BACKGROUND Multiple linear stapler firings during double stapling technique(DST)after laparoscopic low anterior resection(LAR)are associated with an increased risk of anastomotic leakage(AL).However,it is difficult to...BACKGROUND Multiple linear stapler firings during double stapling technique(DST)after laparoscopic low anterior resection(LAR)are associated with an increased risk of anastomotic leakage(AL).However,it is difficult to predict preoperatively the need for multiple linear stapler cartridges during DST anastomosis.AIM To develop a deep learning model to predict multiple firings during DST anastomosis based on pelvic magnetic resonance imaging(MRI).METHODS We collected 9476 MR images from 328 mid-low rectal cancer patients undergoing LAR with DST anastomosis,which were randomly divided into a training set(n=260)and testing set(n=68).Binary logistic regression was adopted to create a clinical model using six factors.The sequence of fast spin-echo T2-weighted MRI of the entire pelvis was segmented and analyzed.Pure-image and clinical-image integrated deep learning models were constructed using the mask region-based convolutional neural network segmentation tool and three-dimensional convolutional networks.Sensitivity,specificity,accuracy,positive predictive value(PPV),and area under the receiver operating characteristic curve(AUC)was calculated for each model.RESULTS The prevalence of≥3 linear stapler cartridges was 17.7%(58/328).The prevalence of AL was statistically significantly higher in patients with≥3 cartridges compared to those with≤2 cartridges(25.0%vs 11.8%,P=0.018).Preoperative carcinoembryonic antigen level>5 ng/mL(OR=2.11,95%CI 1.08-4.12,P=0.028)and tumor size≥5 cm(OR=3.57,95%CI 1.61-7.89,P=0.002)were recognized as independent risk factors for use of≥3 linear stapler cartridges.Diagnostic performance was better with the integrated model(accuracy=94.1%,PPV=87.5%,and AUC=0.88)compared with the clinical model(accuracy=86.7%,PPV=38.9%,and AUC=0.72)and the image model(accuracy=91.2%,PPV=83.3%,and AUC=0.81).CONCLUSION MRI-based deep learning model can predict the use of≥3 linear stapler cartridges during DST anastomosis in laparoscopic LAR surgery.This model might help determine the best anastomosis strategy by avoiding DST when there is a high probability of the need for≥3 linear stapler cartridges.展开更多
Similarity coefficient mapping（SCM） aims to improve the morphological evaluation of T＊2weighted magnetic resonance imaging（T＊2-w MRI）. However, how to interpret the generated SCM map is still pending. Moreover, ...Similarity coefficient mapping（SCM） aims to improve the morphological evaluation of T＊2weighted magnetic resonance imaging（T＊2-w MRI）. However, how to interpret the generated SCM map is still pending. Moreover, is it probable to extract tissue dissimilarity messages based on the theory behind SCM？ The primary purpose of this paper is to address these two questions. First, the theory of SCM was interpreted from the perspective of linear fitting. Then, a term was embedded for tissue dissimilarity information. Finally, our method was validated with sixteen human brain image series from multiecho T＊2-w MRI. Generated maps were investigated from signal-to-noise ratio（SNR） and perceived visual quality, and then interpreted from intra- and inter-tissue intensity. Experimental results show that both perceptibility of anatomical structures and tissue contrast are improved. More importantly, tissue similarity or dissimilarity can be quantified and cross-validated from pixel intensity analysis. This method benefits image enhancement, tissue classification, malformation detection and morphological evaluation.展开更多
This editorial elaborates on the current and future applications of linear endoscopic ultrasound(EUS),a substantial diagnostic and therapeutic modality for various anatomical regions.The scope of endosonographic asses...This editorial elaborates on the current and future applications of linear endoscopic ultrasound(EUS),a substantial diagnostic and therapeutic modality for various anatomical regions.The scope of endosonographic assessment is broad and,among other factors,allows for the evaluation of the mediastinal anatomy and related pathologies,such as mediastinal lymphadenopathy and the staging of central malignant lung lesions.Moreover,EUS assessment has proven more accurate in detecting small lesions missed by standard imaging examinations,such as computed tomography or magnetic resonance imaging.We focus on its current uses in the mediastinum,including lung and esophageal cancer staging,as well as evaluating mediastinal lymphadenopathy and submucosal lesions.The editorial also explores future perspectives of EUS in mediastinal examination,including ultrasound-guided therapies,artificial intelligence integration,advancements in mediastinal modalities,and improved diagnostic approaches for various mediastinal lesions.展开更多
Diffusion tensor imaging is a unique method to visualize white matter fibers three-dimensionally, non-invasively and in vivo, and therefore it is an important tool for observing and researching neural regeneration. Di...Diffusion tensor imaging is a unique method to visualize white matter fibers three-dimensionally, non-invasively and in vivo, and therefore it is an important tool for observing and researching neural regeneration. Different diffusion tensor imaging-based fiber tracking methods have been already investigated, but making the computing faster, fiber tracking longer and smoother and the details shown clearer are needed to be improved for clinical applications. This study proposed a new fiber tracking strategy based on tri-linear interpolation. We selected a patient with acute infarction of the right basal ganglia and designed experiments based on either the tri-linear interpolation algorithm or tensorline algorithm. Fiber tracking in the same regions of interest （genu of the corpus callosum） was performed separately. The validity of the tri-linear interpolation algorithm was verified by quan- titative analysis, and its feasibility in clinical diagnosis was confirmed by the contrast between tracking results and the disease condition of the patient as well as the actual brain anatomy. Statis- tical results showed that the maximum length and average length of the white matter fibers tracked by the tri-linear interpolation algorithm were significantly longer. The tracking images of the fibers indicated that this method can obtain smoother tracked fibers, more obvious orientation and clearer details. Tracking fiber abnormalities are in good agreement with the actual condition of patients, and tracking displayed fibers that passed though the corpus callosum, which was consistent with the anatomical structures of the brain. Therefore, the tri-linear interpolation algorithm can achieve a clear, anatomically correct and reliable tracking result.展开更多
背景:目前压缩感知技术用于踝关节MRI应用的报道较少。目的:探讨不同加速因子的压缩感知技术对踝关节常规2D-MRI图像质量和扫描时间的影响。方法:对24名健康志愿者(38个踝关节)在3.0T MR行常规2D-TSE序列扫描,基于敏感编码SENSE并行成...背景:目前压缩感知技术用于踝关节MRI应用的报道较少。目的:探讨不同加速因子的压缩感知技术对踝关节常规2D-MRI图像质量和扫描时间的影响。方法:对24名健康志愿者(38个踝关节)在3.0T MR行常规2D-TSE序列扫描,基于敏感编码SENSE并行成像(S组)和压缩感知技术(CS组),分别获取轴位T1WI(加速因子分别为S1.3、CS1.3、CS1.9、CS2.7)、矢状位PDWI加速因子分别为(S1.8、CS1.8、CS2.6、CS3.2)、冠状位PDWI(加速因子分别为S1.3、CS1.3、CS1.6、CS2.0)序列图像,每个序列的其他扫描参数保持一致。对踝关节图像的肌腱、软骨、韧带和肌肉结构进行5分主观评分,测量骨、软骨、韧带、肌腱、肌肉、积液、脂肪结构的背景噪声标准差和信号强度,计算其信噪比和对比噪声比,对不同加速因子成像的主观评分和客观评价进行统计学分析,每个序列图像质量以SENSE组(S组)为标准参考。结果与结论:(1)当加速因子相同时,常规序列S组和CS组的主观评分、信噪比和对比噪声比差异均无显著性意义(P>0.05);(2)当CS(轴位T1WIA)、CS(矢状位PDWI)、CS(冠状位PDWI)序列的加速因子分别在1.9、2.6、1.6时,软骨、肌腱、韧带等踝关节主要观察结构的图像质量均无明显差异(P>0.05),扫描时间分别是1 min 32 s、1 min 42 s、1 min 48 s;当CS(轴位T1WI)、CS(矢状位PDWI)、CS(冠状位PDWI)序列的加速因子分别增加至2.7、3.2、2.0时,所有解剖结构的主观评分分数仍大于3分,但每个序列均出现信噪比和对比噪声比显著下降(P<0.05);(3)结果提示,当加速因子相同时,CS组获取的图像质量总体高于S组;当加速因子增大时,CS序列的扫描时间逐渐减少,图像质量也随之降低;在3.0T MR仪上,压缩感知技术应用在常规2D序列轴位T1WI、矢状位PDWI和冠状位PDWI时,分别推荐1.9、2.6、1.6加速因子,整体时间可减少约27%(1 min 53 s)。展开更多
基金Shanghai Jiaotong University,No.YG2019QNB24This study was reviewed and approved by Ruijin Hospital Ethics Committee(Approval No.2019-82).
文摘BACKGROUND Multiple linear stapler firings during double stapling technique(DST)after laparoscopic low anterior resection(LAR)are associated with an increased risk of anastomotic leakage(AL).However,it is difficult to predict preoperatively the need for multiple linear stapler cartridges during DST anastomosis.AIM To develop a deep learning model to predict multiple firings during DST anastomosis based on pelvic magnetic resonance imaging(MRI).METHODS We collected 9476 MR images from 328 mid-low rectal cancer patients undergoing LAR with DST anastomosis,which were randomly divided into a training set(n=260)and testing set(n=68).Binary logistic regression was adopted to create a clinical model using six factors.The sequence of fast spin-echo T2-weighted MRI of the entire pelvis was segmented and analyzed.Pure-image and clinical-image integrated deep learning models were constructed using the mask region-based convolutional neural network segmentation tool and three-dimensional convolutional networks.Sensitivity,specificity,accuracy,positive predictive value(PPV),and area under the receiver operating characteristic curve(AUC)was calculated for each model.RESULTS The prevalence of≥3 linear stapler cartridges was 17.7%(58/328).The prevalence of AL was statistically significantly higher in patients with≥3 cartridges compared to those with≤2 cartridges(25.0%vs 11.8%,P=0.018).Preoperative carcinoembryonic antigen level>5 ng/mL(OR=2.11,95%CI 1.08-4.12,P=0.028)and tumor size≥5 cm(OR=3.57,95%CI 1.61-7.89,P=0.002)were recognized as independent risk factors for use of≥3 linear stapler cartridges.Diagnostic performance was better with the integrated model(accuracy=94.1%,PPV=87.5%,and AUC=0.88)compared with the clinical model(accuracy=86.7%,PPV=38.9%,and AUC=0.72)and the image model(accuracy=91.2%,PPV=83.3%,and AUC=0.81).CONCLUSION MRI-based deep learning model can predict the use of≥3 linear stapler cartridges during DST anastomosis in laparoscopic LAR surgery.This model might help determine the best anastomosis strategy by avoiding DST when there is a high probability of the need for≥3 linear stapler cartridges.
基金Project supported in part by the National High Technology Research and Development Program of China(Grant Nos.2015AA043203 and 2012AA02A604)the National Natural Science Foundation of China(Grant Nos.81171402+8 种基金61471349and 81501463)the Innovative Research Team Program of Guangdong Province,China(Grant No.2011S013)the Science and Technological Program for Higher Education,Science and Researchand Health Care Institutions of Guangdong ProvinceChina(Grant No.2011108101001)the Natural Science Foundation of Guangdong Province,China(Grant No.2014A030310360)the Fundamental Research Program of Shenzhen City,China(Grant No.JCYJ20140417113430639)Beijing Center for Mathematics and Information Interdisciplinary Sciences,China
文摘Similarity coefficient mapping（SCM） aims to improve the morphological evaluation of T＊2weighted magnetic resonance imaging（T＊2-w MRI）. However, how to interpret the generated SCM map is still pending. Moreover, is it probable to extract tissue dissimilarity messages based on the theory behind SCM？ The primary purpose of this paper is to address these two questions. First, the theory of SCM was interpreted from the perspective of linear fitting. Then, a term was embedded for tissue dissimilarity information. Finally, our method was validated with sixteen human brain image series from multiecho T＊2-w MRI. Generated maps were investigated from signal-to-noise ratio（SNR） and perceived visual quality, and then interpreted from intra- and inter-tissue intensity. Experimental results show that both perceptibility of anatomical structures and tissue contrast are improved. More importantly, tissue similarity or dissimilarity can be quantified and cross-validated from pixel intensity analysis. This method benefits image enhancement, tissue classification, malformation detection and morphological evaluation.
文摘This editorial elaborates on the current and future applications of linear endoscopic ultrasound(EUS),a substantial diagnostic and therapeutic modality for various anatomical regions.The scope of endosonographic assessment is broad and,among other factors,allows for the evaluation of the mediastinal anatomy and related pathologies,such as mediastinal lymphadenopathy and the staging of central malignant lung lesions.Moreover,EUS assessment has proven more accurate in detecting small lesions missed by standard imaging examinations,such as computed tomography or magnetic resonance imaging.We focus on its current uses in the mediastinum,including lung and esophageal cancer staging,as well as evaluating mediastinal lymphadenopathy and submucosal lesions.The editorial also explores future perspectives of EUS in mediastinal examination,including ultrasound-guided therapies,artificial intelligence integration,advancements in mediastinal modalities,and improved diagnostic approaches for various mediastinal lesions.
基金supported by the National Natural Science Foundation of China,No.60703045
文摘Diffusion tensor imaging is a unique method to visualize white matter fibers three-dimensionally, non-invasively and in vivo, and therefore it is an important tool for observing and researching neural regeneration. Different diffusion tensor imaging-based fiber tracking methods have been already investigated, but making the computing faster, fiber tracking longer and smoother and the details shown clearer are needed to be improved for clinical applications. This study proposed a new fiber tracking strategy based on tri-linear interpolation. We selected a patient with acute infarction of the right basal ganglia and designed experiments based on either the tri-linear interpolation algorithm or tensorline algorithm. Fiber tracking in the same regions of interest （genu of the corpus callosum） was performed separately. The validity of the tri-linear interpolation algorithm was verified by quan- titative analysis, and its feasibility in clinical diagnosis was confirmed by the contrast between tracking results and the disease condition of the patient as well as the actual brain anatomy. Statis- tical results showed that the maximum length and average length of the white matter fibers tracked by the tri-linear interpolation algorithm were significantly longer. The tracking images of the fibers indicated that this method can obtain smoother tracked fibers, more obvious orientation and clearer details. Tracking fiber abnormalities are in good agreement with the actual condition of patients, and tracking displayed fibers that passed though the corpus callosum, which was consistent with the anatomical structures of the brain. Therefore, the tri-linear interpolation algorithm can achieve a clear, anatomically correct and reliable tracking result.
文摘背景:目前压缩感知技术用于踝关节MRI应用的报道较少。目的:探讨不同加速因子的压缩感知技术对踝关节常规2D-MRI图像质量和扫描时间的影响。方法:对24名健康志愿者(38个踝关节)在3.0T MR行常规2D-TSE序列扫描,基于敏感编码SENSE并行成像(S组)和压缩感知技术(CS组),分别获取轴位T1WI(加速因子分别为S1.3、CS1.3、CS1.9、CS2.7)、矢状位PDWI加速因子分别为(S1.8、CS1.8、CS2.6、CS3.2)、冠状位PDWI(加速因子分别为S1.3、CS1.3、CS1.6、CS2.0)序列图像,每个序列的其他扫描参数保持一致。对踝关节图像的肌腱、软骨、韧带和肌肉结构进行5分主观评分,测量骨、软骨、韧带、肌腱、肌肉、积液、脂肪结构的背景噪声标准差和信号强度,计算其信噪比和对比噪声比,对不同加速因子成像的主观评分和客观评价进行统计学分析,每个序列图像质量以SENSE组(S组)为标准参考。结果与结论:(1)当加速因子相同时,常规序列S组和CS组的主观评分、信噪比和对比噪声比差异均无显著性意义(P>0.05);(2)当CS(轴位T1WIA)、CS(矢状位PDWI)、CS(冠状位PDWI)序列的加速因子分别在1.9、2.6、1.6时,软骨、肌腱、韧带等踝关节主要观察结构的图像质量均无明显差异(P>0.05),扫描时间分别是1 min 32 s、1 min 42 s、1 min 48 s;当CS(轴位T1WI)、CS(矢状位PDWI)、CS(冠状位PDWI)序列的加速因子分别增加至2.7、3.2、2.0时,所有解剖结构的主观评分分数仍大于3分,但每个序列均出现信噪比和对比噪声比显著下降(P<0.05);(3)结果提示,当加速因子相同时,CS组获取的图像质量总体高于S组;当加速因子增大时,CS序列的扫描时间逐渐减少,图像质量也随之降低;在3.0T MR仪上,压缩感知技术应用在常规2D序列轴位T1WI、矢状位PDWI和冠状位PDWI时,分别推荐1.9、2.6、1.6加速因子,整体时间可减少约27%(1 min 53 s)。
