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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 [Formula: see text]×16 [Formula: see text]×8 [Formula: see text], 512 [Formula: see text] and 64 [Formula: see text]).

Nuclear magnetic resonance spectroscopy (MRS) allows for the determination ofatomic structures and concentrations of different chemicals in a biochemicalsample of interest. MRS is used in vivo clinically to aid in the diagnosis ofseveral pathologies that affect metabolic pathways in the body. Typically, thisexperiment produces a one dimensional (1D) 1H spectrum containing several peaksthat are well associated with biochemicals, or metabolites. However, since manyof these peaks overlap, distinguishing chemicals with similar atomic structuresbecomes much more challenging. One technique capable of overcoming this issueis the localized correlated spectroscopy (L-COSY) experiment, which acquires asecond spectral dimension and spreads overlapping signal across this seconddimension. Unfortunately, the acquisition of a two dimensional (2D)spectroscopy experiment is extremely time consuming. Furthermore, quantitationof a 2D spectrum is more complex. Recently, artificial intelligence has emergedin the field of medicine as a powerful force capable of diagnosing disease,aiding in treatment, and even predicting treatment outcome. In this study, weutilize deep learning to: 1) accelerate the L-COSY experiment and 2) quantifyL-COSY spectra. We demonstrate that our deep learning model greatly outperformscompressed sensing based reconstruction of L-COSY spectra at higheracceleration factors. Specifically, at four-fold acceleration, our method hasless than 5% normalized mean squared error, whereas compressed sensing yields20% normalized mean squared error. We also show that at low SNR (25% noisecompared to maximum signal), our deep learning model has less than 8%normalized mean squared error for quantitation of L-COSY spectra. These pilotsimulation results appear promising and may help improve the efficiency andaccuracy of L-COSY experiments in the future.

Magnetic resonance spectroscopy (MRS) is a powerful tool capable of investigating the metabolic status of several tissues in vivo. In particular, single-voxel-based 1 H spectroscopy provides invaluable biochemical information from a volume of interest (VOI) and has therefore been used in a variety of studies. Unfortunately, typical one-dimensional MRS data suffer from severe signal overlap and thus important metabolites are difficult to distinguish. One method that is used to disentangle overlapping resonances is the two-dimensional J-resolved spectroscopy (JPRESS) experiment. Due to the long acquisition duration of the JPRESS experiment, a limited number of points are acquired in the indirect dimension, leading to poor spectral resolution along this dimension. Poor spectral resolution is problematic because proper peak assignment may be hindered, which is why the zero-filling method is often used to improve resolution as a post-processing step. However, zero-filling leads to spectral artifacts, which may affect visualization and quantitation of spectra. A novel method utilizing a covariance transformation, called covariance J-resolved spectroscopy (CovJ), was developed in order to improve spectral resolution along the indirect dimension (F1 ). Comparison of simulated data demonstrates that peak structures remain qualitatively similar between JPRESS and the novel method along the diagonal region (F1 = 0 Hz), whereas differences arise in the cross-peak (F1 ≠0 Hz) regions. In addition, quantitative results of in vivo JPRESS data acquired on a 3T scanner show significant correlations (r2 >0.86, p

1H Magnetic Resonance Spectroscopic imaging (SI) is a powerful tool capable of investigating metabolism in vivo from mul- tiple regions. However, SI techniques are time consuming, and are therefore difficult to implement clinically. By applying non-uniform sampling (NUS) and compressed sensing (CS) reconstruction, it is possible to accelerate these scans while re- taining key spectral information. One recently developed method that utilizes this type of acceleration is the five-dimensional echo planar J-resolved spectroscopic imaging (5D EP-JRESI) sequence, which is capable of obtaining two-dimensional (2D) spectra from three spatial dimensions. The prior-knowledge fitting (ProFit) algorithm is typically used to quantify 2D spectra in vivo, however the effects of NUS and CS reconstruction on the quantitation results are unknown. This study utilized a simulated brain phantom to investigate the errors introduced through the acceleration methods. Errors (normalized root mean square error >15%) were found between metabolite concentrations after twelve-fold acceleration for several low concentra- tion (

Attempts have been made to reduce the total scan time in multi-dimensional J-resolved spectroscopic imaging (JRESI) using an echo-planar (EP) readout gradient, but acquisition duration remains a limitation for routine clinical use in the brain. We present here a significant acceleration achieved with a 4D EP-JRESI sequence that collects dual phase encoded lines within a single repetition time (TR) using two bipolar read-out trains. The performance and reliability of this novel 4D sequence, called Multi-Echo based Echo-Planar J-resolved Spectroscopic Imaging (ME-EP-JRESI), was evaluated in 10 healthy controls and a brain phantom using a 3 T MRI/MRS scanner. The prior knowledge fitting (ProFit) algorithm, with a new simulated basis set consisting of macromolecules and lipids apart from metabolites of interest, was used for quantitation. Both phantom and in-vivo data demonstrated that localization and spatial/spectral profiles of metabolites from the ME-EP-JRESI sequence were in good agreement with that of the EP-JRESI sequence. Both in the occipital and temporal lobe, metabolites with higher physiological concentrations including Glx (Glu+Gln), tNAA (NAA+NAAG), mI all had coefficient of variations between 9-25%. In summary, we have implemented, validated and tested the ME-EP-JRESI sequence, demonstrating that multi-echo acquisition can successfully reduce the total scan duration for EP-JRESI sequences.

Two-dimensional localized correlated spectroscopy (2D L-COSY) offers greater spectral dispersion than conventional one-dimensional (1D) MRS techniques, yet long acquisition times and limited post-processing support have slowed its clinical adoption. Improving acquisition efficiency and developing versatile post-processing techniques can bolster the clinical viability of 2D MRS. The purpose of this study was to implement a non-uniformly weighted sampling (NUWS) scheme for faster acquisition of 2D-MRS. A NUWS 2D L-COSY sequence was developed for 7T whole-body MRI. A phantom containing metabolites commonly observed in the brain at physiological concentrations was scanned ten times with both the NUWS scheme of 12:48 duration and a 17:04 constant eight-average sequence using a 32-channel head coil. 2D L-COSY spectra were also acquired from the occipital lobe of four healthy volunteers using both the proposed NUWS and the conventional uniformly-averaged L-COSY sequence. The NUWS 2D L-COSY sequence facilitated 25% shorter acquisition time while maintaining comparable SNR in humans (+0.3%) and phantom studies (+6.0%) compared to uniform averaging. NUWS schemes successfully demonstrated improved efficiency of L-COSY, by facilitating a reduction in scan time without affecting signal quality.

Several different pathologies, including many neurodegenerative disorders, affect the energy metabolism of the brain. Glutamate, a neurotransmitter in the brain, can be used as a biomarker to monitor these metabolic processes. One method that is capable of quantifying glutamate concentration reliably in several regions of the brain is TE-averaged (1) H spectroscopic imaging. However, this type of method requires the acquisition of multiple TE lines, resulting in long scan durations. The goal of this experiment was to use non-uniform sampling, compressed sensing reconstruction and an echo planar readout gradient to reduce the scan time by a factor of eight to acquire TE-averaged spectra in three spatial dimensions. Simulation of glutamate and glutamine showed that the 2.2-2.4 ppm spectral region contained 95% glutamate signal using the TE-averaged method. Peak integration of this spectral range and home-developed, prior-knowledge-based fitting were used for quantitation. Gray matter brain phantom measurements were acquired on a Siemens 3 T Trio scanner. Non-uniform sampling was applied retrospectively to these phantom measurements and quantitative results of glutamate with respect to creatine 3.0 (Glu/Cr) ratios showed a coefficient of variance of 16% for peak integration and 9% for peak fitting using eight-fold acceleration. In vivo scans of the human brain were acquired as well and five different brain regions were quantified using the prior-knowledge-based algorithm. Glu/Cr ratios from these regions agreed with previously reported results in the literature. The method described here, called accelerated TE-averaged echo planar spectroscopic imaging (TEA-EPSI), is a significant methodological advancement and may be a useful tool for categorizing glutamate changes in pathologies where affected brain regions are not known a priori. Copyright © 2016 John Wiley & Sons, Ltd.

PurposeTo implement an accelerated five-dimensional (5D) echo-planar J-resolved spectroscopic imaging sequence combining 3 spatial and 2 spectral encoding dimensions and to apply the sequence in human brain.MethodsAn echo planar readout was used to acquire a single spatial and a single spectral dimension during one readout. Nonuniform sampling was applied to the two phase-encoded spatial directions and the indirect spectral dimension. Nonlinear reconstruction was used to minimize the ℓ1-norm or the total variation and included a spectral mask to enhance sparsity. Retrospective reconstructions at multiple undersamplings were performed in phantom. Ten healthy volunteers were scanned with 8× undersampling and compared to a fully sampled single slice scan.ResultsRetrospective reconstruction of fully sampled phantom data showed excellent quality at 4×, 8×, 12×, and 16× undersampling using either reconstruction method. Reconstruction of prospectively acquired in vivo scans with 8× undersampling showed excellent quality in the occipito-parietal lobes and good quality in the frontal lobe, consistent with the fully sampled single slice scan.ConclusionBy utilizing nonuniform sampling with nonlinear reconstruction, 2D J-resolved spectra can be acquired over a 3D spatial volume with a total scan time of 20 min, which is reasonable for in vivo studies.

PurposeTo measure cerebral metabolite levels in perinatally HIV-infected youths and healthy controls using the accelerated five dimensional (5D) echo planar J-resolved spectroscopic imaging (EP-JRESI) sequence, which is capable of obtaining two dimensional (2D) J-resolved spectra from three spatial dimensions (3D).Materials and methodsAfter acquisition and reconstruction of the 5D EP-JRESI data, T1-weighted MRIs were used to classify brain regions of interest for HIV patients and healthy controls: right frontal white (FW), medial frontal gray (FG), right basal ganglia (BG), right occipital white (OW), and medial occipital gray (OG). From these locations, respective J-resolved and TE-averaged spectra were extracted and fit using two different quantitation methods. The J-resolved spectra were fit using prior knowledge fitting (ProFit) while the TE-averaged spectra were fit using the advanced method for accurate robust and efficient spectral fitting (AMARES).ResultsQuantitation of the 5D EP-JRESI data using the ProFit algorithm yielded significant metabolic differences in two spatial locations of the perinatally HIV-infected youths compared to controls: elevated NAA/(Cr+Ch) in the FW and elevated Asp/(Cr+Ch) in the BG. Using the TE-averaged data quantified by AMARES, an increase of Glu/(Cr+Ch) was shown in the FW region. A strong negative correlation (r < -0.6) was shown between tCh/(Cr+Ch) quantified using ProFit in the FW and CD4 counts. Also, strong positive correlations (r > 0.6) were shown between Asp/(Cr+Ch) and CD4 counts in the FG and BG.ConclusionThe complimentary results using ProFit fitting of J-resolved spectra and AMARES fitting of TE-averaged spectra, which are a subset of the 5D EP-JRESI acquisition, demonstrate an abnormal energy metabolism in the brains of perinatally HIV-infected youths. This may be a result of the HIV pathology and long-term combinational anti-retroviral therapy (cART). Further studies of larger perinatally HIV-infected cohorts are necessary to confirm these findings.

The overlap of metabolites is a major limitation in one-dimensional (1D) spectral-based single-voxel MRS and multivoxel-based MRSI. By combining echo planar spectroscopic imaging (EPSI) with a two-dimensional (2D) J-resolved spectroscopic (JPRESS) sequence, 2D spectra can be recorded in multiple locations in a single slice of prostate using four-dimensional (4D) echo planar J-resolved spectroscopic imaging (EP-JRESI). The goal of the present work was to validate two different non-linear reconstruction methods independently using compressed sensing-based 4D EP-JRESI in prostate cancer (PCa): maximum entropy (MaxEnt) and total variation (TV). Twenty-two patients with PCa with a mean age of 63.8 years (range, 46-79 years) were investigated in this study. A 4D non-uniformly undersampled (NUS) EP-JRESI sequence was implemented on a Siemens 3-T MRI scanner. The NUS data were reconstructed using two non-linear reconstruction methods, namely MaxEnt and TV. Using both TV and MaxEnt reconstruction methods, the following observations were made in cancerous compared with non-cancerous locations: (i) higher mean (choline + creatine)/citrate metabolite ratios; (ii) increased levels of (choline + creatine)/spermine and (choline + creatine)/myo-inositol; and (iii) decreased levels of (choline + creatine)/(glutamine + glutamate). We have shown that it is possible to accelerate the 4D EP-JRESI sequence by four times and that the data can be reliably reconstructed using the TV and MaxEnt methods. The total acquisition duration was less than 13 min and we were able to detect and quantify several metabolites.

PurposeTo implement a 5D (three spatial + two spectral) correlated spectroscopic imaging sequence for application to human calf.Theory and methodsNonuniform sampling was applied across the two phase encoded dimensions and the indirect spectral dimension of an echo planar-correlated spectroscopic imaging sequence. Reconstruction was applied that minimized the group sparse mixed ℓ2,1-norm of the data. Multichannel data were compressed using a sensitivity map-based approach with a spatially dependent transform matrix and utilized the self-sparsity of the individual coil images to simplify the reconstruction.ResultsSingle channel data with 8× and 16× undersampling are shown in the calf of a diabetic patient. A 15-channel scan with 12× undersampling of a healthy volunteer was reconstructed using 5 virtual channels and compared to a fully sampled single slice scan. Group sparse reconstruction faithfully reconstructs the lipid cross peaks much better than ℓ1 minimization.ConclusionCOSY spectra can be acquired over a 3D spatial volume with scan time under 15 min using echo planar readout with highly undersampled data and group sparse reconstruction.

Bariatric surgery improves dyslipidaemia and reduces body weight, but it remains unclear how bariatric surgery modulates gene expression in fat cells to influence the proprotein convertase subtilisin/kexin type 9 (PCSK-9) and low-density lipoprotein receptor (LDLR) gene expression. The expression of the PCSK9/LDLR/tumor necrosis factor-alpha (TNFα) gene in adipose tissue was measured in two groups of Zucker Diabetic Sprague Dawley (ZDSD) rats after Roux-en-Y gastric bypass (RYGB) surgery or 'SHAM' operation. There was lower PCSK9 (p = 0.02) and higher LDLR gene expression (p = 0.02) in adipose tissue in rats after RYGB. Weight change did not correlate with PCSK9 gene expression (r = −0.5, p = 0.08) or TNFα gene expression (r = −0.4, p = 0.1). TNFα gene expression was positively correlated with PCSK9 gene expression (r = 0.7, p = 0.001) but not correlated with LDLR expression (r = −0.3, p = 0.3). Circulating triglyceride levels were lower in RYGB compared to the SHAM group (1.1 (0.8–1.4) vs. 1.5 (1.0–4.2), p = 0.038) mmol/L with no difference in cholesterol levels. LDLR gene expression was increased post-bariatric surgery with the potential to reduce the number of circulating LDL particles. PCSK9 gene expression and TNFα gene expression were positively correlated after RYGB in ZDSD rats, suggesting that the modulation of pro-inflammatory pathways in adipose tissue after RYGB may partly relate to PCSK9 and LDLR gene expression. [ABSTRACT FROM AUTHOR]

Summary: Bariatric surgery in people with obesity can lead to long‐term remission of type 2 diabetes mellitus (T2DM) and a reduction in the incidence of macrovascular complications. The impact of bariatric surgery on microvascular complications is less clear. In this narrative review, we sought to evaluate the effect of bariatric surgery on microvascular complications in patients with and without diabetes. The risk of developing microvascular complications is increased in people with obesity, and this is amplified in those with T2DM. The impact of metabolic surgery on microvascular complications is limited to a subgroup analysis of studies or statistical modeling to predict the glycemia‐independent effect of bariatric surgery. While bariatric surgery halts the progression of retinopathy in those with minimal retinopathy, it may worsen in those with advanced retinopathy. Bariatric surgery improves proteinuria and major renal outcomes, regardless of the severity of renal impairment. Bariatric surgery in patients with obesity with or without diabetes is associated with an improvement in neuropathic symptoms and regeneration of small nerve fibers. In conclusion, bariatric surgery is associated with an improvement in microvascular complications. Further studies are needed to elucidate the underlying mechanisms for the favorable effect of bariatric surgery on microvascular outcomes. [ABSTRACT FROM AUTHOR]

Composite materials have found widespread applications in the automotive, aerospace, and building industries. Several components are joined together for these applications, by some temporary or permanent bonding approach. The increased use of different materials and their combinations such as composites makes the whole joining process something to be thoroughly considered before continuing. Several aspects need to be studied before spending significant time and financial resources. Considering these challenges in this paper we have provided a review of the investigations that have been made on fiber-reinforced composite joints. The level of development in various types of joints and joining techniques such as mechanical bonding, adhesive bonding, and fusion bonding along with their advantages and disadvantages is given. Several parameters affecting the performance of composite joints such as joint configuration, material selection and properties, geometric parameters, dominating failure modes, and environmental factors are described briefly. To verify the performance of composite joints, guidance on joint testing is given (both destructive and non-destructive). [ABSTRACT FROM AUTHOR]