Artificial Intelligence-Based Prediction of Contrast Medium Doses for Computed Tomography Angiography Using Optimized Clinical Parameter Sets.

OBJECTIVES: In this paper, an artificial intelligence-based algorithm for predicting the optimal contrast medium dose for computed tomography (CT) angiography of the aorta is presented and evaluated in a clinical study. The prediction of the contrast dose reduction is modelled as a classification problem using the image contrast as the main feature. METHODS: This classification is performed by random decision forests (RDF) and k-nearest-neighbor methods (KNN). For the selection of optimal parameter subsets all possible combinations of the 22 clinical parameters (age, blood pressure, etc.) are considered using the classification accuracy and precision of the KNN classifier and RDF as quality criteria. Subsequently, the results of the evaluation were optimized by means of feature transformation using regression neural networks (RNN). These were used for a direct classification based on regressed Hounsfield units as well as preprocessing for a subsequent KNN classification. RESULTS: For feature selection, an RDF model achieved the highest accuracy of 84.42% and a KNN model achieved the best precision of 86.21%. The most important parameters include age, height, and hemoglobin. The feature transformation using an RNN considerably exceeded these values with an accuracy of 90.00% and a precision of 97.62% using all 22 parameters as input. However, also the feasibility of the parameter sets in routine clinical practice has to be considered, because some of the 22 parameters are not measured in routine clinical practice and additional measurement time of 15 to 20 minutes per patient is needed. Using the standard feature set available in clinical routine the best accuracy of 86.67% and precision of 93.18% was achieved by the RNN. CONCLUSION: We developed a reliable hybrid system that helps radiologists determine the optimal contrast dose for CT angiography based on patient-specific parameters.

Random Forest and Gradient Boosted Trees for Patient Individualized Contrast Agent Dose Reduction in CT Angiography.

This work aims to recognize the patient individual possibility of contrast dose reduction in CT angiography. This system should help to identify whether the dose of contrast agent in CT angiography can be reduced to avoid side effects. In a clinical study, 263 CT angiographies were performed and, in addition, 21 clinical parameters were recorded for each patient before contrast agent administration. The resulting images were labeled according to their contrast quality. It is assumed that the contrast dose could be reduced for CT angiography images with excessive contrast. These data was used to develop a model for predicting excessive contrast based on the clinical parameters using logistic regression, random forest, and gradient boosted trees. In addition, the minimization of clinical parameters required was investigated to reduce the overall effort. Therefore, models were tested with all subsets of clinical parameters and each parameter's importance was examined. In predicting excessive contrast in CT angiography images covering the aortic region, a maximum accuracy of 0.84 was achieved by a random forest with 11 clinical parameters; for the leg-pelvis region data, an accuracy of 0.87 was achieved by a random forest with 7 parameters; and for the entire data set, an accuracy of 0.74 was achieved by gradient boosted trees with 9 parameters.

Repeatability and reproducibility of various 4D Flow MRI postprocessing software programs in a multi-software and multi-vendor cross-over comparison study.

BACKGROUND: Different software programs are available for the evaluation of 4D Flow cardiovascular magnetic resonance (CMR). A good agreement of the results between programs is a prerequisite for the acceptance of the method. Therefore, the goal was to compare quantitative results from a cross-over comparison in individuals examined on two scanners of different vendors analyzed with four postprocessing software packages. METHODS: Eight healthy subjects (27 ± 3 years, 3 women) were each examined on two 3T CMR systems (Ingenia, Philips Healthcare; MAGNETOM Skyra, Siemens Healthineers) with a standardized 4D Flow CMR sequence. Six manually placed aortic contours were evaluated with Caas (Pie Medical Imaging, SW-A), cvi42 (Circle Cardiovascular Imaging, SW-B), GTFlow (GyroTools, SW-C), and MevisFlow (Fraunhofer Institute MEVIS, SW-D) to analyze seven clinically used parameters including stroke volume, peak flow, peak velocity, and area as well as typically scientifically used wall shear stress values. Statistical analysis of inter- and intrareader variability, inter-software and inter-scanner comparison included calculation of absolute and relative error (ER), intraclass correlation coefficient (ICC), Bland-Altman analysis, and equivalence testing based on the assumption that inter-software differences needed to be within 80% of the range of intrareader differences. RESULTS: SW-A and SW-C were the only software programs showing agreement for stroke volume (ICC = 0.96; ER = 3 ± 8%), peak flow (ICC: 0.97; ER = -1 ± 7%), and area (ICC = 0.81; ER = 2 ± 22%). Results from SW-A/D and SW-C/D were equivalent only for area and peak flow. Other software pairs did not yield equivalent results for routinely used clinical parameters. Especially peak maximum velocity yielded poor agreement (ICC ≤ 0.4) between all software packages except SW-A/D that showed good agreement (ICC = 0.80). Inter- and intrareader consistency for clinically used parameters was best for SW-A and SW-D (ICC = 0.56-97) and worst for SW-B (ICC = -0.01-0.71). Of note, inter-scanner differences per individual tended to be smaller than inter-software differences. CONCLUSIONS: Of all tested software programs, only SW-A and SW-C can be used equivalently for determination of stroke volume, peak flow, and vessel area. Irrespective of the applied software and scanner, high intra- and interreader variability for all parameters have to be taken into account before introducing 4D Flow CMR in clinical routine. Especially in multicenter clinical trials a single image evaluation software should be applied.

Retrograde Revascularization of Tibial Arteries in Patients with Critical Limb Ischemia: Plantar-Arch Versus Transpedal Approach.

PURPOSE: To compare retrograde plantar-arch and transpedal-access approach for revascularization of below-the-knee (BTK) arteries in patients with critical limb ischemia (CLI) after a failed antegrade approach. MATERIALS AND METHODS: Retrospectively we identified 811 patients who underwent BTK revascularization between 1/2014 and 1/2020. In 115/811 patients (14.2%), antegrade revascularization of at least 1 tibial artery had failed. In 67/115 (58.3%), patients retrograde access to the target vessel was achieved via the femoral access and the plantar-arch (PLANTAR-group); and in 48/115 patients (41.7%) retrograde revascularization was performed by an additional retrograde puncture (TRANSPEDAL-group). Comorbidities, presence of calcification at pedal-plantar-loop/transpedal-access-site, and tibial-target-lesion was recorded. Endpoints were technical success (PLANTAR-group: crossing the plantar-arch; TRANSPEDAL-group: intravascular placement of the pedal access sheath), procedural success [residual stenosis <30% after plain old balloon angioplasty (POBA)], and procedural complications limb salvage and survival. Correlations between calcification at access site/tibial-target-lesion and technical/procedural-success were tested. RESULTS: Technical success was achieved in 50/67 (75%) patients of the PLANTAR-group and in 39/48 (81%) patients of the TRANSPEDAL-group (p=0.1). Procedural success was obtained in 23/67 (34%) patients of the PLANTAR-group and in 25/48 (52%) patients of the TRANSPEDAL-group (p=0.04). In 14/49 (29%) cases with calcification at the pedal-plantar loop, technical success was not achieved (p=0.04), and in 33/44 (75%) patients with calcification at the tibial-target-lesion, procedural success was not attained (PLANTAR-group) (p=0.026). In the TRANSPEDAL-group, correlations between calcification at access site/tibial-target-lesion and technical/procedural-success were not observed (p=0.2/p=0.4). In the PLANTAR-group, minor complications occurred in 13/67 (19%) and in the TRANSPEDAL-group in 4/48 patients (8%) (p=0.08). Limb salvage at 12 (18) months was 90% (82%) (PLANTAR-group; 95%CI 15.771-18.061) and 84% (76%) (TRANSPEDAL-group; 95%CI 14.475-17.823) (Log-rank p=0.46). Survival at 12 (18) months was 94% (86%) (PLANTAR-group; 95%CI 16.642-18.337) and 85% (77%) (TRANSPEDAL; 95%CI 14.296-17.621) (Log-rank p=0.098). CONCLUSION: Procedural success was significantly higher using the transpedal-access approach. Calcifications at pedal-plantar loop and target-lesion significantly influenced technical/procedural failure using the plantar-arch approach. No significant difference between both retrograde techniques in terms of feasibility, safety, and limb salvage/survival was found.

Automatic quality measurement of aortic contrast-enhanced CT angiographies for patient-specific dose optimization.

PURPOSE: Iodine-containing contrast agent (CA) used in contrast-enhanced CT angiography (CTA) can pose a health risk for patients. A system that adjusts the frequently used standard CA dose for individual patients based on their clinical parameters can be useful. As basis the quality of the image contrast in CTA volumes has to be determined, especially to recognize excessive contrast induced by CA overdosing. However, a manual assessment with a ROI-based image contrast classification is a time-consuming step in everyday clinical practice. METHODS: We propose a method to automate the contrast measurement of aortic CTA volumes. The proposed algorithm is based on the mean HU values in selected ROIs that were automatically positioned in the CTA volume. First, an automatic localization algorithm determines the CTA image slices for certain ROIs followed by the localization of these ROIs. A rule-based classification using the mean HU values in the ROIs categorizes images with insufficient, optimal and excessive contrast. RESULTS: In 95.89% (70 out of 73 CTAs obtained with the ulrich medical CT motion contrast media injector) the algorithm chose the same image contrast class as the radiological expert. The critical case of missing an overdose did not occur with a positive predicative value of 100%. CONCLUSION: The resulting system works well within our range of considered scan protocols detecting enhanced areas in CTA volumes. Our work automized an assessment for classifying CA-induced image contrast which reduces the time needed for medical practitioners to perform such an assessment manually.

Computer-Assisted Quality Assessment of Aortic CT Angiographies for Patient-Individual Dose Adjustment.

Iodine-containing contrast agents (CA) are important for enhanced image contrast in CT imaging especially in CT angiography (CTA). CA however poses a risk to the patient since it can e.g. harm the kidneys. In clinical routine often a standard dose is applied that does not take differences between individual patients into account. We propose a method that as a preliminary stage determines excessive image contrast and CA overdosing by assessing the image contrast in CTA images obtained with the ulrich medical CT motion contrast media injector with RIS/PACS interface. A resulting CA dose recommendation is linked to a set of clinical parameters collected for each assessed patient. We used the established data set to implement an automatic classification for individual CA dose adjustment. The classification determines similar cases of new patients to take on the associated CA dose adjustment recommendation. The computation of similar patient data is based on the previously collected patient-individual parameters. The study shows that as basis for a recommendations the largest proportion of patients receive too much CA. A first evaluation of the automatic classification showed an overall error rate of 22% to recognize the correct class for CA dose adjustments using a k-NN-Classifier and a leave-one-out method. The classification's positive predictive value for correctly assigning a CA overdosing was 85.71%.