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

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. Tracer-kinetic (K,v,v) 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 artefacts 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.