Resolution Magnetic Resonance Imaging Research Articles

Perceptual super-resolution in multiple sclerosis MRI.

Magnetic resonance imaging (MRI) is crucial for diagnosing and monitoring of multiple sclerosis (MS) as it is used to assess lesions in the brain and spinal cord. However, in real-world clinical settings, MRI scans are often acquired with thick slices, limiting their utility for automated quantitative analyses. This work presents a single-image super-resolution (SR) reconstruction framework that leverages SR convolutional neural networks (CNN) to enhance the through-plane resolution of structural MRI in people with MS (PwMS). Our strategy involves the supervised fine-tuning of CNN architectures, guided by a content loss function that promotes perceptual quality, as well as reconstruction accuracy, to recover high-level image features. Extensive evaluation with MRI data of PwMS shows that our SR strategy leads to more accurate MRI reconstructions than competing methods. Furthermore, it improves lesion segmentation on low-resolution MRI, approaching the performance achievable with high-resolution images. Results demonstrate the potential of our SR framework to facilitate the use of low-resolution retrospective MRI from real-world clinical settings to investigate quantitative image-based biomarkers of MS.

Synthesizing PET images from high-field and ultra-high-field MR images using joint diffusion attention model.

Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) stand as pivotal diagnostic tools for brain disorders, offering the potential for mutually enriching disease diagnostic perspectives. However, the costs associated with PET scans and the inherent radioactivity have limited the widespread application of PET. Furthermore, it is noteworthy to highlight the promising potential of high-field and ultra-high-field neuroimaging in cognitive neuroscience research and clinical practice. With the enhancement of MRI resolution, a related question arises: can high-resolution MRI improve the quality of PETimages? This study aims to enhance the quality of synthesized PET images by leveraging the superior resolution capabilities provided by high-field and ultra-high-fieldMRI. From a statistical perspective, the joint probability distribution is considered the most direct and fundamental approach for representing the correlation between PET and MRI. In this study, we proposed a novel model, the joint diffusion attention model, namely, the joint diffusion attention model (JDAM), which primarily focuses on learning information about the joint probability distribution. JDAM consists of two primary processes: the diffusion process and the sampling process. During the diffusion process, PET gradually transforms into a Gaussian noise distribution by adding Gaussian noise, while MRI remains fixed. The central objective of the diffusion process is to learn the gradient of the logarithm of the joint probability distribution between MRI and noise PET. The sampling process operates as a predictor-corrector. The predictor initiates a reverse diffusion process, and the corrector applies Langevindynamics. Experimental results from the publicly available Alzheimer's Disease Neuroimaging Initiative dataset highlight the effectiveness of the proposed model compared to state-of-the-art (SOTA) models such as Pix2pix and CycleGAN. Significantly, synthetic PET images guided by ultra-high-field MRI exhibit marked improvements in signal-to-noise characteristics when contrasted with those generated from high-field MRI data. These results have been endorsed by medical experts, who consider the PET images synthesized through JDAM to possess scientific merit. This endorsement is based on their symmetrical features and precise representation of regions displaying hypometabolism, a hallmark of Alzheimer'sdisease. This study establishes the feasibility of generating PET images from MRI. Synthesis of PET by JDAM significantly enhances image quality compared to SOTAmodels.

Latent diffusion model-based MRI superresolution enhances mild cognitive impairment prognostication and Alzheimer's disease classification

IntroductionTimely diagnosis and prognostication of Alzheimer's disease (AD) and mild cognitive impairment (MCI) are pivotal for effective intervention. Artificial intelligence (AI) in neuroradiology may aid in such appropriate diagnosis and prognostication. This study aimed to evaluate the potential of novel diffusion model-based AI for enhancing AD and MCI diagnosis through superresolution (SR) of brain magnetic resonance (MR) images. Methods1.5T brain MR scans of patients with AD or MCI and healthy controls (NC) from Alzheimer's Disease Neuroimaging Initiative 1 (ADNI1) were superresolved to 3T using a novel diffusion model-based generative AI (d3T*) and a convolutional neural network-based model (c3T*). Comparisons of image quality to actual 1.5T and 3T MRI were conducted based on signal-to-noise ratio (SNR), naturalness image quality evaluator (NIQE), and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE). Voxel-based volumetric analysis was then conducted to study whether 3T* images offered more accurate volumetry than 1.5T images. Binary and multiclass classifications of AD, MCI, and NC were conducted to evaluate whether 3T* images offered superior AD classification performance compared to actual 1.5T MRI. Moreover, CNN-based classifiers were used to predict conversion of MCI to AD, to evaluate the prognostication performance of 3T* images. The classification performances were evaluated using accuracy, sensitivity, specificity, F1 score, Matthews correlation coefficient (MCC), and area under the receiver-operating curves (AUROC). ResultsAnalysis of variance (ANOVA) detected significant differences in image quality among the 1.5T, c3T*, d3T*, and 3T groups across all metrics. Both c3T* and d3T* showed superior image quality compared to 1.5T MRI in NIQE and BRISQUE with statistical significance. While the hippocampal volumes measured in 3T* and 3T images were not significantly different, the hippocampal volume measured in 1.5T images showed significant difference. 3T*-based AD classifications showed superior performance across all performance metrics compared to 1.5T-based AD classification. Classification performance between d3T* and actual 3T was not significantly different. 3T* images offered superior accuracy in predicting the conversion of MCI to AD than 1.5T images did. ConclusionsThe diffusion model-based MRI SR enhances the resolution of brain MR images, significantly improving diagnostic and prognostic accuracy for AD and MCI. Superresolved 3T* images closely matched actual 3T MRIs in quality and volumetric accuracy, and notably improved the prediction performance of conversion from MCI to AD.

Spatial attention-based implicit neural representation for arbitrary reduction of MRI slice spacing

Magnetic resonance (MR) images collected in 2D clinical protocols typically have large inter-slice spacing, resulting in high in-plane resolution and reduced through-plane resolution. Super-resolution technique can enhance the through-plane resolution of MR images to facilitate downstream visualization and computer-aided diagnosis. However, most existing works train the super-resolution network at a fixed scaling factor, which is not friendly to clinical scenes of varying inter-slice spacing in MR scanning. Inspired by the recent progress in implicit neural representation, we propose a Spatial Attention-based Implicit Neural Representation (SA-INR) network for arbitrary reduction of MR inter-slice spacing. The SA-INR aims to represent an MR image as a continuous implicit function of 3D coordinates. In this way, the SA-INR can reconstruct the MR image with arbitrary inter-slice spacing by continuously sampling the coordinates in 3D space. In particular, a local-aware spatial attention operation is introduced to model nearby voxels and their affinity more accurately in a larger receptive field. Meanwhile, to improve the computational efficiency, a gradient-guided gating mask is proposed for applying the local-aware spatial attention to selected areas only. We evaluate our method on the public HCP-1200 dataset and the clinical knee MR dataset to demonstrate its superiority over other existing methods.

Artificial intelligence and multimodal data fusion for smart healthcare: topic modeling and bibliometrics

Advancements in artificial intelligence (AI) have driven extensive research into developing diverse multimodal data analysis approaches for smart healthcare. There is a scarcity of large-scale analysis of literature in this field based on quantitative approaches. This study performed a bibliometric and topic modeling examination on 683 articles from 2002 to 2022, focusing on research topics and trends, journals, countries/regions, institutions, authors, and scientific collaborations. Results showed that, firstly, the number of articles has grown from 1 in 2002 to 220 in 2022, with a majority being published in interdisciplinary journals that link healthcare and medical research and information technology and AI. Secondly, the significant rise in the quantity of research articles can be attributed to the increasing contribution of scholars from non-English speaking countries/regions and the noteworthy contributions made by authors in the USA and India. Thirdly, researchers show a high interest in diverse research issues, especially, cross-modality magnetic resonance imaging (MRI) for brain tumor analysis, cancer prognosis through multi-dimensional data analysis, and AI-assisted diagnostics and personalization in healthcare, with each topic experiencing a significant increase in research interest. There is an emerging trend towards issues such as applying generative adversarial networks and contrastive learning for multimodal medical image fusion and synthesis and utilizing the combined spatiotemporal resolution of functional MRI and electroencephalogram in a data-centric manner. This study is valuable in enhancing researchers’ and practitioners’ understanding of the present focal points and upcoming trajectories in AI-powered smart healthcare based on multimodal data analysis.

Potential Application of Hydrops MR Imaging: A Systematic Review.

Diagnostic dilemma between clinical Meniere's disease and radiological endolymphatic hydrops (EH) has emerged since the introduction of hydrops magnetic resonance imaging (MRI). The aim of this study is to explore the potential application of hydrops MRI on diagnosing the EH. This review was developed from peer-reviewed articles published in those journals listed on journal of citation reports. The MEDLINE database of the US National Library of Medicine, Scopus, and Google Scholar were used to collect articles based on the guidelines (PRISMA 2020 statement) for reporting reviews. Initially, 470 articles were retrieved from 1983 to 2023, and 80 relevant articles were ultimately selected. The sensitivity (69%-92%) and specificity (78%-96%) values varied from each laboratory for detecting EH via hydrops MRI, probably due to candidate selection and the grading system employed. The application of hydrops MRI allows (1) differentiation between EH and sudden sensorineural hearing loss; (2) determination of the affected side of EH; and (3) confirmation of the diagnosis of EH concomitant with other disorders. Notably, not all differentials for EH can be visualized on MR images. One of the existing gaps to be filled is that updated hydrops MRI fails to identify distortion, that is, rupture, collapse, fistula, or fibrosis of the inner ear compartments, akin to what histopathological evidence can demonstrate. Hence, enhanced ultrahigh resolution of hydrops MRI is required for demonstrating fine structures of the inner ear compartments in the future.

A polymeric 1H/19F dual-modal MRI contrast agent with a snowman-like Janus nanostructure.

Magnetic resonance imaging (MRI) has emerged as a pivotal tool in contemporary medical diagnostics, offering non-invasive and high-resolution visualization of internal structures. Contrast agents are essential for enhancing MRI resolution, accurate lesion detection, and early pathology identification. While gadolinium-based contrast agents are widely used in clinics, safety concerns have prompted exploration of metal-free alternatives, including fluorine and nitroxide radical-based MRI contrast agents. Fluorine-containing compounds exhibit excellent MRI capabilities, with 19F MRI providing enhanced resolution and quantitative assessment. Nitroxide radicals, such as PROXYL and TEMPO, offer paramagnetic properties for MRI contrast. Despite their versatility, nitroxide radicals suffer from lower relaxivity values (r1) compared to gadolinium. Dual-modal imaging, combining 1H and 19F MRI, has gained prominence for its comprehensive insights into biological processes and disease states. However, existing dual-modal agents predominantly utilize gadolinium-organic ligands without incorporating nitroxide radicals. Here, we introduce a novel dual-modal MRI contrast agent (J-CA) featuring a Janus asymmetric nanostructure synthesized via seeded emulsion polymerization and post-modification. J-CA demonstrates excellent in vitro and in vivo performance in both 19F and 1H MRI, with a T2 relaxation time of 5 ms and an r1 value of 0.31 mM-1 s-1, ensuring dual-modal imaging capability. Moreover, J-CA exhibits superior biocompatibility and organ targeting, making it a promising candidate for precise lesion imaging and disease diagnosis. This work introduces a new avenue for metal-free dual-modal MRI, addressing safety concerns associated with traditional contrast agents.

The Potential of Iron Oxide Nanoparticle-Enhanced MRI at 7 T Compared With 3 T for Detecting Small Suspicious Lymph Nodes in Patients With Prostate Cancer.

Accurate detection of lymph node (LN) metastases in prostate cancer (PCa) is a challenging but crucial step for disease staging. Ultrasmall superparamagnetic iron oxide (USPIO)-enhanced magnetic resonance imaging (MRI) enables distinction between healthy LNs and nodes suspicious for harboring metastases. When combined with MRI at an ultra-high magnetic field, an unprecedented spatial resolution can be exploited to visualize these LNs. The aim of this study was to explore USPIO-enhanced MRI at 7 T in comparison to 3 T for the detection of small suspicious LNs in the same cohort of patients with PCa. Twenty PCa patients with high-risk primary or recurrent disease were referred to our hospital for an investigational USPIO-enhanced 3 T MRI examination with ferumoxtran-10. With consent, they underwent a 7 T MRI on the same day. Three-dimensional anatomical and T2*-weighted images of both examinations were evaluated blinded, with an interval, by 2 readers who annotated LNs suspicious for metastases. Number, size, and level of suspicion (LoS) of LNs were paired within patients and compared between field strengths. At 7 T, both readers annotated significantly more LNs compared with 3 T (474 and 284 vs 344 and 162), with 116 suspicious LNs on 7 T (range, 1-34 per patient) and 79 suspicious LNs on 3 T (range, 1-14 per patient) in 17 patients. For suspicious LNs, the median short axis diameter was 2.6 mm on 7 T (1.3-9.5 mm) and 2.8 mm for 3 T (1.7-10.4 mm, P = 0.05), with large overlap in short axis of annotated LNs between LoS groups. At 7 T, significantly more suspicious LNs had a short axis <2.5 mm compared with 3 T (44% vs 27%). Magnetic resonance imaging at 7 T provided better image quality and structure delineation and a higher LoS score for suspicious nodes. In the same cohort of patients with PCa, more and more small LNs were detected on 7 T USPIO-enhanced MRI compared with 3 T MRI. Suspicious LNs are generally very small, and increased nodal size was not a good indication of suspicion for the presence of metastases. The high spatial resolution of USPIO-enhanced MRI at 7 T improves structure delineation and the visibility of very small suspicious LNs, potentially expanding the in vivo detection limits of pelvic LN metastases in PCa patients.

MRI‐Compatible, Transparent PEDOT:PSS Neural Implants for the Alleviation of Neuropathic Pain with Motor Cortex Stimulation

AbstractSimultaneous monitoring of electrophysiology and magnetic resonance imaging (MRI) could guide the innovative diagnosis and treatment of various neurodegenerative diseases that are previously impossible. However, this technique is difficult because the existing metal‐based implantable neural interface for electrophysiology is not free from signal distortions from its intrinsic magnetic susceptibility while performing an MRI of the implanted area of the neural interface. Moreover, brain tissue heating from neural implants generated by the radiofrequency field from MRI poses potential hazards for patients. Previous studies with soft polymer‐based electrode arrays provide relatively suitable MRI compatibility but does not guarantee high‐resolution electrophysiological signal acquisition and stimulation performance. Here, MRI compatible, optically transparent flexible implantable device capable of electrophysiological multichannel mapping and electrical stimulation is introduced. Using the device, neuropathic pain (NP) relief with a 30‐channel electrophysiological mapping of the somatosensory area before and after motor cortex stimulation (MCS) in allodynia rats after noxious stimulation is confirmed. Additionally, artifact‐free manganese‐enhanced MRI of dramatic relief of pain‐related region activity by MCS is demonstrated. Furthermore, artifact‐free optogenetics with transgenic mice is also investigated by recording light‐evoked potentials. These results suggest a promising neuro‐prosthetic for analyzing and modulating spatiotemporal neurodynamic without MRI or optical modality resolution constraints.

Domain migration representation learning for blind magnetic resonance image super-resolution

Recently, magnetic resonance (MR) imaging has been widely used in clinical treatment to assist doctors in diagnosis because of its high resolution of soft tissue and no radiation. However, limited by imaging equipment and imaging time, the resolution of MR imaging is low. The previous super-resolution algorithms used for MR images use paired datasets to generate down-sampled images by artificially assuming the image degradation process, and forming a sample pair to monitor and train the super-resolution network. However, the super-resolution network obtained by this way will cause serious degradation of the network performance in practical application. In this paper, we proposed DMN, a blind super-resolution framework based on domain migration, which decouples the blind super-resolution network with the idea of domain migration, and avoids the false detail generation problem of the blind super-resolution network in the MR images super-resolution task through independent training. At the same time, we propose a multi-level parallel transformer feature enhancement module (MPTB), which takes noise as the original input and embeds noise features step by step to enhance the fitting ability of the network while reducing the complexity of the network structure design. Besides, we also use the k-space loss function to form a constraint function, so that the domain migration network can fully consider the k-space characteristics of MR images. The experimental results on the MR images of the knee joint and brain in the FastMRI dataset show that the super-resolution performance of our proposed DMN network is better than the previous super-resolution networks.

The effect of smoking on MRI lesion resolution in NMOSD-AQP4 and MOGAD

Background: The effect of smoking on the resolution of magnetic resonance imaging (MRI) lesions in patients with neuromyelitis optica spectrum disorders with aquaporin-4 positive antibody (NMOSD-AQP4) and myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) has not been studied before. Objective: We aimed to determine the effect of smoking on lesion resolution in MRI and assess its correlation with clinical recovery after a relapse. Methods: We conducted a cohort study including NMOSD-AQP4 and MOGAD patients with acute and follow-up MRI scans. We collected demographic, clinical, imaging and smoking data. Logistic regression models were fitted to predict the effect of smoking on lesion resolution and to assess whether clinical recovery was associated with MRI lesion resolution. Results: A total of 105 patients were included (57 with NMOSD-AQP4 and 48 with MOGAD). Current and past smoking was associated with a higher risk of persistent lesions in NMOSD-AQP4 and MOGAD (risk ratio (RR) = 3.4, 95% confidence interval (CI) = 2.5–4.7, p < 0.001). Additionally, the presence of lesion resolution was associated with better clinical recovery (RR = 1.9, 95% CI = 1.7–2.2, p < 0.001). Conclusion: Smoking is associated with worse MRI lesion resolution in patients with NMOSD-AQP4 and MOGAD, and lesion resolution correlates with clinical recovery. Our findings suggest a detrimental effect of smoking in inflammatory central nervous system (CNS) diseases.

A paired dataset of T1- and T2-weighted MRI at 3 Tesla and 7 Tesla

Brain magnetic resonance imaging (MRI) provides detailed soft tissue contrasts that are critical for disease diagnosis and neuroscience research. Higher MRI resolution typically comes at the cost of signal-to-noise ratio (SNR) and tissue contrast, particularly for more common 3 Tesla (3T) MRI scanners. At ultra-high magnetic field strength, 7 Tesla (7T) MRI allows for higher resolution with greater tissue contrast and SNR. However, the prohibitively high costs of 7T MRI scanners deter their widespread adoption in clinical and research centers. To obtain higher-quality images without 7T MRI scanners, algorithms that can synthesize 7T MR images from 3T MR images are under active development. Here, we make available a dataset of paired T1-weighted and T2-weighted MR images at 3T and 7T of 10 healthy subjects to facilitate the development and evaluation of 3T-to-7T MR image synthesis models. The quality of the dataset is assessed using image quality metrics implemented in MRIQC.

A2OURSR: Adaptive adjustment based real MRI super-resolution via opinion-unaware measurements.

High-quality and high-resolution magnetic resonance (MR) images can provide more details for diagnosis and analyses. Recently, MR images guided neurosurgery has become an emerging technique in clinics. Unlike other medical imaging techniques, it is impossible to achieve both real-time imaging and high image quality in MR imaging. The real-time performance is closely related to the nuclear magnetic equipment itself as well as the collection strategy of the k space data. Optimizing the imaging time cost via the corresponding algorithm is harder than enhancing image quality. Further, in reconstructing low-resolution and noise-rich MR images, getting relatively high-definition and resolution MR images as references are difficult or impossible. In addition, the existing methods are restricted in learning the controllable functions under the supervision of known degradation types and levels. As a result, severely bad results are inevitable when the modeling assumptions are far apart from the actual situation. To address these problems, we propose a novel adaptive adjustment method based on real MR images via opinion-unaware measurements for real super-resolution (A2OURSR). It can estimate the degree of blur and noise from the test image itself using two scores. These two scores can be considered pseudo labels to train the adaptive adjustable degradation estimation module. Then, the outputs of the above model are used as the inputs of the conditional network to tweak the generated results. Thus, the results can be automatically adjusted via the whole dynamic model. Extensive experimental results show that the proposed A2OURSR is superior to state-of-the-art methods on benchmarks quantitatively and visually.

Two-photon excited luminescence of structural light enhancement in subwavelength SiO2 coating europium ion-doped paramagnetic gadolinium oxide nanoparticle and application for magnetic resonance imaging

BackgroundOxides of lanthanide rare-earth elements show great potential in the fields of imaging and therapeutics due to their unique electrical, optical and magnetic properties. Oxides of lanthanide-based nanoparticles enable high-resolution imaging of biological tissues by magnetic resonance imaging (MRI), computed tomography (CT) imaging, and fluorescence imaging. In addition, they can be used to detect, treat, and regulate diseases by fine-tuning their structure and function. It remains challenging to achieve safer, efficient, and more sensitive nanoparticles for clinical applications through the structural design of functional and nanostructured rare-earth materials.ResultIn this study, we designed a mesoporous silica-coated core–shell structure of europium oxide ions to obtain near-infrared two-photon excitation fluorescence while maintaining high contrast and resolution in MRI. We designed enhanced 800 nm photoexcitation nanostructures, which were simulated by the finite-difference method (FDM) and finite-difference time-domain method (FDTD). The nanoparticle structure, two-photon absorption, up-conversion fluorescence, magnetic properties, cytotoxicity, and MRI were investigated in vivo and in vitro. The nanoparticle has an extremely strong optical fluorescence response and multiple excitation peaks in the visible light band under the 405 nm continuous-wave laser excitation. The nanoparticle was found to possess typical optical nonlinearity induced by two-photon absorption by ultrafast laser Z-scan technique. Two-photon excited fluorescence of visible red light at wavelengths of 615 nm and 701 nm, respectively, under excitation of the more biocompatible near-infrared (pulsed laser at 800 nm). In an in vitro MRI study, a T1 relaxation rate of 6.24 mM−1 s−1 was observed. MRI in vivo showed that the nanoparticles could significantly enhance the signal intensity in liver tissue.ConclusionsThese results suggest that this sample has applied potential in visible light fluorescence imaging and MRI.

A Systematic Review of Magnetic Resonance Imaging for the Diagnosis of Stroke

Background: With the advancement of magnetic resonance imaging (MRI), a non-invasive imaging technique, ischemic and hemorrhagic stroke diagnostic accuracy has increased significantly. The intricate resolution of MRI enables better visualisation of the structure of the brain and vulnerable regions to ischemia. As a result, the detection of hypoperfused areas is now more accurate. Additionally, MRI makes it possible to see hematomas in the brain, revealing hemorrhagic strokes brought on by burst arteries. MRI has become an essential tool in the fight against strokes because to its crucial role in evaluating damage caused by strokes. Purpose: The purpose of this article is to examine the advantages and disadvantages of utilising MRI scans to identify strokes. The use of MRI technology to identify brain injury and differentiate between strokes caused by blood flow and haemorrhage will be highlighted. When it comes to stroke therapy and prognosis, MRI might potentially affect the advice given by doctors. Methods: The Department of Radiology at KTH Hospital Peshawar thoroughly investigated the possibility of MRI for stroke diagnosis between March 2016 and March 2017. We searched the PubMed and EMBASE archives for papers about the use of MRI in stroke patient investigations in order to assemble the most information possible. We only chose studies that have a main concentration on MRI imaging and stroke patients. Results: Twenty-nine research publications examined the efficiency of MRI with an emphasis on stroke diagnosis. Twenty-four of them examined ischemic stroke diagnosis, while the remaining five concentrated on hemorrhagic stroke diagnosis. Findings showed that MRI was very accurate, with a sensitivity and specificity of up to 97% for ischemic stroke detection. MRI shown success in the diagnosis of hemorrhagic stroke, with a sensitivity and specificity of up to [100%]. Conclusions: MRI is a crucial tool for the diagnosis and treatment of stroke. Because of the great accuracy and sensitivity of this imaging method, both ischemic and hemorrhagic strokes may be identified. It also carefully evaluates the effects of a stroke on the brain. The early and successful diagnosis and treatment of stroke depend heavily on the use of MRI. Keywords: diagnosis, ischemic stroke, hemorrhagic stroke, and magnetic resonance imaging

Optimizing imaging resolution in brain MRI: understanding the impact of technical factors.

Magnetic resonance imaging (MRI) exams are essential for diagnostic procedures, but their lengthy duration and associated costs limit their accessibility. Shorter scan times would reduce expenses and allow for more MRI exams, expanding the range of diagnostic procedures. This study investigated technical factors that could decrease scan time without compromising image quality, including field-of-view (FOV), phase field of view, phase oversampling, cross-talk, brain MRI imaging resolution, and scan time. Data were collected from September 2021 to June 2022. All patients underwent brain scans in the transverse plane following a standardized protocol using a 1.5-tesla Siemens Avanto MRI scanner. The protocol employed T2-weighted Turbo Spin Echo imaging. Twenty-four cases were included in this study. Initially, all participants underwent brain MRI scans using the original protocols with axial sections. The results indicated that altering the FOV phase and phase oversampling significantly affected the scan time, whereas other factors did not have a direct impact. The original protocol had a scan time of 3.47 minutes with a FOV of 230 mm, 90% FOV phase, and 0% phase oversampling. After implementing the modified protocol, the scan time was reduced to 2.18 minutes with a FOV of 217 mm and 93.98% phase oversampling of 13.96%. Statistical analysis confirmed the high significance of FOV phase and phase oversampling in reducing scan time. By optimizing these technical factors, MRI exams can be performed more efficiently, resulting in shorter scan times and potentially reducing costs. This would enhance patient comfort and enable a greater number of MRI exams, facilitating a more comprehensive range of diagnostic procedures.

A Double-Stage 3D U-Net for On-Cloud Brain Extraction and Multi-Structure Segmentation from 7T MR Volumes

The brain is the organ most studied using Magnetic Resonance (MR). The emergence of 7T scanners has increased MR imaging resolution to a sub-millimeter level. However, there is a lack of automatic segmentation techniques for 7T MR volumes. This research aims to develop a novel deep learning-based algorithm for on-cloud brain extraction and multi-structure segmentation from unenhanced 7T MR volumes. To this aim, a double-stage 3D U-Net was implemented in a cloud service, directing its first stage to the automatic extraction of the brain and its second stage to the automatic segmentation of the grey matter, basal ganglia, white matter, ventricles, cerebellum, and brain stem. The training was performed on the 90% (the 10% of which served for validation) and the test on the 10% of the Glasgow database. A mean test Dice Similarity Coefficient (DSC) of 96.33% was achieved for the brain class. Mean test DSCs of 90.24%, 87.55%, 93.82%, 85.77%, 91.53%, and 89.95% were achieved for the brain structure classes, respectively. Therefore, the proposed double-stage 3D U-Net is effective in brain extraction and multi-structure segmentation from 7T MR volumes without any preprocessing and training data augmentation strategy while ensuring its machine-independent reproducibility.

A non-linear triangular split-ring based metaresonator for targeted scanning at 1.5T MRI

In magnetic resonance imaging (MRI), RF signals are initially transmitted to stimulate the body protons which eventually release the electromagnetic energy while returning back to their original states. The image resolution and scanning efficiency of MRI can be improved by enhancing the magnetic fields received from the patient’s body using metamaterials. The major limitation of linear metamaterials is that they amplify RF magnetic fields both during transmission and reception phases. This requires modification of the RF excitation pulses during the transmission phase. Further, local increase of transmitted power poses a potential threat of tissue-heating and high specific absorption rate (SAR) values in addition to perturbing the transmit field homogeneity. In order to circumvent these problems, we propose a self-adaptive metaresonator which has the capability of self-detuning itself during transmission of RF pulses during MRI scans. A triangular split-ring based metaresonator is designed for maximum thirty-fold SNR improvement in 1.5T MRI. Switching diodes have been employed for switching on and off the magnetic field enhancement by the metaresonator. During transmission phase when the switching diodes get turned on, the metaresonator is detuned. During reception phase when the switching diodes get turned off, the metaresonator is tuned to 63.8 MHz which is the Larmor frequency of 1.5T MRI. The proposed metaresonator is thin and compact which enables its easy placement in the multi-element arrays of clinical MRI.

Super-resolution reconstruction in ultrahigh-field MRI

Magnetic resonance imaging (MRI) is a highly significant imaging platform for a variety of medical and research applications. However, the low spatiotemporal resolution of conventional MRI limits its applicability toward rapid acquisition of ultrahigh-resolution scans. Current aims at high-resolution MRI focus on increasing the accuracy of tissue delineation, assessments of structural integrity, and early identification of malignancies. Unfortunately, high-resolution imaging often leads to decreased signal/noise (SNR) and contrast/noise (CNR) ratios and increased time cost, which are unfeasible in many clinical and academic settings, offsetting any potential benefits. In this study, we apply and assess the efficacy of super-resolution reconstruction (SRR) through iterative back-projection utilizing through-plane voxel offsets. SRR allows for high-resolution imaging in condensed time frames. Rat skulls and archerfish samples, typical models in academic settings, were used to demonstrate the impact of SRR on varying sample sizes and applicability for translational and comparative neuroscience. The SNR and CNR increased in samples that did not fully occupy the imaging probe and in instances where the low-resolution data were acquired in three dimensions, while the CNR was found to increase with both 3D and 2D low-resolution data reconstructions when compared with directly acquired high-resolution images. Limitations to the applied SRR algorithm were investigated to determine the maximum ratios between low-resolution inputs and high-resolution reconstructions and the overall cost effectivity of the strategy. Overall, the study revealed that SRR could be used to decrease image acquisition time, increase the CNR in nearly all instances, and increase the SNR in small samples.

Superresolution MRI with a Structured-Illumination Approach

Unlike optical imaging methods, where the Fourier representation of the object is created by wave diffraction, magnetic resonance imaging (MRI) scans the Fourier domain by spatially modulating the nuclear magnetization with magnetic field gradients. Therefore, the resolution of MRI is limited by signal strength rather than the wavelength of the diffracted wave. However, in several important applications, such as the imaging of water diffusion or blood oxygenation in the human brain, MRI must collect all the Fourier data during a single evolution of the signal to avoid artifacts caused by random fluctuations of its phase. The Fourier domain coverage of the ``single-shot'' MRI strategy is limited by the lifetime of the signal and results in a resolution limit analogous to the diffraction limit in optical microscopy. Consequently, recently developed optical superresolution methods may appear helpful. This work employs the recently developed method of phaseless encoding, which implements the idea of linear structured illumination microscopy (SIM) to enable multishot data acquisition and triple the MRI resolution regardless of the presence of spin phase fluctuations, and develops it further to enhance the resolution in two dimensions and to push its gain above a factor of 3 using ``illumination patterns'' with multiple harmonics, a concept borrowed from saturated SIM (SSIM) methodology. Results obtained with enhanced phaseless encoding on a 3-Tesla clinical scanner in phantoms and human brain are presented. This method will be beneficial when conventional multishot MRI is impeded by signal instability due to motion or other factors.