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Abstract-Dynamic contrast-enhanced image data (perfusion data) are used to characterize regional tissue perfusion. Perfusion data consist of a sequence of images, acquired after a contrast agent bolus is applied. Perfusion data are used for diagnostic purposes in oncology, ischemic stroke assessment, or myocardial ischemia. The diagnostic evaluation of perfusion data is challenging, since the data are complex and exhibit various artifacts, e.g., motion artifacts. We provide an overview on existing methods to analyze and visualize CT and MR perfusion data. The integrated visualization of several 2D parameter maps, the 3D visualization of parameter volumes, and exploration techniques are discussed. An essential aspect in the diagnosis of perfusion data is the correlation between perfusion data and derived time-intensity curves as well as with other image data, in particular with high-resolution morphologic image data. We discuss visualization support with respect to the three major application areas: ischemic stroke diagnosis, breast tumor diagnosis, and the diagnosis of coronary heart disease.

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The aim of this study was to investigate the efficacy of a dedicated software tool for automated and semiautomated volume measurement in contrast-enhanced (CE) magnetic resonance mammography (MRM). Ninety-six breast lesions with histopathological workup (27 benign, 69 malignant) were re-evaluated by different volume measurement techniques. Volumes of all lesions were extracted automatically (AVM) and semiautomatically (SAVM) from CE 3D MRM and compared with manual 3D contour segmentation (manual volume measurement, MVM, reference measurement technique) and volume estimates based on maximum diameter measurement (MDM). Compared with MVM as reference method MDM, AVM and SAVM underestimated lesion volumes by 63.8%, 30.9% and 21.5%, respectively, with significantly different accuracy for benign (102.4%, 18.4% and 11.4%) and malignant (54.9%, 33.0% and 23.1%) lesions (p < 0.05). Inter- and intraobserver reproducibility was best for AVM (mean difference +/- 2SD, 1.0 +/- 9.7% and 1.8 +/- 12.1%) followed by SAVM (4.3 +/- 25.7% and 4.3 +/- 7.9%), MVM (2.3 +/- 38.2% and 8.6 +/- 31.8%) and MDM (33.9 +/- 128.4% and 9.3 +/- 55.9%). SAVM is more accurate for volume assessment of breast lesions than MDM and AVM. Volume measurement is less accurate for malignant than benign lesions.

The analysis of myocardial tissue with contrast-enhanced MR yields multiple parameters, which can be used to classify the examined tissue. Perfusion images are often distorted by motion, while late enhancement images are acquired with a different size and resolution. Therefore, it is common to reduce the analysis to a visual inspection, or to the examination of parameters related to the 17-segment-model proposed by the American Heart Association (AHA). As this simplification comes along with a considerable loss of information, our purpose is to provide methods for a more accurate analysis regarding topological and functional tissue features. In order to achieve this, we implemented registration methods for the motion correction of the perfusion sequence and the matching of the late enhancement information onto the perfusion image and vice versa. For the motion corrected perfusion sequence, vector images containing the voxel enhancement curves' semi-quantitative parameters are derived. The resulting vector images are combined with the late enhancement information and form the basis for the tissue examination. For the exploration of data we propose different modes: the inspection of the enhancement curves and parameter distribution in areas automatically segmented using the late enhancement information, the inspection of regions segmented in parameter space by user defined threshold intervals and the topological comparison of regions segmented with different settings. Results showed a more accurate detection of distorted regions in comparison to the AHA-model-based evaluation.