Quality assurance of magnetic resonance spectroscopic imaging-derived metabolic data.
OBJECTIVE
Spatially resolved metabolite maps, as measured by magnetic resonance spectroscopic imaging (MRSI) methods, are being increasingly used to acquire metabolic information to guide therapy, with metabolite ratio maps perhaps providing the most diagnostic information. We present a quality assurance procedure for MRSI-derived metabolic data acquired ultimately for guiding conformal radiotherapy.


METHODS
An MRSI phantom filled with brain-mimicking solutions was custom-built with an insert holding eight vials containing calibration solutions of precisely varying metabolite concentrations that emulated increasing grade/density of brain tumor. Phantom metabolite ratios calculated from fully relaxed 1D, 2D, and 3D MRS data for each vial were compared with calibrated metabolite ratios acquired at 9.4 T. Additionally, 3D ratio maps were "discretized" to eight pseudoabnormality levels on a slice-by-slice basis and the accuracy of this procedure was verified.


RESULTS
Regression analysis revealed expected linear relationships between experimental and calibration metabolite ratios with intercepts close to zero for the three acquisition modes. 1D MRS data agreed most with theoretical considerations (regression coefficient, b = 0.969; intercept 0.008). The 2D (b = 1.049; intercept -0.199) and 3D (correlation coefficient r(2) = 0.9978-0.7336 for five slices) MRSI indicated reduced MRS data quality in regions of degraded B(0) and B(1) homogeneity. Pseudoabnormality levels were found to be consistent with expectations within regions of adequate B(0) homogeneity.


CONCLUSIONS
This simple phantom-based approach to generate baseline calibration curves for all MRS acquisition modes may be useful to identify temporal deviations from acceptable data quality in a routine clinical environment or for testing new MRS and MRSI acquisition software.
Hunjan
Sandeep
S
Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA 94305-5304, USA. shunjan@lucas.stanford.edu
Adalsteinsson
Elfar
E
Kim
Dong-Hyun
DH
Harsh
Griffith R
GR
Boyer
Arthur L
AL
Spielman
Daniel
D
Xing
Lei
L
D001224:Aspartic Acid / Q000031:analogs & derivatives* / Q000378:metabolism*; D015415:Biomarkers; D001921:Brain / Q000378:metabolism*; D002138:Calibration; D002794:Choline / Q000378:metabolism*; D008279:Magnetic Resonance Imaging / Q000592:standards; D009682:Magnetic Resonance Spectroscopy / Q000592:standards*; D019047:Phantoms, Imaging / Q000592:standards*; D020266:Radiotherapy, Conformal / Q000295:instrumentation / Q000592:standards*; D012044:Regression Analysis; D014057:Tomography, X-Ray Computed / Q000592:standards
D015415:Biomarkers; D001224:Aspartic Acid; C000179:N-acetylaspartate; D002794:Choline