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

Pallenberg, René; Fleitmann, Marja; Soika, Kira; Stroth, A; Gerlach, J; Fürschke, Alexander; Barkhausen, Jörg; Bischof, Arpad; Handels, Heinz

doi:10.1007/s11548-020-02238-4

Cited by 4 publications

(5 citation statements)

References 9 publications

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“…1 . In order to generate the ground truth of the classification, we implemented a semiautomatic evaluation in previous works, 15 which evaluates the quality of the image contrast based on mean HU taken in regions of interest (ROI). This work establishes the subsequent classification.…”

Section: Objectivesmentioning

confidence: 99%

“…In addition, radiologists have indicated which parameters are recorded routinely (rr). As a ground truth for the dose optimization in an earlier work 15 we implemented an image contrast quality measure similar to Behrendt et al 24 As shown in ►Fig. 2 under the guidance of radiological experts three ROIs were defined and placed in each CTA volume, respectively, in the aorta and the arteria femoralis communis.…”

Section: Clinical Study For Data Acquisitionmentioning

confidence: 99%

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

Fleitmann,

Uzunova,

Pallenberg

et al. 2024

Methods Inf Med

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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.

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Section: Objectivesmentioning

confidence: 99%

Section: Clinical Study For Data Acquisitionmentioning

confidence: 99%

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

Fleitmann,

Uzunova,

Pallenberg

et al. 2024

Methods Inf Med

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“…In total, the data set comprises 263 CTA images and the corresponding clinical parameters. The images were labeled as "excessive" based on the quality of their contrast [5][6][7]. Excessive means that the contrast is higher than necessary.…”

Section: Datamentioning

confidence: 99%

“…Excessive means that the contrast is higher than necessary. To determine this, the mean HU values of selected regions of interest were determined by a radiologist [6]. The data set includes CTA images of eight different recording protocols covering different body regions.…”

Section: Datamentioning

confidence: 99%

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

Pallenberg

Fleitmann

Stroth

et al. 2023

Caring Is Sharing – Exploiting the Value in Data for Health and Innovation

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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.

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“…Reference slice selection: Baumgartner et al [9] described a real-time CNN-based fetal US slice selection method, focusing on the temporal aspect of the reference slice selection, which differ from our problem as there is more than one reference slice solution. Pallenberg et al [10] described a template-based spatial slice selection for CT.…”

Section: Fetal Brain Roi Detection and Segmentationmentioning

confidence: 99%

Automatic linear measurements of the fetal brain on MRI with deep neural networks

et al. 2021

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Purpose: Timely, accurate and reliable assessment of fetal brain development is essential to reduce short and longterm risks to fetus and mother. Fetal MRI is increasingly used for fetal brain assessment. Three key biometric linear measurements important for fetal brain evaluation are Cerebral Biparietal Diameter (CBD), Bone Biparietal Diameter (BBD), and Trans-Cerebellum Diameter (TCD), obtained manually by expert radiologists on reference slices, which is time consuming and prone to human error. The aim of this study was to develop a fully automatic method computing the CBD, BBD and TCD measurements from fetal brain MRI. Methods:The input is fetal brain MRI volumes which may include the fetal body and the mother's abdomen. The outputs are the measurement values and reference slices on which the measurements were computed. The method, which follows the manual measurements principle, consists of five stages: 1) computation of a Region Of Interest that includes the fetal brain with an anisotropic 3D U-Net classifier; 2) reference slice selection with a Convolutional Neural Network; 3) slice-wise fetal brain structures segmentation with a multiclass U-Net classifier; 4) computation of the fetal brain midsagittal line and fetal brain orientation, and; 5) computation of the measurements. Results:Experimental results on 214 volumes for CBD, BBD and TCD measurements yielded a mean 𝐿 1 difference of 1.55mm, 1.45mm and 1.23mm respectively, and a Bland-Altman 95% confidence interval (𝐶𝐼 95 ) of 3.92mm, 3.98mm and 2.25mm respectively. These results are similar to the manual inter-observer variability, and are consistent across gestational ages and brain conditions. Conclusions:The proposed automatic method for computing biometric linear measurements of the fetal brain from MR imaging achieves human level performance. It has the potential of being a useful method for the assessment of fetal brain biometry in normal and pathological cases, and of improving routine clinical practice.

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

Cited by 4 publications

References 9 publications

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

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

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

Automatic linear measurements of the fetal brain on MRI with deep neural networks

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