Diffusion-Weighted MRI of the Liver in Patients With Chronic Liver Disease: A Comparative Study Between Different Fitting Approaches and Diffusion Models

BACKGROUND

Diffusion-weighted imaging (DWI) has been considered for chronic liver disease (CLD) characterization. Grading of liver fibrosis is important for disease management.

PURPOSE

To investigate the relationship between DWI's parameters and CLD-related features (particularly regarding fibrosis assessment).

STUDY TYPE

Retrospective.

SUBJECTS

Eighty-five patients with CLD (age: 47.9 ± 15.5, 42.4% females).

FIELD STRENGTH/SEQUENCE

3-T, spin echo-echo planar imaging (SE-EPI) with 12 b-values (0-800 s/mm2 ).

ASSESSMENT

Several models statistical models, stretched exponential model, and intravoxel incoherent motion were simulated. The corresponding parameters (Ds , σ, DDC, α, f, D, D*) were estimated on simulation and in vivo data using the nonlinear least squares (NLS), segmented NLS, and Bayesian methods. The fitting accuracy was analyzed on simulated Rician noised DWI. In vivo, the parameters were averaged from five central slices entire liver to compare correlations with histological features (inflammation, fibrosis, and steatosis). Then, the differences between mild (F0-F2) or severe (F3-F6) groups were compared respecting to statistics and classification. A total of 75.3% of patients used to build various classifiers (stratified split strategy and 10-folders cross-validation) and the remaining for testing.

STATISTICAL TESTS

Mean squared error, mean average percentage error, spearman correlation, Mann-Whitney U-test, receiver operating characteristic (ROC) curve, area under ROC curve (AUC), sensitivity, specificity, accuracy, precision. A P-value <0.05 was considered statistically significant.

RESULTS

In simulation, the Bayesian method provided the most accurate parameters. In vivo, the highest negative significant correlation (Ds , steatosis: r = -0.46, D*, fibrosis: r = -0.24) and significant differences (Ds , σ, D*, f) were observed for Bayesian fitted parameters. Fibrosis classification was performed with an AUC of 0.92 (0.91 sensitivity and 0.70 specificity) with the aforementioned diffusion parameters based on the decision tree method.

DATA CONCLUSION

These results indicate that Bayesian fitted parameters may provide a noninvasive evaluation of fibrosis with decision tree.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: Stage 1.

Optimal control in a magnetization-prepared rapid acquisition gradient-echo sequence

This article proposes a numerical framework to determine the optimal magnetization preparation in a three-dimensional magnetization-prepared rapid gradient-echo (MP-RAGE) sequence to obtain the best achievable contrast between target tissues based on differences in their relaxation times. The benefit lies in the adaptation of the algorithm of optimal control, GRAdient Ascent Pulse Engineering (GRAPE), to the optimization of magnetization preparation in a cyclic sequence without full recovery between each cycle. This numerical approach optimizes magnetization preparation of an arbitrary number of radio frequency pulses to enhance contrast, taking into account the establishment of a steady state in the longitudinal component of the magnetization. The optimal control preparation offers an optimized mixed T 1 / T 2 contrast in this traditional T 1 -weighted sequence. To show the versatility of the proposed method, numerical and in vitro results are described. Examples of contrasts acquired on brain regions of a healthy volunteer are presented for potential applications at 3 T.

Alteration of skeletal muscle energy metabolism assessed by phosphorus-31 magnetic resonance spectroscopy in clinical routine, part 1: Advanced quality control pipeline

Implementing a standardized phosphorus-31 magnetic resonance spectroscopy (31 P-MRS) dynamic acquisition protocol to evaluate skeletal muscle energy metabolism and monitor muscle fatigability, while being compatible with various longitudinal clinical studies on diversified patient cohorts, requires a high level of technicality and expertise. Furthermore, processing data to obtain reliable results also demands a great degree of expertise from the operator. In this two-part article, we present an advanced quality control approach for data acquired using a dynamic 31 P-MRS protocol. The aim is to provide decision support to the operator to assist in data processing and obtain reliable results based on objective criteria. We present here, in part 1, an advanced data quality control (QC) approach of a dynamic 31 P-MRS protocol. Part 2 is an impact study that will demonstrate the added value of the QC approach to explore data derived from two clinical populations that experience significant fatigue, patients with coronavirus disease 2019 and multiple sclerosis. In part 1, 31 P-MRS was performed using 3-T clinical MRI in 175 subjects from clinical and healthy control populations conducted in a University Hospital. An advanced data QC score (QCS) was developed using multiple objective criteria. The criteria were based on current recommendations from the literature enriched by new proposals based on clinical experience. The QCS was designed to indicate valid and corrupt data and guide necessary objective data editing to extract as much valid physiological data as possible. Dynamic acquisitions using an MR-compatible ergometer ran over a rest (40 s), exercise (2 min), and a recovery phase (6 min). Using QCS enabled rapid identification of subjects with data anomalies, allowing the user to correct the data series or reject them partially or entirely, as well as identify fully valid datasets. Overall, the use of the QCS resulted in the automatic classification of 45% of the subjects, including 58 participants who had data with no criterion violation and 21 participants with violations that resulted in the rejection of all dynamic data. The remaining datasets were inspected manually with guidance, allowing acceptance of full datasets from an additional 80 participants and recovery phase data from an additional 16 subjects. Overall, more anomalies occurred with patient data (35% of datasets) compared with healthy controls (15% of datasets). In conclusion, the QCS ensures a standardized data rejection procedure and rigorous objective analysis of dynamic 31 P-MRS data obtained from patients. This methodology contributes to efforts made to standardize 31 P-MRS practices that have been underway for a decade, with the goal of making it an empowered tool for clinical research.

Alteration of skeletal muscle energy metabolism assessed by 31 P MRS in clinical routine: Part 2. Clinical application

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). 31 P-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], [H2 PO4 - ], and pH-COVID-19 only, and post-exercise [PCr], [Pi ], and [H2 PO4 - ]-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 31 P-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.

Evaluation of deep learning models for quality control of MR spectra

Purpose

While 3D MR spectroscopic imaging (MRSI) provides valuable spatial metabolic information, one of the hurdles for clinical translation is its interpretation, with voxel-wise quality control (QC) as an essential and the most time-consuming step. This work evaluates the accuracy of machine learning (ML) models for automated QC filtering of individual spectra from 3D healthy control and patient datasets.

Methods

A total of 53 3D MRSI datasets from prior studies (30 neurological diseases, 13 brain tumors, and 10 healthy controls) were included in the study. Three ML models were evaluated: a random forest classifier (RF), a convolutional neural network (CNN), and an inception CNN (ICNN) along with two hybrid models: CNN + RF, ICNN + RF. QC labels used for training were determined manually through consensus of two MRSI experts. Normalized and cropped real-valued spectra was used as input. A cross-validation approach was used to separate datasets into training/validation/testing sets of aggregated voxels.

Results

All models achieved a minimum AUC of 0.964 and accuracy of 0.910. In datasets from neurological disease and controls, the CNN model produced the highest AUC (0.982), while the RF model achieved the highest AUC in patients with brain tumors (0.976). Within tumor lesions, which typically exhibit abnormal metabolism, the CNN AUC was 0.973 while that of the RF was 0.969. Data quality inference times were on the order of seconds for an entire 3D dataset, offering drastic time reduction compared to manual labeling.

Conclusion

ML methods accurately and rapidly performed automated QC. Results in tumors highlights the applicability to a variety of metabolic conditions.

Polyphenol Supplementation Did Not Affect Insulin Sensitivity and Fat Deposition During One-Month Overfeeding in Randomized Placebo-Controlled Trials in Men and in Women

Two randomized placebo-controlled double-blind paralleled trials (42 men in Lyon, 19 women in Lausanne) were designed to test 2 g/day of a grape polyphenol extract during 31 days of high calorie-high fructose overfeeding. Hyperinsulinemic-euglycemic clamps and test meals with [1,1,1-¹³C₃]-triolein were performed before and at the end of the intervention. Changes in body composition were assessed by dual-energy X-ray absorptiometry (DEXA). Fat volumes of the abdominal region and liver fat content were determined in men only, using 3D-magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) at 3T. Adipocyte's size was measured in subcutaneous fat biopsies. Bodyweight and fat mass increased during overfeeding, in men and in women. While whole body insulin sensitivity did not change, homeostasis model assessment of insulin resistance (HOMA-IR) and the hepatic insulin resistance index (HIR) increased during overfeeding. Liver fat increased in men. However, grape polyphenol supplementation did not modify the metabolic and anthropometric parameters or counteract the changes during overfeeding, neither in men nor in women. Polyphenol intake was associated with a reduction in adipocyte size in women femoral fat. Grape polyphenol supplementation did not counteract the moderated metabolic alterations induced by one month of high calorie-high fructose overfeeding in men and women. The clinical trials are registered under the numbers NCT02145780 and NCT02225457 at ClinicalTrials.gov and available at https://clinicaltrials.gov/ct2/show/NCT02145780 and https://clinicaltrials.gov/ct2/show/NCT02225457.

Spurious phase correction in rapid metabolic imaging

IDEAL-type magnetic resonance spectroscopic imaging (MRSI) sequences require the acquisition of several datasets using optimized sampling in the time domain to reconstruct metabolite maps. Each unitary scan consists of a selective slice (2D) or slab (3D) excitation followed by an evolution time and then the acquisition of the spatially encoded signal. It is critical that the phase variation during the evolution time for each scan is only dependent on chemical shifts. In this paper, we described the apparition of spurious phase due to either the transmit or the receive frequency. The presence of this unwanted phase depends on (i) where the commutation between these two frequencies is performed and (ii) how it is done, as there are two phase commutation modes: continuous and coherent. We present the correction needed in function of the different cases. It appears that some solutions are universal. However, it is critical to know which case is implemented on the MRI scanner, which is not always easy information to have. We illustrated several cases with our preclinical MRI by using the IDEAL spiral method on a ¹³C phantom.

Direct Comparison of Bayesian and Fermi Deconvolution Approaches for Myocardial Blood Flow Quantification: In silico and Clinical Validations

Cardiac magnetic resonance myocardial perfusion imaging can detect coronary artery disease and is an alternative to single-photon emission computed tomography or positron emission tomography. However, the complex, non-linear MR signal and the lack of robust quantification of myocardial blood flow have hindered its widespread clinical application thus far. Recently, a new Bayesian approach was developed for brain imaging and evaluation of perfusion indexes (Kudo et al., 2014). In addition to providing accurate perfusion measurements, this probabilistic approach appears more robust than previous approaches, particularly due to its insensitivity to bolus arrival delays. We assessed the performance of this approach against a well-known and commonly deployed model-independent method based on the Fermi function for cardiac magnetic resonance myocardial perfusion imaging. The methods were first evaluated for accuracy and precision using a digital phantom to test them against the ground truth; next, they were applied in a group of coronary artery disease patients. The Bayesian method can be considered an appropriate model-independent method with which to estimate myocardial blood flow and delays. The digital phantom comprised a set of synthetic time-concentration curve combinations generated with a 2-compartment exchange model and a realistic combination of perfusion indexes, arterial input dynamics, noise and delays collected from the clinical dataset. The myocardial blood flow values estimated with the two methods showed an excellent correlation coefficient (r² > 0.9) under all noise and delay conditions. The Bayesian approach showed excellent robustness to bolus arrival delays, with a similar performance to Fermi modeling when delays were considered. Delays were better estimated with the Bayesian approach than with Fermi modeling. An in vivo analysis of coronary artery disease patients revealed that the Bayesian approach had an excellent ability to distinguish between abnormal and normal myocardium. The Bayesian approach was able to discriminate not only flows but also delays with increased sensitivity by offering a clearly enlarged range of distribution for the physiologic parameters.

Automatic myocardial ischemic lesion detection on magnetic resonance perfusion weighted imaging prior perfusion quantification: A pre-modeling strategy

Even if cardiovascular magnetic resonance (CMR) perfusion imaging has proven its relevance for visual detection of ischemia, myocardial blood flow (MBF) quantification at the voxel observation scale remains challenging. Integration of an automated segmentation step, prior to perfusion index estimation, might be a significant reconstruction component that could allow sustainable assumptions and constraint enlargement prior to advanced modeling. Current clustering techniques, such as bullseye representation or manual delineation, are not designed to discriminate voxels belonging to the lesion from healthy areas. Hence, the resulting average time-intensity curve, which is assumed to represent the dynamic contrast enhancement inside of a lesion, might be contaminated by voxels with perfectly healthy microcirculation. This study introduces a hierarchical lesion segmentation approach based on time-intensity curve features that considers the spatial particularities of CMR myocardial perfusion. A first k-means clustering approach enables this method to perform coarse clustering, which is refined by a novel spatiotemporal region-growing (STRG) segmentation, thus ensuring spatial and time-intensity curve homogeneity. Over a cohort of 30 patients, myocardial blood flow (MBF) measured in voxels of lesion regions detected with STRG was significantly lower than in regions drawn manually (mean difference = 0.14, 95% CI [0.07, 0.2]) and defined with the bullseye template (mean difference = 0.25, 95% CI [0.17, 0.36]). Over the 90 analyzed slices, the median Dice score calculated against the ground truth ranged between 0.62 and 0.67, the inclusion coefficients ranged between 0.62 and 0.76 and the centroid distances ranged between 0.97 and 3.88 mm. Therefore, though these metrics highlight spatial differences, they could not be used as an index to evaluate the accuracy and performance of the method, which can only be attested by the variability of the MBF clinical index.

3D Chemical Shift-Encoded MRI for Volume and Composition Quantification of Abdominal Adipose Tissue During an Overfeeding Protocol in Healthy Volunteers

BACKGROUND

Overweight and obesity are major worldwide health concerns characterized by an abnormal accumulation of fat in adipose tissue (AT) and liver.

PURPOSE

To evaluate the volume and the fatty acid (FA) composition of the subcutaneous adipose tissue (SAT) and the visceral adipose tissue (VAT) and the fat content in the liver from 3D chemical-shift-encoded (CSE)-MRI acquisition, before and after a 31-day overfeeding protocol.

STUDY TYPE

Prospective and longitudinal study.

SUBJECTS

Twenty-one nonobese healthy male volunteers.

FIELD STRENGTH/SEQUENCE

A 3D spoiled-gradient multiple echo sequence and STEAM sequence were performed at 3T.

ASSESSMENT

AT volume was automatically segmented on CSE-MRI between L2 to L4 lumbar vertebrae and compared to the dual-energy X-ray absorptiometry (DEXA) measurement. CSE-MRI and MR spectroscopy (MRS) data were analyzed to assess the proton density fat fraction (PDFF) in the liver and the FA composition in SAT and VAT. Gas chromatography-mass spectrometry (GC-MS) analyses were performed on 13 SAT samples as a FA composition countermeasure.

STATISTICAL TESTS

Paired t-test, Pearson's correlation coefficient, and Bland-Altman plots were used to compare measurements.

RESULTS

SAT and VAT volumes significantly increased (P < 0.001). CSE-MRI and DEXA measurements were strongly correlated (r = 0.98, P < 0.001). PDFF significantly increased in the liver (+1.35, P = 0.002 for CSE-MRI, + 1.74, P = 0.002 for MRS). FA composition of SAT and VAT appeared to be consistent between localized-MRS and CSE-MRI (on whole segmented volume) measurements. A significant difference between SAT and VAT FA composition was found (P < 0.001 for CSE-MRI, P = 0.001 for MRS). MRS and CSE-MRI measurements of the FA composition were correlated with the GC-MS results (for ndb: r_MRS/GC-MS  = 0.83 P < 0.001, r_{CSE-MRI/GC-MS}  = 0.84, P = 0.001; for nmidb: r_MRS/GC-MS  = 0.74, P = 0.006, r_{CSE-MRI/GC-MS}  = 0.66, P = 0.020) DATA CONCLUSION: The follow-up of liver PDFF, volume, and FA composition of AT during an overfeeding diet was demonstrated through different methods. The CSE-MRI sequence associated with a dedicated postprocessing was found reliable for such quantification.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1587-1599.

A simplified framework to optimize MRI contrast preparation

PURPOSE

This article proposes a rigorous optimal control framework for the design of preparation schemes that optimize MRI contrast based on relaxation time differences.

METHODS

Compared to previous optimal contrast preparation schemes, a drastic reduction of the optimization parameter number is performed. The preparation scheme is defined as a combination of several block pulses whose flip angles, phase terms and inter-pulse delays are optimized to control the magnetization evolution.

RESULTS

The proposed approach reduces the computation time of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>B</mml:mi> <mml:mn>0</mml:mn></mml:msub> </mml:math> -robust preparation schemes to around a minute (whereas several hours were required with previous schemes), with negligible performance loss. The chosen parameterization allows to formulate the total preparation duration as a constraint, which improves the overall compromise between contrast performance and preparation time. Simulation, in vitro and in vivo results validate this improvement, illustrate the straightforward applicability of the proposed approach, and point out its flexibility in terms of achievable contrasts. Major improvement is especially achieved for short-T₂ enhancement, as shown by the acquisition of a non-trivial contrast on a rat brain, where a short-T₂ white matter structure (corpus callosum) is enhanced compared to surrounding gray matter tissues (hippocampus and neocortex).

CONCLUSIONS

This approach proposes key advances for the design of optimal contrast preparation sequences, that emphasize their ability to generate non-standard contrasts, their potential benefit in a clinical context, and their straightforward applicability on any MR system.

Constant gradient elastography with optimal control RF pulses

This article presents a new motion encoding strategy to perform magnetic resonance elastography (MRE). Instead of using standard motion encoding gradients, a tailored RF pulse is designed to simultaneously perform selective excitation and motion encoding in presence of a constant gradient. The RF pulse is designed with a numerical optimal control algorithm, in order to obtain a magnetization phase distribution that depends on the displacement characteristics inside each voxel. As a consequence, no post-excitation encoding gradients are required. This offers numerous advantages, such as reducing eddy current artifacts, and relaxing the constraint on the gradients maximum switch rate. It also allows to perform MRE with ultra-short TE acquisition schemes, which limits T₂ decay and optimizes signal-to-noise ratio. The pulse design strategy is developed and analytically analyzed to clarify the encoding mechanism. Finally, simulations, phantom and ex vivo experiments show that phase-to-noise ratios are improved when compared to standard MRE encoding strategies.

Comparison of MRI-derived vs. traditional estimations of fatty acid composition from MR spectroscopy signals

INTRODUCTION

The composition of fatty acids in the body is gaining increasing interest, and can be followed up noninvasively by quantitative magnetic resonance spectroscopy (MRS). However, current MRS quantification methods have been shown to provide different quantitative results in terms of lipid signals, with possible varying outcomes for a given biological examination. Quantitative magnetic resonance imaging using multigradient echo sequence (MGE-MRI) has recently been added to MRS approaches. In contrast, these methods fit the undersampled magnetic resonance temporal signal with a simplified model function (expressing the triglyceride [TG] spectrum with only three TG parameters), specific implementations and prior knowledge. In this study, an adaptation of an MGE-MRI method to MRS lipid quantification is proposed.

METHODS

Several versions of the method - with time data fully or undersampled, including or excluding the spectral peak T₂ knowledge in the fitting - were compared theoretically and on Monte Carlo studies with a time-domain, peak-fitting approach. Robustness, repeatability and accuracy were also inspected on in vitro oil acquisitions and test-retest in vivo subcutaneous adipose tissue acquisitions, adding results from the reference LCModel method.

RESULTS

On simulations, the proposed method provided TG parameter estimates with the smallest variability, but with a possible bias, which was mitigated by fitting on undersampled data and considering peak T₂ values. For in vitro measurements, estimates for all approaches were correlated with theoretical values and the best concordance was found for the usual MRS method (LCModel and peak fitting). Limited in vivo test-retest variability was found (4.1% for PUFAindx, 0.6% for MUFAindx and 3.6% for SFAindx), as for LCModel (7.6% for PUFAindx, 7.8% for MUFAindx and 3.0% for SFAindx).

CONCLUSION

This study shows that fitting the three TG parameters directly on MRS data is one valuable solution to circumvent the poor conditioning of the MRS quantification problem.

In vivo MRS for the assessment of mouse colon using a dedicated endorectal coil: initial findings

Inflammatory bowel disease is a common group of inflammation conditions that can affect the colon and the rectum. These pathologies require a careful follow-up of patients to prevent the development of colorectal cancer. Currently, conventional endoscopy is used to depict alterations of the intestinal walls, and biopsies are performed on suspicious lesions for further analysis (histology). MRS enables the in vivo analysis of biochemical content of tissues (i.e. without removing any samples). Combined with dedicated endorectal coils (ERCs), MRS provides new ways of characterizing alterations of tissues. An MRS in vivo protocol was specifically set up on healthy mice and on mice chemically treated to induce colitis. Acquisitions were performed on a 4.7 T system using a linear volume birdcage coil for the transmission of the B₁ magnetic field, and a dedicated ERC was used for signal reception. Colon-wall complex, lumen and visceral fat were assessed on healthy and treated mice with voxel sizes ranging from 0.125 μL to 2 μL while keeping acquisition times below 3 min. The acquired spectra show various biochemical contents such as α- and β-methylene but also glycerol backbone and diacyl. Choline was detected in tumoral regions. Visceral fat regions display a high lipid content with no water, whereas colon-wall complex exhibits both high lipid and high water contents. To the best of our knowledge, this is the first time that in vivo MRS using an ERC has been performed in the assessment of colon walls and surrounding structures. It provides keys for the in vivo characterization of small local suspicious lesions and offers complementary solutions to biopsies.

Active control of the spatial MRI phase distribution with optimal control theory

This paper investigates the use of Optimal Control (OC) theory to design Radio-Frequency (RF) pulses that actively control the spatial distribution of the MRI magnetization phase. The RF pulses are generated through the application of the Pontryagin Maximum Principle and optimized so that the resulting transverse magnetization reproduces various non-trivial and spatial phase patterns. Two different phase patterns are defined and the resulting optimal pulses are tested both numerically with the ODIN MRI simulator and experimentally with an agar gel phantom on a 4.7T small-animal MR scanner. Phase images obtained in simulations and experiments are both consistent with the defined phase patterns. A practical application of phase control with OC-designed pulses is also presented, with the generation of RF pulses adapted for a Magnetic Resonance Elastography experiment. This study demonstrates the possibility to use OC-designed RF pulses to encode information in the magnetization phase and could have applications in MRI sequences using phase images.

Optimal control design of preparation pulses for contrast optimization in MRI

This work investigates the use of MRI radio-frequency (RF) pulses designed within the framework of optimal control theory for image contrast optimization. The magnetization evolution is modeled with Bloch equations, which defines a dynamic system that can be controlled via the application of the Pontryagin Maximum Principle (PMP). This framework allows the computation of optimal RF pulses that bring the magnetization to a given state to obtain the desired contrast after acquisition. Creating contrast through the optimal manipulation of Bloch equations is a new way of handling contrast in MRI, which can explore the theoretical limits of the system. Simulation experiments carried out on-resonance quantify the contrast improvement when compared to standard T₁ or T₂ weighting strategies. The use of optimal pulses is also validated for the first time in both in vitro and in vivo experiments on a small-animal 4.7T MR system. Results demonstrate their robustness to static field inhomogeneities as well as the fact that they can be embedded in standard imaging sequences without affecting standard parameters such as slice selection or echo type. In vivo results on rat and mouse brains illustrate the ability of optimal contrast pulses to create non-trivial contrasts on well-studied structures (white matter versus gray matter).

Creatine, Glutamine plus Glutamate, and Macromolecules Are Decreased in the Central White Matter of Premature Neonates around Term

Preterm birth represents a high risk of neurodevelopmental disabilities when associated with white-matter damage. Recent studies have reported cognitive deficits in children born preterm without brain injury on MRI at term-equivalent age. Understanding the microstructural and metabolic underpinnings of these deficits is essential for their early detection. Here, we used diffusion-weighted imaging and single-voxel 1H magnetic resonance spectroscopy (MRS) to compare brain maturation at term-equivalent age in premature neonates with no evidence of white matter injury on conventional MRI except diffuse excessive high-signal intensity, and normal term neonates. Thirty-two infants, 16 term neonates (mean post-conceptional age at scan: 39.8±1 weeks) and 16 premature neonates (mean gestational age at birth: 29.1±2 weeks, mean post-conceptional age at scan: 39.2±1 weeks) were investigated. The MRI/MRS protocol performed at 1.5T involved diffusion-weighted MRI and localized 1H-MRS with the Point RESolved Spectroscopy (PRESS) sequence. Preterm neonates showed significantly higher ADC values in the temporal white matter (P<0.05), the occipital white matter (P<0.005) and the thalamus (P<0.05). The proton spectrum of the centrum semiovale was characterized by significantly lower taurine/H2O and macromolecules/H2O ratios (P<0.05) at a TE of 30 ms, and reduced (creatine+phosphocreatine)/H2O and (glutamine+glutamate)/H2O ratios (P<0.05) at a TE of 135 ms in the preterm neonates than in full-term neonates. Our findings indicate that premature neonates with normal conventional MRI present a delay in brain maturation affecting the white matter and the thalamus. Their brain metabolic profile is characterized by lower levels of creatine, glutamine plus glutamate, and macromolecules in the centrum semiovale, a finding suggesting altered energy metabolism and protein synthesis.

Localized 2D COSY sequences: Method and experimental evaluation for a whole metabolite quantification approach

Two-dimensional spectroscopy offers the possibility to unambiguously distinguish metabolites by spreading out the multiplet structure of J-coupled spin systems into a second dimension. Quantification methods that perform parametric fitting of the 2D MRS signal have recently been proposed for resolved PRESS (JPRESS) but not explicitly for Localized Correlation Spectroscopy (LCOSY). Here, through a whole metabolite quantification approach, correlation spectroscopy quantification performances are studied. The ability to quantify metabolite relaxation constant times is studied for three localized 2D MRS sequences (LCOSY, LCTCOSY and the JPRESS) in vitro on preclinical MR systems. The issues encountered during implementation and quantification strategies are discussed with the help of the Fisher matrix formalism. The described parameterized models enable the computation of the lower bound for error variance--generally known as the Cramér Rao bounds (CRBs), a standard of precision--on the parameters estimated from these 2D MRS signal fittings. LCOSY has a theoretical net signal loss of two per unit of acquisition time compared to JPRESS. A rapid analysis could point that the relative CRBs of LCOSY compared to JPRESS (expressed as a percentage of the concentration values) should be doubled but we show that this is not necessarily true. Finally, the LCOSY quantification procedure has been applied on data acquired in vivo on a mouse brain.

Fast multidimensional NMR spectroscopy for sparse spectra

Multidimensional NMR spectroscopy is widely used for studies of molecular and biomolecular structure. A major disadvantage of multidimensional NMR is the long acquisition time which, regardless of sensitivity considerations, may be needed to obtain the final multidimensional frequency domain coefficients. In this article, a method for under-sampling multidimensional NMR acquisition of sparse spectra is presented. The approach is presented in the case of two-dimensional NMR acquisitions. It relies on prior knowledge about the support in the two-dimensional frequency domain to recover an over-determined system from the under-determined system induced in the linear acquisition model when under-sampled acquisitions are performed. This over-determined system can then be solved with linear least squares. The prior knowledge is obtained efficiently at a low cost from the one-dimensional NMR acquisition, which is generally acquired as a first step in multidimensional NMR. If this one-dimensional acquisition is intrinsically sparse, it is possible to reconstruct the corresponding two-dimensional acquisition from far fewer observations than those imposed by the Nyquist criterion, and subsequently to reduce the acquisition time. Further improvements are obtained by optimizing the sampling procedure for the least-squares reconstruction using the sequential backward selection algorithm. Theoretical and experimental results are given in the case of a traditional acquisition scheme, which demonstrate reliable and fast reconstructions with acceleration factors in the range 3-6. The proposed method outperforms the CS methods (OMP, L1) in terms of the reconstruction performance, implementation and computation time. The approach can be easily extended to higher dimensions and spectroscopic imaging.

Liver fat volume fraction quantification with fat and water T1 and T 2* estimation and accounting for NMR multiple components in patients with chronic liver disease at 1.5 and 3.0 T

OBJECTIVE

To validate a magnitude-based method for fat volume fraction (FVF) quantification in the liver without any dominant component ambiguity problems and with the aim of transferring this method to any imaging system (clinical fields of 1.5 and 3.0 T).

METHODS

MR imaging was performed at 1.5 and 3.0 T using a multiple-angle multiple-gradient echo sequence. A quantification algorithm correcting for relaxation time effects using a disjointed estimation of T1 and T2* of fat and water and accounting for the NMR spectrum of fat was developed. Validations were performed on fat-water emulsion at 1.5 and 3.0 T and compared with (1)H-MRS. This was followed by a prospective in-vivo comparative study on 28 patients with chronic liver disease and included histology.

RESULTS

Phantom study showed good agreement between MRI and MRS. MR-estimated FVF and histological results correlated strongly and FVF allowed the diagnosis of mild (cutoff = 5.5 %) and moderate steatosis (cutoff = 15.2 %) with a sensitivity/specificity of 100 %.

CONCLUSION

FVF calculation worked independently of the field strength. FVF may be a relevant biomarker for the clinical follow-up of patients (1) with or at risk of NAFLD (2) of steatosis in patients with other chronic liver diseases.

KEY POINTS

• Non-invasive techniques to diagnose non-alcoholic fatty liver diseases (NAFLD) are important. • Liver fat volume fraction quantified using MRI correlates well with histology. • Fat volume fraction could be a relevant marker for NAFLD clinical follow-up. • Disjointed relaxation time estimation could potentially identify factors contributing to NAFLD.

3D localized 2D ultrafast J-resolved magnetic resonance spectroscopy: in vitro study on a 7 T imaging system

2D Magnetic Resonance Spectroscopy (MRS) is a well known tool for the analysis of complicated and overlapped MR spectra and was therefore originally used for structural analysis. It also presents a potential for biomedical applications as shown by an increasing number of works related to localized in vivo experiments. However, 2D MRS suffers from long acquisition times due to the necessary collection of numerous increments in the indirect dimension (t(1)). This paper presents the first 3D localized 2D ultrafast J-resolved MRS sequence, developed on a small animal imaging system, allowing the acquisition of a 3D localized 2D J-resolved MRS spectrum in a single scan. Sequence parameters were optimized regarding Signal-to-Noise ratio and spectral resolution. Sensitivity and spatial localization properties were characterized and discussed. An automatic post-processing method allowing the reduction of artifacts inherent to ultrafast excitation is also presented. This sequence offers an efficient signal localization and shows a great potential for in vivo dynamic spectroscopy.

Quantification method using the Morlet wavelet for magnetic resonance spectroscopic signals with macromolecular contamination

We study the Morlet wavelet transform on characterizing Magnetic Resonance Spectroscopic (MRS) signals acquired at short echo-time. These signals contain contributions from metabolites, water and a baseline which mainly originates from large molecules, known as macromolecules, and lipids. The baseline signal decays faster than the metabolite ones. Therefore, by making use of the time-scale representation of the wavelet, the two signals can be distinguished without any additional pre-processing. This is confirmed by the experimental results which show that the Morlet wavelet can correctly quantify the metabolite contributions even when a baseline is embedded in the MRS signals.

Estimation of metabolite T1 relaxation times using tissue specific analysis, signal averaging and bootstrapping from magnetic resonance spectroscopic imaging data

Object A novel method of estimating metabolite T₁ relaxation times using MR spectroscopic imaging (MRSI) is proposed. As opposed to conventional single-voxel metabolite T₁ estimation methods, this method investigates regional and gray matter (GM)/white matter (WM) differences in metabolite T₁ by taking advantage of the spatial distribution information provided by MRSI.

Material and methods The method, validated by Monte Carlo studies, involves a voxel averaging to preserve the GM/WM distribution, a non-linear least squares fit of the metabolite T₁ and an estimation of its standard error by bootstrapping. It was applied in vivo to estimate the T₁ of N-acetyl compounds (NAA), choline, creatine and myo-inositol in eight normal volunteers, at 1.5 T, using a short echo time 2D-MRSI slice located above the ventricles.

Results WM-T_1,NAA was significantly (P < 0.05) longer in anterior regions compared to posterior regions of the brain. The anterior region showed a trend of a longer WM T₁ compared to GM for NAA, creatine and myo-Inositol. Lastly, accounting for the bootstrapped standard error estimate in a group mean T₁ calculation yielded a more accurate T₁ estimation.

Conclusion The method successfully measured in vivo metabolite T₁ using MRSI and can now be applied to diseased brain.

Metabolite concentrations of healthy mouse brain by Magnetic Resonance Spectroscopy at 7 Tesla

In vivo¹H short echo-time Magnetic Resonance spectra are made up of overlapping spectral components from many metabolites. Typically, they exibit low signal-to-noise ratio. Metabolite concentrations are obtained by quantitating such spectra. Quantitation is difficult due to the superposition of metabolite resonances, macromolecules, lipids and water residue contributions. A fitting algorithm invoking extensive prior knowledge is needed. We quantitated¹H in vivo mouse brain spectra obtained at 7 Tesla using the time-domain QUEST method combined with in vitro metabolite basis set signals. Brain metabolite concentrations estimated from eight mouse brain signals are compared to previously reported results.

Semi-parametric estimation in magnetic resonance spectroscopy: automation of the disentanglement procedure

Semi-parametric disentanglement of parametric parts from non-parametric parts of a signal is a universal problem. This study concerns estimation of metabolite concentrations from in vivo Magnetic Resonance Spectroscopy (MRS) signals. Due to in vivo conditions, so-called macro-molecules contribute non-parametric components to the signals. Disentanglement is achieved by exploiting prior knowledge about the parametric and non-parametric parts directly in the measurement domain. Moreover, Cramér-Rao bounds on the non-parametric part are derived. These expressions are used to automate the disentanglement procedure.

Time-domain semi-parametric estimation based on a metabolite basis set

A novel and fast time-domain quantitation algorithm--quantitation based on semi-parametric quantum estimation (QUEST)--invoking optimal prior knowledge is proposed and tested. This nonlinear least-squares algorithm fits a time-domain model function, made up from a basis set of quantum-mechanically simulated whole-metabolite signals, to low-SNR in vivo data. A basis set of in vitro measured signals can be used too. The simulated basis set was created with the software package NMR-SCOPE which can invoke various experimental protocols. Quantitation of 1H short echo-time signals is often hampered by a background signal originating mainly from macromolecules and lipids. Here, we propose and compare three novel semi-parametric approaches to handle such signals in terms of bias-variance trade-off. The performances of our methods are evaluated through extensive Monte-Carlo studies. Uncertainty caused by the background is accounted for in the Cramér-Rao lower bounds calculation. Valuable insight about quantitation precision is obtained from the correlation matrices. Quantitation with QUEST of 1H in vitro data, 1H in vivo short echo-time and 31P human brain signals at 1.5 T, as well as 1H spectroscopic imaging data of human brain at 1.5 T, is demonstrated.

Time-domain quantitation of 1H short echo-time signals: background accommodation

Quantitation of 1H short echo-time signals is often hampered by a background signal originating mainly from macromolecules and lipids. While the model function of the metabolite signal is known, that of the macromolecules is only partially known. We present time-domain semi-parametric estimation approaches based on the QUEST quantitation algorithm (QUantitation based on QUantum ESTimation) and encompassing Cramér-Rao bounds that handle the influence of 'nuisance' parameters related to the background. Three novel methods for background accommodation are presented. They are based on the fast decay of the background signal in the time domain. After automatic estimation, the background signal can be automatically (1) subtracted from the raw data, (2) included in the basis set as multiple components, or (3) included in the basis set as a single entity. The performances of these methods combined with QUEST are evaluated through extensive Monte Carlo studies. They are compared in terms of bias-variance trade-off. Because error bars on the amplitudes are of paramount importance for diagnostic reliability, Cramér-Rao bounds accounting for the uncertainty caused by the background are proposed. Quantitation with QUEST of in vivo short echo-time (1)H human brain with estimation of the background is demonstrated.

Dynamic magnetic resonance imaging paragraph sign with radial scanning: a post-acquisition paragraph sign keyhole approach

A method - PA-keyhole - for 2D/3D dynamic magnetic resonance imaging with radial scanning is proposed. PA-keyhole exploits the inherent strong oversampling in the center of k-space, which contains crucial temporal information regarding contrast evolution. The method is based on: (1). a rearrangement of the temporal order of 2D/3D isotropic distributions of trajectories during the scan into subdistributions according to the desired time resolution, (2). a new post-acquisition keyhole approach based on the replacement of the central disk/sphere in k-space using data solely from a subdistribution, and (3). reconstruction of 2D/3D dynamic (time-resolved) images using 2D/3D-gridding with Pipe's approach to the sampling density compensation and 2D/3D-IFFT. The scan time is not increased with respect to a conventional 2D/3D radial scan of the same spatial resolution; in addition, one benefits from the dynamic information. The abilities of PA-keyhole and the sliding window techniques to restore simulated dynamic contrast changes are compared. Results are shown both for 2D and 3D dynamic imaging using experimental data. An application to in-vivo ventilation of rat lungs using hyperpolarized helium is demonstrated.