Publications by authors named "Ratiney H"

Precision medicine research benefits from machine learning in the creation of robust models adapted to the processing of patient data. This applies both to pathology identification in images, i.e.

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Implementing a standardized phosphorus-31 magnetic resonance spectroscopy ( 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 P-MRS protocol.

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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). P-MRS was performed on a 3 T clinical MRI, in 19 COVID-19 patients, 38 MS patients, and 40 matched healthy controls.

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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.

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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.

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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).

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Article Synopsis
  • Two double-blind trials involving 42 men and 19 women assessed the impact of 2 g/day of grape polyphenol extract during a 31-day period of high calorie-high fructose overfeeding.
  • While body weight and fat mass increased in both men and women, grape polyphenol supplementation did not prevent changes in insulin sensitivity or fat accumulation, particularly in the liver for men.
  • Interestingly, the intake of polyphenols was linked to a decrease in fat cell size in women's femoral fat, indicating some localized effects despite overall metabolic alterations remaining unchanged.
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Magnetic Resonance Elastography (MRE) quantifies the mechanical properties of tissues, typically applying motion encoding gradients (MEG). Multifrequency results allow better characterizations of tissues using data usually acquired through sequential monofrequency experiments. High frequencies are difficult to reach due to slew rate limitations and low frequencies induce long TEs, yielding magnitude images with low SNR.

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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.

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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.

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Magnetic resonance elastography (MRE) is used to non-invasively quantify viscoelastic properties of tissues based on the measurement of propagation characteristics of shear waves. Because some of these viscoelastic parameters show a frequency dependence, multifrequency analysis allows us to measure the wave propagation dispersion, leading to a better characterization of tissue properties. Conventionally, motion encoding gradients (MEGs) oscillating at the same frequency as the mechanical excitation encode motion.

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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.

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The aim of this study was to investigate the feasibility of measuring the effects of a 14-day Periodic Fasting (PF) intervention (<200 cal) on multi-organs of primary interest (liver, visceral/subcutaneous/bone marrow fat, muscle) using non-invasive advanced magnetic resonance spectroscopic (MRS) and imaging (MRI) methods. One subject participated in a 14-day PF under daily supervision of nurses and specialized physicians, ingesting a highly reduced intake: 200 Kcal/day coupled with active walking and drinking at least 3 L of liquids/day. The fasting was preceded by a 7-day pre-fasting vegetarian period and followed by 14 days of stepwise reintroduction of food.

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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.

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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.

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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.

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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.

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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).

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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.

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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.

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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.

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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.

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Importance: Predicting disease evolution is becoming essential for optimizing treatment decision making in multiple sclerosis (MS). Multiple sclerosis pathologic damage typically includes demyelination, neuro-axonal loss, and astrogliosis.

Objective: To evaluate the potential of magnetic resonance markers of central nervous system injury to predict brain-volume loss and clinical disability in multiple sclerosis.

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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.

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