Insights into Imaging最新文献

{"title":"Porto-mesenteric four-dimensional flow MRI: a novel non-invasive technique for assessment of gastro-oesophageal varices.","authors":"Rasha Karam, Basma A Elged, Omar Elmetwally, Shahira El-Etreby, Mostafa Elmansy, Mohammed Elhawary","doi":"10.1186/s13244-024-01805-6","DOIUrl":"https://doi.org/10.1186/s13244-024-01805-6","url":null,"abstract":"<p><strong>Objectives: </strong>To assess the role of 4D flow MRI in the assessment of gastro-oesophageal varices and in the prediction of high-risk varices in patients with chronic liver disease.</p><p><strong>Methods: </strong>Thirty-eight patients diagnosed with either oesophageal or gastric varices were included in this single-centre prospective study. 4D flow MRI was used to calculate peak flow, average flow and peak velocity at the portal vein confluence (PV1) and hilum (PV2), splenic vein hilum (SV1) and confluence (SV2), and superior mesenteric vein (SMV). PV and SV fractional flow changes were also measured.</p><p><strong>Results: </strong>ROC analysis revealed that both PV2 average flow and PV fractional average flow change had 100% sensitivity to predict high-risk patients with the PV fractional peak flow change having the widest area under the curve (AUC) and the highest specificity (92.3%). SV1 average flow, SV2 average flow, SV2 peak flow, and SV2 peak velocity increased significantly in patients with oesophageal compared to gastric varices included (p = 0.022, < 0.001, < 0.001 and 0.001, respectively).</p><p><strong>Conclusion: </strong>Based on certain porto-mesenteric blood flow, velocity, and fractional flow change parameters, 4D flow MRI showed excellent performance in identifying high-risk patients and giving an idea about the grade and location of varices.</p><p><strong>Critical relevance statement: </strong>Variceal bleeding is a major consequence of unidentified risky upper GI varices. Thus, by identifying and locating high-risk varices early, either oesophageal or gastric, using a non-invasive method like MRI, adverse events might be avoided.</p><p><strong>Key points: </strong>4D flow MRI can be used as a potential alternative for endoscopy to predict patients with high-risk varices. Based on portal vein fractional flow change, splenic flow and velocity, 4D MRI can predict and locate high-risk varices. Earlier identification of high-risk varices can allow for interventions to prevent adverse events.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"231"},"PeriodicalIF":4.1,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11436693/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142346028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detecting and characterizing creeping fat in Crohn's disease: agreement between intestinal ultrasound and computed tomography enterography.","authors":"Mengyuan Zhou, Zihan Niu, Li Ma, Wenbo Li, Mengsu Xiao, Yudi He, Jing Qin, Yuxin Jiang, Wei Liu, Qingli Zhu","doi":"10.1186/s13244-024-01807-4","DOIUrl":"10.1186/s13244-024-01807-4","url":null,"abstract":"<p><strong>Objectives: </strong>Creeping fat (CF) is associated with stricture formation in Crohn's disease (CD). This study evaluated the feasibility of intestinal ultrasound (IUS) for semiquantitative analysis of CF and compared the agreement between IUS and computed tomography enterography (CTE).</p><p><strong>Methods: </strong>In this retrospective study, we recruited consecutive CD patients who underwent IUS and CTE. CF wrapping angle was analyzed on the most affected bowel segment and was independently evaluated by IUS and CTE. We evaluated the wrapping angle of CF in the cross- and vertical sections of the diseased bowel. CF wrapping angle was divided into < 180° and ≥ 180°. IUS performance was assessed using CTE as a reference standard, and IUS interobserver consistency was evaluated.</p><p><strong>Results: </strong>We enrolled 96 patients. CTE showed that CF wrapping angle was < 180° in 35 patients and ≥ 180° in 61 patients. We excluded three cases in which the observation positions were inconsistent between the IUS and CTE. Excellent agreement was shown between US and CTE (82/93, 88.2%). The eleven remaining cases showed inconsistencies mostly in the terminal ileum (n = 5) and small intestine (n = 4). Total agreement between IUS observers was 89.6% (86/96, κ = 0.839, p = 0.000), with perfect agreement for the ileocecal and colonic segments (35/37, 94.6% and 20/21, 95.2%, respectively) and moderate agreement for small intestinal segments (16/21, 76.2%).</p><p><strong>Conclusions: </strong>IUS could be of value and complementary to CTE for assessing CF, particularly in patients with affected terminal ileum and colon. IUS is a non-invasive technique for monitoring CD patients.</p><p><strong>Critical relevance statement: </strong>In our study, excellent agreement was shown between intestinal US observers as well as between US and CT enterography (CTE) for assessing creeping fat (CF), which showed that ultrasound could be of value and complementary to CTE.</p><p><strong>Key points: </strong>Creeping fat (CF) is a potential therapeutic target in Crohn's disease. Excellent agreement was shown between US and CT Enterography (CTE) for assessing CF. Ultrasound could be complementary to CTE for assessing CF.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"229"},"PeriodicalIF":4.1,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11420403/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142286270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of microvascular ultrasound-assisted thyroid imaging report and data system in thyroid nodule risk stratification.","authors":"Guangrong Ma, Libin Chen, Yong Wang, Zhiyan Luo, Yiqing Zeng, Xue Wang, Zhan Shi, Tao Zhang, Yurong Hong, Pintong Huang","doi":"10.1186/s13244-024-01806-5","DOIUrl":"10.1186/s13244-024-01806-5","url":null,"abstract":"<p><strong>Objectives: </strong>To establish superb microvascular imaging (SMI) based thyroid imaging reporting and data system (SMI TI-RADS) for risk stratification of malignancy in thyroid nodules.</p><p><strong>Methods: </strong>In total, 471 patients, comprising 643 thyroid nodules, who received conventional ultrasound (US), SMI, and a final diagnosis were extensively analyzed. A qualitative assessment of US features of the nodules was performed followed by univariable and multivariable logistic regression analyses, leading to the construction of the SMI TI-RADS, which was further verified using internal and external validation cohorts.</p><p><strong>Results: </strong>Among the stand-alone US, predictive factors were the shape and margins of the nodules, echogenicity and echogenic foci, vascularity, extrathyroidal extension, ring-SMI patterns, penetrating vascularity, flow-signal enlarged, and vascularity area ratio. SMI TI-RADS depicted an enhanced area under the receiver operating characteristic curve (AUC) of 0.94 (95% CI: 0.92, 0.96; p < 0.001 relative to other stratification systems), a 79% biopsy yield of malignancy (BYM, 189/240 nodules), and a 21% unnecessary biopsy rate (UBR, 51/240 nodules). In the verification cohorts, we demonstrated AUCs, malignancy biopsy yields, and unnecessary biopsy rates of 0.88 (95% CI: 0.83, 0.94), 79% (59/75 nodules), and 21% (16/75 nodules) for the internal cohort, respectively, and 0.91 (95% CI: 0.85, 0.96), 72% (31/43 nodules), and 28% (12/43 nodules) for the external cohort, respectively.</p><p><strong>Conclusion: </strong>SMI TI-RADS was found to be superior in diagnostic sensitivity, specificity, and efficiency than existing TI-RADSs, showing better stratification of the malignancy risk, and thus decreasing the rate of unnecessary needle biopsy.</p><p><strong>Critical relevance statement: </strong>To develop an imaging and data system based on conventional US and SMI features for stratifying the malignancy risk in thyroid nodules.</p><p><strong>Key points: </strong>SMI features could improve thyroid nodule risk stratification. SMI TI-RADS showed superior diagnostic efficiency and accuracy for biopsy guidance. SMI TI-RADS can provide better guidance for clinical diagnosis and treatment of thyroid nodules.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"230"},"PeriodicalIF":4.1,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11420409/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142286269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An ensemble machine learning model assists in the diagnosis of gastric ectopic pancreas and gastric stromal tumors.","authors":"Kui Sun, Ying Wang, Rongchao Shi, Siyu Wu, Ximing Wang","doi":"10.1186/s13244-024-01809-2","DOIUrl":"https://doi.org/10.1186/s13244-024-01809-2","url":null,"abstract":"<p><strong>Objective: </strong>To develop an ensemble machine learning (eML) model using multiphase computed tomography (MPCT) for distinguishing between gastric ectopic pancreas (GEP) and gastric stromal tumors (GIST) in lesions < 3 cm.</p><p><strong>Methods: </strong>In this study, we retrospectively collected MPCT images from 138 patients between April 2017 and June 2023 across two centers. Cohort 1 comprised 94 patients divided into a training cohort and an internal validation cohort, while the 44 patients from Cohort 2 constituted the external validation cohort. Deep learning (DL) models were constructed based on the lesion region, and radiomics features were extracted to develop radiomics models, which were later integrated into the fusion model. Model performance was assessed through the analysis of the area under the receiver operating characteristic curve (AUROC). The diagnostic efficacy of the optimal model was compared with that of a radiologist. Additionally, the radiologist with the assistance of the eML model provides a secondary diagnosis, to assess the potential clinical value of the model.</p><p><strong>Results: </strong>After evaluation using an external validation cohort, the radiomics model demonstrated the highest performance in the venous phase, achieving AUROC of 0.87. The DL model showed optimal performance in the non-contrast phase, with AUROC of 0.81. The eML achieved the best performance across all models, with AUROC of 0.90. The use of eML-assisted analysis resulted in a significant improvement in the junior radiologist's accuracy, rising from 0.77 to 0.93 (p < 0.05). However, the senior radiologist's accuracy, while improving from 0.86 to 0.95, did not exhibit a statistically significant difference.</p><p><strong>Conclusion: </strong>eML model based on MPCT can effectively distinguish between GEPs and GISTs < 3 cm.</p><p><strong>Critical relevance statement: </strong>The multiphase CT-based fusion model, incorporating radiomics and DL technology, proves effective in distinguishing between GEP and gastric stromal tumors, serving as a valuable tool to enhance diagnoses and offering references for clinical decision-making.</p><p><strong>Key points: </strong>No studies yet differentiated these tumors via radiomics or DL. Radiomics and DL methodologies unveil potentially distinct phenotypes within lesions. Quantitative analysis on CT for GIST and ectopic pancreas. Ensemble learning aids accurate diagnoses, assisting treatment decisions.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"225"},"PeriodicalIF":4.1,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11424595/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142346025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A practical risk stratification system based on ultrasonography and clinical characteristics for predicting the malignancy of soft tissue masses.","authors":"Ying-Lun Zhang, Meng-Jie Wu, Yu Hu, Xiao-Jing Peng, Qian Ma, Cui-Lian Mao, Ye Dong, Zong-Kai Wei, Ying-Qian Gao, Qi-Yu Yao, Jing Yao, Xin-Hua Ye, Ju-Ming Li, Ao Li","doi":"10.1186/s13244-024-01802-9","DOIUrl":"https://doi.org/10.1186/s13244-024-01802-9","url":null,"abstract":"<p><strong>Objective: </strong>To establish a practical risk stratification system (RSS) based on ultrasonography (US) and clinical characteristics for predicting soft tissue masses (STMs) malignancy.</p><p><strong>Methods: </strong>This retrospective multicenter study included patients with STMs who underwent US and pathological examinations between April 2018 and April 2023. Chi-square tests and multivariable logistic regression analyses were performed to assess the association of US and clinical characteristics with the malignancy of STMs in the training set. The RSS was constructed based on the scores of risk factors and validated externally.</p><p><strong>Results: </strong>The training and validation sets included 1027 STMs (mean age, 50.90 ± 16.64, 442 benign and 585 malignant) and 120 STMs (mean age, 51.93 ± 17.90, 69 benign and 51 malignant), respectively. The RSS was constructed based on three clinical characteristics (age, duration, and history of malignancy) and six US characteristics (size, shape, margin, echogenicity, bone invasion, and vascularity). STMs were assigned to six categories in the RSS, including no abnormal findings, benign, probably benign (fitted probabilities [FP] for malignancy: 0.001-0.008), low suspicion (FP: 0.008-0.365), moderate suspicion (FP: 0.189-0.911), and high suspicion (FP: 0.798-0.999) for malignancy. The RSS displayed good diagnostic performance in the training and validation sets with area under the receiver operating characteristic curve (AUC) values of 0.883 and 0.849, respectively.</p><p><strong>Conclusion: </strong>The practical RSS based on US and clinical characteristics could be useful for predicting STM malignancy, thereby providing the benefit of timely treatment strategy management to STM patients.</p><p><strong>Critical relevance statement: </strong>With the help of the RSS, better communication between radiologists and clinicians can be realized, thus facilitating tumor management.</p><p><strong>Key points: </strong>There is no recognized grading system for STM management. A stratification system based on US and clinical features was built. The system realized great communication between radiologists and clinicians in tumor management.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"226"},"PeriodicalIF":4.1,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11424597/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142346015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"I thought it was a hemangioma! A pictorial essay about common and uncommon liver hemangiomas' mimickers.","authors":"Matteo Bonatti, Riccardo Valletta, Valentina Corato, Tommaso Gorgatti, Andrea Posteraro, Vincenzo Vingiani, Fabio Lombardo, Giacomo Avesani, Andrea Mega, Giulia A Zamboni","doi":"10.1186/s13244-024-01745-1","DOIUrl":"https://doi.org/10.1186/s13244-024-01745-1","url":null,"abstract":"<p><p>Focal liver lesions are frequently encountered during imaging studies, and hemangiomas represent the most common solid liver lesion. Liver hemangiomas usually show characteristic imaging features that enable characterization without the need for biopsy or follow-up. On the other hand, there are many benign and malignant liver lesions that may show one or more imaging features resembling hemangiomas that radiologists must be aware of. In this article we will review the typical imaging features of liver hemangiomas and will show a series of potential liver hemangiomas' mimickers, giving radiologists some hints for improving differential diagnoses. CRITICAL RELEVANCE STATEMENT: The knowledge of imaging features of potential liver hemangiomas mimickers is fundamental to avoid misinterpretation. KEY POINTS: Liver hemangiomas typically show imaging features that enable avoiding a biopsy. Many benign and malignant liver lesions show imaging features resembling hemangiomas. Radiologists must know the potentially misleading imaging features of hemangiomas' mimickers.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"228"},"PeriodicalIF":4.1,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11413281/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142286271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explainable breast cancer molecular expression prediction using multi-task deep-learning based on 3D whole breast ultrasound.","authors":"Zengan Huang, Xin Zhang, Yan Ju, Ge Zhang, Wanying Chang, Hongping Song, Yi Gao","doi":"10.1186/s13244-024-01810-9","DOIUrl":"https://doi.org/10.1186/s13244-024-01810-9","url":null,"abstract":"<p><strong>Objectives: </strong>To noninvasively estimate three breast cancer biomarkers, estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) and enhance performance and interpretability via multi-task deep learning.</p><p><strong>Methods: </strong>The study included 388 breast cancer patients who received the 3D whole breast ultrasound system (3DWBUS) examinations at Xijing Hospital between October 2020 and September 2021. Two predictive models, a single-task and a multi-task, were developed; the former predicts biomarker expression, while the latter combines tumor segmentation with biomarker prediction to enhance interpretability. Performance evaluation included individual and overall prediction metrics, and Delong's test was used for performance comparison. The models' attention regions were visualized using Grad-CAM + + technology.</p><p><strong>Results: </strong>All patients were randomly split into a training set (n = 240, 62%), a validation set (n = 60, 15%), and a test set (n = 88, 23%). In the individual evaluation of ER, PR, and HER2 expression prediction, the single-task and multi-task models achieved respective AUCs of 0.809 and 0.735 for ER, 0.688 and 0.767 for PR, and 0.626 and 0.697 for HER2, as observed in the test set. In the overall evaluation, the multi-task model demonstrated superior performance in the test set, achieving a higher macro AUC of 0.733, in contrast to 0.708 for the single-task model. The Grad-CAM + + method revealed that the multi-task model exhibited a stronger focus on diseased tissue areas, improving the interpretability of how the model worked.</p><p><strong>Conclusion: </strong>Both models demonstrated impressive performance, with the multi-task model excelling in accuracy and offering improved interpretability on noninvasive 3DWBUS images using Grad-CAM + + technology.</p><p><strong>Critical relevance statement: </strong>The multi-task deep learning model exhibits effective prediction for breast cancer biomarkers, offering direct biomarker identification and improved clinical interpretability, potentially boosting the efficiency of targeted drug screening.</p><p><strong>Key points: </strong>Tumoral biomarkers are paramount for determining breast cancer treatment. The multi-task model can improve prediction performance, and improve interpretability in clinical practice. The 3D whole breast ultrasound system-based deep learning models excelled in predicting breast cancer biomarkers.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"227"},"PeriodicalIF":4.1,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11424596/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142346027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accelerated 3D whole-heart non-contrast-enhanced mDIXON coronary MR angiography using deep learning-constrained compressed sensing reconstruction","authors":"Xi Wu, Xun Yue, Pengfei Peng, Xianzheng Tan, Feng Huang, Lei Cai, Lei Li, Shuai He, Xiaoyong Zhang, Peng Liu, Jiayu Sun","doi":"10.1186/s13244-024-01797-3","DOIUrl":"https://doi.org/10.1186/s13244-024-01797-3","url":null,"abstract":"To investigate the feasibility of a deep learning-constrained compressed sensing (DL-CS) method in non-contrast-enhanced modified DIXON (mDIXON) coronary magnetic resonance angiography (MRA) and compare its diagnostic accuracy using coronary CT angiography (CCTA) as a reference standard. Ninety-nine participants were prospectively recruited for this study. Thirty healthy subjects (age range: 20–65 years; 50% female) underwent three non-contrast mDIXON-based coronary MRA sequences including DL-CS, CS, and conventional sequences. The three groups were compared based on the scan time, subjective image quality score, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). The remaining 69 patients suspected of coronary artery disease (CAD) (age range: 39–83 years; 51% female) underwent the DL-CS coronary MRA and its diagnostic performance was compared with that of CCTA. The scan time for the DL-CS and CS sequences was notably shorter than that of the conventional sequence (9.6 ± 3.1 min vs 10.0 ± 3.4 min vs 13.0 ± 4.9 min; p < 0.001). The DL-CS sequence obtained the highest image quality score, mean SNR, and CNR compared to CS and conventional methods (all p < 0.001). Compared to CCTA, the accuracy, sensitivity, and specificity of DL-CS mDIXON coronary MRA per patient were 84.1%, 92.0%, and 79.5%; those per vessel were 90.3%, 82.6%, and 92.5%; and those per segment were 98.0%, 85.1%, and 98.0%, respectively. The DL-CS mDIXON coronary MRA provided superior image quality and short scan time for visualizing coronary arteries in healthy individuals and demonstrated high diagnostic value compared to CCTA in CAD patients. DL-CS resulted in improved image quality with an acceptable scan time, and demonstrated excellent diagnostic performance compared to CCTA, which could be an alternative to enhance the workflow of coronary MRA. ","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"77 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ECR 2024 Book of Abstracts","authors":"","doi":"10.1186/s13244-024-01766-w","DOIUrl":"https://doi.org/10.1186/s13244-024-01766-w","url":null,"abstract":"&lt;p&gt;&lt;b&gt;Open Access&lt;/b&gt; This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.&lt;/p&gt;\u0000&lt;p&gt;Reprints and permissions&lt;/p&gt;&lt;img alt=\"Check for updates. Verify currency and authenticity via CrossMark\" height=\"81\" loading=\"lazy\" src=\"data:image/svg+xml;base64,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","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"80 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Focal cortical dysplasia lesion segmentation using multiscale transformer","authors":"Xiaodong Zhang, Yongquan Zhang, Changmiao Wang, Lin Li, Fengjun Zhu, Yang Sun, Tong Mo, Qingmao Hu, Jinping Xu, Dezhi Cao","doi":"10.1186/s13244-024-01803-8","DOIUrl":"https://doi.org/10.1186/s13244-024-01803-8","url":null,"abstract":"Accurate segmentation of focal cortical dysplasia (FCD) lesions from MR images plays an important role in surgical planning and decision but is still challenging for radiologists and clinicians. In this study, we introduce a novel transformer-based model, designed for the end-to-end segmentation of FCD lesions from multi-channel MR images. The core innovation of our proposed model is the integration of a convolutional neural network-based encoder-decoder structure with a multiscale transformer to augment the feature representation of lesions in the global field of view. Transformer pathways, composed of memory- and computation-efficient dual-self-attention modules, leverage feature maps from varying depths of the encoder to discern long-range interdependencies among feature positions and channels, thereby emphasizing areas and channels relevant to lesions. The proposed model was trained and evaluated on a public-open dataset including MR images of 85 patients using both subject-level and voxel-level metrics. Experimental results indicate that our model offers superior performance both quantitatively and qualitatively. It successfully identified lesions in 82.4% of patients, with a low false-positive lesion cluster rate of 0.176 ± 0.381 per patient. Furthermore, the model achieved an average Dice coefficient of 0.410 ± 0.288, outperforming five established methods. Integration of the transformer could enhance the feature presentation and segmentation performance of FCD lesions. The proposed model has the potential to serve as a valuable assistive tool for physicians, enabling rapid and accurate identification of FCD lesions. The source code and pre-trained model weights are available at https://github.com/zhangxd0530/MS-DSA-NET . This multiscale transformer-based model performs segmentation of focal cortical dysplasia lesions, aiming to help radiologists and clinicians make accurate and efficient preoperative evaluations of focal cortical dysplasia patients from MR images. ","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"7 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142178901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}