Midterm follow-up after computed tomography angiography planned left atrial appendage closure.

BACKGROUND: While studies have shown the advantages of computed tomography angiography (CTA) over transesophageal echocardiography (TEE) in left atrial appendage closure (LAAC) preprocedural planning for WATCHMAN™ legacy and FLX devices, there has been no reported long-term data for this approach. OBJECTIVES: We sought to evaluate long-term outcomes using CTA-based preprocedural planning for LAAC using the WATCHMAN™ device. METHODS: A prospective analysis of 231 consecutive patients who underwent LAAC in a single, large academic hospital in the United States was conducted over a 5-year period. CTA-guided preprocedural planning was performed in all. Procedural success, adverse events, length of procedure, number of devices used, and length of stay were evaluated. Rates of death, cerebral embolism, systemic embolism, and major and minor bleeding were recorded. Adjusted predicted stroke and major bleeding rates were derived from CHA2DS2-Vasc and HAS-BLED scores, respectively. RESULTS: From January 26, 2017, to November 23, 2021, 231 patients underwent LAAC with CTA preprocedural planning by two operating physicians. The mean age of patients was 76.5 ± 8.4. 59.7% of patients were male. Mean CHA2DS2VASc and HAS-BLED scores were 4.5 ± 1.4 and 3.9 ± 0.9, respectively. All procedures were performed with intracardiac echo (100%). The procedural success rate was 99.1%. The CTA sizing strategy accurately predicted the implant size in 93.5% of patients. Mean number of devices used was 1.10 ± 0.3. Peri-procedural complication rate was 2.2%. 6 patients were lost to follow-up. Mean follow-up was 608.94 days with a total of 377.04 patient years. Median follow-up period of 368 days (interquartile range: 209-1067 days). There were 51 deaths from all causes (13.52 per 100 patient-years), 10 cases of cerebral embolism (2.65 per 100 patient-years), 2 cases of systemic embolism (0.53 per 100 patient-years), 17 cases of major bleeding (4.50 per 100 patient-years), and 31 cases of minor bleeding (8.2 per 100 patient-years). All-cause mortality at 1, 2, and 3 years was 12.7%, 20.9%, and 29.2%, respectively. CV event rates at 1, 2, and 3 years were 2.1%, 6.6%, and 10.5%, respectively. CONCLUSIONS: CTA-based preprocedural planning is accurate in predicting device size for LAAC and associated with excellent clinical outcomes at 5 years.

Cinematic rendering in the evaluation of complex vascular injury of the lower extremities: how we do it.

Lower extremity trauma is one of the most common injury patterns seen in emergency medical and surgical practice. Vascular injuries occur in less than one percent of all civilian fractures. However, if not treated promptly, such injuries can lead to ischemia and death. Computed tomography angiography (CTA) is the non-invasive imaging gold standard and plays a crucial part in the decision-making process for treating lower extremity trauma. A novel, FDA-approved 3D reconstruction technique known as cinematic rendering (CR) yields photorealistic reconstructions of lower extremity vascular injuries depicting clinically important aspects of those injuries, aiding in patient workup and surgical planning, and thus improving patient outcomes. In this article, we provide clinical examples of the use of CR in evaluating lower extremity vascular injuries, including the relationship of these injuries to adjacent osseous structures and overlying soft tissues, and its role in management of lower extremity trauma.

Novel computed tomography angiography-based sizing methodology for WATCHMAN FLX device in left atrial appendage closure.

BACKGROUND: While there is recent data suggesting an advantage of computed tomography angiography (CTA) over transesophageal echocardiography (TEE) for preprocedural left atrial appendage closure (LAAC) planning, there is limited published experience for sizing strategies. Device sizing for LAAC may be challenging and noninvasive algorithms that improve this selection process are warranted. OBJECTIVES: We sought to evaluate the safety and the feasibility for the implementation of a novel CTA-based sizing methodology for WATCHMAN™ FLX device in a series of patients undergoing LAAC using the TruPlan™ software package. METHODS: A prospective analysis of 136 consecutive patients who underwent LAAC over a 12-month period in a single, large academic hospital in the United States was conducted. CTA-guided preprocedural planning and intracardiac echocardiography (ICE) was performed in all. Procedural success, adverse events, length of procedure, number of devices used, and length of stay were evaluated. RESULTS: A total of 136 patients who underwent LAAC procedure with WATCHMAN™ FLX platform between October 1, 2020 until September 30, 2021 were included. The pre-specified protocol using CTA and ICE was implemented in all patients (100%). Mean CHA2 DS2 VASc score was 4.4 ± 1.3 and the mean HAS-BLED score was 3.9 ± 0.8. ICE-guided 100% transseptal puncture success rate was 100% with 98.5% of overall procedural success rate. Preprocedural CTA sizing strategy accurately predicted the implanted size in 91.1% of patients. Ten patients (7.4%) required another sized device and 2 cases were aborted. At 45-day follow-up, only 1 patient (0.7%) had significant peri-device leak (≥5 mm) on TEE. CONCLUSIONS: CTA-based preprocedural sizing methodology for WATCHMAN™ FLX in LAAC was safe, feasible and associated with excellent procedural outcomes. Further studies are warranted to confirm if the features specific to TruPlan™ may reduce the number of deployment attempts, the number of devices utilized in the procedure, and the risk of complications.

Exploring value of CT coronary imaging combined with machine-learning methods to predict myocardial ischemia.

PURPOSE: To establish a machine-learning (ML) model based on coronary computed tomography angiography (CTA) images for evaluating myocardial ischemia in patients diagnosed with coronary atherosclerosis. METHODS: This retrospective analysis includes CTA images acquired from 110 patients. Among them, 58 have myocardial ischemia and 52 have normal myocardial blood supply. The patients are divided into training and test datasets with a ratio 7 : 3. Deep learning model-based CQK software is used to automatically segment myocardium on CTA images and extract texture features. Then, seven ML models are constructed to classify between myocardial ischemia and normal myocardial blood supply cases. Predictive performance and stability of the classifiers are determined by receiver operating characteristic curve with cross validation. The optimal ML model is then validated using an independent test dataset. RESULTS: Accuracy and areas under ROC curves (AUC) obtained from the support vector machine with extreme gradient boosting linear method are 0.821 and 0.777, respectively, while accuracy and AUC achieved by the neural network (NN) method are 0.818 and 0.757, respectively. The naive Bayes model yields the highest sensitivity (0.942), and the random forest model yields the highest specificity (0.85). The k-nearest neighbors model yields the lowest accuracy (0.74). Additionally, NN model demonstrates the lowest relative standard deviations (0.16 for accuracy and 0.08 for AUC) indicating the high stability of this model, and its AUC applying to the independent test dataset is 0.72. CONCLUSION: The NN model demonstrates the best performance in predicting myocardial ischemia using radiomics features computed from CTA images, which suggests that this ML model has promising potential in guiding clinical decision-making.

Application of deep learning image reconstruction algorithm to improve image quality in CT angiography of children with Takayasu arteritis.

BACKGROUND: The inflammatory indexes of children with Takayasu arteritis (TAK) usually tend to be normal immediately after treatment, therefore, CT angiography (CTA) has become an important method to evaluate the status of TAK and sometime is even more sensitive than laboratory test results. OBJECTIVE: To evaluate image quality improvement in CTA of children diagnosed with TAK using a deep learning image reconstruction (DLIR) in comparison to other image reconstruction algorithms. METHODS: hirty-two TAK patients (9.14±4.51 years old) underwent neck, chest and abdominal CTA using 100 kVp were enrolled. Images were reconstructed at 0.625 mm slice thickness using Filtered Back-Projection (FBP), 50%adaptive statistical iterative reconstruction-V (ASIR-V), 100%ASIR-V and DLIR with high setting (DLIR-H). CT number and standard deviation (SD) of the descending aorta and back muscle were measured and contrast-to-noise ratio (CNR) for aorta was calculated. The vessel visualization, overall image noise and diagnostic confidence were evaluated using a 5-point scale (5, excellent; 3, acceptable) by 2 observers. RESULTS: There was no significant difference in CT number across images reconstructed using different algorithms. Image noise values (in HU) were 31.36±6.01, 24.96±4.69, 18.46±3.91 and 15.58±3.65, and CNR values for aorta were 11.93±2.12, 15.66±2.37, 22.54±3.34 and 24.02±4.55 using FBP, 50%ASIR-V, 100%ASIR-V and DLIR-H, respectively. The 100%ASIR-V and DLIR-H images had similar noise and CNR (all P > 0.05), and both had lower noise and higher CNR than FBP and 50%ASIR-V images (all P < 0.05). The subjective evaluation suggested that all images were diagnostic for large arteries, however, only 50%ASIR-V and DLIR-H met the diagnostic requirement for small arteries (3.03±0.18 and 3.53±0.51). CONCLUSION: DLIR-H improves CTA image quality and diagnostic confidence for TAK patients compared with 50%ASIR-V, and best balances image noise and spatial resolution compared with 100%ASIR-V.

A coronary artery CTA segmentation approach based on deep learning.

Presence of plaque and coronary artery stenosis are the main causes of coronary heart disease. Detection of plaque and coronary artery segmentation have become the first choice in detecting coronary artery disease. The purpose of this study is to investigate a new method for plaque detection and automatic segmentation and diagnosis of coronary arteries and to test its feasibility of applying to clinical medical image diagnosis. A multi-model fusion coronary CT angiography (CTA) vessel segmentation method is proposed based on deep learning. The method includes three network layer models namely, an original 3-dimensional full convolutional network (3D FCN) and two networks that embed the attention gating (AG) model in the original 3D FCN. Then, the prediction results of the three networks are merged by using the majority voting algorithm and thus the final prediction result of the networks is obtained. In the post-processing stage, the level set function is used to further iteratively optimize the results of network fusion prediction. The JI (Jaccard index) and DSC (Dice similarity coefficient) scores are calculated to evaluate accuracy of blood vessel segmentations. Applying to a CTA dataset of 20 patients, accuracy of coronary blood vessel segmentation using FCN, FCN-AG1, FCN-AG2 network and the fusion method are tested. The average values of JI and DSC of using the first three networks are (0.7962, 0.8843), (0.8154, 0.8966) and (0.8119, 0.8936), respectively. When using new fusion method, average JI and DSC of segmentation results increase to (0.8214, 0.9005), which are better than the best result of using FCN, FCN-AG1 and FCN-AG2 model independently.

Application of 70 kVp in abdominal CT angiography to reduce both radiation and contrast dosage and improve patient comfort for children.

BACKGROUND: Low-tube voltage scanning improves CT attenuation value of contrast medium (CM). Thus, we hypothesized that 70 kVp in pediatric abdominal CT angiography (CTA) could be used to reduce both radiation and CM dose and improve patient comfort at the same time. OBJECTIVE: To evaluate the feasibility of using 70 kVp in pediatric abdominal CTA to reduce radiation dose and CM dose and improve patient care for children. MATERIALS AND METHODS: Forty-six children needing abdominal CTA were enrolled in the study group using low-dose scanning protocol with 70 kVp and 0.7-1.1 ml/kg contrast dose, and reconstructed with 50%ASIR-V. They were compared with other 46 children in control group with matching body weight and underwent conventional CT scans with 100 kVp, 1.2-1.8 ml/kg contrast dose and reconstructed using 50%ASIR. Image quality of large vessels was evaluated using a 5-point scale. CT value and standard deviation of descending aorta (Ao) was measured, and signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. Radiation dose, contrast dose, the maximum injection pressure between the two groups were also compared. RESULTS: Score for displaying large vessels by 70 kVp images was 3.91±0.28, lower than that (4.17±0.38) of the control group (p < 0.05), but fully met the diagnostic requirements. CT value of Ao was 390.87±86.79HU in study group, which is higher than 343.93±49.94HU in control group, while there was no difference in SNR and CNR between two groups; the radiation dose, contrast dosage and injection pressure of the study group were 1.23±0.39mGy, 12.67±7.27 ml and 43.83±17.16psi, respectively, which are significantly lower than the 1.95±0.37mGy, 22.67±7.39 ml, and 77.59±19.68psi of control group. CONCLUSION: Use of 70 kVp in pediatric abdominal CTA provides diagnostic quality images while significantly reduce radiation and contrast dose, as well as injection pressure to improve patient comfort for children.

Automated vessel segmentation in lung CT and CTA images via deep neural networks.

BACKGROUND: The distribution of pulmonary vessels in computed tomography (CT) and computed tomography angiography (CTA) images of lung is important for diagnosing disease, formulating surgical plans and pulmonary research. PURPOSE: Based on the pulmonary vascular segmentation task of International Symposium on Image Computing and Digital Medicine 2020 challenge, this paper reviews 12 different pulmonary vascular segmentation algorithms of lung CT and CTA images and then objectively evaluates and compares their performances. METHODS: First, we present the annotated reference dataset of lung CT and CTA images. A subset of the dataset consisting 7,307 slices for training and 3,888 slices for testing was made available for participants. Second, by analyzing the performance comparison of different convolutional neural networks from 12 different institutions for pulmonary vascular segmentation, the reasons for some defects and improvements are summarized. The models are mainly based on U-Net, Attention, GAN, and multi-scale fusion network. The performance is measured in terms of Dice coefficient, over segmentation rate and under segmentation rate. Finally, we discuss several proposed methods to improve the pulmonary vessel segmentation results using deep neural networks. RESULTS: By comparing with the annotated ground truth from both lung CT and CTA images, most of 12 deep neural network algorithms do an admirable job in pulmonary vascular extraction and segmentation with the dice coefficients ranging from 0.70 to 0.85. The dice coefficients for the top three algorithms are about 0.80. CONCLUSIONS: Study results show that integrating methods that consider spatial information, fuse multi-scale feature map, or have an excellent post-processing to deep neural network training and optimization process are significant for further improving the accuracy of pulmonary vascular segmentation.

Analysis of segmentation of lung parenchyma based on deep learning methods.

Precise segmentation of lung parenchyma is essential for effective analysis of the lung. Due to the obvious contrast and large regional area compared to other tissues in the chest, lung tissue is less difficult to segment. Special attention to details of lung segmentation is also needed. To improve the quality and speed of segmentation of lung parenchyma based on computed tomography (CT) or computed tomography angiography (CTA) images, the 4th International Symposium on Image Computing and Digital Medicine (ISICDM 2020) provides interesting and valuable research ideas and approaches. For the work of lung parenchyma segmentation, 9 of the 12 participating teams used the U-Net network or its modified forms, and others used the methods to improve the segmentation accuracy include attention mechanism, multi-scale feature information fusion. Among them, U-Net achieves the best results including that the final dice coefficient of CT segmentation is 0.991 and the final dice coefficient of CTA segmentation is 0.984. In addition, attention U-Net and nnU-Net network also performs well. In this paper, the methods chosen by 12 teams from different research groups are evaluated and their segmentation results are analyzed for the study and references to those involved.

Health Care Monitoring and Treatment for Coronary Artery Diseases: Challenges and Issues.

In-stent restenosis concerning the coronary artery refers to the blood clotting-caused re-narrowing of the blocked section of the artery, which is opened using a stent. The failure rate for stents is in the range of 10% to 15%, where they do not remain open, thereby leading to about 40% of the patients with stent implantations requiring repeat procedure within one year, despite increased risk factors and the administration of expensive medicines. Hence, today stent restenosis is a significant cause of deaths globally. Monitoring and treatment matter a lot when it comes to early diagnosis and treatment. A review of the present stent monitoring technology as well as the practical treatment for addressing stent restenosis was conducted. The problems and challenges associated with current stent monitoring technology were illustrated, along with its typical applications. Brief suggestions were given and the progress of stent implants was discussed. It was revealed that prime requisites are needed to achieve good quality implanted stent devices in terms of their size, reliability, etc. This review would positively prompt researchers to augment their efforts towards the expansion of healthcare systems. Lastly, the challenges and concerns associated with nurturing a healthcare system were deliberated with meaningful evaluations.

Fully-automatic segmentation of coronary artery using growing algorithm.

Currently, cardiac computed tomography angiography (CTA) is widely applied to coronary artery disease diagnosis. Automatic segmentation of coronary artery has played an important role in coronary artery disease diagnosis. In this study, we propose and test a fully automatic coronary artery segmentation method that does not require any human-computer interaction. The proposed method uses a growing strategy and contains three main parts namely, (1) the initial seed detection that automatically detects the root points of the left and right coronary arteries where the ascending aorta meets the coronary arteries, (2) the growing strategy that searches for the neighborhood blocks to decide the existence of coronary arteries with an improved convolutional neural network, and (3) the iterative termination condition that decides whether the growing iteration finishes. The proposed framework is validated using a dataset containing 32 cardiac CTA volumes from different patients for training and testing. Experimental results show that the proposed method obtained a Dice loss ranged from 0.70 to 0.83, which indicates that the new method outperforms the traditional methods such as level set.

The Role of CT Angiography in Assessing Deep Inferior Epigastric Perforator Flap Patency in Patients With Pre-existing Abdominal Scars.

BACKGROUND: Abdominal scars can affect the patency of deep inferior epigastric artery (DIEA) perforators and are a concern when planning free flap breast reconstruction (FFBR). Computed tomography angiography (CTA) is routinely used for preoperative DIEA flap imaging. We investigated CTA utility in predicting the most clinically useful DIEA perforators in scarred abdomens. METHODS: A single surgeon's CTA FFBR patients were studied. All were imaged by one radiologist. CTA reports, abdominal scars, and flap intraoperative details were analyzed. The operative findings were then correlated with the CTA "predictions." RESULTS: A hundred and six patients with preoperative CTAs underwent 132 FFBRs, 44% (58) from scarred and 56% (74) from virgin abdomens. All flap transfers were successful. Concordance between perforators identified by CTA preoperatively and those selected by the surgeon intraoperatively was 95% (scarred 93%; non-scarred 96%, P = 0.470). There was a significant difference in the proportion of single-perforator flaps between the two groups (scarred 46%; non-scarred 28%, P = 0.041). "Scarred" flaps were heavier (789 vs 676 g, P = 0.0244) than those harvested from virgin abdomens. CONCLUSIONS: CTA accurately predicted perforator choice in flaps from scarred and virgin abdomens. "Scarred" flaps are more likely to be heavier and based on one perforator suggesting that scarring may have an effect on intraflap vascular anatomy. Further investigations are needed to delineate the mechanism by which this occurs.

Comparison of Magnetic Resonance Angiography and Computed Tomography Angiography Stereoscopic Cerebral Vascular Models.

In this paper, we will discuss and compare the stereoscopic models developed from two types of radiographic data, Magnetic Resonance Angiography (MRA) images and Computed Tomography Angiography (CTA) images. Stereoscopic models were created using surface or volume segmentation and semi-auto combined segmentation techniques. Although, the CTA data were found to improve the speed and quality of constructing virtual vascular models compared to conventional CT data, small blood vessels were difficult to capture during the imaging and reconstruction process thereby limiting the fidelity of the stereoscopic models. Thus, high contrast Magnetic Resonance Angiography (MRA) images offer better resolution to visualize and capture the smaller branches of the cerebral vasculature than CTA images.

[Usefulness of Fenestrated Catheters for i.v. Contrast Infusion Cardiac CT Angiography for Newborn Patients during the Congenital Heart Disease].

PURPOSE: A three-dimensional (3D) image from computed tomography (CT) angiography is a useful method for evaluation of complex anatomy such as congenital heart disease. However, 3D imaging requires high contrast enhancement for distinguishing between blood vessels and soft tissue. To improve the contrast enhancement, many are increasing the injection rate. However, one method is the use of fenestrated catheters, it allows use of a smaller gauge catheter for high-flow protocols. The purpose of this study was to compare the pressure of injection rate and CT number of a 24-gauge fenestrated catheter with an 22-gauge non-fenestrated catheter for i.v. contrast infusion during CT. METHODS: Between December 2014 and March 2015, 50 newborn patients were randomly divided into two protocols; 22-gauge conventional non-fenestrated catheter (24 newborn; age range 0.25-8 months, body weight 3.6±1.2 kg) and 24-gauge new fenestrated catheter (22 newborn; age range 0.25-12 months, body weight 3.3±0.9 kg). Helical scan of the heart was performed using a 64-detector CT (LightSpeed VCT, GE Healthcare) (tube voltage 80 kV; detector configuration 64×0.625 mm, rotation time 0.4 s/rot, helical pitch 1.375, preset noise index for automatic tube current modulation 40 at 0.625 mm slice thickness). RESULTS: We compared the maximum pressure of injection rate, CT number of aortic enhancement, and CT number of pulmonary artery enhancement between both protocols. The median injection rate, CT number of aortic enhancement, and CT number of pulmonary artery enhancement were 0.9 (0.5-3.4) ml/s, 455.5 (398-659) HU, and 500.0 (437-701) HU in 22-gauge conventional non-fenestrated catheter and 0.9 (0.5-2.0) ml/s, 436.5 (406-632) HU, and 479.5 (445-695) HU in the 24-gauge fenestrated catheter, respectively. There are no significantly different between a 24-gauge fenestrated catheter and 22-gauge non-fenestrated catheters at injection rate and CT number. Maximum pressure of injection rate was lower with 24-gauge non-fenestrated catheters (0.33 kg/cm2) than 22-gauge non-fenestrated catheters (0.55 kg/cm2) (p<0.01Conclusion: A 24-gauge fenestrated catheter performs similarly to an 22-gauge non-fenestrated catheter with respect to i.v. contrast infusion and aortic enhancement levels and can be placed in most subjects whose veins are deemed insufficient for an 22-gauge catheter.

Tailored CT angiography in follow-up after endovascular aneurysm repair (EVAR): combined dose reduction techniques.

Background Endovascular aneurysm repair (EVAR) requires lifelong surveillance by computed tomography angiography (CTA). This is attended by a substantial accumulation of radiation exposure. Iterative reconstruction (IR) has been introduced to approach dose reduction. Purpose To evaluate adaptive statistical iterative reconstruction (ASIR) at different levels of tube voltage concerning image quality and dose reduction potential in follow-up post EVAR. Material and Methods One hundred CTAs in 67 patients with EVAR were examined using five protocols: protocol A (n = 40) as biphasic standard using filtered back projection (FBP) at 120 kV; protocols B (n = 40), C (n = 10), and D1 (n = 5) biphasic using ASIR at 120, 100, and 80 kV, respectively; and protocol D2 (n = 5) with a monophasic splitbolus ASIR protocol at 80 kV. Image quality was assessed quantitatively and qualitatively. Applied doses were determined. Results Applied doses in ASIR protocols were significantly lower than FBP standard (up to 75%). Compared to protocol A, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) (e.g. arterial CNR intra-/extra-stent lumen: A = 35.4 ± 13.5, B = 34.2 ± 10.0, C = 29.6 ± 6.8, D1 = 32.1 ± 6.3, D2 = 40.8 ± 23.1) in protocol B were equal and in protocols C and D equal to partially inferior, however not decisive for diagnostic quality. Subjective image quality ratings in all protocols were good to excellent without impairments of diagnostic confidence (A-D2: 5), with high inter-rater agreement (60-100%). Conclusion ASIR contributes to significant dose reduction without decisive impairments of image quality and diagnostic confidence. We recommend an adapted follow-up introducing ASIR and combined low-kV in the long-term surveillance after EVAR.

Computed tomography angiography spot sign predicts intraprocedural aneurysm rupture in subarachnoid hemorrhage.

INTRODUCTION: To analyze whether the computed tomography angiography (CTA) spot sign predicts the intraprocedural rupture rate and outcome in patients with aneurysmal subarachnoid hemorrhage (aSAH). METHODS: From a prospective nationwide multicenter registry database, 1023 patients with aneurysmal subarachnoid hemorrhage (aSAH) were analyzed retrospectively. Descriptive statistics and logistic regression analysis were used to compare spot sign-positive and -negative patients with aneurysmal intracerebral hemorrhage (aICH) for baseline characteristics, aneurysmal and ICH imaging characteristics, treatment and admission status as well as outcome at discharge and 1-year follow-up (1YFU) using the modified Rankin Scale (mRS). RESULTS: A total of 218 out of 1023 aSAH patients (21%) presented with aICH including 23/218 (11%) patients with spot sign. Baseline characteristics were comparable between spot sign-positive and -negative patients. There was a higher clip-to-coil ratio in patients with than without aICH (both spot sign positive and negative). Median aICH volume was significantly higher in the spot sign-positive group (50 ml, 13-223 ml) than in the spot sign-negative group (18 ml, 1-416; p < 0.0001). Patients with a spot sign-positive aICH thus were three times as likely as those with spot sign-negative aICH to show an intraoperative aneurysm rupture [odds ratio (OR) 3.04, 95% confidence interval (CI) 1.04-8.92, p = 0.046]. Spot sign-positive aICH patients showed a significantly worse mRS at discharge (p = 0.039) than patients with spot sign-negative aICH (median mRS 5 vs. 4). Logistic regression analysis showed that the spot sign was an aICH volume-dependent predictor for outcome. Both spot sign-positive and -negative aICH patients showed comparable rates of hospital death, death at 1YFU and mRS at 1YFU. CONCLUSION: In this multicenter data analysis, patients with spot sign-positive aICH showed higher aICH volumes and a higher rate of intraprocedural aneurysm rupture, but comparable long-term outcome to spot sign-negative aICH patients.

Revival of monophasic contrast injection protocols: superiority of a monophasic injection protocol compared to a biphasic injection protocol in high-pitch CT angiography.

BACKGROUND: Biphasic injection protocols are frequently used because they yield homogenous contrast enhancement. We hypothesize that with faster scanners and shorter scan times, biphasic injection protocols are no longer necessary. PURPOSE: To evaluate whether a monophasic injection protocol is equivalent to a biphasic protocol in terms of contrast enhancement and homogeneity. MATERIAL AND METHODS: Repeated high-pitch CTA (pitch 3) and conventional standard-pitch computed tomography angiography (CTA) (pitch 1.2) from the cervical region to the symphysis was performed in seven beagles (11.2 ± 2.5 kg) in a cross-over study design. Arterial contrast enhancement was measured along the z-axis in the ascending, descending, and abdominal aorta and the iliac arteries. The z-axis is the longitudinal axis of the human body and at the same time the direction in which the CT table is moving. The data were analyzed using repeated measures ANOVA with a post-hoc t-test and visual assessment of the scans. RESULTS: In high-pitch CTA, monophasic injection protocols were superior to biphasic injection protocols in enhancement levels (P < 0.05) and enhancement homogeneity along the z-axis (P < 0.05). In conventional CTA, enhancement levels did not differ. Contrast homogeneity was better for biphasic protocols. CONCLUSION: High-pitch CTA monophasic injection protocols are superior to biphasic injection protocols, due to a higher and more homogeneous contrast enhancement with the same amount of contrast medium used.

Software-based on-site estimation of fractional flow reserve using standard coronary CT angiography data.

BACKGROUND: The significance of a coronary stenosis can be determined by measuring the fractional flow reserve (FFR) during invasive coronary angiography. Recently, methods have been developed which claim to be able to estimate FFR using image data from standard coronary computed tomography angiography (CCTA) exams. PURPOSE: To evaluate the accuracy of non-invasively computed fractional flow reserve (cFFR) from CCTA. MATERIAL AND METHODS: A total of 23 vessels in 21 patients who had undergone both CCTA and invasive angiography with FFR measurement were evaluated using a cFFR software prototype. The cFFR results were compared to the invasively obtained FFR values. Correlation was calculated using Spearman's rank correlation, and agreement using intraclass correlation coefficient (ICC). Sensitivity, specificity, accuracy, negative predictive value, and positive predictive value for significant stenosis (defined as both FFR ≤0.80 and FFR ≤0.75) were calculated. RESULTS: The mean cFFR value for the whole group was 0.81 and the corresponding mean invFFR value was 0.84. The cFFR sensitivity for significant stenosis (FFR ≤0.80/0.75) on a per-lesion basis was 0.83/0.80, specificity was 0.76/0.89, and accuracy 0.78/0.87. The positive predictive value was 0.56/0.67 and the negative predictive value was 0.93/0.94. The Spearman rank correlation coefficient was ρ = 0.77 (P < 0.001) and ICC = 0.73 (P < 0.001). CONCLUSION: This particular CCTA-based cFFR software prototype allows for a rapid, non-invasive on-site evaluation of cFFR. The results are encouraging and cFFR may in the future be of help in the triage to invasive coronary angiography.

Measurements of Coronary Artery Aneurysms Due to Kawasaki Disease by Dual-Source Computed Tomography (DSCT).

Diameters of coronary artery aneurysms (CAAs) complicating acute phase KD can strongly predict the long-term prognosis of coronary artery lesions (CAL). Recently, computed tomographic angiography (CTA) has been used to detect CAL, and the purpose of this study was to determine whether coronary artery diameters measurements by CTA using dual-source computed tomography (DSCT) can be used instead of coronary angiogram (CAG) measurements. Twenty-five patients (22 males and three females) with CAL due to KD, who had undergone both CTA and CAG within one year, were retrospectively evaluated between 2007 and 2013. A prospective electrocardiogram-triggered CTA was performed on a DSCT (SOMATOM(®) Definition, Siemens Healthcare, Germany). Two pediatric cardiologists independently measured the diameters of CAAs twice in each maximum intensity projection (MIP), curved multiplaner reconstruction (MPR) and CAG. We measured 161 segments in total (segment 1-3, 5-7, 11, 13). Diagnostic accuracy was expressed as κ coefficient. A Bland-Altman analysis was also used to assess the intra-observer, inter-observer and inter-modality agreement. The diagnostic quality of CTA was excellent (κ = 0.93). Excellent inter-observer agreement for the diameters of CAAs was obtained for MIP, MPR and CAG and for the intra-observer agreement. The inter-modality agreement was also excellent in measurements of CAA (MPR-CAG: y = 0.9x + 0.40, r = 0.97, p < 0.0001 MIP-CAG: y = x + 0.1, r = 0.94, p < 0.0001). These values in normal coronary arteries were also obtained. We found a significant correlation between CTA and CAG in measuring the coronary arteries. We conclude that measuring coronary artery diameters by CTA is reliable and useful.

The rupture risk of intracranial saccular aneurysm: a case-control study based on a three-dimensional computed tomography angiography model.

Background: Assessing the risk of rupture in intracranial aneurysms is crucial. Advancements in medical imaging now allow for three-dimensional (3D) assessments of aneurysms, providing a more detailed understanding of their morphology and associated risks. This study aimed to compare the 3D morphological parameters of ruptured and unruptured intracranial saccular aneurysms (ISAs) using computed tomography angiography (CTA) and to analyze risk factors linked to ISA rupture. Methods: This retrospective case-control study included patients diagnosed with ISAs via CTA, for which data were sourced from both the Emergency Department and Inpatient Unit in The First Affiliated Hospital of Jinan University. The patients were categorized into rupture and unrupture groups. We used 3D-Slicer (version 5.2.2, Slicer Community) to construct morphological models of the ISAs and their parent arteries. These models facilitated assessments of intracranial aneurysmal volume (IAV), aneurysmal surface area (ASA), and maximum sectional area (MSA). Differences in 3D morphological parameters between ruptured and unruptured ISAs were then analyzed. For statistical analysis, we first performed single factor analysis on the data, constructed a receiver operating characteristic (ROC) curve one by one with statistically significant parameters, and screened out ROC curves that met the sample requirements. Second, we performed multiparameter logistic regression analysis to construct a ROC curve model and analyzed its predictive performance. Results: The analysis encompassed 97 patients comprising 97 ISAs diagnosed from March 2016 to March 2022. Significant differences in morphological parameters were observed between the rupture and unrupture groups (P<0.05), including IAV, ASA, MSA, IAV/diameter (IAV/D), IAV/neck width (IAV/N), MSA/diameter (MSA/D), MSA/neck width (MSA/N), ASA/neck width (ASA/N), and ASA/MSA. It was found that the IAV, ASA, and MSA values of the rupture group were larger than those of the unrupture group. Meanwhile, the IAV/D, IAV/N, MSA/D, MSA/N, and ASA/N values were larger in the rupture group, while ASA/MSA and ASA/IAV were smaller. Conclusions: This study underscores the significance of specific morphological indicators, such as ASA/N and ASA/MSA, in predicting the rupture risk of ISAs. The IAV, MSA, and ASA parameters, especially in relation to diameter and neck width, provide crucial insights into the rupture potential of ISAs.