Dynamic contrast-enhanced imaging using magnetic resonance (DCE-MRI) has become a widely utilized and adopted clinical tool for non-invasive assessment of the vascular support of various tumors [1-3]. DCE analysis is performed using a time-series of images acquired after injecting contrast material (tracer), and investigating temporal changes of contrast attenuation in vessels and tissues (e.g., a tumor) [4]. Biomarkers extracted from such imagery have been shown to be correlated with physiological and molecular processes which can be observed in tumor angiogenesis (they are morphologically characterized by...

Data: Our study encompassed real-life image data acquired for 44 low-grade glioma WHO II DCE-MRI patients (age: 40.8±13.9, 23 males; 3T MRI; The MRI sequences were acquired in the axial plane with a field of view of 230x190 mm, matrix size 256x256 and 1 mm slice thickness with no slice gap). We used the series with TE=386 ms, TR=5000 ms, and inversion time of 1800 ms for segmentation of brain tumors. The scans were analyzed retrospectively: ground truth (GT) lesion delineation was provided by an...

Our experimental validation shed light on the following issues concerned with the ECONIB performance. Tofts’ modeling The average RMS error for Ktrans obtained for the QIBA phantom dataset was 1.510%, and for ve it amounted to 2.782%. Brain tumor detection and segmentation The experimental part which was aimed at verifying the detection and segmentation performance of ECONIB consisted of two experiments – the first experiment was performed using our image dataset encompassing 44 WHO II (low-grade glioma) patients, and the second was executed over two...

The purpose of this work was to verify our fully-automatic DCE analysis approach. We applied it to process low-grade glioma patient data and extract DCE biomarkers (Ktransand vecontrast-concentration parameters, alongside the corresponding parameter maps) without any user intervention. Our experimental study included verification of ECONIB’s most important steps (brain tumor detection and segmentation, vascular input region determination and modeling of contrast flow in tissue) and showed that it delivers high-quality results over real-life and benchmark datasets. Finally, the average processing time of our technique was...

[1]Kenneth A. Miles. Functional computed tomography in oncology. European Journal of Cancer, 38(16):2079–2084, 2002. [2]Pradeep Kumar Gupta, Jitender Saini, Prativa Sahoo, Rana Patir, Sunita Ahlawat, Manish Beniwal, Kandavel Thennarasu, Vani Santosh, Rakesh Kumar Gupta.Role of Dynamic Contrast-Enhanced Perfusion Magnetic Resonance Imaging in Grading of Pediatric Brain Tumors on 3T.Pediatric Neurosurgery, 52(5):298-305, 2017. [3] Vaios Hatzoglou, Jamie Tisnado, Alpesh Mehta, Kyung K. Peck, Mariza Daras, Antonio M. Omuro, Kathryn Beal, Andrei I. Holodny. Dynamic contrast‐enhanced MRI perfusion for differentiating between melanoma and lung cancer brain metastases....