Evaluating feasibility of high resolution T1-perfusion MRI with whole brain coverage using compressed SENSE: Application to glioma grading.

Abstract Purpose To evaluate the efficacy of optimized T1-Perfusion MRI protocol(protocol-2) with whole brain coverage and improved spatial resolution using Compressed-SENSE(CSENSE) to differentiate high-grade-glioma(HGG) and low-grade-glioma(LGG) and to compare it with the conventional protocol(protocol-1) with partial brain coverage used in our center. Methods This study included MRI data from 5 healthy volunteers, a phantom and 126 brain tumor patients. Current study had two parts: To analyze the effect of CSENSE on 3D-T1-weighted(W) fast-field-echo(FFE) images, T1-W, dual-PDT2-W turbo-spin-echo images and T1 maps, and to evaluate the performance of high resolution T1-Perfusion MRI protocol with whole brain coverage optimized using CSENSE. Coefficient-of-Variation(COV), Relative-Percentage-Error(RPE), Normalized-Mean-Squared-Error(NMSE) and qualitative scoring were used for the former study. The performance of tracer-kinetic(Ktrans,ve,vp) and hemodynamic(rCBV,rCBF) parameters computed from both protocols were used to differentiate LGG and HGG. Results The image quality of all structural images was found to be of diagnostic quality till R = 4. NMSE in healthy T1-W-FFE images and COV in phantom images increased with-respect-to R and images provided optimum quality till R = 4. Structural images and maps exhibited artifacts from R = 6. All parameters in tumor tissue and hemodynamic parameters in healthy gray matter tissue computed from both protocols were not significantly different. Parameters computed from protocol-2 performed better in terms of glioma grading. For both protocols, rCBF performed least (AUC = 0.759 and 0.851) and combination of all parameters performed best (AUC = 0.890 and 0.964). Conclusion CSENSE(R = 4) can be used to improve the resolution and brain coverage for T1-Perfusion analysis used to differentiate gliomas.