To evaluate the efficacy of the “double-low” scanning protocol combined with the artificial intelligence iterative reconstruction (AIIR) algorithm for abdominal computed tomography (CT) enhancement in obese patients and to identify the optimal AIIR algorithm level. Patients with a body mass index ≥ 30.00 kg/m2 who underwent abdominal CT enhancement were randomly assigned to groups A or B. Group A underwent conventional protocol with the Karl 3D iterative reconstruction algorithm at levels 3–5. Group B underwent the “double-low” protocol with AIIR algorithm at levels 1–5. Radiation dose, total iodine intake, along with subjective and objective image quality were recorded. The optimal reconstruction levels for arterial-phase and portal-venous-phase images were identified. Comparisons were made in terms of radiation dose, iodine intake, and image quality. Overall, 150 patients with obesity were collected, and each group consisted of 75 cases. Karl 3D level 5 was the optimal algorithm level for group A, while AIIR level 4 was the optimal algorithm level for group B. AIIR level 4 images in group B exhibited significantly superior subjective and objective image quality than those in Karl 3D level 5 images in group A (P < 0.001). Group B showed reductions in mean CT dose index values, dose-length product, size-specific dose estimate based on water-equivalent diameter, and total iodine intake, compared with group A (P < 0.001). The “double-low” scanning protocol combined with the AIIR algorithm significantly reduces radiation dose and iodine intake during abdominal CT enhancement in obese patients. AIIR level 4 is the optimal reconstruction level for arterial-phase and portal-venous-phase in this patient population.
该文报道1例胃壁异位支气管源性囊肿和肠源性囊肿患者。男性，23岁，CT表现为左侧肾上腺区与胃间见1个类圆形囊性低密度病变，胃大弯侧另见1个类圆形囊性低密度病变，增强扫描囊壁轻度强化。病理诊断分别为黏膜下支气管源性囊肿和黏膜固有肌内肠源性囊肿。
Purpose: To develop predictive models based on the integration of radiomics with the Vesical Imaging-Reporting and Data System (VIRADS) for determining muscle invasion of bladder cancer. Materials and Methods: One hundred ninety-one patients were retrospectively included in this study from January 2015 to March 2022. Of these, 121 data were randomly divided into training and validation sets at a ratio of 7:3. The remaining data (n = 70) served as the independent testing set. The radiomics features were extracted from bladder cancer on high-b-value DWI images. The pipelines of radiomics models were trained in the training set. One optimal model was selected based on the performance in the validation set. Then, the selected model was tested in the independent testing set. Two radiologists evaluated the VI-RADS based on T2WI and DWI. Reader 1 was an experienced reader, and Reader 2 was an inexperienced reader. A clinical-radiomics model was built by integrating the radiomics signature and VI-RADS. The performance was assessed using receiver operating characteristic curve analysis. The histopathological results were used as the standard reference to assess the diagnostic accuracy of muscle invasion. Results: The radiomics model had area under the curve (AUC) values of 0.801, 0.867, and 0.806 in the training, validation, and testing sets, respectively. The VI-RADS scores of Readers 1/2 yielded AUC values of 0.831/0.781, 0.909/0.815, and 0.871/0.776 in the training, validation, and testing sets, respectively. The clinical-radiomics model for Readers 1/2 revealed AUC values of 0.889/0.854, 0.961/0.919, and 0.881/0.844 in the training, validation, and testing sets, respectively. The performance of the clinical-radiomics model was improved compared to the VI-RADS score for inexperienced Reader 2 (P < 0.05). Conclusion: The radiomics model was useful in the diagnosis of muscle invasion of bladder cancer. The clinical-radiomics model integrating radiomics and VI-RADS further improved the performance compared to VI-RADS alone, which was helpful for readers with less diagnostic experience. & COPY; 2022 Elsevier Inc. All rights reserved.
Objective:To investigate the value of intratumoral and peritumoral radiomics features of multi-parameter MRI in evaluation of the status of human epithelial growth factor receptor 2 in breast cancer.Methods:The clinical, pathological and imaging data of 340 patients with pathologically confirmed breast cancer in Henan Provincial People′s Hospital from September 2019 to December 2020 were retrospectively collected. All patients were female, 48 (42, 55) years old. All patients underwent multi-parameter breast MRI before surgery, including dynamic contrast-enhanced T 1WI (DCE-T 1WI), fat-suppressed T 2WI (T 2WI) and diffusion-weighted imaging (DWI). The region of interest (ROI) for lesions were manually delineated and the segmented ROIs were zoomed in ring shape by 4 mm to acquire ROI intra and ROI prei, respectively. Then six sets of radiomics features were extracted from ROI intra and ROI prei of DCE-T 1WI, T 2WI and DWI. The cases were divided into a training set (272 cases) and a test set (68 cases) by stratified sampling at a ratio of 4∶1. The Mann-Whitney U test, Select K Best and minimum absolute contraction and selection operator were used for feature selection of the 6 sets of radiomics features. The feature subsets after reduction were used to construct independent and combined radiomics signatures with support vector machine algorithm to predict the HER2 status of breast cancer. Receiver operating characteristic curve was generated and area under curve (AUC) was calculated to compare the prediction performance of different models. Results:Of the 340 patients, 80 were HER2-positive and 260 were HER2-negative. Among the radiomics signatures based on single sequence, the DWI peri showed the best performance in predicting HER2 status of breast cancer, with an AUC of 0.678 for the test set. Among the combination of intratumoral and peritumoral radiomics signatures based on same sequence, the DWI intra+DWI peri had the highest prediction value, achieving an AUC of 0.774 for the testing set. Among the intratumoral or peritumoral radiomics signatures derived from two different sequences, the DCE-T 1WI intra+DWI intra and T 2WI peri+DWI peri showed the best predictive performance, yielding AUC of 0.766 and 0.769 in the testing set, respectively. Among the combination of intratumoral or peritumoral radiomics signatures derived from all 3 sequences or combinations of all features, the DCE-T 1WI intra+T 2WI intra+DWI intra+DCE-T 1WI peri+T 2WI peri+DWI peri obtained the highest prediction efficiency, with an AUC of 0.913 for the testing set. Conclusion:The radiomics features of intratumoral and peritumoral regions based on multi-parameter MRI have a certain value in non-invasive evaluation of HER2 status of breast cancer, which can help clinicians to provide scientific basis for decision-making of targeted therapy in patients with breast cancer.
Objective:To investigate the changes in gray matter volume of the subsystems as well as intra-subsystem and inter-subsystem functional connectivity in the default mode network (DMN) of relapsing-remitting multiple sclerosis (RRMS) patients with preserved cognitive function.Methods:In this prospective study, thirty-seven RRMS patients with preserved cognitive function who were admitted to Huashan Hospital of Fudan University from April 2020 to January 2021 (RRMS group) and 43 healthy volunteers (HC group) were recruited. Patients in the RRMS group received the cognitive assessment using a clinical cognitive functioning scale. Three-dimensional T 1WI and resting-state functional MRI were performed to obtain the brain structural and functional data. The DMN was divided into three subsystems: CORE, dorsal medial prefrontal cortex (DMPFC), and medial temporal lobe (MTL). The gray matter volume of the three subsystems were extracted from the gray matter volume map generated by spatial normalization; 24 regions of interest (ROIs) of the DMN were defined based on Yeo′s 17 networks, and their functional connectivity values were calculated to derive the mean intra-subsystem and inter-subsystem functional connectivity values. Differences in gray matter volume and functional connectivity between the RRMS and HC groups were compared using independent sample t-tests; Spearman′s partial correlation was used to analyze the correlation between subsystems′ gray matter volume and functional connectivity, as well as between subsystems′ functional connectivity and clinical scale scores. Results:Compared to the HC group, the gray matter volume of the three subsystems of the DMN were considerably reduced in the RRMS group ( P<0.05). The functional connectivity within and between the three subsystems were not statistically significantly different between the HC and RRMS groups ( P>0.05). Based on the ROI analysis, patients with RRMS the brain regions with significantly reduced DMN intra-subsystem functional connectivity values were mainly located in the left dorsomedial prefrontal cortex of the DMPFC, the right lateral temporal cortex of the DMPFC, and the left medial temporal cortex of the MTL, as compared with the HC group ( P<0.01). The gray matter volume of DMPFC was positively correlated with the functional connectivity within DMPFC in the control group ( r=0.326, P=0.040). In the RRMS group, the gray matter volume of CORE was positively correlated with the functional connectivity between CORE and DMPFC ( r=0.363, P=0.038), and the functional connectivity within CORE was positively correlated with scores on the memory and executive screening scale ( r=0.430, P=0.036). Conclusions:RRMS patients with preserved cognitive function exhibit gray matter atrophy in all three DMN subsystems. There is no correlation between the structure and function of the DMPFC subsystem. The functional connectivity within CORE subsystem may reflect memory and execution status; DMPFC and CORE may be critical encephalic regions for neurodegeneration and brain functional changes in RRMS patients with preserved cognitive function.
新型冠状病毒（COVID-19）可导致心肌损伤，包括暴发性心肌炎、恶性心律失常、心肌梗死和心源性猝死等。COVID-19感染后出现胸闷、心悸、胸痛等症状，应及时行心电图、超声心动图和心肌酶三联检查来筛查是否存在心肌损伤并初步判断病因。心脏MRI检查在进一步明确诊断、判断病情动态演变及危险分层等方面发挥关键作用。本文将概括COVID-19相关心肌损伤的病因分类、致病机制及病理基础，并重点介绍不同类型COVID-19相关心肌损伤的MRI表现。
Objective:To investigate the value of a deep learning method based on MobileNet in classification of bedside chest radiograph and improvement of the work efficiency.Methods:A total of 6, 320 bedside chest radiographs from January 2017 to December 2022 in the Second Peoples′ Hospital of Changzhou were retrospectively collected. The included cases were divided into normal group (885 images), pneumonia group (1 927 images), pleural effusion group (373 images), and pneumonia with pleural effusion group (3 135 images). Three hundred and fifty images were selected as a validation set, while the remaining images were divided into a train set (4 775 images) and a test set (1 195 images) using simple randomization, by 8∶2 ratio. Two lightweight convolutional neural network models (MobileNetV1 and MobileNetV2) were used to construct a bedside chest radiograph classification model, based on which two fine-tuning strategies were designed. Four models were generated namely MobileNetV1_False (V1_False), MobileNetV1_True (V1_True), MobileNetV2_False (V2_False) and MobileNetV2_True (V2_True). In the first stage, a binary classification model was established to divide the images into normal and lesion groups; then a four-class classification model was established in the second stage, with which the images were divided into four groups: normal, pneumonia, pleural effusion and pneumonia with pleural effusion. Metrics for model performance evaluation including accuracy (Ac), precision (Pr), recall rate (Rc), F1 score (F1) and area under the receiver operating characteristic curve (AUC) were calculated.Results:In both the first and second stages, V1_True and V2_True had higher Ac, Pr, Rc, and F1 than V1_False and V2_False in both the training set and validation set; and the V1_True model outperformed the other three models in classification. The classification Ac of the V1_True model in the validation set was higher than that of radiologists in the first stage [95.71% (335/350) vs. 90.29% (316/350)] and in the second stage [93.43% (327/350) vs. 87.14% (305/350)]. The recognition time of V1_True model′s in the validation set of 350 bedside chest radiographs was significantly less than that of the radiologists (mean: 17 s vs. 300 min).Conclusions:V1_True is an optimal MobileNet model for classifying bedside chest radiographs. The application of this model in clinical practice may help to accurately identify the information of lung lesions from bedside chest radiographs in time, and may improve the work efficiency in the radiology department.
回顾性分析2021年2月至2023年2月在河南省人民医院确诊为黏液样胶质神经元肿瘤的4例患者的临床病理及影像学资料。患者中男3例、女1例。1例病灶位于鞍区、1例位于左侧脑室旁、1例位于四叠体池旁、1例位于胼胝体。CT显示病灶呈囊实性改变，以囊性为主，边缘见少许钙化；4例MRI显示病灶囊性部分呈T 1WI低信号和T 2WI高信号改变，液体衰减反转恢复序列呈等高信号，囊性部分内见条状、线状或结节状T 1WI等信号，增强扫描实质部分呈明显强化。病灶囊性成分病理为黏液样成分，呈黏液湖改变，实性成分为漂浮在黏液之中的少突胶质样细胞和小血管，部分病灶可见钙化成分。病灶的强化程度取决于实性成分占比及血供。
Objective:To investigate the effect of calcification on the diagnostic accuracy of the quantitative flow fraction (CT-QFR) derived from coronary CT angiography (CCTA).Methods:A total of 244 patients (471 coronary arteries) who underwent both CCTA and invasive coronary angiography (ICA) for suspected coronary artery disease between 2019 and 2021 were included in the study. All analyses were conducted at the vessel level using CCTA and ICA images, and the morphological and hemodynamic parameters of all enrolled vessels were assessed. The group was divided into severe calcification (206 cases) and non-severe calcification (265 cases) based on whether the arc of lesion calcification was greater than 180°. Subsequently, the two groups were evaluated to the degree of coronary stenosis, the length of the target lesion, the length of calcification, the ratio of the length of calcification, the remodeling index of calcification, the quantitative flow fraction (QFR), the CT-QFR, and the distribution of the involved vessels. Pearson correlation analysis and the Bland-Altman scatterplot were used to analyze the correlation and consistency between CT-QFR and QFR values from different subgroups. The benchmark for coronary ischemia was QFR≤0.80, and the criteria for diagnosing coronary ischemia were CT-QFR≤0.80 and luminal stenosis≥50%, respectively, and the effectiveness of CT-QFR for coronary ischemia was evaluated by plotting the ROC curves in various calcification subgroups.Results:The degree of luminal stenosis, lesion length, calcification length ratio, and calcification remodeling index were substantially higher in the severely calcified group than in the non-severely calcified group (all P<0.05). The results of the Pearson correlation analysis demonstrated a significant association between CT-QFR and QFR in both the severe and non-severe calcification groups ( r=0.85, 95%CI 0.81-0.88, P<0.001; r=0.91, 95%CI 0.89-0.93, P<0.001); in contrast, the Bland-Altman analysis indicated that the CT-QFR and QFR measurements in the severely calcified group exhibited a high level of agreement, with a mean difference of -0.01 (95% limits of agreement -0.22 to 0.20) for measurements in the severely calcified group and 0 (95% limits of agreement -0.15 to 0.16). The specificity, positive predictive value, negative predictive value, and area under the curve (AUC) for the diagnosis of ischaemic lesions by CT-QFR and CCTA alone were lower in the severely calcified group than in the non-severely calcified group, but the difference in AUC between the two groups for CT-QFR was not statistically significant ( P>0.05), and the difference in AUC for the morphological assessment of CCTA was statistically significant. The diagnostic effectiveness of CCTA alone was considerably worse than the specificity and AUC of CT-QFR for the various calcified subgroups for the diagnosis of ischemic lesions (all P<0.001). Conclusions:Severe calcification somewhat affected the diagnosis of ischaemic lesions by CT-QFR, but there was still a high correlation and concordance between CT-QFR and QFR within the severely calcified group, and the diagnostic efficacy was significantly better than that assessed by CCTA morphology alone.
Objective:To access the efficacy of monoenergetic imaging from spectral CT combined with metal artifact reduction for orthopedic implants (O-MAR) on reducing contrast hardening artifacts in the vein on the injection side, and determining the optimal monoenergetic spectral range to improve the display of axillary lymph node.Methods:A total of 35 patients with breast cancer who underwent chest-enhanced CT scans were enrolled in this retrospective study. The original data were reconstructed to obtain a total of 35 sets of images, including one conventional image, 17 groups of monoenergetic images, and 17 groups of monoenergetic+O-MAR images. The areas of interest were delineated in the high and low-density artifact area on the injection side of the same layer contrast agent, and the contralateral ectopectoralis. The CT value and its standard deviation (SD) were recorded respectively, the artifact area was measured, and the number of axillary lymph nodes was recorded. The difference in CT values (ΔCT 1, ΔCT 2) and the artifact index (AI1 and AI 2) of the high and low-density artifact areas relative to the contralateral ectopectoralis in the same layer were calculated respectively. Friedman test and Wilcoxon signed-rank test were used to compare the differences of ΔCT, AI, artifact area, and number of lymph nodes among the three imaging modalities, and the Kappa test was used to compare the differences in subjective evaluation. Results:As the energy level increased, compared to the conventional image, monoenergetic image, ΔCT 1 absolute value, ΔCT 2 absolute value, AI 1, and AI 2 showed a trend of initially low and then high, artifact area decreased, and the number of detected lymph nodes increased ( P<0.01). Compared to other energy levels, when the monoenergetic image was 100 keV, ΔCT 1 value, 140 keV for ΔCT 2 value, 120 keV for AI 1 value, and 130 keV for AI 2 value were close to zero, and the number of detected lymph nodes was highest at 110-200 keV. In contrast, in the monoenergetic+O-MAR images, ΔCT 1 absolute value showed a trend of initially low and then high, but, ΔCT 2 absolute value, AI 1, AI 2, and artifact area all significantly decreased, whereas the number of detected lymph nodes significantly increased (χ 2 values were 916.23, 895.93, 387.08, 519.41, 890.10, and 1027.98, respectively. All P<0.01). Compared to other energy levels, when the monoenergetic+O-MAR image was at 100 keV, ΔCT 1 value was close to zero, while ΔCT 2 value became close to zero with increasing energy level, and the number of detected lymph nodes was highest at 110-200 keV. As the energy level increased, the ΔCT 1, AI 1, AI 2, and artifact area of monoenergetic+O-MAR images were significantly smaller than those of monoenergetic images at the same energy level, and the number of detected lymph nodes was significantly higher than that of monoenergetic images ( P<0.01). The subjective scores for 110-200 keV monoenergetic images and 100-200 keV monoenergetic+O-MAR images were both higher than 4, and the score for monoenergetic+O-MAR images was significantly higher than that of single-energy spectrum images. The agreement between the two radiologists in assessing subjective scores was good. Conclusion:At 100-120 keV level, spectral CT monoenergetic combined with O-MAR imaging technique has the best performance in removing hardening-induced artifacts of chest-enhanced CT contrast agent and detecting and displaying axillary lymph nodes.
人工智能越来越多地应用于图像后处理、疾病诊断与预后评估。最近，以ChatGPT为代表的大型语言模型在自然语言处理领域取得了重大进展，这引起了人们对其在医疗环境中应用的兴趣和担忧。本文从医学影像学的视角讨论如何在医疗环境中利用大型语言模型，以及它们在提高影像学实践、研究和教学工作的效率及有效性方面的潜力。同时，将聚焦大型语言模型在未来实际应用过程中的一些局限性。
马拉松运动日益流行，其导致的运动损伤也越来越常见。但目前对马拉松运动损伤的影像学研究集中在运动后损伤方面，运动损伤的防范和损伤风险筛查方面的研究相对少见，大样本的研究更为缺乏。中国马拉松运动参与者众多，应重视运动前风险筛查与防范，为马拉松跑者的运动安全保驾护航。
专家引言：脑血管疾病如脑梗死、脑动脉瘤是全球范围内困扰人类健康、带来社会医疗负担的重要议题。近年来，四川省人民医院放射科王玉婷教授团队围绕脑血管影像学开展了一系列的研究，一方面在缺血性疾病如脑梗死领域，探索急性和慢性期不同的影像学任务；另一方面在出血性疾病如脑动脉瘤领域，开展人群研究和新型标志物的探索和验证。基于较长期的系统性研究，开发验证了一系列较关键的影像标志物，可应用于脑梗死、脑动脉瘤等血管疾病的临床管理全流程的多个场景（病因诊断、预后预测、破裂出血风险评估、复发风险评估、药物疗效监测等），为辅助脑血管疾病的诊疗提供了影像学依据。北京天坛医院刘亚欧教授指出，脑血管病作为全球范围内导致成人死亡和残疾的重要原因，如何减少其发病、改善预后并减少复发是目前的重要研究方向。王玉婷教授团队从西南人群入手，着眼于脑血管病领域的临床关键问题，从临床需求出发进行研究设计，进行了一系列临床影像研究，并与国内外研究团队深入合作，将影像学新技术应用于脑卒中病因探索、临床预后预测、疾病复发风险评估、药物疗效监测等方面，使影像研究结果能够应用于临床实践。
Objective:To explore the distribution of perihip stress in male marathon runners and normal volunteers during the gait cycle using finite element analysis.Methods:In November 2022, the hip joint CT image data of a male marathon runner and a male healthy volunteer was collected. The gait cycle was divided into 7 phases, including the foot to the ground, the early, middle, and late period of one-foot support, the toe push and extension period, and the beginning and end of the swing. A three-dimensional finite element model was constructed based on the gray value of CT and according to the individualized material attribute assigning scheme. The dynamic change of hip joint contact force during the gait cycle was applied to the load, and the biomechanical qualitative analysis of the model was carried out through the equivalent stress results. Based on qualitative analysis, the same 10 nodes on the front, middle, and back of the model acetabulum, the front, and back of the femoral head, and the neck of the femur were extracted as equivalent stress reference points, and the biomechanical quantitative analysis of the model was carried out.Results:During the gait cycle, according to the hip equivalent stress distribution diagram, the contact on the articular surface of the two subjects mainly occurred in the posterior upper part of the acetabulum and the posterior part of the femoral head and gradually moved outward and backward with the joint force during walking. From the point of view of the stress distribution nodes around the hip joint, the stress changes of the nodes in each part of the two subjects showed bimodal characteristics with the load inside the hip joint at each phase. The internal stress value of the hip joint showed a gradual upward trend from the foot to the ground, reached the maximum stress in the middle period of one-foot support, then decreased, and slightly increased in the toe push and extension period. Then, the stress values at the beginning and end of the swing gradually decreased until entering the next gait cycle. The stress values in the posterior acetabulum and posterior femoral head of the marathon runner were smaller than those of the healthy volunteer.Conclusion:The stress around the hip joint was different between marathon runner and healthy volunteer, but the stress distribution and transmission path were the same.
Objective:To explore the value of high-resolution CT (HRCT) visual scores and quantitative analysis in assessing pulmonary Langerhans cell histiocytosis (PLCH) in adults.Methods:In total 51 adult patients with PLCH confirmed by pathology in Peking Union Medical College Hospital from August 2014 to December 2021 were retrospectively analyzed. All patients underwent HRCT and pulmonary function tests (PFT). The involvement of the nodular and cystic lesions were evaluated by two experienced radiologists using CT visual scores. The cases were divided into three groups based on the nodular scores, and into four groups based on the cystic scores, respectively. Ratio of low attenuation areas (LAA%) was measured by an automatic post-processing software. Pulmonary function indices including forced expiratory volume in the first second (FEV 1), forced vital capacity (FVC), FEV 1/FVC, diffusion capacity for carbon monoxide of lung (D LCO), alveolar ventilation (V A), D LCO/V A, D LCO corrected for hemoglobin (D LCOc), D LCOc/V A were collected. FEV 1/FVC was expressed as measured values and other indices were expressed as percent predicted (%pred). Spearman correlation analysis was used to evaluate the correlation between HRCT visual scores, LAA% and PFT. The lung function indices among different nodular groups as well as among different cystic groups were compared using the Kruskal‐Wallis test. Results:Both nodular and cystic lesions were found on HRCT images of all 51 patients. There were no correlation between the visual scores of nodular lesions and lung function indices (all P>0.05). There were no significant differences in lung function indices among different nodular groups (all P>0.05). The visual scores of cystic lesions were negatively correlated with FEV 1/FVC, D LCO%pred, D LCO/V A%pred, D LCOc%pred, D LCOc/V A%pred ( r=-0.491, -0.347, -0.330, -0.373, -0.346, respectively, all P<0.05); the pulmonary function indices among different cystic groups had significant difference (all P<0.05). LAA% were negatively correlated with FEV 1/FVC, D LCO%pred, D LCO/V A%pred, D LCOc%pred, D LCOc/V A%pred ( r=-0.278, -0.378, -0.418, -0.395, -0.451, respectively, all P<0.05). Conclusion:HRCT visual scores of nodular lesions do not correlate with lung function in patients with PLCH. Visual scores and quantitative analysis of the cystic lesions can reflect the impairment degree of pulmonary ventilation and diffusion function to a certain extent, and may be used in assessment of patients with PLCH.
Objective:To investigate the impact of the deep learning reconstruction algorithm TrueFidelity TM for Gemstone Spectral Imaging (TF-GSI) and the adaptive statistical iterative reconstruction algorithm (ASiR-V, hereinafter referred to as ASiR-V) based on phantom and animal models on the image quality of dual-energy CT images. Methods:GE Revolution Apex CT was used to scan the ACR 464 phantom and a mouse model of gastric cancer with lymph node metastasis ( n=16). TF-GSI and ASiR-V were separately used to reconstruct middle and high-grade images (TF-GSI-M, TF-GSI-H, ASiR-V-50%, and ASiR-V-100%) on the phantom and mouse based on virtual monoenergetic images at 70 keV. The task transfer function (TTF) of bone and acrylic, image noise power spectrum (NPS), and detectability index (d′) of the phantom images were evaluated. One-way ANOVA analysis was used to compare the image noise, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) for brain and liver on images of mice. The consistency of the two reconstruction-algorithm images (TF-GSI-H and ASiR-V100%) in the detection of small lesions by two radiologists (A and B) was evaluated using kappa test. Results:In terms of the phantom, the TF-GSI-H group had the best performance in TTF, NPS, and d′. Compared to ASiR-V-100%, the TTF50% of bone and acrylic in the TF-GSI-H group increased by 2.4% and 8.9%, respectively; the NPS peak decreased by 54.1%, compared to ASiR-V-100%; the d′ of bone and acrylic in the TF-GSI-H group relative to ASiR-V-100% increased by 52.7% and 59.5%, respectively. The TF-GSI group had reduced image noise compared to the ASiR-V group, and both SNR and CNR of the two tissues increased, but the differences between the groups were not statistically significant (all P>0.05). The two reconstruction-algorithm images showed good consistency in image evaluation by the two radiologists (A, Kappa=0.875, P<0.001; B, Kappa=0.625, P=0.012). In terms of the detection of micro-metastases in mice, the TF-GSI group outperformed the ASiR-V group (average accuracy: 83.5% vs 71.9%; average sensitivity: 77.8% vs 61.2%; average specificity: 85.7% vs 85.7%). Conclusion:Compared with iterative reconstruction algorithm, the DLIR algorithm showed improved spatial resolution, reduced image noise, and enabled detectability of micro-lesion for images from dual-energy CT.
Objective:To evaluate the calf muscle cross-sectional area (CSA) and fat fraction (FF) in amateur marathon runners based on T 2WI-Flex fat-suppression sequence and iterative decomposition of water and fat with echo asymmetrical and least-squares estimation quantitation sequence (IDEAL-IQ) technique, and to explore the correlation between them and age, body mass index (BMI), running age, speed exercise, and monthly exercise. Methods:From September to November 2022, 37 amateur marathon group (marathon group) and 12 healthy volunteers (control group) who did not often exercise were recruited in Shijiazhuang. T 2WI-Flex fat-suppression sequence and IDEAL-IQ sequence were performed on all subjects. The anterior group of the right calf (tibialis anterior, extensor hallucis longus, extensor digital longus), external group of the calf (peroneus longus, peroneus brevis), soleus, medial head and lateral head of the gastrocnemius were delineated along the edge of the muscle contour as regions of interest, and the CSA and FF values of the muscles were obtained. Twelve subjects matched with the clinical data of the control group were selected from the marathon group (marathon matched group), and the differences in CSA and FF were compared by independent sample t test or Mann-Whitney U test. Spearman correlation analysis was used to evaluate the correlation between CSA and FF values of the right calf and running-related indicators including age, BMI, running age, speed exercise, and monthly exercise in 37 amateur marathon runners. Results:The CSA of the right calf soleus muscle in the marathon matched matched group was larger than that in the control group ( t=-2.09, P=0.048). There was no significant difference in CSA of other calf muscles between the two groups ( P>0.05). The FF values of the right calf anterior group, the outer group, the soleus muscle, the medial head and the lateral head of the gastrocnemius muscle in the marathon matched group were lower than those in the control group ( P<0.05). The BMI of 37 amateur marathon runners was positively correlated with the CSA and FF values of the anterior group, the outer group, the soleus, the medial head and the lateral head of the gastrocnemius muscle (CSA: r=0.628, 0.468, 0.680, 0.566, 0.615, respectively, all P<0.05; FF value: r=0.395, 0.567, 0.631, 0.482, 0.516, respectively, all P<0.05). The FF values of the anterior group, the outer group, the soleus, the medial head and the lateral head of the gastrocnemius muscle were negatively correlated with monthly exercise ( r=-0.337, -0.405, -0.437, -0.338, -0.446, respectively, all P<0.05). Conclusions:Long-term running training can reduce the FF value of each calf muscle group, and the FF value of calf muscles is negatively correlated with the amount of running each month. Long-term running training has the greatest effect on the CSA of soleus muscle.
该文报道1例IgG4相关性疾病累及鼻咽部患者的影像学表现。患者女，65岁，影像学检查示右侧鼻咽部、垂体、海绵窦、硬脑膜、肺部支气管均有不同程度受累，表现为右侧鼻咽部肿胀，垂体肿大，垂体柄增粗，双侧海绵窦增宽，颅底硬脑膜较对称性增厚，右肺上叶尖段及下叶内基底段支气管壁明显增厚，PET-CT示相应病灶区域代谢增高。实验室检查血清IgG4未见升高，鼻咽部组织病理学可见弥漫淋巴细胞、浆细胞浸润，局灶散在异型细胞，纤维组织增生，IgG4（+），约30个/高倍视野，IgG4+/IgG+>40%。综合诊断为IgG4相关性疾病。
Objective:To establish and verify a nomogram model based on MRI liver imaging reporting and data system (LI-RADS) features for predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC) following the Milan criteria.Methods:A retrospective analysis was conducted on data from 118 HCC patients (121 lesions) confirmed by pathology from June 2016 to June 2022 at the Third Affiliated Hospital of Soochow University. Forty-seven HCCs were diagnosed as MVI-positive and 74 HCCs as MVI-negative. The data was randomly divided into the training set (83 patients with 84 HCCs, including 31 MVI-positive and 53 MVI-negative HCCs) and the test set (35 patients with 37 HCCs, including 16 MVI-positive and 21 MVI-negative HCCs) using cross-validation method. HCC imaging features were evaluated based on LI-RADS (version 2018). In the training set, the χ 2 test was used to compare the differences in LI-RADS features between the MVI-positive group and the MVI-negative group. The logistic regression analysis was conducted to identify independent risk factors for predicting MVI-positive and to construct the nomogram model. The receiver operating characteristic (ROC) curves and decision curve analysis (DCA) were used to evaluate the performance and clinical benefits of the nomogram model in predicting MVI tumors. Results:There were statistically significant differences between the MVI-positive group and the MVI-negative group in terms of tumor size, tumor margin, mosaic architecture, and corona enhancement ( P<0.05). Multivariate logistic analysis results showed that HCC maximum diameter>3 cm (OR=1.427, 95%CI 1.314-12.227, P=0.009), nonsmooth tumor margin (OR=3.167, 95%CI 1.227-461.232, P=0.041), mosaic architecture (OR=1.769, 95%CI 1.812-61.434, P=0.022), and corona enhancement (OR=4.015, 95%CI 3.327-836.384, P=0.011) were independent risk factors for predicting MVI-positive tumors. Based on the independent predictors, the constructed nomogram model demonstrated an area under the ROC curve of 0.863 (95%CI 0.768-0.947) and 0.887 (95%CI 0.804-0.987) in the training and test sets for predicting MVI tumors, respectively. DCA showed that the curve of the nomogram model was consistently above the treat-all and treat-none strategies across all reasonable threshold probabilities in the training set, indicating that patients could obtain clinical benefits from the model. Conclusions:The preoperative nomogram model based on MRI LI-RADS features can effectively predict MVI in HCC following the Milan criteria, which could benefit the patients.
Objective:To explore the value of ultra-short echo time magnetization transfer (UTE-MT) techniques for quantitatively dynamic monitoring of anterior patellar tendon (patellar tendon, quadriceps tendon) changes in amateur marathon runners before and after competition.Methods:Between October 2020 and January 2021, 23 amateur marathoners in Zhuhai, aged 28-50 (40±6) years, were prospectively recruited. Three-dimensional UTE-MT and dual-echo UTE-T 2* sequence scans of bilateral knee joints were performed before, 48 hours and 4 weeks after the marathon running, respectively. Another 5 non-running volunteers were recruited for verification of sequence stability. UTE-magnetization transfer ratio (MTR) and UTE-T 2* value of the patellar tendon, quadriceps tendon, and 3 tendon-bone insertion points (patellar tendon-tibial insertion point, patellar tendon-patellar insertion point, and quadriceps tendon-patellar insertion point) were measured independently on sagittal images of the knee joint by 2 radiologists. The stability of the 2 serial measurements and consistency tests between the 2 radiologists were assessed with a two-way mixed intraclass correlation coefficient (ICC). Repeated-measures analysis of variance was used to compare the differences in UTE-MTR and UTE-T 2* values of the prepatellar tendon before and after the marathon running. Results:Both UTE-MT and dual-echo UTE-T 2* sequence measurements had good stability, with ICC values of 0.98 and 0.92, respectively. Measurements of UTE-MTR and UTE-T 2* value of the patellar tendon, quadriceps tendon, and the 3 tendon-bone insertion points by the 2 radiologists were in good agreement (ICC>0.80). Forty-eight hours after the marathon running, the UTE-MTR of the patellar tendon, quadriceps tendon, and the 3 tendon-bone insertion points decreased, and UTE-MTR of the patellar tendon continued to decrease 4 weeks after the race, while UTE-MTR of other regions increased. Only the difference in UTE-MTR for the patellar tendon was statistically significant ( F=7.46, P=0.001) among pre-marathon (0.34±0.04), 48 h after the race (0.32±0.04), and 4 weeks after the race (0.31±0.04). UTE-T 2* value was mildly elevated in all regions at 48 h after the marathon running, but the differences among the three points were not statistically significant ( P>0.05). Conclusion:The UTE-MT has better reproducibility and inter-rater reliability. The UTE-MT can be used to monitor the dynamic changes of the prepatellar tendon before and after marathon exercise, where the UTE-MTR of the patellar tendon consistently decreases after marathon exercise.
