Synthetic mri

Data scarcity and data imbalance are two major challenges in training deep learning models on medical images, such as brain tumor MRI data. The recent advancements in generative artificial intelligence have opened new possibilities for synthetically generating MRI data, including brain tumor MRI scans. This approach can be a potential solution to mitigate the data scarcity problem and enhance training data availability. This work focused on adapting the 2D latent diffusion models to generate 3D multi-contrast brain tumor MRI data with a tumor mask as the condition...

PURPOSE: Explore the function and dose calculation accuracy of MRI images in radiotherapy planning through deep learning methods. METHODS: 131 brain tumor patients undergoing radiotherapy with previous MR and CT images were recruited for this study. A new series of MRI from the aligned MR was firstly registered to CT images strictly using MIM software and then resampled. A deep learning method (U-NET) was used to establish a MRI-to-CT conversion model, for which 105 patient images were used as the training set and 26 patient images were used as the tuning set...

Manganese(II)-based contrast agents (MBCAs) are potential candidates for gadolinium-free enhanced magnetic resonance imaging (MRI). In this work, a rigid binuclear MBCA (Mn2 -PhDTA2 ) with a zero-length linker was developed via facile synthetic routes, while the other dimer (Mn2 -TPA-PhDTA2 ) with a longer rigid linker was also synthesized via more complex steps. Although the molecular weight of Mn2 -PhDTA2 is lower than that of Mn2 -TPA-PhDTA2 , their T 1 relaxivities are similar, being increased by over 71% compared to the mononuclear Mn-PhDTA...

Dual-energy computed tomography (CT) is an excellent substitute for identifying bone marrow edema in magnetic resonance imaging. However, it is rarely used in practice owing to its low contrast. To overcome this problem, we constructed a framework based on deep learning techniques to screen for diseases using axial bone images and to identify the local positions of bone lesions. To address the limited availability of labeled samples, we developed a new generative adversarial network (GAN) that extends expressions beyond conventional augmentation (CA) methods based on geometric transformations...

The combination of magnetic resonance and fluorescence imaging in dual-modality imaging not only resolves the limitations of conventional single molecular imaging techniques in terms of specificity, sensitivity, and resolution but also expands the possibilities of molecular imaging techniques in diagnostics and therapeutic monitoring. Herein, a novel pH-responsive magnetic resonance/near-infrared fluorescence (MR/NIRF) nanoprobe (MnO2 @BSA-Cy5.5) was successfully prepared by biomineralizing manganese dioxide (MnO2 ) with bovine serum albumin (BSA) while coupling fluorescent dye Cy5...

OBJECTIVES: Dark-blood late gadolinium enhancement (DB-LGE) cardiac magnetic resonance has been proposed as an alternative to standard white-blood LGE (WB-LGE) imaging protocols to enhance scar-to-blood contrast without compromising scar-to-myocardium contrast. In practice, both DB and WB contrasts may have clinical utility, but acquiring both has the drawback of additional acquisition time. The aim of this study was to develop and evaluate a deep learning method to generate synthetic WB-LGE images from DB-LGE, allowing the assessment of both contrasts without additional scan time...

OBJECTIVES: Surgery for congenital malformation of the hand is complex and protocols are not available. Simulation could help optimize results. The objective of the present study was to design, produce and assess a 3D-printed anatomical support, to improve success in rare and complex surgeries of the hand. MATERIAL AND METHODS: We acquired MRI imaging of the right hand of a 30 year-old subject, and analyzed and split the various skin layers for segmentation. We created the prototype of a healthy hand, using 3D multi-material and silicone printing devices, and drew up a printing protocol suitable for all patients...

Neuroimaging studies often involve predicting a scalar outcome from an array of images collectively called tensor. The use of magnetic resonance imaging (MRI) provides a unique opportunity to investigate the structures of the brain. To learn the association between MRI images and human intelligence, we formulate a scalar-on-image quantile regression framework. However, the high dimensionality of the tensor makes estimating the coefficients for all elements computationally challenging. To address this, we propose a low-rank coefficient array estimation algorithm based on tensor train (TT) decomposition which we demonstrate can effectively reduce the dimensionality of the coefficient tensor to a feasible level while ensuring adequacy to the data...

This study aimed to develop multifunctional nanoplatforms for both cancer imaging and therapy using superparamagnetic iron oxide nanoparticles (SPIONs). Two distinct synthetic methods, reduction-precipitation (MR/P ) and co-precipitation at controlled pH (MpH ), were explored, including the assessment of the coating's influence, namely dextran and gold, on their magnetic properties. These SPIONs were further functionalized with gadolinium to act as dual T1/T2 contrast agents for magnetic resonance imaging (MRI)...

Nuclear magnetic resonance (NMR) spectroscopy and magnetic resonance imaging (MRI) are indispensable tools in science and medicine, offering insights into the functions of biological processes [...].

OBJECTIVE: Insidious disability worsening is a common feature in relapsing-remitting multiple sclerosis (RRMS). Many patients experience progression independent of relapse activity (PIRA) despite being treated with high efficacy disease-modifying therapies. We prospectively investigated associations of body-fluid and imaging biomarkers with PIRA. METHODS: Patients with early RRMS (n = 104) were prospectively included and followed up for 60 months...

Brain segmentation from neonatal MRI images is a very challenging task due to large changes in the shape of cerebral structures and variations in signal intensities reflecting the gestational process. In this context, there is a clear need for segmentation techniques that are robust to variations in image contrast and to the spatial configuration of anatomical structures. In this work, we evaluate the potential of synthetic learning, a contrast-independent model trained using synthetic images generated from the ground truth labels of very few subjects...

Aquaporin-1 (Aqp1), a water channel, has garnered significant interest for cell-based medicine and in vivo synthetic biology due to its ability to be genetically encoded to produce magnetic resonance signals by increasing the rate of water diffusion in cells. However, concerns regarding the effects of Aqp1 overexpression and increased membrane diffusivity on cell physiology have limited its widespread use as a deep-tissue reporter. In this study, we present evidence that Aqp1 generates strong diffusion-based magnetic resonance signals without adversely affecting cell viability or morphology in diverse cell lines derived from mice and humans...

BACKGROUND AND OBJECTIVES: Menopausal hormone therapy (MHT) is generally thought to be neuroprotective, yet results have been inconsistent. Here, we present a comprehensive study of MHT use and brain characteristics in middle-to older aged females from the UK Biobank, assessing detailed MHT data, APOE ε4 genotype, and tissue-specific gray (GM) and white matter (WM) brain age gap (BAG), as well as hippocampal and white matter hyperintensity (WMH) volumes. METHODS: A total of 19,846 females with magnetic resonance imaging data were included (current-users = 1,153, 60...

In the last two decades, metal-organic frameworks (MOFs) with highly tunable structure and porosity, have emerged as drug nanocarriers in the biomedical field. In particular, nanoscaled MOFs (nanoMOFs) have been widely investigated because of their potential biocompatibility, high drug loadings, and progressive release. To enhance their properties, MOFs have been combined with magnetic nanoparticles (MNPs) to form magnetic nanocomposites (MNP@MOF) with additional functionalities. Due to the magnetic properties of the MNPs, their presence in the nanosystems enables potential combinatorial magnetic targeted therapy and diagnosis...

PURPOSE: 23 Na MRI can be used to quantify in-vivo tissue sodium concentration (TSC), but the inherently low 23 Na signal leads to long scan times and/or noisy or low-resolution images. Reconstruction algorithms such as compressed sensing (CS) have been proposed to mitigate low signal-to-noise ratio (SNR); although, these can result in unnatural images, suboptimal denoising and long processing times. Recently, machine learning has been increasingly used to denoise 1 H MRI acquisitions; however, this approach typically requires large volumes of high-quality training data, which is not readily available for 23 Na MRI...

BACKGROUND: Observational assessments of infant temperament have provided unparalleled insight into prediction of risk for social anxiety. Yet, it is challenging to administer and score these assessments alongside high-quality infant neuroimaging data. The current study aims to identify infant resting state functional connectivity (rsFC) associated with both parent-report and observed behavioral estimates of infant novelty-evoked distress. METHODS: Using data from the Origins of Infant Temperament (OIT) study which includes deep phenotyping of infant temperament, we identified parent-report measures that were associated with observed novelty-evoked distress...

Deep brain stimulation (DBS) is a method of electrical neuromodulation used to treat a variety of neuropsychiatric conditions including essential tremor, Parkinson's disease, epilepsy, and obsessive-compulsive disorder. The procedure requires precise placement of electrodes such that the electrical contacts lie within or in close proximity to specific target nuclei and tracts located deep within the brain. DBS electrode trajectory planning has become increasingly dependent on direct targeting with the need for precise visualization of targets...

Diffusion magnetic resonance imaging is an important tool for mapping tissue microstructure and structural connectivity non-invasively in the in vivo human brain. Numerous diffusion signal models are proposed to quantify microstructural properties. Nonetheless, accurate estimation of model parameters is computationally expensive and impeded by image noise. Supervised deep learning-based estimation approaches exhibit efficiency and superior performance but require additional training data and may be not generalizable...

BACKGROUND: 7 Tesla (7T) apparent diffusion coefficient (ADC) maps derived from diffusion-weighted imaging (DWI) demonstrate improved image quality and spatial resolution over 3 Tesla (3T) ADC maps. However, 7T magnetic resonance imaging (MRI) currently suffers from limited clinical unavailability, higher cost, and increased susceptibility to artifacts. PURPOSE: To address these issues, we propose a hybrid CNN-transformer model to synthesize high-resolution 7T ADC maps from multimodal 3T MRI...