Spectral characterization of tissues in high spectral and spatial resolution MR images: Implications for a classification-based synthetic CT algorithm

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To characterize the spectral parameters of tissues with high spectral and spatial resolution magnetic resonance images to be used as a foundation for a classification-based synthetic CT algorithm.


A phantom was constructed consisting of a section of fresh beef leg with bone embedded in 1% agarose gel. The high spectral and spatial (HiSS) resolution MR imaging sequence used had 1.0 mm in-plane resolution and 11.1 Hz spectral resolution. This sequence was used to image the phantom and one patient. Post-processing was performed off-line with IDL and included Fourier transformation of the time-domain data, labeling of fat and water peaks, and fitting the magnitude spectra with Lorentzian functions. Images of the peak height and peak integral of both the water and fat resonances were generated and analyzed. Several regions-of-interest (ROIs) were identified in phantom: bone marrow, cortical bone, adipose tissue, muscle, agar gel, and air; in the patient, no agar gel was present but an ROI of saline in the bladder was analyzed. All spectra were normalized by the noise within each voxel; thus, all parameters are reported in terms of signal-to-noise (SNR). The distributions of tissue spectral parameters were analyzed and scatterplots generated. Water peak height in cortical bone was compared to air using a nonparametric t-test. Composition of the various ROIs in terms of water, fat, or fat and water was also reported.


In phantom, the scatterplot of peak height (water versus fat) showed good separation of bone marrow and adipose tissue. Water versus fat integral scatterplot showed better separation of muscle and cortical bone than the peak height scatterplot. In the patient data, the distributions of water and fat peak heights were similar to that in phantom, with more overlap of bone marrow and cortical bone than observed in phantom. The relationship between bone marrow and cortical bone for peak integral was better separated than those of peak heights in the patient data. For both the phantom and patient, there was a significant amount of overlap in spectral parameters of cortical bone versus air.


These results show promising results for utilizing HiSS imaging in a classification-based synthetic CT algorithm. Cortical bone and air overlap was expected due to the short T2* of bone; reducing early echo times would improve the SNR in bone and image data from these early echoes could help differentiate these tissue types. Further studies need to be done with the goal of better separation of air and bone, and to extend the concept to volumetric imaging before it can be clinically applied.

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