Magnetic Resonance Spectroscopic Imaging Data Research Articles

Generating Synthetic MR Spectroscopic Imaging Data with Generative Adversarial Networks to Train Machine Learning Models.

To develop a new method to generate synthetic MR spectroscopic imaging (MRSI) data for training machine learning models. This study targeted routine MRI examination protocols with single voxel spectroscopy (SVS). A novel model derived from pix2pix generative adversarial networks was proposed to generate synthetic MRSI data using MRI and SVS data as inputs. T1- and T2-weighted, SVS, and reference MRSI data were acquired from healthy brains with clinically available sequences. The proposed model was trained to generate synthetic MRSI data. Quantitative evaluation involved the calculation of the mean squared error (MSE) against the reference and metabolite ratio value. The effect of the location of and the number of the SVS data on the quality of the synthetic MRSI data was investigated using the MSE. The synthetic MRSI data generated from the proposed model were visually closer to the reference. The 95% confidence interval (CI) of the metabolite ratio value of synthetic MRSI data overlapped with the reference for seven of eight metabolite ratios. The MSEs tended to be lower in the same location than in different locations. The MSEs among groups of numbers of SVS data were not significantly different. A new method was developed to generate MRSI data by integrating MRI and SVS data. Our method can potentially increase the volume of MRSI data training for other machine learning models by adding SVS acquisition to routine MRI examinations.

31P MR Spectroscopy in the Pancreas: Repeatability, Comparison With Liver, and Pilot Pancreatic Cancer Data.

Non-invasive evaluation of phosphomonoesters (PMEs) and phosphodiesters (PDEs) by 31-phosphorus MR spectroscopy (31P MRS) may have potential for early therapy (non-)response assessment in cancer. However, 31P MRS has not yet been applied to investigate the human pancreas in vivo. To assess the technical feasibility and repeatability of 31P MR spectroscopic imaging (MRSI) of the pancreas, compare 31P metabolite levels between pancreas and liver, and determine the feasibility of 31P MRSI in pancreatic cancer. Prospective cohort study. 10 healthy subjects (age 34 ± 12 years, four females) and one patient (73-year-old female) with pancreatic ductal adenocarcinoma. 7-T, 31P FID-MRSI, 1H gradient-echo MRI. 31P FID-MRSI of the abdomen (including the pancreas and liver) was performed with a nominal voxel size of 20 mm (isotropic). For repeatability measurements, healthy subjects were scanned twice on the same day. The patient was only scanned once. Test-retest 31P MRSI data of pancreas and liver voxels (segmented on 1H MRI) of healthy subjects were quantified by fitting in the time domain and signal amplitudes were normalized to γ-adenosine triphosphate. In addition, the PME/PDE ratio was calculated. Metabolite levels were averaged over all voxels within the pancreas, right liver lobe and left liver lobe, respectively. Repeatability of test-retest data from healthy pancreas was assessed by paired t-tests, Bland-Altman analyses, and calculation of the intrasubject coefficients of variation (CoVs). Significant differences between healthy pancreas and right and left liver lobes were assessed with a two-way analysis of variance (ANOVA) for repeated measures. A P-value <0.05 was considered statistically significant. The intrasubject CoVs for PME, PDE, and PME/PDE in healthy pancreas were below 20%. Furthermore, PME and PME/PDE were significantly higher in pancreas compared to liver. In the patient with pancreatic cancer, qualitatively, elevated relative PME signals were observed in comparison with healthy pancreas. In vivo 31P MRSI of the human healthy pancreas and in pancreatic cancer may be feasible at 7 T. 3 TECHNICAL EFFICACY: Stage 2.

NIMG-64. IN VIVO METABOLIC PROFILING FOR RECURRENT GLIOMA

Abstract INTRODUCTION The 2021 WHO classification has identified the importance of molecular features in diagnosing adult glioma. The goal of this study was to use short echo MR spectroscopic imaging (MRSI) to evaluate the metabolite profiles at recurrence and characterize in vivo metabolism in relation to pathology by combining it with image-guided tissue samples. METHODS Fifty-one patients with an initial diagnosis of glioma (18 astrocytoma, 14 oligodendroglioma, and 19 glioblastoma) received MR examinations before surgical resection of progressive disease confirmed histologically. The 3D MRSI data were combined, processed, and quantified by LCModel with a simulated basis set. Metabolite levels were compared between contrast-enhancing lesions (CEL), non-enhancing lesions (NEL), and patient groups. Spectra data centered at the tissue target location were correlated to pathological parameters from the samples (0-4 samples/subject). RESULTS 74.5% (38/51) patients had enhancing tumors, with median CEL volume of 4.2 cm3 (min-max: 0.2-28.5 cm3) and median NEL volumes of 50.6 cm3 (4.8-167.2 cm3). The patients with GBM had significantly larger CEL volumes than the others (p=0.009).18/29 (62%) patients with lower-grade glioma had malignant progression to higher grades. Those who progressed from grade 2 to 3 or 4 had higher myo-inositol/choline (mean±SD, 2.62±0.64, N=18) compared to those who recurred as grade 2 (1.88±0.52, N=5, p = 0.015). Of the 51 patients, 40/65 in vivo spectra voxels centered where the tissues were sampled displayed good quality, with 27 having active tumors, 7 having treatment effects, and 6 having mixed tumors/treatment effects. The levels of choline/creatine and myo-inositol+glycline/creatine were significantly correlated with MIB-1 (p&amp;lt; 0.0001, =0.034; r=0.563, 0.349), but no difference was found between the locations with active tumors and treatment effects. DISCUSSION: This study demonstrated metabolite variations at the recurrence. Ongoing studies will further characterize the metabolite profiles for malignant progression and differences between active tumors and treatment effects.

SLIPMAT: A pipeline for extracting tissue-specific spectral profiles from 1H MR spectroscopic imaging data

1H Magnetic Resonance Spectroscopy (MRS) is an important non-invasive tool for measuring brain metabolism, with numerous applications in the neuroscientific and clinical domains. In this work we present a new analysis pipeline (SLIPMAT), designed to extract high-quality, tissue-specific, spectral profiles from MR spectroscopic imaging data (MRSI). Spectral decomposition is combined with spatially dependant frequency and phase correction to yield high SNR white and grey matter spectra without partial-volume contamination. A subsequent series of spectral processing steps are applied to reduce unwanted spectral variation, such as baseline correction and linewidth matching, before direct spectral analysis with machine learning and traditional statistical methods. The method is validated using a 2D semi-LASER MRSI sequence, with a 5-minute duration, from data acquired in triplicate across 8 healthy participants. Reliable spectral profiles are confirmed with principal component analysis, revealing the importance of total-choline and scyllo-inositol levels in distinguishing between individuals – in good agreement with our previous work. Furthermore, since the method allows the simultaneous measurement of metabolites in grey and white matter, we show the strong discriminative value of these metabolites in both tissue types for the first time. In conclusion, we present a novel and time efficient MRSI acquisition and processing pipeline, capable of detecting reliable neuro-metabolic differences between healthy individuals, and suitable for the sensitive neurometabolic profiling of in-vivo brain tissue.

Spatio-temporal reconstruction of substance dynamics using compressed sensing in multi-spectral magnetic resonance spectroscopic imaging

The objective of our study is to observe dynamics of multiple substances in vivo with high temporal resolution from multi-spectral magnetic resonance spectroscopic imaging (MRSI) data. The multi-spectral MRSI can effectively separate spectral peaks of multiple substances and is useful to measure spatial distributions of substances. However it is difficult to measure time-varying substance distributions directly by ordinary full sampling because the measurement requires a significantly long time. In this study, we propose a novel method to reconstruct the spatio-temporal distributions of substances from randomly undersampled multi-spectral MRSI data on the basis of compressed sensing (CS) and the partially separable function model with base spectra of substances. In our method, we have employed spatio-temporal sparsity and temporal smoothness of the substance distributions as prior knowledge to perform CS. By directly reconstructing the spatio-temporal distributions of the substances themselves without reconstructing the spectra, this method significantly reduces the amount of MRSI data required per single time frame. We have formulated a regularized minimization problem for reconstruction and solved it by the alternating direction method of multipliers (ADMM). The effectiveness of our method has been evaluated using phantom data sets of glass tubes filled with glucose or lactate solution in increasing amounts over time and animal data sets of a tumor-bearing mouse to observe the metabolic dynamics involved in the Warburg effect in vivo. The reconstructed results are consistent with the expected behaviors, showing that our method can reconstruct the spatio-temporal distribution of substances with a temporal resolution of four seconds which is extremely short time scale compared with that of full sampling. Since this method utilizes only prior knowledge naturally assumed for the spatio-temporal distributions of substances and is independent of the number of the spectral and spatial dimensions or the acquisition sequence of MRSI, it is expected to contribute to revealing the underlying substance dynamics in MRSI data already acquired or to be acquired in the future.

Vascular disease risk factors in multiple sclerosis: Effect on metabolism and brain volumes.

Vascular disease risk factors (VDRF) such as hypertension, hyperlipidemia, obesity, diabetes and heart disease likely play a role in disease progression in people with multiple sclerosis (PwMS) (Marrie, Rudick etal. 2010). Studies exploring the mechanistic connection between vascular disease and MS disease progression are scant. We hypothesized that phosphate energy metabolism impairment in PwMS with VDRFs (VDRF+) will be greater compared to PwMS without VDRFs (VDRF-) and is related to increased brain atrophy in VDRF+. To test this hypothesis, we planned to study the differences in the high energy phosphate (HEP) metabolites in cerebral gray matter as assessed by 31P magnetic resonance spectroscopic imaging (MRSI) and MRI brain volumetric in the VDRF+ and VDRF- PwMS at four different timepoints over a 3 yearlong period using a 7T MR system. We present here the results from the cross-sectional evaluation of HEP metabolites and brain volumes. We also evaluated the differences in clinical impairment, blood metabolic biomarkers and quality of life in VDRF+ and VDRF- PwMS in this cohort. Group differences in high energy phosphate metabolites were assessed from a volume of interest in the occipital region using linear mixed models. Brain parenchymal and white matter lesion volumes were determined from MR anatomic images. We present here the cross-sectional analysis of the baseline data collected as part of a longitudinal 3 yearlong study where we obtained baseline and subsequent 6-monthly clinical and laboratory data and annual 7T MRI volumetric and 31P MR spectroscopic imaging (MRSI) data on 52 PwMS with and without VDRF. Key clinical and laboratory outcomes included: body mass index (BMI), waist and thigh circumferences and disability [Expanded Disability Status Scale (EDSS)], safety (complete blood count with differential, complete metabolic), lipid panel including total cholesterol and HbA1C. We analyzed clinical and laboratory data for the group differences using student's t or χ2 test. We investigated relationship between phosphate metabolites and VDRF using mixed effect linear regression. Complete MRI data were available for 29 VDRF+, age 56.3 (6.8) years [mean (SD)] (83% female), and 23 VDRF-, age 52.5 (7.5) years (57% female) individuals with MS. The mean value of normalized adenosine triphosphate (ATP) (calculated as the ratio of ATP to total phosphate signal in a voxel) was decreased by 4.5% (p < .05) in VDRF+ compared to VDRF- MS group. White matter lesion (WML) volume fraction in VDRF+ individuals {0.007 (0.007)} was more than doubled compared to VDRF- participants {0.003 (0.006), p= .02}. We found significantly lower brain ATP and higher inorganic phosphate (Pi) in those PwMS with VDRFs compared to those without. ATP depletion may reflect mitochondrial dysfunction. Ongoing longitudinal data analysis from this study, not presented here, will evaluate the relationship of phosphate metabolites, brain atrophy and disease progression in PwMS with and without vascular disease.

Physics-informed deep learning approach to quantification of human brain metabolites from magnetic resonance spectroscopy data

PurposeWhile the recommended analysis method for magnetic resonance spectroscopy data is linear combination model (LCM) fitting, the supervised deep learning (DL) approach for quantification of MR spectroscopy (MRS) and MR spectroscopic imaging (MRSI) data recently showed encouraging results; however, supervised learning requires ground truth fitted spectra, which is not practical. Moreover, this work investigates the feasibility and efficiency of the LCM-based self-supervised DL method for the analysis of MRS data. MethodWe present a novel DL-based method for the quantification of relative metabolite concentrations, using quantum-mechanics simulated metabolite responses and neural networks. We trained, validated, and evaluated the proposed networks with simulated and publicly accessible in-vivo human brain MRS data and compared the performance with traditional methods. A novel adaptive macromolecule fitting algorithm is included. We investigated the performance of the proposed methods in a Monte Carlo (MC) study. ResultThe validation using low-SNR simulated data demonstrated that the proposed methods could perform quantification comparably to other methods. The applicability of the proposed method for the quantification of in-vivo MRS data was demonstrated. Our proposed networks have the potential to reduce computation time significantly. ConclusionThe proposed model-constrained deep neural networks trained in a self-supervised manner can offer fast and efficient quantification of MRS and MRSI data. Our proposed method has the potential to facilitate clinical practice by enabling faster processing of large datasets such as high-resolution MRSI datasets, which may have thousands of spectra.

Is pre-radiotherapy metabolic heterogeneity of glioblastoma predictive of progression-free survival?

Background and purposeAll glioblastoma subtypes share the hallmark of aggressive invasion, meaning that it is crucial to identify their different components if we are to ensure effective treatment and improve survival. Proton MR spectroscopic imaging (MRSI) is a noninvasive technique that yields metabolic information and is able to identify pathological tissue with high accuracy. The aim of the present study was to identify clusters of metabolic heterogeneity, using a large MRSI dataset, and determine which of these clusters are predictive of progression-free survival (PFS). Materials and methodsMRSI data of 180 patients acquired in a pre-radiotherapy examination were included in the prospective SPECTRO-GLIO trial. Eight features were extracted for each spectrum: Cho/NAA, NAA/Cr, Cho/Cr, Lac/NAA, and the ratio of each metabolite to the sum of all the metabolites. Clustering of data was performed using a mini-batch k-means algorithm. The Cox model and logrank test were used for PFS analysis. ResultsFive clusters were identified as sharing similar metabolic information and being predictive of PFS. Two clusters revealed metabolic abnormalities. PFS was lower when Cluster 2 was the dominant cluster in patients’ MRSI data. Among the metabolites, lactate (present in this cluster and in Cluster 5) was the most statistically significant predictor of poor outcome. ConclusionResults showed that pre-radiotherapy MRSI can be used to reveal tumor heterogeneity. Groups of spectra, which have the same metabolic information, reflect the different tissue components representative of tumor burden proliferation and hypoxia. Clusters with metabolic abnormalities and high lactate are predictive of PFS.

Whole-brain MR spectroscopic imaging reveals regional metabolite abnormalities in perinatally HIV infected young adults.

Perinatally acquired HIV (PHIV) has been associated with brain structural and functional deficiencies, and with poorer cognitive performance despite the advent of antiretroviral therapy (ART). However, investigation of brain metabolite levels in PHIV measured by proton magnetic resonance spectroscopy (MRS) methods, is still limited with often inconclusive or contradictory findings. In general, these MRS-based methods have used a single voxel approach that can only evaluate metabolite concentrations in a few select brain anatomical regions. Additionally, most of the published data have been on children perinatally infected with HIV with only a few studies examining adult populations, though not exclusively. Therefore, this prospective and cross-sectional study aims to evaluate metabolite differences at the whole-brain level, using a unique whole-brain proton magnetic resonance spectroscopy imaging (MRSI) method, in a group of PHIV infected young adults (N = 28) compared to age and gender matched control sample (N = 28), and to find associations with HIV clinical factors and neurocognitive scores. MRSI data were acquired on a 3T scanner with a TE of 70 ms. Brain metabolites levels of total N-acetylaspartate (tNAA), total choline (tCho) and total creatine (tCre), as well as ratios of tNAA/tCre, tCho/tCre, and tNAA/tCho, were obtained from the whole brain level and evaluated at the level of gray matter (GM) and white matter (WM) tissue types and anatomical regions of interest (ROI). Our results indicate extensive metabolic abnormalities throughout the brains of PHIV infected subjects with significantly elevated levels of tCre and tCho, notably in GM regions. Decreases in tNAA and ratios of tNAA/tCre and tNAA/tCho were also found mostly in WM regions. These metabolic alterations indicate increased glial activation, inflammation, neuronal dysfunction, and energy metabolism in PHIV infected individuals, which correlated with a reduction in CD4 cell count, and lower cognitive scores. Our findings suggest that significant brain metabolite alterations and associated neurological complications persist in the brains of those with PHIV on long-term ART, and advocates the need for continued monitoring of their brain health.

Correction to ‘ORYX‐MRSI: A fully‐automated open‐source software for proton magnetic resonance spectroscopic imaging data analysis’

International Journal of Imaging Systems and TechnologyVolume 33, Issue 2 p. 770-770 CORRIGENDUMFree Access Correction to ‘ORYX-MRSI: A fully-automated open-source software for proton magnetic resonance spectroscopic imaging data analysis’ This article corrects the following: ORYX-MRSI: A fully-automated open-source software for proton magnetic resonance spectroscopic imaging data analysis Sevim Cengiz, Muhammed Yildirim, Abdullah Bas, Esin Ozturk-Isik, Volume 32Issue 4International Journal of Imaging Systems and Technology pages: 1068-1083 First Published online: May 17, 2022 First published: 27 January 2023 https://doi.org/10.1002/ima.22857AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat We regret to report a technical error in our manuscript entitled ‘ORYX-MRSI: A fully-automated open-source software for proton magnetic resonance spectroscopic imaging data analysis’. The volume of interest (VOI) for all the MR spectroscopic metabolites, other than the reference metabolite, are shifted in space by their respective chemical shift amounts as calculated by Equation 2 due to the chemical shift error. Even though there is a shift in the VOIs of different metabolites, it was incorrect to shift the metabolite maps along with their corresponding VOIs. This technical error has been corrected in the Oryx-MRSI source code, which has been updated on our laboratory's GitHub page. Currently, if the chemical shift correction is set to ‘on’, the corresponding VOIs for the metabolites are shifted in space by their respective chemical shift amounts, and the metabolite intensities are evaluated at their respective locations. Additionally, the metabolite ratios are evaluated only in the region that is located at the intersection of the corresponding metabolite VOIs. Moreover, the tissue fractions are calculated only once at the reference VOI. As a result, Figure 5 is not accurate after correction and hence should not be considered for further discussion. The authors thank Wolfgang Bogner, Ph.D. and Gilbert Hangel, Ph.D. for their careful reviews of the published manuscript and apologize for this error. REFERENCE 1Cengiz S, Yildirim M, Bas A, Ozturk-Isik E. ORYX-MRSI: a fully-automated open-source software for proton magnetic resonance spectroscopic imaging data analysis. Int J Imaging Syst Technol. 2022; 32: 1068- 1083. doi:10.1002/ima.22748 Volume33, Issue2March 2023Pages 770-770 ReferencesRelatedInformation

Joint spectral quantification of MR spectroscopic imaging using linear tangent space alignment-based manifold learning.

To develop a manifold learning-based method that leverages the intrinsic low-dimensional structure of MR Spectroscopic Imaging (MRSI) signals for joint spectral quantification. A linear tangent space alignment (LTSA) model was proposed to represent MRSI signals. In the proposed model, the signals of each metabolite were represented using a subspace model and the local coordinates of the subspaces were aligned to the global coordinates of the underlying low-dimensional manifold via linear transform. With the basis functions of the subspaces predetermined via quantum mechanics simulations, the global coordinates and the matrices for the local-to-global coordinate alignment were estimated by fitting the proposed LTSA model to noisy MRSI data with a spatial smoothness constraint on the global coordinates and a sparsity constraint on the matrices. The performance of the proposed method was validated using numerical simulation data and in vivo proton-MRSI experimental data acquired on healthy volunteers at 3T. The results of the proposed method were compared with the QUEST method and the subspace-based method. In all the compared cases, the proposed method achieved superior performance over the QUEST and the subspace-based methods both qualitatively in terms of noise and artifacts in the estimated metabolite concentration maps, and quantitatively in terms of spectral quantification accuracy measured by normalized root mean square errors. Joint spectral quantification using linear tangent space alignment-based manifold learning improves the accuracy of MRSI spectral quantification.

NIMG-24. LONGITUDINAL MR SPECTROSCOPY TO DETECT PROGRESSION IN PATIENTS WITH LOWER-GRADE GLIOMA IN THE SURVEILLANCE PHASE

Abstract BACKGROUND Monitoring lower-grade gliomas (LrGGs) for disease progression is made difficult by the limits of anatomical MRI to distinguish treatment-related tissue changes from tumor progression. MR spectroscopic imaging (MRSI) offers additional metabolic information that can help address these challenges. The goal of this study was to compare longitudinal changes in multiparametric MRI, including diffusion-weighted imaging, perfusion imaging, and 3D MRSI, for LrGG patients who progressed at the final time point and those who remained clinically stable. METHODS Forty-one patients with LrGG who were clinically stable were longitudinally assessed for progression. Changes in anatomical, diffusion, perfusion, and MRSI data were acquired and compared between patients who remained clinically stable and those who progressed. RESULTS Thirty-one patients remained stable, and 10 patients progressed. Over the study period, progressed patients had a significantly greater increase in normalized choline, choline-to-N-acetylaspartic acid index (CNI), normalized creatine, and creatine-to-N-acetylaspartic acid index (CRNI), than stable patients. CRNI was significantly associated with progression status and WHO type. Progressed astrocytoma patients had greater increases in CRNI than stable astrocytoma patients. CONCLUSIONS LrGG patients in surveillance with tumors that progressed had significantly increasing choline and creatine metabolite signals on MRSI, with a trend of increasing T2 FLAIR volumes, compared to LrGG patients who remained stable. These data show that MRSI can be used in conjunction with anatomical imaging studies to gain a clearer picture of LrGG progression, especially in the setting of clinical ambiguity.

SNR Enhancement for Multi-TE MRSI Using Joint Low-Dimensional Model and Spatial Constraints.

We present a novel method to enhance the SNR for multi-TE MR spectroscopic imaging (MRSI) data by integrating learned nonlinear low-dimensional model and spatial constraints. A deep complex convolutional autoencoder (DCCAE) was developed to learn a nonlinear low-dimensional representation of the high-dimensional multi-TE 1H spectroscopy signals. The learned model significantly reduces the data dimension thus serving as an effective constraint for noise reduction. A reconstruction formulation was proposed to integrate the spatiospectral encoding model, the learned model, and a spatial constraint for an SNR-enhancing reconstruction from multi-TE data. The proposed method has been evaluated using both numerical simulations and in vivo brain MRSI experiments. The superior denoising performance of the proposed over alternative methods was demonstrated, both qualitatively and quantitatively. In vivo multi-TE data was used to assess the improved metabolite quantification reproducibility and accuracy achieved by the proposed method. We expect the proposed SNR-enhancing reconstruction to enable faster and/or higher-resolution multi-TE 1H-MRSI of the brain, potentially useful for various clinical applications.

Convolutional neural networks to predict brain tumor grades and Alzheimer's disease with MR spectroscopic imaging data.

PurposeTo evaluate the value of convolutional neural network (CNN) in the diagnosis of human brain tumor or Alzheimer’s disease by MR spectroscopic imaging (MRSI) and to compare its Matthews correlation coefficient (MCC) score against that of other machine learning methods and previous evaluation of the same data. We address two challenges: 1) limited number of cases in MRSI datasets and 2) interpretability of results in the form of relevant spectral regions.MethodsA shallow CNN with only one hidden layer and an ad-hoc loss function was constructed involving two branches for processing spectral and image features of a brain voxel respectively. Each branch consists of a single convolutional hidden layer. The output of the two convolutional layers is merged and fed to a classification layer that outputs class predictions for the given brain voxel.ResultsOur CNN method separated glioma grades 3 and 4 and identified Alzheimer’s disease patients using MRSI and complementary MRI data with high MCC score (Area Under the Curve were 0.87 and 0.91 respectively). The results demonstrated superior effectiveness over other popular methods as Partial Least Squares or Support Vector Machines. Also, our method automatically identified the spectral regions most important in the diagnosis process and we show that these are in good agreement with existing biomarkers from the literature.ConclusionShallow CNNs models integrating image and spectral features improved quantitative and exploration and diagnosis of brain diseases for research and clinical purposes. Software is available at https://bitbucket.org/TeslaH2O/cnn_mrsi.

Mapping early tumor response to radiotherapy using diffusion kurtosis imaging*.

In this pilot study, DKI measures of diffusivity and kurtosis were compared in active tumor regions and correlated to radiologic response to radiotherapy after completion of 2weeks of treatment to derive potential early measures of tumor response. MRI and Magnetic Resonance Spectroscopic Imaging (MRSI) data were acquired before the beginning of RT (pre-RT) and 2weeks after the initiation of treatment (during-RT) in 14 glioblastoma patients. The active tumor region was outlined as the union of the residual contrast-enhancing region and metabolically active tumor region. Average and standard deviation of mean, axial, and radial diffusivity (MD, AD, RD) and mean, axial, and radial kurtosis (MK, AK, RK) values were calculated for the active tumor VOI from images acquired pre-RT and during-RT and paired t-tests were executed to estimate pairwise differences. Receiver operating characteristic (ROC) curve analysis was conducted to evaluate the predictive capabilities of changes in diffusion metrics for progression-free survival (PFS). Analysis showed significant pairwise differences for AD (p = 0.035; Cohen's d of 0.659) and AK (p = 0.019; Cohen's d of 0.753) in diffusion measures after 2weeks of RT. ROC curve analysis showed that percentage change differences in AD and AK between pre-RT and during-RT scans provided an Area Under the Curve (AUC) of 0.524 and 0.762, respectively, in discriminating responders (PFS>180 days) and non-responders (PFS<180days). This pilot study, although preliminary in nature, showed significant changes in AD and AK maps, with kurtosis derived AK maps showing an increased sensitivity in mapping early changes in the active tumor regions.

Dictionary learning compressed sensing reconstruction: pilot validation of accelerated echo planar J-resolved spectroscopic imaging in prostate cancer

ObjectivesThis study aimed at developing dictionary learning (DL) based compressed sensing (CS) reconstruction for randomly undersampled five-dimensional (5D) MR Spectroscopic Imaging (3D spatial + 2D spectral) data acquired in prostate cancer patients and healthy controls, and test its feasibility at 8x and 12x undersampling factors.Materials and methodsProspectively undersampled 5D echo-planar J-resolved spectroscopic imaging (EP-JRESI) data were acquired in nine prostate cancer (PCa) patients and three healthy males. The 5D EP-JRESI data were reconstructed using DL and compared with gradient sparsity-based Total Variation (TV) and Perona-Malik (PM) methods. A hybrid reconstruction technique, Dictionary Learning-Total Variation (DLTV), was also designed to further improve the quality of reconstructed spectra.ResultsThe CS reconstruction of prospectively undersampled (8x and 12x) 5D EP-JRESI data acquired in prostate cancer and healthy subjects were performed using DL, DLTV, TV and PM. It is evident that the hybrid DLTV method can unambiguously resolve 2D J-resolved peaks including myo-inositol, citrate, creatine, spermine and choline.ConclusionImproved reconstruction of the accelerated 5D EP-JRESI data was observed using the hybrid DLTV. Accelerated acquisition of in vivo 5D data with as low as 8.33% samples (12x) corresponds to a total scan time of 14 min as opposed to a fully sampled scan that needs a total duration of 2.4 h (TR = 1.2 s, 32 {k}_{x}×16 {k}_{y}×8 {k}_{z}, 512 {t}_{2} and 64 {t}_{1}).

Mapping of the brain from water-unsuppressed 1 H-MRSIand turbo spin-echo data.

To obtain high-quality maps of brain tissues from water-unsuppressed magnetic resonance spectroscopic imaging (MRSI)and turbo spin-echo (TSE) data. mapping can be achieved using mapping from water-unsuppressed MRSI data and mapping from TSE data. However, mapping often suffers from signal dephasing and distortions caused by field inhomogeneity; measurements may be biased due to system imperfections, especially for -weighted image with small number of TEs. In this work, we corrected the field inhomogeneity effect on mapping using a subspace model-based method, incorporating pre-learned spectral basis functions of the water signals. estimation bias was corrected using a TE-adjustment method, which modeled the deviation between measured and reference decays as TE shifts. In vivo experiments were performed to evaluate the performance of the proposed method. High-quality maps were obtained in the presence of large field inhomogeneity in the prefrontal cortex. Bias in measurements obtained from TSE data was effectively reduced. Based on the and measurements produced by the proposed method, high-quality maps were obtained, along with neurometabolite maps, from MRSI and TSE data that were acquired in about 9min. The results obtained from acute stroke and glioma patients demonstrated the feasibility of the proposed method in the clinical setting. High-quality maps can be obtained from water-unsuppressed 1 H-MRSI and TSE data using the proposed method. With further development, this method may lay a foundation for simultaneously imaging oxygenation and neurometabolic alterations of brain disorders.

Convolutional neural networks to predict brain tumor grades and Alzheimer's disease with MR Spectroscopic Imaging data

Convolutional neural networks to predict brain tumor grades and Alzheimer's disease with MR Spectroscopic Imaging data

ORYX‐MRSI: A fully‐automated open‐source software for proton magnetic resonance spectroscopic imaging data analysis

AbstractProton magnetic resonance spectroscopic imaging (1H‐MRSI) provides a noninvasive, spatially resolved evaluation of brain metabolism. However, there are some limitations of 1H‐MRSI preventing its wider use in the clinics, including the spectral quality issues, partial volume effect and chemical shift artifact. Additionally, it is necessary to create metabolite maps for analyzing spectral data along with other MRI modalities. In this study, a MATLAB‐based open‐source data analysis software for three‐dimensional 1H‐MRSI, called Oryx‐MRSI, which includes modules for visualization of raw 1H‐MRSI data and LCModel outputs, chemical shift correction, tissue fraction calculation, metabolite map production, and registration onto standard MNI152 brain atlas while providing automatic spectral quality control, is presented. Oryx‐MRSI implements region of interest analysis at brain parcellations defined on MNI152 brain atlas. All generated metabolite maps are stored in NIfTI format. Oryx‐MRSI is publicly available at https://github.com/Computational-Imaging-LAB/Oryx-MRSI along with six example datasets.

7T HR FID-MRSI Compared to Amino Acid PET: Glutamine and Glycine as Promising Biomarkers in Brain Tumors.

Simple SummaryMagnetic resonance spectroscopic imaging is an imaging method that can map the distribution of multiple biochemicals in the human brain in one scan. Using stronger magnetic fields, such as 7 Tesla, allows for higher resolution images and more biochemical maps. To test these results, we compared it to positron emission tomography, the established clinical standard for metabolic imaging. This comparison mainly looked at the overlap between regions with increased signal between both methods. We found that the molecules glutamine and glycine, only mappable at 7 Tesla, corresponded better to positron emission tomography than the commonly used choline.(1) Background: Recent developments in 7T magnetic resonance spectroscopic imaging (MRSI) made the acquisition of high-resolution metabolic images in clinically feasible measurement times possible. The amino acids glutamine (Gln) and glycine (Gly) were identified as potential neuro-oncological markers of importance. For the first time, we compared 7T MRSI to amino acid PET in a cohort of glioma patients. (2) Methods: In 24 patients, we co-registered 7T MRSI and routine PET and compared hotspot volumes of interest (VOI). We evaluated dice similarity coefficients (DSC), volume, center of intensity distance (CoI), median and threshold values for VOIs of PET and ratios of total choline (tCho), Gln, Gly, myo-inositol (Ins) to total N-acetylaspartate (tNAA) or total creatine (tCr). (3) Results: We found that Gln and Gly ratios generally resulted in a higher correspondence to PET than tCho. Using cutoffs of 1.6-times median values of a control region, DSCs to PET were 0.53 ± 0.36 for tCho/tNAA, 0.66 ± 0.40 for Gln/tNAA, 0.57 ± 0.36 for Gly/tNAA, and 0.38 ± 0.31 for Ins/tNAA. (4) Conclusions: Our 7T MRSI data corresponded better to PET than previous studies at lower fields. Our results for Gln and Gly highlight the importance of future research (e.g., using Gln PET tracers) into the role of both amino acids.