Dataset Information

Human cardiac 31P magnetic resonance spectroscopy at 7 Tesla.

ABSTRACT:

Purpose

Phosphorus magnetic resonance spectroscopy ((31)P-MRS) affords unique insight into cardiac energetics but has a low intrinsic signal-to-noise ratio (SNR) in humans. Theory predicts an increased (31)P-MRS SNR at 7T, offering exciting possibilities to better investigate cardiac metabolism. We therefore compare the performance of human cardiac (31)P-MRS at 7T to 3T, and measure T1s for (31)P metabolites at 7T.

Methods

Matched (31)P-MRS data were acquired at 3T and 7T, on nine normal volunteers. A novel Look-Locker CSI acquisition and fitting approach was used to measure T1s on six normal volunteers.

Results

T1s in the heart at 7T were: phosphocreatine (PCr) 3.05 ± 0.41s, γ-ATP 1.82 ± 0.09s, α-ATP 1.39 ± 0.09s, β-ATP 1.02 ± 0.17s and 2,3-DPG (2,3-diphosphoglycerate) 3.05 ± 0.41s (N = 6). In the field comparison (N = 9), PCr SNR increased 2.8× at 7T relative to 3T, the Cramer-Ráo uncertainty (CRLB) in PCr concentration decreased 2.4×, the mean CRLB in PCr/ATP decreased 2.7× and the PCr/ATP SD decreased 2×.

Conclusion

Cardiac (31)P-MRS at 7T has higher SNR and the spectra can be quantified more precisely than at 3T. Cardiac (31)P T1s are shorter at 7T than at 3T. We predict that 7T will become the field strength of choice for cardiac (31)P-MRS.

SUBMITTER: Rodgers CT

PROVIDER: S-EPMC4106879 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:The heart's energy demand per gram of tissue is the body's highest and creatine kinase (CK) metabolism, its primary energy reserve, is compromised in common heart diseases. Here, neural-network analysis is used to test whether noninvasive phosphorus (31P) cardiovascular magnetic resonance spectroscopy (CMRS) measurements of cardiac adenosine triphosphate (ATP) energy, phosphocreatine (PCr), the first-order CK reaction rate kf, and the rate of ATP synthesis through CK (CK flux), can predict specific human heart disease and clinical severity. METHODS:The data comprised the extant 178 complete sets of PCr and ATP concentrations, kf, and CK flux data from human CMRS studies performed on clinical 1.5 and 3 Tesla scanners. Healthy subjects and patients with nonischemic cardiomyopathy, dilated (DCM) or hypertrophic disease, New York Heart Association (NYHA) class I-IV heart failure (HF), or with anterior myocardial infarction are included. Three-layer neural-networks were created to classify disease and to differentiate DCM, hypertrophy and clinical NYHA class in HF patients using leave-one-out training. Network performance was assessed using 'confusion matrices' and 'area-under-the-curve' (AUC) analyses of 'receiver operating curves'. Possible methodological bias and network imbalance were tested by segregating 1.5 and 3 Tesla data, and by data augmentation by random interpolation of nearest neighbors, respectively. RESULTS:The network differentiated healthy, HF and non-HF cardiac disease with an overall accuracy of 84% and AUC?>?90% for each category using the four CK metabolic parameters, alone. HF patients with DCM, hypertrophy, and different NYHA severity were differentiated with ~?80% overall accuracy independent of CMRS methodology. CONCLUSIONS:While sample-size was limited in some sub-classes, a neural network classifier applied to noninvasive cardiac 31P CMRS data, could serve as a metabolic biomarker for common disease types and HF severity with clinically-relevant accuracy. Moreover, the network's ability to individually classify disease and HF severity using CK metabolism alone, implies an intimate relationship between CK metabolism and disease, with subtle underlying phenotypic differences that enable their differentiation. TRIAL REGISTRATION:ClinicalTrials.gov Identifier: NCT00181259.