No abstract

Compared with the control group, 3D fusion imaging is associated with a significant reduction in the volume of contrast employed for standard and complex endovascular aortic procedures, which can be particularly important in patients with renal failure. Radiation doses, procedure times, and fluoroscopy times were reduced when 3D fusion was used.

The aim of the study was to evaluate the performance of a computer-aided detection (CAD) algorithm in low-dose and full-dose multidetector-row CT (MDCT) of the thorax and its impact on radiologists' performance. Chest CT examinations of 77 patients were evaluated retrospectively for pulmonary nodules. All patients had undergone a 16-slice MDCT chest examination with a standard acquisition protocol. Artificial image noise was added to the raw data to simulate image acquisition at 10 mAs(eff.) The data were transferred to dedicated lung analysis software (LungCare) with a prototype CAD algorithm (LungCAD). CAD was applied to both dose settings. Images were read by a radiologist and a first-year resident with and without the software at both dose settings. All images were reviewed in consensus by the two radiologists to set the reference standard. Sensitivity results with respect to the reference standard were compared. No statistically significant differences in the detection rate for all pulmonary nodules could be found between low-dose and full-dose settings for the CAD software alone (p = 0.0065). Both radiologists displayed a statistically significant increase in sensitivity with the use of CAD (p<0.0001). In conclusion, CAD is beneficial in both low-dose and standard-dose settings. This may be beneficial in reducing false-negative diagnosis in lung cancer screening, standard chest examinations and the search for metastases.

BackgroundCurrently, the decision to treat iliac artery stenoses is mainly based on visual inspection of digital subtraction angiographies. Intra‐arterial pressure measurements can provide clinicians with accurate hemodynamic information. However, pressure measurements are rarely performed because of their invasiveness and the time required. Therefore, the aim of the study was to test the feasibility of a computational model that can predict translesional pressure gradients across iliac artery stenoses on the basis of imaging data only.Methods and ResultsPatients (N=21) with symptomatic peripheral arterial disease and a peak systolic velocity ratio between 2.5 and 5.0 were included in the study. Patients underwent per‐procedural 3‐dimensional rotational angiography and hyperemic intra‐arterial translesional pressure measurements. Vascular anatomical features were reconstructed from the 3‐dimensional rotational angiography data into an axisymmetrical 2‐dimensional computational mesh, and flow was estimated on the basis of the stenosis geometry. Computational fluid dynamics were performed to predict the pressure gradient and were compared with the measured pressure gradients. A good agreement by overlapping error bars of the predicted and measured pressure gradients was found in 21 of 25 lesions. Stratification of the stenosis on the basis of the predicted pressure gradient into hemodynamic not significant (<10 mm Hg) and hemodynamic significant (≥10 mm Hg) resulted in sensitivity, specificity, and overall predictive values of 95%, 60%, and 88%, respectively.ConclusionsThe feasibility of the patient‐specific computational model to predict the hyperemic translesional pressure gradient over iliac artery stenosis was successfully tested. Presented results suggest that, with further optimization and corroboration, the model can become a valuable aid to the diagnosis of equivocal iliac artery stenosis.Clinical Trial Registration URL: http://www.trialregister.nl. Unique identifier: NTR5085.