Cardiac thrombus dissolution in acute ischemic stroke: A substudy of Mind the Heart.

Background: Cardiac thrombi are an important cause of ischemic stroke but are infrequently detected on cardiac imaging. We hypothesized that this might be explained by early dissolution of these cardiac thrombi after stroke occurrence. Methods: We performed a single-center observational pilot study between November 2019 and November 2020, embedded in the larger "Mind-the-Heart" study. We included patients with AIS and a cardiac thrombus in the left atrium or ventricle (filling defect <100 Hounsfield Units) diagnosed on cardiac CT that was acquired during the initial stroke imaging protocol. We repeated cardiac CT within one week to determine if the thrombus had dissolved. Results: Five patients (four men, median age 52 years, three with atrial fibrillation and one with anticoagulation therapy at baseline) were included. Median time from symptom onset to first cardiac CT was 383 (range 42-852) minutes and median time from first to second cardiac CT was three days (range 1-7). Two patients received intravenous thrombolysis (IVT). In total, six thrombi were seen on initial CT imaging (one in the left ventricle, four in the left atrial appendage, one in the left atrium). The left atrium thrombus and one left atrial appendage thrombus had dissolved on follow-up cardiac CT, one of which was in a patient with IVT treatment. Conclusion: This pilot study illustrates that cardiac thrombi can dissolve within days of stroke occurrence both with and without IVT treatment.

Detection of patent foramen ovale in patients with ischemic stroke on prospective ECG-gated cardiac CT compared to transthoracic echocardiography.

BACKGROUND: Cardiac CT acquired during the acute stroke imaging protocol is an emerging alternative to transthoracic echocardiography (TTE) to screen for sources of cardioembolism. Currently, its diagnostic accuracy to detect patent foramen ovale (PFO) is unclear. METHODS: This was a substudy of Mind the Heart, a prospective cohort in which consecutive adult patients with acute ischemic stroke underwent prospective ECG-gated cardiac CT during the initial stroke imaging protocol. Patients also underwent TTE. We included patients < 60 years who underwent TTE with agitated saline contrast (cTTE) and assessed sensitivity, specificity, negative and positive predictive value of cardiac CT for the detection of PFO using cTTE as the reference standard. RESULTS: Of 452 patients in Mind the Heart, 92 were younger than 60 years. Of these, 59 (64%) patients underwent both cardiac CT and cTTE and were included. Median age was 54 (IQR 49-57) years and 41/59 (70%) were male. Cardiac CT detected a PFO in 5/59 (8%) patients, 3 of which were confirmed on cTTE. cTTE detected a PFO in 12/59 (20%) patients. Sensitivity and specificity of cardiac CT were 25% (95% CI 5-57%) and 96% (95% CI 85-99%), respectively. Positive and negative predictive values were 59% (95% CI 14-95) and 84% (95% CI 71-92). CONCLUSION: Prospective ECG-gated cardiac CT acquired during the acute stroke imaging protocol does not appear to be a suitable screening method for PFO due to its low sensitivity. Our data suggest that if cardiac CT is used as a first-line screening method for cardioembolism, additional echocardiography remains indicated in young patients with cryptogenic stroke, in whom PFO detection would have therapeutic consequences. These results need to be confirmed in larger cohorts.

Comparison of two-dimensional measurement with three-dimensional volume rendering for the assessment of loss of domain in incisional hernia patients.

PURPOSE: We aimed to compare simple two-dimensional (2D) measurement with comprehensive three-dimensional (3D) volume rendering to determine loss of domain (LOD), a clinically important decision-making feature for incisional hernia repair. METHODS: In this single-center retrospective study, we analyzed the CT scans of a consecutive cohort of adult patients with a midline incisional hernia. The hernia sac- and abdominal cavity volumes were obtained by two different methods. The 2D method estimated the volumes using the corresponding height, width, and depth. The 3D method comprised of a volume rendering tool. For both methods, LOD was calculated according to the Sabbagh ratio (hernia sac volume / (hernia sac volume + abdominal cavity volume)). Taking the 3D method as the reference standard, the performance of the 2D method was expressed as positive predictive value (PPV) and negative predictive value (NPV) for LOD of more than- and less than 20%. The agreement between both methods was expressed as Cohen's kappa coefficient (kappa). RESULTS: We analyzed 92 CT scans. Agreement between both methods was high (kappa = 0.854, p = 0.0001); all 67 measurements for which the 2D method assessed LOD to be less than 20% were correctly classified (NPV = 100%), and 20 of 25 measurements for which the 2D method assessed LOD to be more than 20% were correctly classified (PPV = 80%). CONCLUSIONS: The 2D method can exclude patients from perioperative actions needed for a more complex hernia. Since this method is easy to use and less time-consuming, it seems useful for the routine radiological assessment of LOD in clinical practice.

Machine learning-based prediction of insufficient contrast enhancement in coronary computed tomography angiography.

OBJECTIVES: Patient-tailored contrast delivery protocols strongly reduce the total iodine load and in general improve image quality in CT coronary angiography (CTCA). We aim to use machine learning to predict cases with insufficient contrast enhancement and to identify parameters with the highest predictive value. METHODS: Machine learning models were developed using data from 1,447 CTs. We included patient features, imaging settings, and test bolus features. The models were trained to predict CTCA images with a mean attenuation value in the ascending aorta below 400 HU. The accuracy was assessed by the area under the receiver operating characteristic (AUROC) and precision-recall curves (AUPRC). Shapley Additive exPlanations was used to assess the impact of features on the prediction of insufficient contrast enhancement. RESULTS: A total of 399 out of 1,447 scans revealed attenuation values in the ascending aorta below 400 HU. The best model trained using only patient features and CT settings achieved an AUROC of 0.78 (95% CI: 0.73-0.83) and AUPRC of 0.65 (95% CI: 0.58-0.71). With the inclusion of the test bolus features, it achieved an AUROC of 0.84 (95% CI: 0.81-0.87), an AUPRC of 0.71 (95% CI: 0.66-0.76), and a sensitivity of 0.66 and specificity of 0.88. The test bolus' peak height was the feature that impacted low attenuation prediction most. CONCLUSION: Prediction of insufficient contrast enhancement in CT coronary angiography scans can be achieved using machine learning models. Our experiments suggest that test bolus features are strongly predictive of low attenuation values and can be used to further improve patient-specific contrast delivery protocols. KEY POINTS: • Prediction of insufficient contrast enhancement in CT coronary angiography scans can be achieved using machine learning models. • The peak height of the test bolus curve is the most impacting feature for the best performing model.

Reducing soft-tissue shrinkage artefacts caused by staining with Lugol's solution.

Diffusible iodine-based contrast-enhanced computed tomography (diceCT) is progressively used in clinical and morphological research to study developmental anatomy. Lugol's solution (Lugol) has gained interest as an effective contrast agent; however, usage is limited due to extensive soft-tissue shrinkage. The mechanism of Lugol-induced shrinkage and how to prevent it is largely unknown, hampering applications of Lugol in clinical or forensic cases where tissue shrinkage can lead to erroneous diagnostic conclusions. Shrinkage was suggested to be due to an osmotic imbalance between tissue and solution. Pilot experiments pointed to acidification of Lugol, but the relation of acidification and tissue shrinkage was not evaluated. In this study, we analyzed the relation between tissue shrinkage, osmolarity and acidification of the solution during staining. Changes in tissue volume were measured on 2D-segmented magnetic resonance and diceCT images using AMIRA software. Partial correlation and stepwise regression analysis showed that acidification of Lugol is the main cause of tissue shrinkage. To prevent acidification, we developed a buffered Lugol's solution (B-Lugol) and showed that stabilizing its pH almost completely prevented shrinkage without affecting staining. Changing from Lugol to B-Lugol is a major improvement for clinical and morphological research and only requires a minor adaptation of the staining protocol.