Comparison of MRI-derived vs. traditional estimations of fatty acid composition from MR spectroscopy signals.

INTRODUCTION: The composition of fatty acids in the body is gaining increasing interest, and can be followed up noninvasively by quantitative magnetic resonance spectroscopy (MRS). However, current MRS quantification methods have been shown to provide different quantitative results in terms of lipid signals, with possible varying outcomes for a given biological examination. Quantitative magnetic resonance imaging using multigradient echo sequence (MGE-MRI) has recently been added to MRS approaches. In contrast, these methods fit the undersampled magnetic resonance temporal signal with a simplified model function (expressing the triglyceride [TG] spectrum with only three TG parameters), specific implementations and prior knowledge. In this study, an adaptation of an MGE-MRI method to MRS lipid quantification is proposed. METHODS: Several versions of the method - with time data fully or undersampled, including or excluding the spectral peak T2 knowledge in the fitting - were compared theoretically and on Monte Carlo studies with a time-domain, peak-fitting approach. Robustness, repeatability and accuracy were also inspected on in vitro oil acquisitions and test-retest in vivo subcutaneous adipose tissue acquisitions, adding results from the reference LCModel method. RESULTS: On simulations, the proposed method provided TG parameter estimates with the smallest variability, but with a possible bias, which was mitigated by fitting on undersampled data and considering peak T2 values. For in vitro measurements, estimates for all approaches were correlated with theoretical values and the best concordance was found for the usual MRS method (LCModel and peak fitting). Limited in vivo test-retest variability was found (4.1% for PUFAindx, 0.6% for MUFAindx and 3.6% for SFAindx), as for LCModel (7.6% for PUFAindx, 7.8% for MUFAindx and 3.0% for SFAindx). CONCLUSION: This study shows that fitting the three TG parameters directly on MRS data is one valuable solution to circumvent the poor conditioning of the MRS quantification problem.

In vivo MRS for the assessment of mouse colon using a dedicated endorectal coil: initial findings.

Inflammatory bowel disease is a common group of inflammation conditions that can affect the colon and the rectum. These pathologies require a careful follow-up of patients to prevent the development of colorectal cancer. Currently, conventional endoscopy is used to depict alterations of the intestinal walls, and biopsies are performed on suspicious lesions for further analysis (histology). MRS enables the in vivo analysis of biochemical content of tissues (i.e. without removing any samples). Combined with dedicated endorectal coils (ERCs), MRS provides new ways of characterizing alterations of tissues. An MRS in vivo protocol was specifically set up on healthy mice and on mice chemically treated to induce colitis. Acquisitions were performed on a 4.7 T system using a linear volume birdcage coil for the transmission of the B1 magnetic field, and a dedicated ERC was used for signal reception. Colon-wall complex, lumen and visceral fat were assessed on healthy and treated mice with voxel sizes ranging from 0.125 µL to 2 µL while keeping acquisition times below 3 min. The acquired spectra show various biochemical contents such as α- and ß-methylene but also glycerol backbone and diacyl. Choline was detected in tumoral regions. Visceral fat regions display a high lipid content with no water, whereas colon-wall complex exhibits both high lipid and high water contents. To the best of our knowledge, this is the first time that in vivo MRS using an ERC has been performed in the assessment of colon walls and surrounding structures. It provides keys for the in vivo characterization of small local suspicious lesions and offers complementary solutions to biopsies.

Semi-parametric time-domain quantification of HR-MAS data from prostate tissue.

High Resolution--Magic Angle Spinning (HR-MAS) spectroscopy provides rich biochemical profiles that require accurate quantification to permit biomarker identification and to understand the underlying pathological mechanisms. Meanwhile, quantification of HR-MAS data from prostate tissue samples is challenging due to significant overlap between the resonant peaks, the presence of short T2* metabolites such as citrate or polyamines (T2 from 25 to 100 msec) and macromolecules, and variations in chemical shifts and T2*s within a metabolite's spin systems. Since existing methods do not address these challenges completely, a new quantification method was developed and optimized for HR-MAS data acquired with an ultra short T(E) and over 30,000 data points. The proposed method, named HR-QUEST (High Resolution--QUEST), iteratively employs the QUEST time-domain semi-parametric strategy with a new model function that incorporates prior knowledge from whole and subdivided metabolite signals. With these features, HR-QUEST is able to independently fit the chemical shifts and T2*s of a metabolite's spin systems, a necessity for HR-MAS data. By using the iterative fitting approach, it is able to account for significant contributions from macromolecules and to handle shorter T2 metabolites, such as citrate and polyamines. After subdividing the necessary metabolite basis signals, the root mean square (RMS) of the residual was reduced by 52% for measured HR-MAS data from prostate tissue. Monte Carlo studies on simulated spectra with varied macromolecular contributions showed that the iterative fitting approach (6 iterations) coupled with inclusion of long T2 macromolecule components in the basis set improve the quality of the fit, as assessed by the reduction of the RMS of the residual and of the RMS error of the metabolite signal estimate, by 27% and 71% respectively. With this optimized configuration, HR-QUEST was applied to measured HR-MAS prostate data and reliably quantified 16 metabolites and reference signals with estimated Cramér Rao Bounds ≤5%.