Context Sickle cell anemia (SCA) is a chronic illness causing progressive deterioration in quality of life. Brain dysfunction may be the most important and least studied problem affecting individuals with this disease. Objective To measure neurocognitive dysfunction in neurologically asymptomatic adults with SCA vs healthy control individuals. Design, Setting, and Participants Cross-sectional study comparing neuropsychological function and neuroimaging findings in neurologically asymptomatic adults with SCA and controls from 12 SCA centers, conducted between December 2004 and May 2008. Participants were patients with SCA (hemoglobin [Hb] SS and hemoglobin level ≤10 mg/dL) aged 19 to 55 years and of African descent (n=149) or community controls (Hb AA and normal hemoglobin level) (n=47). Participants were stratified on age, sex, and education. Main Outcome Measures The primary outcome measure was nonverbal function assessed by the Wechsler Adult Intelligence Scale, third edition (WAIS-III) Performance IQ Index. Secondary exploratory outcomes included performance on neurocognitive tests of executive function, memory, attention, and language and magnetic resonance imaging measurement of total intracranial and hippocampal volume, cortical gray and white matter, and lacunae. Results The mean WAIS-III Performance IQ score of patients with SCA was significantly lower than that of controls (adjusted mean, 86.69 for patients with SCA vs 95.19 for controls [mean difference, −5.50; 95% confidence interval {CI}, −9.55 to −1.44]; P =.008), with 33% performing more than 1 SD (<85) below the population mean. Among secondary measures, differences were observed in adjusted mean values for global cognitive function (full-scale IQ) (90.47 for patients with SCA vs 95.66 for controls [mean difference, −5.19; 95% CI, −9.24 to −1.13]; P =.01), working memory (90.75 vs 95.25 [mean difference, −4.50; 95% CI, −8.55 to −0.45]; P =.03), processing speed (86.50 vs 97.95 [mean difference, −11.46; 95% CI, −15.51 to −7.40]; P <.001), and measures of executive function. Anemia was associated with poorer neurocognitive function in older patients. No differences in total gray matter or hippocampal volume were observed. Lacunae were more frequent in patients with SCA but not independently related to neurocognitive function. Conclusion Compared with healthy controls, adults with SCA had poorer cognitive performance, which was associated with anemia and age.

Abstract-Magnetic resonance spectroscopic imaging (MRSI) is a powerful tool capable of providing spatially localized maps of metabolite concentrations. Its utility, however, is often depreciated by spectral leakage artifacts resulting from low spatial resolution measurements through an effort to reduce acquisition times. Though model-based techniques can help circumvent these drawbacks, they require strong prior knowledge, and can introduce additional artifacts when the underlying models are inaccurate. We introduce a novel scheme in which a generative model is estimated from the raw MRSI data via a regularized variational framework that minimizes the model approximation error within a measurement-prescribed subspace. As additional a priori information, our approach relies only upon a measured field inhomogeneity map at high spatial resolution. We demonstrate the feasibility of our approach on both synthetic and experimental data.

Purpose Epitomizing the advantages of ultra short echo time and no chemical shift displacement error, high‐resolution‐free induction decay magnetic resonance spectroscopic imaging (FID‐MRSI) sequences have proven to be highly effective in providing unbiased characterizations of metabolite distributions. However, its merits are often overshadowed in high‐resolution settings by reduced signal‐to‐noise ratios resulting from the smaller voxel volumes procured by extensive phase encoding and the related acquisition times. Methods To address these limitations, we here propose an acquisition and reconstruction scheme that offers both implicit dataset denoising and acquisition acceleration. Specifically, a slice selective high‐resolution FID‐MRSI sequence was implemented. Spectroscopic datasets were processed to remove fat contamination, and then reconstructed using a total generalized variation (TGV) regularized low‐rank model. We further measured reconstruction performance for random undersampled data to assess feasibility of a compressed‐sensing SENSE acceleration scheme. Performance of the lipid suppression was assessed using an ad hoc phantom, while that of the low‐rank TGV reconstruction model was benchmarked using simulated MRSI data. To assess real‐world performance, 2D FID‐MRSI acquisitions of the brain in healthy volunteers were reconstructed using the proposed framework. Results Results from the phantom and simulated data demonstrate that skull lipid contamination is effectively removed and that data reconstruction quality is improved with the low‐rank TGV model. Also, we demonstrated that the presented acquisition and reconstruction methods are compatible with a compressed‐sensing SENSE acceleration scheme. Conclusions An original reconstruction pipeline for 2D 1H‐FID‐MRSI datasets was presented that places high‐resolution metabolite mapping on 3T MR scanners within clinically feasible limits.

The notion of non-invasive, high-resolution spatial mapping of metabolite concentrations has long enticed the medical community. While magnetic resonance spectroscopic imaging (MRSI) is capable of achieving the requisite spatio-spectral localization, it has traditionally been encumbered by significant resolution constraints that have thus far undermined its clinical utility. To surpass these obstacles, research efforts have primarily focused on hardware enhancements or the development of accelerated acquisition strategies to improve the experimental sensitivity per unit time. Concomitantly, a number of innovative reconstruction techniques have emerged as alternatives to the standard inverse discrete Fourier transform (DFT). While perhaps lesser known, these latter methods strive to effect commensurate resolution gains by exploiting known properties of the underlying MRSI signal in concert with advanced image and signal processing techniques. This review article aims to aggregate and provide an overview of the past few decades of so-called "superresolution" MRSI reconstruction methodologies, and to introduce readers to current state-of-the-art approaches. A number of perspectives are then offered as to the future of high-resolution MRSI, with a particular focus on translation into clinical settings.