Litcius/Paper detail

Assessing the reproducibility of high temporal and spatial resolution dynamic contrast-enhanced magnetic resonance imaging in patients with gliomas

Woo Hyeon Lim, Joon Sik Park, Jaeseok Park, Seung Hong Choi

2021Scientific Reports12 citationsDOIOpen Access PDF

Abstract

Abstract Temporal and spatial resolution of dynamic contrast-enhanced MR imaging (DCE-MRI) is critical to reproducibility, and the reproducibility of high-resolution (HR) DCE-MRI was evaluated. Thirty consecutive patients suspected to have brain tumors were prospectively enrolled with written informed consent. All patients underwent both HR-DCE (voxel size, 1.1 × 1.1 × 1.1 mm 3 ; scan interval, 1.6 s) and conventional DCE (C-DCE; voxel size, 1.25 × 1.25 × 3.0 mm 3 ; scan interval, 4.0 s) MRI. Regions of interests (ROIs) for enhancing lesions were segmented twice in each patient with glioblastoma ( n = 7) to calculate DCE parameters (K trans , V p , and V e ). Intraclass correlation coefficients (ICCs) of DCE parameters were obtained. In patients with gliomas ( n = 25), arterial input functions (AIFs) and DCE parameters derived from T2 hyperintense lesions were obtained, and DCE parameters were compared according to WHO grades. ICCs of HR-DCE parameters were good to excellent (0.84–0.95), and ICCs of C-DCE parameters were moderate to excellent (0.66–0.96). Maximal signal intensity and wash-in slope of AIFs from HR-DCE MRI were significantly greater than those from C-DCE MRI (31.85 vs. 7.09 and 2.14 vs. 0.63; p < 0.001). Both 95 th percentile K trans and V e from HR-DCE and C-DCE MRI could differentiate grade 4 from grade 2 and 3 gliomas ( p < 0.05). In conclusion, HR-DCE parameters generally showed better reproducibility than C-DCE parameters, and HR-DCE MRI provided better quality of AIFs.

Topics & Concepts

ReproducibilityDynamic contrastMedicineIntraclass correlationMagnetic resonance imagingPercentileNuclear medicineDynamic contrast-enhanced MRIVoxelRadiologyMathematicsStatisticsMRI in cancer diagnosisAdvanced Neuroimaging Techniques and ApplicationsRadiomics and Machine Learning in Medical Imaging