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Background: In recent years, MRS has benefited from increased MRI field strengths, new acquisition protocols and new processing techniques. This review aims to determine how this has altered our understanding of MRS neurometabolic markers in neurodegenerative dementias. Methods: Our systematic review of human in vivo MRS literature since 2002 pertains to Alzheimer's disease (AD), dementia with Lewy bodies (DLB), Parkinson's disease dementia, frontotemporal dementia (FTD), prodromal and 'at‐risk' states. Studies using field strengths of 3 T or more were included. Results: Of 85 studies, AD and/or mild cognitive impairment (MCI) were the most common conditions of interest (58 papers, 68%). Only 14 (16%) studies included other dementia syndromes and 13 (15%) investigated 'at‐risk' cohorts. Earlier findings of lower N‐acetylaspartate and higher myo‐inositol were confirmed. Additionally, lower choline and creatine in AD and MCI were reported, though inconsistently. Previously challenging‐to‐measure metabolites (glutathione, glutamate and gamma‐aminobutyric acid) were reportedly lower in AD, FTD and DLB compared with controls. Discussion: Increasing field strength alongside targeted acquisition protocols has revealed additional metabolite changes. Most studies were small and regional metabolite differences between dementia types may not have been captured due to the predominant placement of voxels in the posterior cingulate cortex. The standard of data collection, quality control and analysis is improving due to greater consensus regarding acquisition and processing techniques. Ongoing harmonization of techniques, creation of larger and longitudinal cohorts, and placement of MRS voxels in more diverse regions will strengthen future research. [ABSTRACT FROM AUTHOR]

MR spectroscopy (MRS) is one of the oldest MR techniques for clinical use, with its applications expanding to in vivo studies over four decades ago. Terminology and concepts for the characterization of in vivo MR spectroscopy methods and MR spectra: background and experts' consensus recommendations. 15 This special issue also includes authoritative reviews of the current state of fast MRSI methodology,16 hyperpolarized SP 13 sp C MRI and MRS,17 and functional MRS in rodents.18 Fifteen proferred original research contributions complete this special issue. It has been a common experience for many of the experienced MRS methods experts to find essential details about employed MRS methods missing from published papers or in manuscripts submitted for review. [Extracted from the article]

Attention deficit hyperactivity disorder (ADHD) is a common mental health condition that significantly affects school‐age children, causing difficulties with learning and daily functioning. Early identification is crucial, and reliable and objective diagnostic tools are necessary. However, current clinical evaluations of behavioral symptoms can be inconsistent and subjective. Functional magnetic resonance imaging (fMRI) is a non‐invasive technique that has proven effective in detecting brain abnormalities in individuals with ADHD. Recent studies have shown promising outcomes in using resting state fMRI (rsfMRI)‐based brain functional networks to diagnose various brain disorders, including ADHD. Several review papers have examined the detection of other diseases using fMRI data and machine learning or deep learning methods. However, no review paper has specifically addressed ADHD. Therefore, this study aims to contribute to the literature by reviewing the use of rsfMRI data and machine learning methods for detection of ADHD. The study provides general information about fMRI databases and detailed knowledge of the ADHD‐200 database, which is commonly used for ADHD detection. It also emphasizes the importance of examining all stages of the process, including network and atlas selection, feature extraction, and feature selection, before the classification stage. The study compares the performance, advantages, and disadvantages of previous studies in detail. This comprehensive approach may be a useful starting point for new researchers in this area. [ABSTRACT FROM AUTHOR]

The translation of MRS to clinical practice has been impeded by the lack of technical standardization. There are multiple methods of acquisition, post‐processing, and analysis whose details greatly impact the interpretation of the results. These details are often not fully reported, making it difficult to assess MRS studies on a standardized basis. This hampers the reviewing of manuscripts, limits the reproducibility of study results, and complicates meta‐analysis of the literature. In this paper a consensus group of MRS experts provides minimum guidelines for the reporting of MRS methods and results, including the standardized description of MRS hardware, data acquisition, analysis, and quality assessment. This consensus statement describes each of these requirements in detail and includes a checklist to assist authors and journal reviewers and to provide a practical way for journal editors to ensure that MRS studies are reported in full. [ABSTRACT FROM AUTHOR]

Amide proton transfer (APT) imaging, a technique sensitive to tissue pH, holds promise in the diagnosis of ischemic stroke. Achieving accurate and rapid APT imaging is crucial for this application. However, conventional APT quantification methods either lack accuracy or are time‐consuming. Machine learning (ML) has recently been recognized as a potential solution to improve APT quantification. In this paper, we applied an ML model trained on a new type of partially synthetic data, along with an optimization approach utilizing recursive feature elimination, to predict APT imaging in an animal stroke model. This partially synthetic datum is not a simple blend of measured and simulated chemical exchange saturation transfer (CEST) signals. Rather, it integrates the underlying components including all CEST, direct water saturation, and magnetization transfer effects partly derived from measurements and simulations to reconstruct the CEST signals using an inverse summation relationship. Training with partially synthetic data requires less in vivo data compared to training entirely with fully synthetic or in vivo data, making it a more practical approach. Since this type of data closely resembles real tissue, it leads to more accurate predictions than ML models trained on fully synthetic data. Results indicate that an ML model trained on this partially synthetic data can successfully predict the APT effect with enhanced accuracy, providing significant contrast between stroke lesions and normal tissues, thus clearly delineating lesions. In contrast, conventional quantification methods such as the asymmetric analysis method, three‐point method, and multiple‐pool model Lorentzian fit showed inadequate accuracy in quantifying the APT effect. Moreover, ML methods trained using in vivo data and fully synthetic data exhibited poor predictive performance due to insufficient training data and inaccurate simulation pool settings or parameter ranges, respectively. Following optimization, only 13 frequency offsets were selected from the initial 69, resulting in significantly reduced scan time. [ABSTRACT FROM AUTHOR]

Magnetic Resonance Fingerprinting (MRF) can be accelerated with simultaneous multislice (SMS) imaging for joint T1 and T2 quantification. However, the high inter‐slice and in‐plane acceleration in SMS‐MRF causes severe aliasing artifacts, limiting the multiband (MB) factors to typically 2 or 3. Deep learning has demonstrated superior performance compared to the conventional dictionary matching approach for single‐slice MRF, but its effectiveness in SMS‐MRF remains unexplored. In this paper, we introduced a new deep learning approach with decoupled spatiotemporal feature learning for SMS‐MRF to achieve high MB factors for accurate and volumetric T1 and T2 quantification in neuroimaging. The proposed method leverages information from both spatial and temporal domains to mitigate the significant aliasing in SMS‐MRF. Neural networks, trained using either acquired SMS‐MRF data or simulated data generated from single‐slice MRF acquisitions, were evaluated. The performance was further compared with both dictionary matching and a deep learning approach based on residual channel attention U‐Net. Experimental results demonstrated that the proposed method, trained with acquired SMS‐MRF data, achieves the best performance in brain T1 and T2 quantification, outperforming dictionary matching and residual channel attention U‐Net. With a MB factor of 4, rapid T1 and T2 mapping was achieved with 1.5 s per slice for quantitative brain imaging. [ABSTRACT FROM AUTHOR]

Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) has emerged as a powerful imaging technique sensitive to tissue molecular composition, pH, and metabolic processes in situ. CEST MRI uniquely probes the physical exchange of protons between water and specific molecules within tissues, providing a window into physiological phenomena that remain invisible to standard MRI. However, given the very low concentration (millimolar range) of CEST compounds, the effects measured are generally only on the order of a few percent of the water signal. Consequently, a few critical challenges, including correction of motion artifacts and magnetic field (B0 and B1+) inhomogeneities, have to be addressed in order to unlock the full potential of CEST MRI. Motion, whether from patient movement or inherent physiological pulsations, can distort the CEST signal, hindering accurate quantification. B0 and B1+ inhomogeneities, arising from scanner hardware imperfections, further complicate data interpretation by introducing spurious variations in the signal intensity. Without proper correction of these confounding factors, reliable analysis and clinical translation of CEST MRI remain challenging. Motion correction methods aim to compensate for patient movement during (prospective) or after (retrospective) image acquisition, reducing artifacts and preserving data quality. Similarly, B0 and B1+ inhomogeneity correction techniques enhance the spatial and spectral accuracy of CEST MRI. This paper aims to provide a comprehensive review of the current landscape of motion and magnetic field inhomogeneity correction methods in CEST MRI. The methods discussed apply to saturation transfer (ST) MRI in general, including semisolid magnetization transfer contrast (MTC) and relayed nuclear Overhauser enhancement (rNOE) studies. [ABSTRACT FROM AUTHOR]

Baluns are crucial in MRI RF coils, essential for minimizing common‐mode currents, maintaining signal‐to‐noise ratio, and ensuring patient safety. This paper introduces the innovative float solenoid balun, based on the renowned solenoid cable trap, and conducts a comparative analysis with the widely used float bazooka balun. Leveraging robust inductive coupling between the cable shield and float resonator, the float solenoid balun offers compact dimensions and post‐installation adjustability. Through electromagnetic simulations and bench testing across static fields (1.5, 3, and 7 T), the float solenoid balun demonstrates superior common‐mode rejection ratios compared to the float bazooka balun. Notably, its float design facilitates easy post‐installation adjustment and eliminates the need for soldering on the cable shield, enhancing usability and reducing risks. Furthermore, the solenoid balun's compact footprint addresses the increasing demand for smaller baluns in modern MRI scanners with denser coil arrays. The float solenoid balun offers a promising solution by conserving valuable space within the RF coil, simplifying practical hardware implementation and cable routing, and accommodating more elements in RF arrays, with great potential for enhancing MRI performance. [ABSTRACT FROM AUTHOR]

In this second part of a two‐part paper, we intend to demonstrate the impact of the previously proposed advanced quality control pipeline. To understand its benefit and challenge the proposed methodology in a real scenario, we chose to compare the outcome when applying it to the analysis of two patient populations with significant but highly different types of fatigue: COVID‐19 and multiple sclerosis (MS). 31P‐MRS was performed on a 3 T clinical MRI, in 19 COVID‐19 patients, 38 MS patients, and 40 matched healthy controls. Dynamic acquisitions using an MR‐compatible ergometer ran over a rest (40 s), exercise (2 min), and a recovery phase (6 min). Long and short TR acquisitions were also made at rest for T1 correction. The advanced data quality control pipeline presented in Part 1 is applied to the selected patient cohorts to investigate its impact on clinical outcomes. We first used power and sample size analysis to estimate objectively the impact of adding the quality control score (QCS). Then, comparisons between patients and healthy control groups using the validated QCS were performed using unpaired t tests or Mann–Whitney tests (p < 0.05). The application of the QCS resulted in increased statistical power, changed the values of several outcome measures, and reduced variability (standard deviation). A significant difference was found between the T1PCr and T1Pi values of MS patients and healthy controls. Furthermore, the use of a fixed correction factor led to systematically higher estimated concentrations of PCr and Pi than when using individually corrected factors. We observed significant differences between the two patient populations and healthy controls for resting [PCr]—MS only, [Pi], [ADP], [H2PO4−], and pH—COVID‐19 only, and post‐exercise [PCr], [Pi], and [H2PO4−]—MS only. The dynamic indicators τPCr, τPi, ViPCr, and Vmax were reduced for COVID‐19 and MS patients compared with controls. Our results show that QCS in dynamic 31P‐MRS studies results in smaller data variability and therefore impacts study sample size and power. Although QCS resulted in discarded data and therefore reduced the acceptable data and subject numbers, this rigorous and unbiased approach allowed for proper assessment of muscle metabolites and metabolism in patient populations. The outcomes include an increased metabolite T1, which directly affects the T1 correction factor applied to the amplitudes of the metabolite, and a prolonged τPCr, indicating reduced muscle oxidative capacity for patients with MS and COVID‐19. [ABSTRACT FROM AUTHOR]

Cerebral small vessel disease (cSVD), a common cause of stroke and dementia, is traditionally considered the small vessel equivalent of large artery occlusion or rupture that leads to cortical and subcortical brain damage. Microvessel endothelial dysfunction can also contribute to it. Brain imaging, including MRI, is useful to show the presence of lesions of several types, although the association between conventional MRI measures and clinical features of cSVD is not always concordant. We assessed the additional contribution of contrast‐agent‐free, state‐of‐the‐art MRI techniques such as arterial spin labeling (ASL), diffusion tensor imaging, functional MRI, and intravoxel incoherent motion (IVIM) applied to cSVD in the existing literature. We performed a review following the PICO Worksheet and Search Strategy, including original papers in English, published between 2000 and 2022. For each MRI method, we extracted information about their contributions, in addition to those established with traditional MRI methods and related information about the origins, pathology, markers, and clinical outcomes in cSVD. This paper presents the first part of the review, which includes 37 studies focusing on ASL, IVIM, and cerebrovascular reactivity (CVR) measures. In general, they have shown that, in addition to white matter hyperintensities, alterations in other neuroimaging parameters such as blood flow and CVR also indicate the presence of cSVD. Such quantitative parameters were also related to cSVD risk factors. Therefore, they are promising, noninvasive tools to explore questions that have not yet been clarified about this clinical condition. However, protocol standardization is essential to increase their clinical use. Quantitative neuroimaging parameters such as blood flow and vascular reactivity indicate the presence of cerebral small vessel disease. They are related to cSVD risk factors. Contrast‐agent‐free, state‐of‐the‐art MRI techniques, such as arterial spin labeling (ASL), blood oxygenation level dependent MRI (BOLD‐MRI), and intravoxel incoherent motion (IVIM), are promising noninvasive tools to explore questions that have not yet been clarified about this clinical condition. However, protocol standardization is essential to increase their clinical use. [ABSTRACT FROM AUTHOR]

Proton MRS is used clinically to collect localized, quantitative metabolic data from living tissues. However, the presence of baselines in the spectra complicates accurate MRS data quantification. The occurrence of baselines is not specific to short‐echo‐time MRS data. In short‐echo‐time MRS, the baseline consists typically of a dominating macromolecular (MM) part, and can, depending on B0 shimming, poor voxel placement, and/or localization sequences, also contain broad water and lipid resonance components, indicated by broad components (BCs). In long‐echo‐time MRS, the MM part is usually much smaller, but BCs may still be present. The sum of MM and BCs is denoted by the baseline. Many algorithms have been proposed over the years to tackle these artefacts. A first approach is to identify the baseline itself in a preprocessing step, and a second approach is to model the baseline in the quantification of the MRS data themselves. This paper gives an overview of baseline handling algorithms and also proposes a new algorithm for baseline correction. A subset of suitable baseline removal algorithms were tested on in vivo MRSI data (semi‐LASER at TE = 40 ms) and compared with the new algorithm. The baselines in all datasets were removed using the different methods and subsequently fitted using spectrIm‐QMRS with a TDFDFit fitting model that contained only a metabolite basis set and lacked a baseline model. The same spectra were also fitted using a spectrIm‐QMRS model that explicitly models the metabolites and the baseline of the spectrum. The quantification results of the latter quantification were regarded as ground truth. The fit quality number (FQN) was used to assess baseline removal effectiveness, and correlations between metabolite peak areas and ground truth models were also examined. The results show a competitive performance of our new proposed algorithm, underscoring its automatic approach and efficiency. Nevertheless, none of the tested baseline correction methods achieved FQNs as good as the ground truth model. All separately applied baseline correction methods introduce a bias in the observed metabolite peak areas. We conclude that all baseline correction methods tested, when applied as a separate preprocessing step, yield poorer FQNs and biased quantification results. While they may enhance visual display, they are not advisable for use before spectral fitting. [ABSTRACT FROM AUTHOR]

Quantitative mapping of MR tissue parameters such as the spin‐lattice relaxation time (T1), the spin‐spin relaxation time (T2), and the spin‐lattice relaxation in the rotating frame (T1ρ), referred to as MR relaxometry in general, has demonstrated improved assessment in a wide range of clinical applications. Compared with conventional contrast‐weighted (eg T1‐, T2‐, or T1ρ‐weighted) MRI, MR relaxometry provides increased sensitivity to pathologies and delivers important information that can be more specific to tissue composition and microenvironment. The rise of deep learning in the past several years has been revolutionizing many aspects of MRI research, including image reconstruction, image analysis, and disease diagnosis and prognosis. Although deep learning has also shown great potential for MR relaxometry and quantitative MRI in general, this research direction has been much less explored to date. The goal of this paper is to discuss the applications of deep learning for rapid MR relaxometry and to review emerging deep‐learning‐based techniques that can be applied to improve MR relaxometry in terms of imaging speed, image quality, and quantification robustness. The paper is comprised of an introduction and four more sections. Section 2 describes a summary of the imaging models of quantitative MR relaxometry. In Section 3, we review existing "classical" methods for accelerating MR relaxometry, including state‐of‐the‐art spatiotemporal acceleration techniques, model‐based reconstruction methods, and efficient parameter generation approaches. Section 4 then presents how deep learning can be used to improve MR relaxometry and how it is linked to conventional techniques. The final section concludes the review by discussing the promise and existing challenges of deep learning for rapid MR relaxometry and potential solutions to address these challenges. [ABSTRACT FROM AUTHOR]

The human visual pathway starts from the retina, passes through the retinogeniculate visual pathway, the optic radiation, and finally connects to the primary visual cortex. Diffusion MRI tractography is the only technology that can noninvasively reconstruct the visual pathway. However, complete and accurate visual pathway reconstruction is challenging because of the skull base environment and complex fiber geometries. Specifically, the optic nerve within the complex skull base environment can cause abnormal diffusion signals. The crossing and fanning fibers at the optic chiasm, and a sharp turn of Meyer's loop at the optic radiation, contribute to complex fiber geometries of the visual pathway. A fiber trajectory distribution (FTD) function‐based tractography method of our previous work and several high sensitivity tractography methods can reveal these complex fiber geometries, but are accompanied by false‐positive fibers. Thus, the related studies of the visual pathway mostly applied the expert region of interest selection strategy. However, interobserver variability is an issue in reconstructing an accurate visual pathway. In this paper, we propose a unified global tractography framework to automatically reconstruct the visual pathway. We first extend the FTD function to a high‐order streamline differential equation for global trajectory estimation. At the global level, the tractography process is simplified as the estimation of global trajectory distribution coefficients by minimizing the cost between trajectory distribution and the selected directions under the prior guidance by introducing the tractography template as anatomic priors. Furthermore, we use a deep learning‐based method and tractography template prior information to automatically generate the mask for tractography. The experimental results demonstrate that our proposed method can successfully reconstruct the visual pathway with high accuracy. [ABSTRACT FROM AUTHOR]

To protect implant carriers in MRI from excessive radiofrequency (RF) heating it has previously been suggested to assess that hazard via sensors on the implant. Other work recommended parallel transmission (pTx) to actively mitigate implant‐related heating. Here, both ideas are integrated into one comprehensive safety concept where native pTx safety (without implant) is ensured by state‐of‐the‐art field simulations and the implant‐specific hazard is quantified in situ using physical sensors. The concept is demonstrated by electromagnetic simulations performed on a human voxel model with a simplified spinal‐cord implant in an eight‐channel pTx body coil at 3T. To integrate implant and native safety, the sensor signal must be calibrated in terms of an established safety metric (e.g., specific absorption rate [SAR]). Virtual experiments show that E‐field and implant‐current sensors are well suited for this purpose, while temperature sensors require some caution, and B1 probes are inadequate. Based on an implant sensor matrix Qs, constructed in situ from sensor readings, and precomputed native SAR limits, a vector space of safe RF excitations is determined where both global (native) and local (implant‐related) safety requirements are satisfied. Within this safe‐excitation subspace, the solution with the best image quality in terms of B1+ magnitude and homogeneity is then found by a straightforward optimization algorithm. In the investigated example, the optimized pTx shim provides a 3‐fold higher meanB1+ magnitude compared with circularly polarized excitation for a maximum implant‐related temperature increase ∆Timp≤1K. To date, sensor‐equipped implants interfaced to a pTx scanner exist as demonstrator items in research labs, but commercial devices are not yet within sight. This paper aims to demonstrate the significant benefits of such an approach and how this could impact implant‐related RF safety in MRI. Today, the responsibility for safe implant scanning lies with the implant manufacturer and the MRI operator; within the sensor concept, the MRI manufacturer would assume much of the operator's current responsibility. [ABSTRACT FROM AUTHOR]

Amide proton transfer (APT) imaging, a variant of chemical exchange saturation transfer MRI, has shown promise in detecting ischemic tissue acidosis following impaired aerobic metabolism in animal models and in human stroke patients due to the sensitivity of the amide proton exchange rate to changes in pH within the physiological range. Recent studies have demonstrated the possibility of using APT‐MRI to detect acidosis of the ischemic penumbra, enabling the assessment of stroke severity and risk of progression, monitoring of treatment progress, and prognostication of clinical outcome. This paper reviews current APT imaging methods actively used in ischemic stroke research and explores the clinical aspects of ischemic stroke and future applications for these methods. [ABSTRACT FROM AUTHOR]

Chemical exchange saturation transfer (CEST) imaging is an important molecular magnetic resonance imaging technique that can image numerous low‐concentration biomolecules with water‐exchangeable protons (such as cellular proteins) and tissue pH. CEST, or more specially amide proton transfer‐weighted imaging, has been widely used for the detection, diagnosis, and response assessment of brain tumors, and its feasibility in identifying molecular markers in gliomas has also been explored in recent years. In this paper, after briefing on the basic principles and quantification methods of CEST imaging, we review its early applications in identifying isocitrate dehydrogenase mutation status, MGMT methylation status, 1p/19q deletion status, and H3K27M mutation status in gliomas. Finally, we discuss the limitations or weaknesses in these studies. [ABSTRACT FROM AUTHOR]

Magnetic resonance imaging (MRI) is a ubiquitous medical imaging technology with applications in disease diagnostics, intervention, and treatment planning. Accurate MRI segmentation is critical for diagnosing abnormalities, monitoring diseases, and deciding on a course of treatment. With the advent of advanced deep learning frameworks, fully automated and accurate MRI segmentation is advancing. Traditional supervised deep learning techniques have advanced tremendously, reaching clinical‐level accuracy in the field of segmentation. However, these algorithms still require a large amount of annotated data, which is oftentimes unavailable or impractical. One way to circumvent this issue is to utilize algorithms that exploit a limited amount of labeled data. This paper aims to review such state‐of‐the‐art algorithms that use a limited number of annotated samples. We explain the fundamental principles of self‐supervised learning, generative models, few‐shot learning, and semi‐supervised learning and summarize their applications in cardiac, abdomen, and brain MRI segmentation. Throughout this review, we highlight algorithms that can be employed based on the quantity of annotated data available. We also present a comprehensive list of notable publicly available MRI segmentation datasets. To conclude, we discuss possible future directions of the field—including emerging algorithms, such as contrastive language‐image pretraining, and potential combinations across the methods discussed—that can further increase the efficacy of image segmentation with limited labels. [ABSTRACT FROM AUTHOR]

The global disparity of magnetic resonance imaging (MRI) is a major challenge, with many low‐ and middle‐income countries (LMICs) experiencing limited access to MRI. The reasons for limited access are technological, economic and social. With the advancement of MRI technology, we explore why these challenges still prevail, highlighting the importance of MRI as the epidemiology of disease changes in LMICs. In this paper, we establish a framework to develop MRI with these challenges in mind and discuss the different aspects of MRI development, including maximising image quality using cost‐effective components, integrating local technology and infrastructure and implementing sustainable practices. We also highlight the current solutions—including teleradiology, artificial intelligence and doctor and patient education strategies—and how these might be further improved to achieve greater access to MRI. [ABSTRACT FROM AUTHOR]

This paper provides a brief description of the early use of ex vivo nuclear magnetic resonance (NMR) studies of tissue and tissue extracts performed in the laboratory of Dr. Robert G. Shulman from 1975 through 1995 at Bell Laboratories, then later at Yale University. During that period, ex vivo NMR provided critical information in support of resonance assignments and the quantitation of concentrations for magnetic resonance spectroscopy studies. The period covered saw rapid advances in magnet technology, starting with studies of microorganisms in vertical bore high‐resolution NMR studies, then by 1981 studies of small mammals in a horizontal bore magnet, and then studies of humans in 1984. Ex vivo NMR played a critical role in all these studies. A general strategy developed in the lab for using ex vivo NMR to support in vivo studies is presented, as well as illustrative examples. [ABSTRACT FROM AUTHOR]

This paper proposes a new method for optimizing feature sharing in deep neural network‐based, rapid, multicontrast magnetic resonance imaging (MC‐MRI). Using the shareable information of MC images for accelerated MC‐MRI reconstruction, current algorithms stack the MC images or features without optimizing the sharing protocols, leading to suboptimal reconstruction results. In this paper, we propose a novel feature aggregation and selection scheme in a deep neural network to better leverage the MC features and improve the reconstruction results. First, we propose to extract and use the shareable information by mapping the MC images into multiresolution feature maps with multilevel layers of the neural network. In this way, the extracted features capture complementary image properties, including local patterns from the shallow layers and semantic information from the deep layers. Then, an explicit selection module is designed to compile the extracted features optimally. That is, larger weights are learned to incorporate the constructive, shareable features; and smaller weights are assigned to the unshareable information. We conduct comparative studies on publicly available T2‐weighted and T2‐weighted fluid attenuated inversion recovery brain images, and the results show that the proposed network consistently outperforms existing algorithms. In addition, the proposed method can recover the images with high fidelity under 16 times acceleration. The ablation studies are conducted to evaluate the effectiveness of the proposed feature aggregation and selection mechanism. The results and the visualization of the weighted features show that the proposed method does effectively improve the usage of the useful features and suppress useless information, leading to overall enhanced reconstruction results. Additionally, the selection module can zero‐out repeated and redundant features and improve network efficiency. [ABSTRACT FROM AUTHOR]

Proton MR spectra of the brain, especially those measured at short and intermediate echo times, contain signals from mobile macromolecules (MM). A description of the main MM is provided in this consensus paper. These broad peaks of MM underlie the narrower peaks of metabolites and often complicate their quantification but they also may have potential importance as biomarkers in specific diseases. Thus, separation of broad MM signals from low molecular weight metabolites enables accurate determination of metabolite concentrations and is of primary interest in many studies. Other studies attempt to understand the origin of the MM spectrum, to decompose it into individual spectral regions or peaks and to use the components of the MM spectrum as markers of various physiological or pathological conditions in biomedical research or clinical practice. The aim of this consensus paper is to provide an overview and some recommendations on how to handle the MM signals in different types of studies together with a list of open issues in the field, which are all summarized at the end of the paper. [ABSTRACT FROM AUTHOR]

Monitoring the variation in phosphocreatine (PCr) levels following exercise provides valuable insights into muscle function. Chemical exchange saturation transfer (CEST) has emerged as a sensitive method with which to measure PCr levels in muscle, surpassing conventional MR spectroscopy. However, existing approaches for quantifying PCr CEST signals rely on time‐consuming fitting methods that require the acquisition of the entire or a section of the CEST Z‐spectrum. Additionally, traditional fitting methods often necessitate clear CEST peaks, which may be challenging to obtain at low magnetic fields. This paper evaluated the application of a new model‐free method using double saturation power (DSP), termed DSP‐CEST, to estimate the PCr CEST signal in muscle. The DSP‐CEST method requires the acquisition of only two or a few CEST signals at the PCr frequency offset with two different saturation powers, enabling rapid dynamic imaging. Additionally, the DSP‐CEST approach inherently eliminates confounding signals, offering enhanced robustness compared with fitting methods. Furthermore, DSP‐CEST does not demand clear CEST peaks, making it suitable for low‐field applications. We evaluated the capability of DSP‐CEST to enhance the specificity of PCr CEST imaging through simulations and experiments on muscle tissue phantoms at 4.7 T. Furthermore, we applied DSP‐CEST to animal leg muscle both before and after euthanasia and observed successful reduction of confounding signals. The DSP‐CEST signal still has contaminations from a residual magnetization transfer (MT) effect and an aromatic nuclear Overhauser enhancement effect, and thus only provides a PCr‐weighted imaging. The residual MT effect can be reduced by a subtraction of DSP‐CEST signals at 2.6 and 5 ppm. Results show that the residual MT‐corrected DSP‐CEST signal at 2.6 ppm has significant variation in postmortem tissues. By contrast, both the CEST signal at 2.6 ppm and a conventional Lorentzian difference analysis of CEST signal at 2.6 ppm demonstrate no significant variation in postmortem tissues. [ABSTRACT FROM AUTHOR]

In proton magnetic resonance spectroscopy (1H MRS)‐based thermometry of brain, averaging temperatures measured from more than one reference peak offers several advantages, including improving the reproducibility (i.e., precision) of the measurement. This paper proposes theoretically and empirically optimal weighting factors to improve the weighted average of temperatures measured from three references. We first proposed concepts of equivalent noise and equivalent signal‐to‐noise ratio in terms of frequency measurement and a concept of relative frequency that allows the combination of different peaks in a spectrum for improving the precision of frequency measurement. Based on these, we then derived a theoretically optimal weighting factor and proposed an empirical weighting factor, both involving equivalent noise levels, for a weighted average of temperatures measured from three references (i.e., the singlets of NAA, Cr, and Ch in the 1H MR spectrum). We assessed these two weighting factors by comparing their errors in measurement of temperatures with the errors of temperatures measured from individual references; we also compared these two new weighting factors with two previously proposed weighting factors. These errors were defined as the standard deviations in repeated measurements or in Monte Carlo studies. Both the proposed theoretical and empirical weighting factors outperformed the two previously proposed weighting factors as well as the three individual references in all phantom and in vivo experiments. In phantom experiments with 4‐ or 10‐Hz line broadening, the theoretical weighting factor outperformed the empirical one, but the latter was superior in all other repeated and Monte Carlo tests performed on phantom and in vivo data. The proposed weighting factors are superior to the two previously proposed weighting factors and can improve the reproducibility of temperature measurement using 1H MRS‐based thermometry. [ABSTRACT FROM AUTHOR]

We present a sequence building block (SBB) that embeds magnetic resonance spectroscopy (MRS) into another sequence on the Siemens VE platform without any custom hardware. This enables dynamic studies such as functional MRS (fMRS), dynamic shimming and frequency correction, and acquisition of navigator images for motion correction. The SBB supports nonlocalised spectroscopy (free induction decay), STimulated Echo Acquisition Mode single voxel spectroscopy, and 1D, 2D and 3D phase-encoded chemical shift imaging. It can embed 1 H or X-nuclear MRS into a 1 H sequence; and 1 H-MRS into an X-nuclear sequence. We demonstrate integration into the vendor's gradient-recalled echo sequence. We acquire test data in phantoms with three coils ( 31 P/ 1 H, 13 C/ 1 H and 2 H/ 1 H) and in two volunteers on a 7-T Terra MRI scanner. Fifteen lines of code are required to insert the SBB into a sequence. Spectra and images are acquired successfully in all cases in phantoms, and in human abdomen and calf muscle. Phantom comparison of signal-to-noise ratio and linewidth showed that the SBB has negligible effects on image and spectral quality, except that it sometimes produces a nuclear Overhauser effect (NOE) signal enhancement for multinuclear applications in line with conventional 1 H NOE pulses. Our new SBB embeds MRS into a host imaging or spectroscopy sequence in 15 lines of code. It allows homonuclear and heteronuclear interleaving. The package is available through the standard C2P procedure. We hope this will lower the barrier for entry to studies applying dynamic fMRS and for online motion correction and B 0 -shim updating., (© 2024 The Authors. NMR in Biomedicine published by John Wiley & Sons Ltd.)

Partial Fourier encoding is popular in single-shot (ss) diffusion-weighted (DW) echo planar imaging (EPI) because it enables a shorter echo time (TE) and, hence, improves the signal-to-noise-ratio. Motion during diffusion encoding causes k-space shifting and dispersion, which compromises the quality of the homodyne reconstruction. This work provides a comprehensive understanding of the artifacts in homodyne reconstruction of partial Fourier ss-DW-EPI data in the presence of motion-induced phase and proposes the motion-induced phase-corrected homodyne (mpc-hdyne) reconstruction method to ameliorate these artifacts. Simulations with different types of motion-induced phase were performed to provide an understanding of the potential artifacts that occur in the homodyne reconstruction of partial Fourier ss-DW-EPI data. To correct for the artifacts, the mpc-hdyne reconstruction is proposed. The algorithm recenters k-space, updates the partial Fourier factor according to detected global k-space shifts, and removes low-resolution nonlinear phase before the conventional homodyne reconstruction. The mpc-hdyne reconstruction is tested on both simulation and in vivo data. Motion-induced phase can cause signal overestimation, worm artifacts, and signal loss in partial Fourier ss-DW-EPI data with the conventional homodyne reconstruction. Simulation and in vivo data showed that the proposed mpc-hdyne reconstruction ameliorated artifacts, yielding higher quality DW images compared with conventional homodyne reconstruction. Based on the understanding of the artifacts in homodyne reconstruction of partial Fourier ss-DW-EPI data, the mpc-hdyne reconstruction was proposed and showed superior performance compared with the conventional homodyne reconstruction on both simulation and in vivo data., (© 2024 The Authors. NMR in Biomedicine published by John Wiley & Sons Ltd.)

Cerebral small vessel disease (cSVD) has been widely studied using conventional magnetic resonance imaging (MRI) methods, although the association between MRI findings and clinical features of cSVD is not always concordant. We assessed the additional contribution of contrast agent‐free, state‐of‐the‐art MRI techniques, particularly diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI), to understand brain damage and structural and functional connectivity impairment related to cSVD. We performed a review following the PICOS worksheet and Search Strategy, including 152 original papers in English, published from 2000 to 2022. For each MRI method, we extracted information about their contributions regarding the origins, pathology, markers, and clinical outcomes in cSVD. In general, DTI studies have shown that changes in mean, radial, and axial diffusivity measures are related to the presence of cSVD. In addition to the classical deficit in executive functions and processing speed, fMRI studies indicate connectivity dysfunctions in other domains, such as sensorimotor, memory, and attention. Neuroimaging metrics have been correlated with the diagnosis, prognosis, and rehabilitation of patients with cSVD. In short, the application of contrast agent‐free, state‐of‐the‐art MRI techniques has provided a complete picture of cSVD markers and tools to explore questions that have not yet been clarified about this clinical condition. Longitudinal studies are desirable to look for causal relationships between image biomarkers and clinical outcomes. [ABSTRACT FROM AUTHOR]

In proton magnetic resonance spectroscopy (1H MRS) thermometry, separately acquired full water and partially suppressed water are commonly used for measuring temperature. This paper compares these two approaches. Single‐voxel 1H MRS data were collected on a 3‐T GE scanner from 26 human subjects. Every subject underwent five continuous MRS sessions, each separated by a 2‐min phase. Each MRS session lasted 13 min and consisted of two free induction decays (FIDs) without water suppression (with full water [FW or w]) and 64 FIDs with partial water suppression (with partially suppressed water [PW or w']). Frequency differences between the two FWs, the first two PWs, the second FW and the first PW (FW2, PW1), or between averaged water (wav′) and N‐acetylaspartate (NAA), were measured. Intrasubject and intersubject variations of the frequency differences were used as a metric for the error in temperature measurement. The intrasubject variations of frequency differences between FW2 and PW1fw2−fw1′, calculated from the five MRS sessions for each subject, were larger than those between the two FWs or between the first two PWs (p = 1.54 x 10−4 and p = 1.72 x 10−4, respectively). The mean values of intrasubject variations of fw2−fw1′ for all subjects were 4.7 and 4.5 times those of fw2−fw1 and fw2′−fw1′, respectively. The intrasubject variations of the temperatures based on frequency differences, fw2−fNAA or (fw1′−fNAA), were about 2.5 times greater than those based on averaged water and NAA frequencies (fwav′−fNAA). The mean temperature measured from (fwav′−fNAA) (n = 26) was 0.29°C lower than that measured from fw2−fNAA and was 0.83°C higher than that from (fw1′−fNAA). It was concluded that the use of separately acquired unsuppressed or partially suppressed water signals may result in large errors in frequency and, consequently, temperature measurement. [ABSTRACT FROM AUTHOR]

Great attention is being paid to solving, or mitigating, the technical problems associated with MRI at ultrahigh field strengths of 7 T and higher. This paper explores the use of the semiadiabatic spin‐echo (SA‐SE) pulse sequence, which uses semiadiabatic radiofrequency (RF) pulses to remove and/or mitigate the effects of the nonuniform B1 excitation field and B0 inhomogeneity associated with the electromagnetic properties of the human brain. A semiadiabatic RF pulse version of the recently published serial transmit excitation pulse (STEP) RF pulse sequence is also presented that now incorporates semiadiabatic pulses, henceforth is called SA‐STEP. As demonstrated by computer simulation, and confirmed using head imaging, both techniques can produce multislice SE MR imaging at 7 T. These new methods use relatively low RF power and achieve good coverage of the human brain in a single scan. [ABSTRACT FROM AUTHOR]

Magnetic resonance spectroscopy (MRS) is a robust, non-invasive means of defining aspects of human neurochemistry. After more than two decades, it is clear that in addition to its scientific interest, MRS has diagnostic value in tumor diagnosis, prognosis, therapeutic outcome, dementia diagnosis and prognosis, multiple sclerosis, infections, trauma, development, stroke, perinatal ischemia, xenobiotics and inborn errors (as determined from a meta-analysis included in this paper). However, in many healthcare systems, a new radiological technique requires evidence-based medicine (EBM) before it is recommended for reimbursement. Much of the reason why MRS is thought to be non-reimbursable in the USA is due to recent announcements that this 15-year-old technique is still considered 'investigational' by these EBM assessments. An analysis is presented of the technology assessments that brought about this situation. Based on the conclusions of the EBM assessments, strategies are suggested that involve all entities responsible for spectroscopy including the scientists' role in ensuring the future for clinical spectroscopy. Copyright © 2006 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Dynamic MR image reconstruction from incomplete k‐space data has generated great research interest due to its capability in reducing scan time. Nevertheless, the reconstruction problem is still challenging due to its ill‐posed nature. Most existing methods either suffer from long iterative reconstruction time or explore limited prior knowledge. This paper proposes a dynamic MR imaging method with both k‐space and spatial prior knowledge integrated via multi‐supervised network training, dubbed as DIMENSION. Specifically, the DIMENSION architecture consists of a frequential prior network for updating the k‐space with its network prediction and a spatial prior network for capturing image structures and details. Furthermore, a multi‐supervised network training technique is developed to constrain the frequency domain information and the spatial domain information. The comparisons with classical k‐t FOCUSS, k‐t SLR, L+S and the state‐of‐the‐art CNN‐based method on in vivo datasets show our method can achieve improved reconstruction results in shorter time. [ABSTRACT FROM AUTHOR]

Despite the success of automated pattern recognition methods in problems of human brain tumor diagnostic classification, limited attention has been paid to the issue of automated data quality assessment in the field of MRS for neuro‐oncology. Beyond some early attempts to address this issue, the current standard in practice is MRS quality control through human (expert‐based) assessment. One aspect of automatic quality control is the problem of detecting artefacts in MRS data. Artefacts, whose variety has already been reviewed in some detail and some of which may even escape human quality control, have a negative influence in pattern recognition methods attempting to assist tumor characterization. The automatic detection of MRS artefacts should be beneficial for radiology as it guarantees more reliable tumor characterizations, as well as the development of more robust pattern recognition‐based tumor classifiers and more trustable MRS data processing and analysis pipelines. Feature extraction methods have previously been used to help distinguishing between good and bad quality spectra to apply subsequent supervised pattern recognition techniques. In this study, we apply feature extraction differently and use a variant of a method for blind source separation, namely Convex Non‐Negative Matrix Factorization, to unveil MRS signal sources in a completely unsupervised way. We hypothesize that, while most sources will correspond to the different tumor patterns, some of them will reflect signal artefacts. The experimental work reported in this paper, analyzing a combined short and long echo time 1H‐MRS database of more than 2000 spectra acquired at 1.5T and corresponding to different tumor types and other anomalous masses, provides a first proof of concept that points to the possible validity of this approach. [ABSTRACT FROM AUTHOR]

In this paper, several radiomics‐based predictive models of response to induction chemotherapy (IC) in sinonasal cancers (SNCs) are built and tested. Models were built as a combination of radiomic features extracted from three types of MRI images: T1‐weighted images, T2‐weighted images and apparent diffusion coefficient (ADC) maps. Fifty patients (aged 54 ± 12 years, 41 men) were included in this study. Patients were classified according to their response to IC (25 responders and 25 nonresponders). Not all types of images were acquired for all of the patients: 49 had T1‐weighted images, 50 had T2‐weighted images and 34 had ADC maps. Only in a subset of 33 patients were all three types of image acquired. Eighty‐nine radiomic features were extracted from the MRI images. Dimensionality reduction was performed by using principal component analysis (PCA) and by selecting only the three main components. Different algorithms (trees ensemble, K‐nearest neighbors, support vector machine, naïve Bayes) were used to classify the patients as either responders or nonresponders. Several radiomic models (either monomodality or multimodality obtained by a combination of T1‐weighted, T2‐weighted and ADC images) were developed and the performance was assessed through 100 iterations of train and test split. The area under the curve (AUC) of the models ranged from 0.56 to 0.78. Trees ensemble, support vector machine and naïve Bayes performed similarly, but in all cases ADC‐based models performed better. Trees ensemble gave the highest AUC (0.78 for the T1‐weighted+T2‐weighted+ADC model) and was used for further analyses. For trees ensemble, the models based on ADC features performed better than those models that did not use those features (P < 0.02 for one‐tail Hanley test, AUC range 0.68–0.78 vs 0.56–0.69) except the T1‐weighted+ADC model (AUC 0.71 vs 0.69, nonsignificant differences). The results suggest the relevance of ADC‐based radiomics for prediction of response to IC in SNCs. [ABSTRACT FROM AUTHOR]

The suppression of motion artefacts from MR images is a challenging task. The purpose of this paper was to develop a standalone novel technique to suppress motion artefacts in MR images using a data‐driven deep learning approach. A simulation framework was developed to generate motion‐corrupted images from motion‐free images using randomly generated motion profiles. An Inception‐ResNet deep learning network architecture was used as the encoder and was augmented with a stack of convolution and upsampling layers to form an encoder‐decoder network. The network was trained on simulated motion‐corrupted images to identify and suppress those artefacts attributable to motion. The network was validated on unseen simulated datasets and real‐world experimental motion‐corrupted in vivo brain datasets. The trained network was able to suppress the motion artefacts in the reconstructed images, and the mean structural similarity (SSIM) increased from 0.9058 to 0.9338. The network was also able to suppress the motion artefacts from the real‐world experimental dataset, and the mean SSIM increased from 0.8671 to 0.9145. The motion correction of the experimental datasets demonstrated the effectiveness of the motion simulation generation process. The proposed method successfully removed motion artefacts and outperformed an iterative entropy minimization method in terms of the SSIM index and normalized root mean squared error, which were 5–10% better for the proposed method. In conclusion, a novel, data‐driven motion correction technique has been developed that can suppress motion artefacts from motion‐corrupted MR images. The proposed technique is a standalone, post‐processing method that does not interfere with data acquisition or reconstruction parameters, thus making it suitable for routine clinical practice. [ABSTRACT FROM AUTHOR]

Magnetic susceptibility imaging may provide valuable information about chemical composition and microstructural organization of tissue. However, its estimation from the MRI signal phase is particularly difficult as it is sensitive to magnetic tissue properties ranging from the molecular to the macroscopic scale. The MRI Larmor frequency shift measured in white matter (WM) tissue depends on the myelinated axons and other magnetizable sources such as iron-filled ferritin. We have previously derived the Larmor frequency shift arising from a dense medium of cylinders with scalar susceptibility and arbitrary orientation dispersion. Here, we extend our model to include microscopic WM susceptibility anisotropy as well as spherical inclusions with scalar susceptibility to represent subcellular structures, biologically stored iron, and so forth. We validate our analytical results with computer simulations and investigate the feasibility of estimating susceptibility using simple iterative linear least squares without regularization or preconditioning. This is done in a digital brain phantom synthesized from diffusion MRI measurements of an ex vivo mouse brain at ultra-high field., (© 2024 The Authors. NMR in Biomedicine published by John Wiley & Sons Ltd.)

We present a review of the characterization of healthy brain aging using MRI with an emphasis on morphology, lesions, and quantitative MR parameters. A scope review found 6612 articles encompassing the keywords "Brain Aging" and "Magnetic Resonance"; papers involving functional MRI or not involving imaging of healthy human brain aging were discarded, leaving 2246 articles. We first consider some of the biogerontological mechanisms of aging, and the consequences of aging in terms of cognition and onset of disease. Morphological changes with aging are reviewed for the whole brain, cerebral cortex, white matter, subcortical gray matter, and other individual structures. In general, volume and cortical thickness decline with age, beginning in mid‐life. Prevalent silent lesions such as white matter hyperintensities, microbleeds, and lacunar infarcts are also observed with increasing frequency. The literature regarding quantitative MR parameter changes includes T1, T2, T2*, magnetic susceptibility, spectroscopy, magnetization transfer, diffusion, and blood flow. We summarize the findings on how each of these parameters varies with aging. Finally, we examine how the aforementioned techniques have been used for age prediction. While relatively large in scope, we present a comprehensive review that should provide the reader with sound understanding of what MRI has been able to tell us about how the healthy brain ages. [ABSTRACT FROM AUTHOR]

Amide proton transfer‐chemical exchange saturation transfer (APT‐CEST) imaging provides important information for the diagnosis and monitoring of tumors. For such analysis, complete coverage of the brain is advantageous, especially when registration is performed with other magnetic resonance (MR) modalities, such as MR spectroscopy (MRS). However, the acquisition of Z‐spectra across several slices via multislice imaging may be time‐consuming. Therefore, in this paper, we present a new approach for fast multislice imaging, allowing us to acquire 16 slices per frequency offset within 8 s. The proposed fast CEST‐EPI sequence employs a presaturation module, which drives the magnetization into the steady‐state equilibrium for the first frequency offset. A second module, consisting of a single CEST pulse (for maintaining the steady‐state) followed by an EPI acquisition, passes through a loop to acquire multiple slices and adjacent frequency offsets. Thus, the whole Z‐spectrum can be recorded much faster than the conventional saturation scheme, which employs a presaturation for each single frequency offset. The validation of the CEST sequence parameters was performed by using the conventional saturation scheme. Subsequently, the proposed and a modified version of the conventional CEST sequence were compared in vitro on a phantom with different T1 times and in vivo on a brain tumor patient. No significant differences between both sequences could be found in vitro. The in vivo data yielded almost identical MTRasym contrasts for the white and gray matter as well as for tumor tissue. Our results show that the proposed fast CEST‐EPI sequence allows for rapid data acquisition and provides similar CEST contrasts as the modified conventional scheme while reducing the scanning time by approximately 50%. [ABSTRACT FROM AUTHOR]

The neurochemical information provided by proton magnetic resonance spectroscopy (MRS) or MR spectroscopic imaging (MRSI) can be severely compromised if strong signals originating from brain water and extracranial lipids are not properly suppressed. The authors of this paper present an overview of advanced water/lipid‐suppression techniques and describe their advantages and disadvantages. Moreover, they provide recommendations for choosing the most appropriate techniques for proper use. Methods of water signal handling are primarily focused on the VAPOR technique and on MRS without water suppression (metabolite cycling). The section on lipid‐suppression methods in MRSI is divided into three parts. First, lipid‐suppression techniques that can be implemented on most clinical MR scanners (volume preselection, outer‐volume suppression, selective lipid suppression) are described. Second, lipid‐suppression techniques utilizing the combination of k‐space filtering, high spatial resolutions and lipid regularization are presented. Finally, three promising new lipid‐suppression techniques, which require special hardware (a multi‐channel transmit system for dynamic B1+ shimming, a dedicated second‐order gradient system or an outer volume crusher coil) are introduced. [ABSTRACT FROM AUTHOR]

Spectral editing in in vivo 1H‐MRS provides an effective means to measure low‐concentration metabolite signals that cannot be reliably measured by conventional MRS techniques due to signal overlap, for example, γ‐aminobutyric acid, glutathione and D‐2‐hydroxyglutarate. Spectral editing strategies utilize known J‐coupling relationships within the metabolite of interest to discriminate their resonances from overlying signals. This consensus recommendation paper provides a brief overview of commonly used homonuclear editing techniques and considerations for data acquisition, processing and quantification. Also, we have listed the experts' recommendations for minimum requirements to achieve adequate spectral editing and reliable quantification. These include selecting the right editing sequence, dealing with frequency drift, handling unwanted coedited resonances, spectral fitting of edited spectra, setting up multicenter clinical trials and recommending sequence parameters to be reported in publications. [ABSTRACT FROM AUTHOR]

With a 40‐year history of use for in vivo studies, the terminology used to describe the methodology and results of magnetic resonance spectroscopy (MRS) has grown substantially and is not consistent in many aspects. Given the platform offered by this special issue on advanced MRS methodology, the authors decided to describe many of the implicated terms, to pinpoint differences in their meanings and to suggest specific uses or definitions. This work covers terms used to describe all aspects of MRS, starting from the description of the MR signal and its theoretical basis to acquisition methods, processing and to quantification procedures, as well as terms involved in describing results, for example, those used with regard to aspects of quality, reproducibility or indications of error. The descriptions of the meanings of such terms emerge from the descriptions of the basic concepts involved in MRS methods and examinations. This paper also includes specific suggestions for future use of terms where multiple conventions have emerged or coexisted in the past. [ABSTRACT FROM AUTHOR]

Once an MRS dataset has been acquired, several important steps must be taken to obtain the desired metabolite concentration measures. First, the data must be preprocessed to prepare them for analysis. Next, the intensity of the metabolite signal(s) of interest must be estimated. Finally, the measured metabolite signal intensities must be converted into scaled concentration units employing a quantitative reference signal to allow meaningful interpretation. In this paper, we review these three main steps in the post‐acquisition workflow of a single‐voxel MRS experiment (preprocessing, analysis and quantification) and provide recommendations for best practices at each step. [ABSTRACT FROM AUTHOR]

Apoptosis maintains an equilibrium between cell proliferation and cell death. Many diseases, including cancer, develop because of defects in apoptosis. A known metabolic marker of apoptosis is a notable increase in 1H NMR‐observable resonances associated with lipids stored in lipid droplets. However, standard one‐dimensional NMR experiments allow the quantification of lipid concentration only, without providing information about physical characteristics such as the size of lipid droplets, viscosity of the cytosol, or cytoskeletal rigidity. This additional information can improve monitoring of apoptosis‐based cancer treatments in intact cells and provide us with mechanistic insight into why these changes occur. In this paper, we use high‐resolution magic angle spinning (HRMAS) 1H NMR spectroscopy to monitor lipid concentrations and apparent diffusion coefficients of mobile lipid in intact cells treated with the apoptotic agents cisplatin or etoposide. We also use solution‐state NMR spectroscopy to study changes in lipid profiles of organic solvent cell extracts. Both NMR techniques show an increase in the concentration of lipids but the relative changes are 10 times larger by HRMAS 1H NMR spectroscopy. Moreover, the apparent diffusion rates of lipids in apoptotic cells measured by HRMAS 1H NMR spectroscopy decrease significantly as compared with control cells. Slower diffusion rates of mobile lipids in apoptotic cells correlate well with the formation of larger lipid droplets as observed by microscopy. We also compared the mean lipid droplet displacement values calculated from the two methods. Both methods showed shorter displacements of lipid droplets in apoptotic cells. Our results demonstrate that the NMR‐based diffusion experiments on intact cells discriminate between control and apoptotic cells. Apparent diffusion measurements in conjunction with 1H NMR spectroscopy‐derived lipid signals provide a novel means of following apoptosis in intact cells. This method could have potential application in enhancing drug discovery by monitoring drug treatments in vitro, particularly for agents that cause portioning of lipids such as apoptosis. [ABSTRACT FROM AUTHOR]

Ultra‐high field MRI offers many opportunities to expand the applications of MRI. In order for this to be realized, the technical problems associated with MRI at field strengths of 7 T and greater need to be solved or mitigated. This paper explores the use of new variations of composite RF pulses, named serial transmit excitation pulses (STEP), in contrast to parallel pulse techniques, in order to remove and/or mitigate the effects of non‐uniform B1 excitation fields associated with the subject (eg the human brain). Several techniques based on STEP sequences are introduced and their application to human brain imaging is presented and evaluated. [ABSTRACT FROM AUTHOR]

NMR relaxometry can provide information about the relaxation of the magnetization in different tissues, increasing our understanding of molecular dynamics and biochemical composition in biological systems. In general, tissues have complex and heterogeneous structures composed of multiple pools. As a result, bulk magnetization returns to its original state with different relaxation times, in a multicomponent relaxation. Recovering the distribution of relaxation times in each voxel is a difficult inverse problem; it is usually unstable and requires long acquisition time, especially on clinical scanners. MRI can also be viewed as an inverse problem, especially when compressed sensing (CS) is used. The solution of these two inverse problems, CS and relaxometry, can be obtained very efficiently in a synergistically combined manner, leading to a more stable multicomponent relaxometry obtained with short scan times. In this paper, we will discuss the details of this technique from the viewpoint of inverse problems. [ABSTRACT FROM AUTHOR]

In this paper we address the possibility to perform imaging of two samples within the same acquisition time using coupled ceramic resonators and one transmit/receive channel. We theoretically and experimentally compare the operation of our ceramic dual‐resonator probe with a wire‐wound solenoid probe, which is the standard probe used in ultrahigh‐field magnetic resonance microscopy. We show that due to the low‐loss ceramics used to fabricate the resonators, and a favorable distribution of the electric field within the conducting sample, a dual probe, which contains two samples, achieves an SNR enhancement by a factor close to the square root of 2 compared with a solenoid optimized for one sample. [ABSTRACT FROM AUTHOR]

The complex transverse water proton magnetization subject to diffusion‐encoding magnetic field gradient pulses in a heterogeneous medium such as brain tissue can be modeled by the Bloch‐Torrey partial differential equation. The spatial integral of the solution of this equation in realistic geometry provides a gold‐standard reference model for the diffusion MRI signal arising from different tissue micro‐structures of interest. A closed form representation of this reference diffusion MRI signal called matrix formalism, which makes explicit the link between the Laplace eigenvalues and eigenfunctions of the biological cell and its diffusion MRI signal, was derived 20 years ago. In addition, once the Laplace eigendecomposition has been computed and saved, the diffusion MRI signal can be calculated for arbitrary diffusion‐encoding sequences and b‐values at negligible additional cost. Up to now, this representation, though mathematically elegant, has not been often used as a practical model of the diffusion MRI signal, due to the difficulties of calculating the Laplace eigendecomposition in complicated geometries. In this paper, we present a simulation framework that we have implemented inside the MATLAB‐based diffusion MRI simulator SpinDoctor that efficiently computes the matrix formalism representation for realistic neurons using the finite element method. We show that the matrix formalism representation requires a few hundred eigenmodes to match the reference signal computed by solving the Bloch‐Torrey equation when the cell geometry originates from realistic neurons. As expected, the number of eigenmodes required to match the reference signal increases with smaller diffusion time and higher b‐values. We also convert the eigenvalues to a length scale and illustrate the link between the length scale and the oscillation frequency of the eigenmode in the cell geometry. We give the transformation that links the Laplace eigenfunctions to the eigenfunctions of the Bloch‐Torrey operator and compute the Bloch‐Torrey eigenfunctions and eigenvalues. This work is another step in bringing advanced mathematical tools and numerical method development to the simulation and modeling of diffusion MRI. [ABSTRACT FROM AUTHOR]

The purpose of the current study was to introduce a Deep learning‐based Accelerated and Noise‐Suppressed Estimation (DANSE) method for reconstructing quantitative maps of biological tissue cellular‐specific, R2t*, and hemodynamic‐specific, R2', metrics of quantitative gradient‐recalled echo (qGRE) MRI. The DANSE method adapts a supervised learning paradigm to train a convolutional neural network for robust estimation of R2t* and R2' maps with significantly reduced sensitivity to noise and the adverse effects of macroscopic (B0) magnetic field inhomogeneities directly from the gradient‐recalled echo (GRE) magnitude images. The R2t* and R2' maps for training were generated by means of a voxel‐by‐voxel fitting of a previously developed biophysical quantitative qGRE model accounting for tissue, hemodynamic, and B0‐inhomogeneities contributions to multigradient‐echo GRE signal using a nonlinear least squares (NLLS) algorithm. We show that the DANSE model efficiently estimates the aforementioned qGRE maps and preserves all the features of the NLLS approach with significant improvements including noise suppression and computation speed (from many hours to seconds). The noise‐suppression feature of DANSE is especially prominent for data with low signal‐to‐noise ratio (SNR ~ 50–100), where DANSE‐generated R2t* and R2' maps had up to three times smaller errors than that of the NLLS method. The DANSE method enables fast reconstruction of qGRE maps with significantly reduced sensitivity to noise and magnetic field inhomogeneities. The DANSE method does not require any information about field inhomogeneities during application. It exploits spatial and gradient echo time‐dependent patterns in the GRE data and previously gained knowledge from the biophysical model, thus producing high quality qGRE maps, even in environments with high noise levels. These features along with fast computational speed can lead to broad qGRE clinical and research applications. [ABSTRACT FROM AUTHOR]

In the last 25 years 13C MRS has been established as the only noninvasive method for measuring glutamate neurotransmission and cell specific neuroenergetics. Although technically and experimentally challenging 13C MRS has already provided important new information on the relationship between neuroenergetics and neuronal function, the high energy cost of brain function in the resting state and the role of altered neuroenergetics and neurotransmitter cycling in disease. In this paper we review the metabolic and neurotransmitter pathways that can be measured by 13C MRS and key findings on the linkage between neuroenergetics, neurotransmitter cycling, and brain function. Applications of 13C MRS to neurological and psychiatric disease as well as brain cancer are reviewed. Recent technological developments that may help to overcome spatial resolution and brain coverage limitations of 13C MRS are discussed. [ABSTRACT FROM AUTHOR]