Vascular malformations and tumors comprise a wide, heterogeneous spectrum of lesions that often represent a diagnostic and therapeutic challenge. Frequent use of an inaccurate nomenclature has led to considerable confusion. Since the treatment strategy depends on the type of vascular anomaly, correct diagnosis and classification are crucial. Magnetic resonance (MR) imaging is the most valuable modality for classification of vascular anomalies because it accurately demonstrates their extension and their anatomic relationship to adjacent structures. A comprehensive assessment of vascular anomalies requires functional analysis of the involved vessels. Dynamic time-resolved contrast material-enhanced MR angiography provides information about the hemodynamics of vascular anomalies and allows differentiation of high-flow and low-flow vascular malformations. Furthermore, MR imaging is useful in assessment of treatment success and establishment of a long-term management strategy. Radiologists should be familiar with the clinical and MR imaging features that aid in diagnosis of vascular anomalies and their proper classification. Furthermore, they should be familiar with MR imaging protocols optimized for evaluation of vascular anomalies and with their posttreatment appearances. Supplemental material available at http://radiographics.rsna.org/lookup/suppl/doi:10.1148/rg.315105213/-/DC1.

http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.2531082269/-/DC1.

Purpose: To develop an automated segmentation method to differentiate the ventilated lung volume on 3 He magnetic resonance imaging (MRI). Materials and Methods:Computational processing (CP) for each subject consisted of the following three essential steps: 1) inhomogeneity bias correction, 2) whole lung segmentation, and 3) subdivision of the lung segmentation into regions of similar ventilation. Evaluation consisted of two comparative analyses: i) comparison of the number of defects scored by two human readers in 43 subjects, and ii) simultaneous truth and performance level estimation (STAPLE) in 18 subjects in which the ventilation defects were manually segmented by four human readers. Conclusion:We developed and evaluated an automated method for quantifying the ventilated lung volume on 3 He MRI. The findings strongly indicate that our proposed algorithmic processing may be a reliable, automatic method for quantitating ventilation defects. (2) investigatory technique that provides high spatial and temporal resolution images of the air spaces of the lungs and has been used to investigate a variety of lung diseases. Automated or semiautomated approaches for classifying areas of varying degrees of ventilation are of potential benefit for facilitating such investigation.Although various approaches have been previously proposed in the literature, several potential confounds continue to complicate the task. These confounds include the presence of a low-frequency intensity bias due to the inhomogeneity field, ventilation defects on the boundary of the lung complicating whole lung segmentation from the background, and the intensity signature of the vasculature, which appear in the same intensity range as ventilation defects. Further complicating 3 He MRI quantitative assessment is that signal intensity is not solely dependent on the density of 3 He atoms in each pixel and, therefore, does not directly reflect regional ventilation. This is due to the fact that the coil transmit and receive sensitivity and the regional partial pressure of oxygen within the lung contribute to the measured signal intensity. For this reason, the 3 He spin-density images provide information about the homogeneity of ventilation within the lung but do not provide a quantitative measure of absolute regional ventilation.We present an automated algorithmic pipeline for ventilation-based partitioning of the lungs in hyperpolarized 3 He MRI which attempts to address the aforementioned complexities. The workflow of the major components of this pipeline is illustrated in Fig. 1. Offline processing includes building the unbiased template and statistical description of the lung shape from sets of normal data. These two descriptions of the data are then used for individual subject processing. We describe the major components of this pipeline and compare its effectiveness in identifying ventilation defects with human readers. Our software pipeline is made available to the research community as open source software through our Advanced Normalization ...

The proposed framework yields accurate automated quantification in near real time. CNNs drastically reduce processing time after offline model construction and demonstrate significant future potential for facilitating quantitative analysis of functional lung MRI.