MR Spectroscopic Imaging of Peripheral Zone in Prostate Cancer Using a 3T MRI Scanner: Endorectal versus External Phased Array Coils.

Magnetic resonance spectroscopic imaging (MRSI) detects alterations in major prostate metabolites, such as citrate (Cit), creatine (Cr), and choline (Ch). We evaluated the sensitivity and accuracy of three-dimensional MRSI of prostate using an endorectal compared to an external phased array "receive" coil on a 3T MRI scanner. Eighteen patients with prostate cancer (PCa) who underwent endorectal MR imaging and proton (1H) MRSI were included in this study. Immediately after the endorectal MRSI scan, the PCa patients were scanned with the external phased array coil. The endorectal coil-detected metabolite ratio [(Ch+Cr)/Cit] was significantly higher in cancer locations (1.667 ± 0.663) compared to non-cancer locations (0.978 ± 0.420) (P < 0.001). Similarly, for the external phased array, the ratio was significantly higher in cancer locations (1.070 ± 0.525) compared to non-cancer locations (0.521 ± 0.310) (P < 0.001). The sensitivity and accuracy of cancer detection were 81% and 78% using the endorectal 'receive' coil, and 69% and 75%, respectively using the external phased array 'receive' coil

Phase I study of dose escalation to dominant intraprostatic lesions using high-dose-rate brachytherapy.

PurposeRadiation dose escalation for prostate cancer improves biochemical control but is limited by toxicity. Magnetic resonance spectroscopic imaging (MRSI) can define dominant intraprostatic lesions (DIL). This phase I study evaluated dose escalation to MRSI-defined DIL using high-dose-rate (HDR) brachytherapy.Material and methodsEnrollment was closed early due to low accrual. Ten patients with prostate cancer (T2a-3b, Gleason 6-9, PSA < 20) underwent pre-treatment MRSI, and eight patients had one to three DIL identified. The eight enrolled patients received external beam radiation therapy to 45 Gy and HDR brachytherapy boost to the prostate of 19 Gy in 2 fractions. MRSI images were registered to planning CT images and DIL dose-escalated up to 150% of prescription dose while maintaining normal tissue constraints. The primary endpoint was genitourinary (GU) toxicity.ResultsThe median total DIL volume was 1.31 ml (range, 0.67-6.33 ml). Median DIL boost was 130% of prescription dose (range, 110-150%). Median urethra V120 was 0.15 ml (range, 0-0.4 ml) and median rectum V75 was 0.74 ml (range, 0.1-1.0 ml). Three patients had acute grade 2 GU toxicity, and two patients had late grade 2 GU toxicity. No patients had grade 2 or higher gastrointestinal toxicity, and no grade 3 or higher toxicities were noted. There were no biochemical failures with median follow-up of 4.9 years (range, 2-8.5 years).ConclusionsDose escalation to MRSI-defined DIL is feasible. Toxicity was low but incompletely assessed due to limited patients' enrollment

Manifold Learning in MR spectroscopy using nonlinear dimensionality reduction and unsupervised clustering

Purpose To investigate whether nonlinear dimensionality reduction improves unsupervised classification of 1H MRS brain tumor data compared with a linear method. Methods In vivo single-voxel 1H magnetic resonance spectroscopy (55 patients) and 1H magnetic resonance spectroscopy imaging (MRSI) (29 patients) data were acquired from histopathologically diagnosed gliomas. Data reduction using Laplacian eigenmaps (LE) or independent component analysis (ICA) was followed by k-means clustering or agglomerative hierarchical clustering (AHC) for unsupervised learning to assess tumor grade and for tissue type segmentation of MRSI data. Results An accuracy of 93% in classification of glioma grade II and grade IV, with 100% accuracy in distinguishing tumor and normal spectra, was obtained by LE with unsupervised clustering, but not with the combination of k-means and ICA. With 1H MRSI data, LE provided a more linear distribution of data for cluster analysis and better cluster stability than ICA. LE combined with k-means or AHC provided 91% accuracy for classifying tumor grade and 100% accuracy for identifying normal tissue voxels. Color-coded visualization of normal brain, tumor core, and infiltration regions was achieved with LE combined with AHC. Conclusion Purpose To investigate whether nonlinear dimensionality reduction improves unsupervised classification of 1H MRS brain tumor data compared with a linear method. Methods In vivo single-voxel 1H magnetic resonance spectroscopy (55 patients) and 1H magnetic resonance spectroscopy imaging (MRSI) (29 patients) data were acquired from histopathologically diagnosed gliomas. Data reduction using Laplacian eigenmaps (LE) or independent component analysis (ICA) was followed by k-means clustering or agglomerative hierarchical clustering (AHC) for unsupervised learning to assess tumor grade and for tissue type segmentation of MRSI data. Results An accuracy of 93% in classification of glioma grade II and grade IV, with 100% accuracy in distinguishing tumor and normal spectra, was obtained by LE with unsupervised clustering, but not with the combination of k-means and ICA. With 1H MRSI data, LE provided a more linear distribution of data for cluster analysis and better cluster stability than ICA. LE combined with k-means or AHC provided 91% accuracy for classifying tumor grade and 100% accuracy for identifying normal tissue voxels. Color-coded visualization of normal brain, tumor core, and infiltration regions was achieved with LE combined with AHC. Conclusion The LE method is promising for unsupervised clustering to separate brain and tumor tissue with automated color-coding for visualization of 1H MRSI data after cluster analysis

N-Acetyl and Glutamatergic Neurometabolites in Perisylvian Brain Regions of Methamphetamine Users.

Background:Methamphetamine induces neuronal N-acetyl-aspartate synthesis in preclinical studies. In a preliminary human proton magnetic resonance spectroscopic imaging investigation, we also observed that N-acetyl-aspartate+N-acetyl-aspartyl-glutamate in right inferior frontal cortex correlated with years of heavy methamphetamine abuse. In the same brain region, glutamate+glutamine is lower in methamphetamine users than in controls and is negatively correlated with depression. N-acetyl and glutamatergic neurochemistries therefore merit further investigation in methamphetamine abuse and the associated mood symptoms. Methods:Magnetic resonance spectroscopic imaging was used to measure N-acetyl-aspartate+N-acetyl-aspartyl-glutamate and glutamate+glutamine in bilateral inferior frontal cortex and insula, a neighboring perisylvian region affected by methamphetamine, of 45 abstinent methamphetamine-dependent and 45 healthy control participants. Regional neurometabolite levels were tested for group differences and associations with duration of heavy methamphetamine use, depressive symptoms, and state anxiety. Results:In right inferior frontal cortex, N-acetyl-aspartate+N-acetyl-aspartyl-glutamate correlated with years of heavy methamphetamine use (r = +0.45); glutamate+glutamine was lower in methamphetamine users than in controls (9.3%) and correlated negatively with depressive symptoms (r = -0.44). In left insula, N-acetyl-aspartate+N-acetyl-aspartyl-glutamate was 9.1% higher in methamphetamine users than controls. In right insula, glutamate+glutamine was 12.3% lower in methamphetamine users than controls and correlated negatively with depressive symptoms (r = -0.51) and state anxiety (r = -0.47). Conclusions:The inferior frontal cortex and insula show methamphetamine-related abnormalities, consistent with prior observations of increased cortical N-acetyl-aspartate in methamphetamine-exposed animal models and associations between cortical glutamate and mood in human methamphetamine users

A Multiscale Approach for Statistical Characterization of Functional Images

Increasingly, scientific studies yield functional image data, in which the observed data consist of sets of curves recorded on the pixels of the image. Examples include temporal brain response intensities measured by fMRI and NMR frequency spectra measured at each pixel. This article presents a new methodology for improving the characterization of pixels in functional imaging, formulated as a spatial curve clustering problem. Our method operates on curves as a unit. It is nonparametric and involves multiple stages: (i) wavelet thresholding, aggregation, and Neyman truncation to effectively reduce dimensionality; (ii) clustering based on an extended EM algorithm; and (iii) multiscale penalized dyadic partitioning to create a spatial segmentation. We motivate the different stages with theoretical considerations and arguments, and illustrate the overall procedure on simulated and real datasets. Our method appears to offer substantial improvements over monoscale pixel-wise methods. An Appendix which gives some theoretical justifications of the methodology, computer code, documentation and dataset are available in the online supplements

Evaluating metabolites in patients with major depressive disorder who received mindfulness-based cognitive therapy and healthy controls using short echo MRSI at 7 Tesla.

ObjectivesOur aim was to evaluate differences in metabolite levels between unmedicated patients with major depressive disorder (MDD) and healthy controls, to assess changes in metabolites in patients after they completed an 8-week course of mindfulness-based cognitive therapy (MBCT), and to exam the correlation between metabolites and depression severity.Materials and methodsSixteen patients with MDD and ten age- and gender-matched healthy controls were studied using 3D short echo-time (20 ms) magnetic resonance spectroscopic imaging (MRSI) at 7 Tesla. Relative metabolite ratios were estimated in five regions of interest corresponding to insula, anterior cingulate cortex (ACC), caudate, putamen, and thalamus.ResultsIn all cases, MBCT reduced severity of depression. The ratio of total choline-containing compounds/total creatine (tCr) in the right caudate was significantly increased compared to that in healthy controls, while ratios of N-acetyl aspartate (NAA)/tCr in the left ACC, myo-inositol/tCr in the right insula, and glutathione/tCr in the left putamen were significantly decreased. At baseline, the severity of depression was negatively correlated with my-inositol/tCr in the left insula and putamen. The improvement in depression severity was significantly associated with changes in NAA/tCr in the left ACC.ConclusionsThis study has successfully evaluated regional differences in metabolites for patients with MDD who received MBCT treatment and in controls using 7 Tesla MRSI

In vivo metabolic imaging of Traumatic Brain Injury.

Complex alterations in cerebral energetic metabolism arise after traumatic brain injury (TBI). To date, methods allowing for metabolic evaluation are highly invasive, limiting our understanding of metabolic impairments associated with TBI pathogenesis. We investigated whether 13C MRSI of hyperpolarized (HP) [1-13C] pyruvate, a non-invasive metabolic imaging method, could detect metabolic changes in controlled cortical injury (CCI) mice (n = 57). Our results show that HP [1-13C] lactate-to-pyruvate ratios were increased in the injured cortex at acute (12/24 hours) and sub-acute (7 days) time points after injury, in line with decreased pyruvate dehydrogenase (PDH) activity, suggesting impairment of the oxidative phosphorylation pathway. We then used the colony-stimulating factor-1 receptor inhibitor PLX5622 to deplete brain resident microglia prior to and after CCI, in order to confirm that modulations of HP [1-13C] lactate-to-pyruvate ratios were linked to microglial activation. Despite CCI, the HP [1-13C] lactate-to-pyruvate ratio at the injury cortex of microglia-depleted animals at 7 days post-injury remained unchanged compared to contralateral hemisphere, and PDH activity was not affected. Altogether, our results demonstrate that HP [1-13C] pyruvate has great potential for in vivo non-invasive detection of cerebral metabolism post-TBI, providing a new tool to monitor the effect of therapies targeting microglia/macrophages activation after TBI

Elevated glutamatergic compounds in pregenual anterior cingulate in pediatric autism spectrum disorder demonstrated by 1H MRS and 1H MRSI.

Recent research in autism spectrum disorder (ASD) has aroused interest in anterior cingulate cortex and in the neurometabolite glutamate. We report two studies of pregenual anterior cingulate cortex (pACC) in pediatric ASD. First, we acquired in vivo single-voxel proton magnetic resonance spectroscopy ((1)H MRS) in 8 children with ASD and 10 typically developing controls who were well matched for age, but with fewer males and higher IQ. In the ASD group in midline pACC, we found mean 17.7% elevation of glutamate + glutamine (Glx) (p<0.05) and 21.2% (p<0.001) decrement in creatine + phosphocreatine (Cr). We then performed a larger (26 subjects with ASD, 16 controls) follow-up study in samples now matched for age, gender, and IQ using proton magnetic resonance spectroscopic imaging ((1)H MRSI). Higher spatial resolution enabled bilateral pACC acquisition. Significant effects were restricted to right pACC where Glx (9.5%, p<0.05), Cr (6.7%, p<0.05), and N-acetyl-aspartate + N-acetyl-aspartyl-glutamate (10.2%, p<0.01) in the ASD sample were elevated above control. These two independent studies suggest hyperglutamatergia and other neurometabolic abnormalities in pACC in ASD, with possible right-lateralization. The hyperglutamatergic state may reflect an imbalance of excitation over inhibition in the brain as proposed in recent neurodevelopmental models of ASD

Whole-brain patterns of 1H-magnetic resonance spectroscopy imaging in Alzheimer's disease and dementia with Lewy bodies

Acknowledgements We thank Craig Lambert for his help in processing the MRS data. The study was funded by the Sir Jules Thorn Charitable Trust (grant ref: 05/JTA) and was supported by the National Institute for Health Research (NIHR) Newcastle Biomedical Research Centre and the Biomedical Research Unit in Lewy Body Dementia based at Newcastle upon Tyne Hospitals National Health Service (NHS) Foundation Trust and Newcastle University and the NIHR Biomedical Research Centre and Biomedical Research Unit in Dementia based at Cambridge University Hospitals NHS Foundation Trust and the University of Cambridge.Peer reviewedPublisher PD