF":1.9,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11924957/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143694064","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":"Enhancing breast cancer detection on screening mammogram using self-supervised learning and a hybrid deep model of Swin Transformer and convolutional neural networks.","authors":"Han Chen, Anne L Martel","doi":"10.1117/1.JMI.12.S2.S22007","DOIUrl":"10.1117/1.JMI.12.S2.S22007","url":null,"abstract":"<p><strong>Purpose: </strong>The scarcity of high-quality curated labeled medical training data remains one of the major limitations in applying artificial intelligence systems to breast cancer diagnosis. Deep models for mammogram analysis and mass (or micro-calcification) detection require training with a large volume of labeled images, which are often expensive and time-consuming to collect. To reduce this challenge, we proposed a method that leverages self-supervised learning (SSL) and a deep hybrid model, named HybMNet, which combines local self-attention and fine-grained feature extraction to enhance breast cancer detection on screening mammograms.</p><p><strong>Approach: </strong>Our method employs a two-stage learning process: (1) SSL pretraining: We utilize Efficient Self-Supervised Vision Transformers, an SSL technique, to pretrain a Swin Transformer (Swin-T) using a limited set of mammograms. The pretrained Swin-T then serves as the backbone for the downstream task. (2) Downstream training: The proposed HybMNet combines the Swin-T backbone with a convolutional neural network (CNN)-based network and a fusion strategy. The Swin-T employs local self-attention to identify informative patch regions from the high-resolution mammogram, whereas the CNN-based network extracts fine-grained local features from the selected patches. A fusion module then integrates global and local information from both networks to generate robust predictions. The HybMNet is trained end-to-end, with the loss function combining the outputs of the Swin-T and CNN modules to optimize feature extraction and classification performance.</p><p><strong>Results: </strong>The proposed method was evaluated for its ability to detect breast cancer by distinguishing between benign (normal) and malignant mammograms. Leveraging SSL pretraining and the HybMNet model, it achieved an area under the ROC curve of 0.864 (95% CI: 0.852, 0.875) on the Chinese Mammogram Database (CMMD) dataset and 0.889 (95% CI: 0.875, 0.903) on the INbreast dataset, highlighting its effectiveness.</p><p><strong>Conclusions: </strong>The quantitative results highlight the effectiveness of our proposed HybMNet and the SSL pretraining approach. In addition, visualizations of the selected region of interest patches show the model's potential for weakly supervised detection of microcalcifications, despite being trained using only image-level labels.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 Suppl 2","pages":"S22007"},"PeriodicalIF":1.9,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12076021/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144081336","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":"Simulating dynamic tumor contrast enhancement in breast MRI using conditional generative adversarial networks.","authors":"Richard Osuala, Smriti Joshi, Apostolia Tsirikoglou, Lidia Garrucho, Walter H L Pinaya, Daniel M Lang, Julia A Schnabel, Oliver Diaz, Karim Lekadir","doi":"10.1117/1.JMI.12.S2.S22014","DOIUrl":"10.1117/1.JMI.12.S2.S22014","url":null,"abstract":"<p><strong>Purpose: </strong>Deep generative models and synthetic data generation have become essential for advancing computer-assisted diagnosis and treatment. We explore one such emerging and particularly promising application of deep generative models, namely, the generation of virtual contrast enhancement. This allows to predict and simulate contrast enhancement in breast magnetic resonance imaging (MRI) without physical contrast agent injection, thereby unlocking lesion localization and categorization even in patient populations where the lengthy, costly, and invasive process of physical contrast agent injection is contraindicated.</p><p><strong>Approach: </strong>We define a framework for desirable properties of synthetic data, which leads us to propose the scaled aggregate measure (SAMe) consisting of a balanced set of scaled complementary metrics for generative model training and convergence evaluation. We further adopt a conditional generative adversarial network to translate from non-contrast-enhanced <math><mrow><mi>T</mi> <mn>1</mn></mrow> </math> -weighted fat-saturated breast MRI slices to their dynamic contrast-enhanced (DCE) counterparts, thus learning to detect, localize, and adequately highlight breast cancer lesions. Next, we extend our model approach to jointly generate multiple DCE-MRI time points, enabling the simulation of contrast enhancement across temporal DCE-MRI acquisitions. In addition, three-dimensional U-Net tumor segmentation models are implemented and trained on combinations of synthetic and real DCE-MRI data to investigate the effect of data augmentation with synthetic DCE-MRI volumes.</p><p><strong>Results: </strong>Conducting four main sets of experiments, (i) the variation across single metrics demonstrated the value of SAMe, and (ii) the quality and potential of virtual contrast injection for tumor detection and localization were shown. Segmentation models (iii) augmented with synthetic DCE-MRI data were more robust in the presence of domain shifts between pre-contrast and DCE-MRI domains. The joint synthesis approach of multi-sequence DCE-MRI (iv) resulted in temporally coherent synthetic DCE-MRI sequences and indicated the generative model's capability of learning complex contrast enhancement patterns.</p><p><strong>Conclusions: </strong>Virtual contrast injection can result in accurate synthetic DCE-MRI images, potentially enhancing breast cancer diagnosis and treatment protocols. We demonstrate that detecting, localizing, and segmenting tumors using synthetic DCE-MRI is feasible and promising, particularly considering patients where contrast agent injection is risky or contraindicated. Jointly generating multiple subsequent DCE-MRI sequences can increase image quality and unlock clinical applications assessing tumor characteristics related to its response to contrast media injection as a pillar for personalized treatment planning.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 Suppl 2","pages":"S22014"},"PeriodicalIF":1.9,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12205897/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144530364","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":"Introduction to the JMI Special Issue on Advances in Breast Imaging.","authors":"Maryellen L Giger, Susan Astley Theodossiadis, Karen Drukker, Hui Li, Andrew D A Maidment, Heather M Whitney","doi":"10.1117/1.JMI.12.S2.S22001","DOIUrl":"https://doi.org/10.1117/1.JMI.12.S2.S22001","url":null,"abstract":"<p><p>The editorial introduces the JMI Special Issue on Advances in Breast Imaging, reflecting on the current forefront of breast imaging research.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 Suppl 2","pages":"S22001"},"PeriodicalIF":1.7,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12422285/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145041826","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":"Frequency-based texture analysis of non-Gaussian properties of digital breast tomosynthesis images and comparison across two vendors.","authors":"Kai Yang, Craig K Abbey, Bruno Barufaldi, Xinhua Li, Theodore A Marschall, Bob Liu","doi":"10.1117/1.JMI.12.S2.S22004","DOIUrl":"10.1117/1.JMI.12.S2.S22004","url":null,"abstract":"<p><strong>Purpose: </strong>We aim to analyze higher-order textural components of digital breast tomosynthesis (DBT) images to quantify differences in the appearance of breast parenchyma produced by different vendors.</p><p><strong>Approach: </strong>We included consecutive women who had normal screening DBT exams in January 2018 from a GE system and in adjacent years from Hologic systems. Laplacian fractional entropy (LFE), as a measure of non-Gaussian statistical properties of breast tissue texture, was calculated from for-presentation Craniocaudal (CC) view DBT slices and synthetic mammograms (SMs) through frequency-based filtering with Gabor filters, which were considered mathematical models for human visual response to image textures. The LFE values were compared within and across subjects and vendors along with secondary parameters (laterality, year-to-year, modality, and breast density) via two-way analysis of variance (ANOVA) tests using frequency as one of the two independent variables, and a <math><mrow><mi>P</mi></mrow> </math> -value <math><mrow><mo><</mo> <mn>0.05</mn></mrow> </math> was considered statistically significant.</p><p><strong>Results: </strong>A total of 8529 CC view DBT slices and SM images from 73 screening exams in 25 women were analyzed. Significant differences in LFE were observed for different frequencies ( <math><mrow><mi>P</mi> <mo><</mo> <mn>0.001</mn></mrow> </math> ) and across vendors (GE versus Hologic DBT: <math><mrow><mi>P</mi> <mo><</mo> <mn>0.001</mn></mrow> </math> , GE versus Hologic SM: <math><mrow><mi>P</mi> <mo><</mo> <mn>0.001</mn></mrow> </math> ).</p><p><strong>Conclusion: </strong>Significant differences in perception of breast parenchyma textures among two DBT vendors were demonstrated via higher-order non-Gaussian statistical properties. This finding extends previously observed differences in anatomical noise power spectra in DBT images and provides quantitative evidence to support caution in across-vendor comparative reading and will be beneficial to facilitate future development of vendor-neutral artificial intelligence algorithms for breast cancer screening.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 Suppl 2","pages":"S22004"},"PeriodicalIF":1.9,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11925074/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143694062","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":"Investigating the effect of adding comparisons with prior mammograms to standalone digital breast tomosynthesis screening.","authors":"Pontus Timberg, Gustav Hellgren, Magnus Dustler, Anders Tingberg","doi":"10.1117/1.JMI.12.S2.S22003","DOIUrl":"10.1117/1.JMI.12.S2.S22003","url":null,"abstract":"<p><strong>Purpose: </strong>The purpose is to retrospectively investigate how the addition of prior and concurrent mammograms affects wide-angle digital breast tomosynthesis (DBT) screening false-positive recall rates, malignancy scoring, and recall agreement.</p><p><strong>Approach: </strong>A total of 200 cases were selected from the Malmö Breast Tomosynthesis Screening Trial. They consist of 150 recalled cases [30 true positives (TPs), 120 false positives (FPs), and 50 healthy, non-recalled true-negative (TN) cases]. The positive cases were categorized based on being recalled by either DBT, digital mammography (DM), or both. Each case had DBT, synthetic mammography (SM), and DM (prior screening round) images. Five radiologists participated in a reading study where detection, risk of malignancy, and recall were assessed. They read each case twice, once using only DBT and once using DBT together with SM and DM priors.</p><p><strong>Results: </strong>The results showed a significant reduction in recall rates for all FP categories, as well as for the TN cases, when adding SM and prior DM to DBT. This resulted also in a significant increase in recall agreement for these categories, with more of the negative cases being recalled by few or no readers. These categories were overall rated as appearing more malignant in the DBT reading arm. For the TP categories, there was a significant decrease in recalls for DM-recalled cancers ( <math><mrow><mi>p</mi> <mo>=</mo> <mn>0.047</mn></mrow> </math> ), but no significant difference for DBT-recalled cancers ( <math><mrow><mi>p</mi> <mo>=</mo> <mn>0.063</mn></mrow> </math> ), or DBT/DM-recalled cancers ( <math><mrow><mi>p</mi> <mo>=</mo> <mn>0.208</mn></mrow> </math> ).</p><p><strong>Conclusions: </strong>Similar to the documented effect of priors in DM screening, we suggest that added two-dimensional priors improve the specificity of DBT screening but may reduce the sensitivity.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 Suppl 2","pages":"S22003"},"PeriodicalIF":1.9,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11931293/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143711591","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":"Exploring the impact of image restoration in simulating higher dose mammography: effects on the detectability of microcalcifications across different sizes using model observer analysis.","authors":"Renann F Brandão, Lucas E Soares, Lucas R Borges, Predrag R Bakic, Anders Tingberg, Marcelo A C Vieira","doi":"10.1117/1.JMI.12.S2.S22013","DOIUrl":"10.1117/1.JMI.12.S2.S22013","url":null,"abstract":"<p><strong>Purpose: </strong>Breast cancer is one of the leading causes of cancer-related deaths among women, and digital mammography plays a key role in screening and early detection. The radiation dose on mammographic exams directly influences image quality and radiologists' performance. We evaluate the impact of an image restoration pipeline-designed to simulate higher dose acquisitions-on the detectability of microcalcifications of various sizes in mammograms acquired at different radiation doses.</p><p><strong>Approach: </strong>The restoration pipeline denoises the image using a Poisson-Gaussian noise model, combining it with the noisy image to achieve a signal-to-noise ratio comparable with an acquisition at twice the original dose. We created a database of images using a physical breast phantom at doses ranging from 50% to 200% of the standard dose. Clustered microcalcifications were computationally inserted into the phantom images. The channelized Hotelling observer was employed in a four-alternative forced-choice to evaluate the detectability of microcalcifications across different sizes and exposure levels.</p><p><strong>Results: </strong>The restoration of low-dose images acquired at <math><mrow><mo>∼</mo> <mn>75</mn> <mo>%</mo></mrow> </math> of the standard dose resulted in detectability levels comparable with those of images acquired at the standard dose. Moreover, images restored at the standard dose demonstrated detectability similar to those acquired at 160% of the nominal radiation dose, with no statistically significant differences.</p><p><strong>Conclusions: </strong>We demonstrate the potential of an image restoration pipeline to simulate higher quality mammography images. The results indicate that reducing noise through denoising and restoration impacts the detectability of microcalcifications. This method improves image quality without hardware modifications or additional radiation exposure.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 Suppl 2","pages":"S22013"},"PeriodicalIF":1.9,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12175087/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144334186","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}