Deep learning is starting to offer promising results for reconstruction in Magnetic Resonance Imaging (MRI). A lot of networks are being developed, but the comparisons remain hard because the frameworks used are not the same among studies, the networ...
Magnetic Resonance Imaging (MRI) is an essential medical imaging modality that provides excellent soft-tissue contrast and high-resolution images of the human body, allowing us to understand detailed information on morphology, structural integrity, a...
Practical applications of compressed sensing often restrict the choice of its two main ingredients. They may (i) prescribe the use of particular redundant dictionaries for certain classes of signals to become sparsely represented or (ii) dictate spec...
The task of fast magnetic resonance (MR) image reconstruction is to reconstruct high-quality MR images from undersampled images. Most of the existing methods are based on U-Net, and these methods mainly adopt several simple connections within the net...
MRI (magnetic resonance imaging) technology aims to map the internal structure image of organisms. It is an important application scenario of Non-Uniform Fast Fourier Transform (NUFFT), which can help doctors quickly locate the lesion site of patient...
The 3D reconstruction of an accurate face model is essential for delivering reliable feedback for clinical decision support. Medical imaging and specific depth sensors are accurate but not suitable for an easy-to-use and portable tool. The recent dev...
The topic of phonation onset gestures is of great interest to singers, acousticians, and voice physiologists alike. The vocal pedagogue and voice researcher Manuel Garcia, in the mid-19th century, first coined the term “coup de la glotte”...
We propose fast phase-contrast cine magnetic resonance imaging (PC-cine MRI) to allow breath-hold acquisition, and we compared intracranial hemo- and hydrodynamic parameters obtained during breath holding between full inspiration and end expiration....
Magnetic Resonance Imaging (MRI) reconstruction is computationally heavy from under sampled data, and centralized data sharing within deep learning models was met with privacy concerns. We therefore propose FL-Swarm MRCM, a novel federated learning f...
Magnetic Resonance Imaging (MRI) serves as a pivotal medical diagnostic technique widely deployed in clinical practice, yet high-resolution reconstruction frequently introduces motion artifacts and degrades signal-to-noise ratios. To enhance imaging...
MRI is an imaging technology that non-invasively obtains high-quality medical images for diagnosis. However, MRI has the major disadvantage of long scan times which cause patient discomfort and image artifacts. As one of the methods for reducing the...
Most existing methods for magnetic resonance imaging (MRI) reconstruction with deep learning use fully supervised training, which assumes that a fully sampled dataset with a high signal-to-noise ratio (SNR) is available for training. In many circumst...
Magnetic resonance imaging (MRI) faces ongoing challenges associated with prolonged acquisition times and susceptibility to motion artifacts. Compressed Sensing (CS) principles have emerged as a significant advancement, addressing these issues by sub...
Functional magnetic resonance imaging (fMRI) is a non-invasive brain imaging technique, which has been regularly used for studying brain’s functional activities in the past few years. A very well-used measure for capturing functional associatio...
Over the last decade, the combination of compressed sensing (CS) with acquisition over multiple receiver coils in magnetic resonance imaging (MRI) has allowed the emergence of faster scans while maintaining a good signal-to-noise ratio (SNR). Self-ca...
The aim of the present study was to identify whether the quantitative metrics from pre-treatment (TX) non-Gaussian intravoxel incoherent motion (NGIVIM) diffusion weighted (DW-) and fast exchange regime (FXR) dynamic contrast enhanced (DCE)-MRI can p...
Compressed sensing in magnetic resonance imaging essentially involves the optimization of (1) the sampling pattern in k-space under MR hardware constraints and (2) image reconstruction from undersampled k-space data. Recently, deep learning methods h...
In order to improve the image quality of BLADE magnetic resonance imaging (MRI) using the index tensor solvers and to evaluate MRI image quality in a clinical setting, we implemented BLADE MRI reconstructions using two tensor solvers (the least-squar...
This study aims to introduce a new compressed sensing averaging (CSA) technique for the reduction of blurring and/or ringing artifacts, depending on the k-space sampling ratio. A full k-space dataset and three randomly undersampled datasets were obta...
Hyperpolarized metabolic MRI with
The need for stable and well-defined magnetic nanoparticles is constantly increasing in biomedical applications; however, their preparation remains challenging. We used two different solvothermal methods (12 h reflux and a 4 min microwave, MW) to syn...
Background: Diffusion-weighted images (DWI) obtained by echo-planar imaging (EPI) are frequently degraded by susceptibility artifacts. It has been suggested that DWI obtained by fast advanced spin-echo (FASE) or reconstructed with deep learning recon...
Magnetic resonance imaging (MRI) is essential for studying brain development and psychiatric disorders in adolescents. However, the imaging consistency remains challenging, highlighting the need for advanced methodologies to improve the diagnostic an...
Background and Objectives: This study aimed to determine whether there is a relationship between preoperative fasting time, fasting blood glucose (FBG), and postoperative emergence delirium (ED) in pediatric patients undergoing MRI under sedation. Ma...
Magnetic resonance imaging (MRI) and MR cholangiopancreatography (MRCP) have emerged as non-invasive diagnostic techniques for the diagnosis of pancreatic and pancreatic duct disorders in humans. The number of studies focused on MR and MRCP for pancr...
Nondestructive and fast measurement and characterization of fish is highly desired during various processing treatments. This research investigated the effectiveness of low field LF-NMR and MRI as fast monitoring techniques to estimate the qualities...
Background/Objectives: Posterior circulation stroke (PCS) poses a diagnostic challenge due to the diverse and subtle clinical manifestations. While the FAST (Face, Arms, Speech, Time) mnemonic has proven effective in identifying anterior circulation...
Background/Objectives: Segmental non-mass enhancement (NME) is the breast MRI distribution pattern with the highest positive predictive value (PPV) for malignancy. Despite its diagnostic relevance, its imaging characteristics have rarely been examine...
Magnetic resonance imaging (MRI) is widely used in diagnostic medicine. MRI uses the static magnetic field to polarize nuclei spins, fast-switching magnetic field gradients to generate temporal and spatial resolution, and radiofrequency (RF) electrom...
This study aims to analyze the sensitivity of vaginosonography (VGS) and magnetic resonance imaging (MRI) in the preoperative local evaluation of early-stage cervical cancers and to assess their accuracy in the detection of tumors, size of the lesion...
Xenon magnetic resonance imaging (MRI) provides excellent sensitivity through the combination of spin hyperpolarization and chemical exchange saturation transfer (CEST). To this end, molecular hosts such as cryptophane-A or cucurbit[n]urils provide u...
A comprehensive review of the literature-to-date on functional magnetic resonance imaging (fMRI) of the spinal cord is presented. Spinal fMRI has been shown, over more than two decades of work, to be a reliable tool for detecting neural activity. We...
Thanks to its innocuousness and high spatiotemporal resolution, light is used in several established and emerging applications in biomedicine. Among them is the modulation of magnetic resonance imaging (MRI) contrast agents’ relaxivity with the...
Nanoparticles are considered potential candidates for a new class of magnetic resonance imaging (MRI) contrast agents. Negative MRI contrast agents require high magnetic moments. However, if nanoparticles can exclusively induce transverse water proto...
In this paper, a fully automatic and computationally efficient midsagittal plane (MSP) extraction technique in brain magnetic resonance images (MRIs) has been proposed. Automatic detection of MSP in neuroimages can significantly aid in registration o...
Whole-Body Magnetic Resonance Imaging (WB-MRI) is increasingly used for metastatic screening in oncology. This prospective single center study assesses the diagnostic value of WB-MRI including diffusion weighted imaging (DWI) and identifies the suffi...
Background: Amyotrophic lateral sclerosis (ALS) is a neuromuscular progressive disorder characterized by limb and bulbar muscle wasting and weakness. A total of 30% of patients present a bulbar onset, while 70% have a spinal outbreak. Respiratory inv...
Autism spectrum disorder (ASD) is a wide range of diseases characterized by difficulties with social skills, repetitive activities, speech, and nonverbal communication. The Centers for Disease Control (CDC) estimates that 1 in 44 American children cu...
A 1.5T MRI combined with a linear accelerator (Unity®, Elekta; Stockholm, Sweden) is a device that shows promise in MRI-guided stereotactic body radiation treatment (SBRT). Previous studies utilized the manufacturer’s pre-set MRI sequences...
Background: Magnetic resonance imaging (MRI) is predominant in the therapeutic management of cancer patients, unfortunately, patients have to wait a long time to get an appointment for examination. Therefore, new MRI devices include deep-learning (DL...
Few studies have investigated the underlying neural substrates of food addiction (FA) in humans using a recognised assessment tool. In addition, no studies have investigated subregions of the amygdala (basolateral (BLA) and central amygdala), which h...
Brain MRI segmentation plays a crucial role in neuroimaging studies and clinical trials by enabling the precise localization and quantification of brain tissues and structures. The advent of deep learning has transformed the field, offering accurate...
Two new DTPA-bis(amide) based ligands conjugated with the arylpiperazinyl moiety were synthesized and subsequently transformed into their corresponding Gd(III) complexes 1 and 2 of the type [Gd(L)H2O]·nH2O. The relaxivity (R1) of these complexes was...
This study presents a fast, non-invasive method to determine the iodine value (IV) of edible oils using Time Domain Nuclear Magnetic Resonance (TD-NMR) and Magnetic Resonance Imaging (MRI) techniques. The IV, which quantifies the degree of unsaturati...
Aim of the study: To determine the value of dynamic examinations ultrasound (US) and MRI in the 1.5T field in the assessment of the mobility of vocal folds (VF) in comparison to laryngoscopy in patients with thyroid gland resection. Materials and met...
Magnetic resonance imaging (MRI) is a standard procedure in medical imaging, on a par with echography and tomodensitometry. In contrast to radiological procedures, no harmful radiation is produced. The constant development of magnetic resonance imagi...
A relationship between an acidic pH in the joints, osteoarthritis (OA), and pain has been previously demonstrated. Acidosis Chemical Exchange Saturation Transfer (acidoCEST) indirectly measures the extracellular pH through the assessment of the excha...
Deep learning (DL) in magnetic resonance imaging (MRI) shows excellent performance in image reconstruction from undersampled k-space data. Artifact-free and high-quality MRI reconstruction is essential for ensuring accurate diagnosis, supporting clin...
MRI is the gold standard modality for speech imaging. However, it remains relatively slow, which complicates imaging of fast movements. Thus, an MRI of the vocal tract is often performed in 2D. While 3D MRI provides more information, the quality of s...
This study proposes a fast 3D dynamic programming expansion to find a shortest surface in a 3D matrix. This algorithm can detect boundaries in an image sequence. Using phantom image studies with added uniform distributed noise from different SNRs, th...
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