Application Of Magnetic Resonance Imaging Research Articles

Exploration and expectation of magnetic resonance imaging in the evaluation of prostate cancer

With the progress of imaging technology, magnetic resonance imaging (MRI) has become the preferred imaging method for prostate cancer due to its excellent soft tissue resolution and the capability of multiparametric and multi-planar imaging. This paper briefly describes the current application and research progress of MRI in the preoperative qualitative diagnosis, staging assessment and postoperative recurrence monitoring of prostate cancer. The purpose is to deepen the understanding of clinicians and radiologists on the value of MRI in prostate cancer, and to promote the exploration of MRI in the management of prostate cancer.

A New Breakpoint to Classify 3D Voxels in MRI: A Space Transform Strategy with 3t2FTS-v2 and Its Application for ResNet50-Based Categorization of Brain Tumors.

Three-dimensional (3D) image analyses are frequently applied to perform classification tasks. Herein, 3D-based machine learning systems are generally used/generated by examining two designs: a 3D-based deep learning model or a 3D-based task-specific framework. However, except for a new approach named 3t2FTS, a promising feature transform operating from 3D to two-dimensional (2D) space has not been efficiently investigated for classification applications in 3D magnetic resonance imaging (3D MRI). In other words, a state-of-the-art feature transform strategy is not available that achieves high accuracy and provides the adaptation of 2D-based deep learning models for 3D MRI-based classification. With this aim, this paper presents a new version of the 3t2FTS approach (3t2FTS-v2) to apply a transfer learning model for tumor categorization of 3D MRI data. For performance evaluation, the BraTS 2017/2018 dataset is handled that involves high-grade glioma (HGG) and low-grade glioma (LGG) samples in four different sequences/phases. 3t2FTS-v2 is proposed to effectively transform the features from 3D to 2D space by using two textural features: first-order statistics (FOS) and gray level run length matrix (GLRLM). In 3t2FTS-v2, normalization analyses are assessed to be different from 3t2FTS to accurately transform the space information apart from the usage of GLRLM features. The ResNet50 architecture is preferred to fulfill the HGG/LGG classification due to its remarkable performance in tumor grading. As a result, for the classification of 3D data, the proposed model achieves a 99.64% accuracy by guiding the literature about the importance of 3t2FTS-v2 that can be utilized not only for tumor grading but also for whole brain tissue-based disease classification.

Selectively Fluorinated PAMAM-Arginine Conjugates as Gene Delivery Vectors.

Polyamidoamine (PAMAM) dendrimers are among the most studied cationic polymers as non-viral gene delivery vectors. However, an "ideal" PAMAM-based gene delivery vector is still missing due to the high manufacturing costs and non-negligible cytotoxicity associated with the use of high-generation dendrimers, whereas low-generation dendrimers are far from displaying efficient gene transfection. In order to cover this gap in the literature, in this study, we propose the functionalization of the outer primary amines of PAMAM G2 and PAMAM G4 with building blocks bearing fluorinated moieties along with a guanidino functional group. We have designed and synthetized two fluorinated arginine (Arg)-based Michael acceptors which were straightforwardly "clicked" to PAMAM dendrimers without the need for coupling reagents and/or catalysts. The obtained conjugates, in particular, derivative 1 formed starting from the low-cost PAMAM G2 and a building block bearing two trifluoromethyl groups, were able to efficiently complex plasmid DNA, had negligible cytotoxicity, and showed improved gene transfection efficiency as compared to undecorated PAMAM dendrimers and a corresponding unfluorinated PAMAM-Arg derivative, with derivative 1 being two orders of magnitude more efficient than the gold standard branched polyethylenimine, bPEI, 25 kDa. These results highlight the importance of the presence of trifluoromethyl moieties for both gene transfection and a possible future application in 19F magnetic resonance imaging.

Miniaturized lumped‐element low‐temperature co‐fired ceramic balun using capacitance‐enhanced capacitors

SummaryA capacitance‐enhanced capacitor (CEC) is proposed and applied to build a low‐temperature co‐fired ceramic (LTCC) balun for miniaturization. The capacitance enhancement is by adding horizontal fingers to traditional vertically interdigital capacitor (VIC) for capacitance increasing without size enlargement. The proposed CEC increased 7.2% capacitance comparing with that of traditional VIC. The balun using the proposed CEC is only 9.5 × 8.4 × 1 mm3, which is equivalent to 0.0034 × 0.0031 × 0.0004 λg3. The measured inband S11, S21, and S31 are better than −15, −3.6, and 3.5 dB from 46 to 58 MHz, respectively. A wide fractional bandwidth (FBW) of 24% based on 15 dB return loss is also reached. The proposed balun has widely applications in maritime navigation, aircraft navigation, and 1–1.5 Tesla (T) magnetic resonance imaging (MRI) applications.

Accelerated partial separable model using dimension-reduced optimization technique for ultra-fast cardiac MRI

Objective. Imaging dynamic objects with high temporal resolution is challenging in magnetic resonance imaging (MRI). The partial separable (PS) model was proposed to improve imaging quality by reducing the degrees of freedom of the inverse problem. However, the PS model still suffers from a long acquisition time and an even longer reconstruction time. The main objective of this study is to accelerate the PS model, shorten the time required for acquisition and reconstruction, and maintain good image quality simultaneously. Approach. We proposed to fully exploit the dimension-reduction property of the PS model, which means implementing the optimization algorithm in subspace. We optimized the data consistency term and used a Tikhonov regularization term based on the Frobenius norm of temporal difference. The proposed dimension-reduced optimization technique was validated in free-running cardiac MRI. We have performed both retrospective experiments on a public dataset and prospective experiments on in vivo data. The proposed method was compared with four competing algorithms based on the PS model and two non-PS model methods. Main results. The proposed method has robust performance against a shortened acquisition time or suboptimal hyper-parameter settings, and achieves superior image quality over all other competing algorithms. The proposed method is 20-fold faster than the widely accepted PS+sparse method, enabling image reconstruction to be finished in just a few seconds. Significance. The accelerated PS model has the potential to save a great deal of time in clinical dynamic MRI examinations and is promising for real-time MRI applications.

SDMS CME Credit – The Acute Female Nongravid Pelvis: A Review of Appropriate Clinical Applications of Sonography, Computed Tomography, and Magnetic Resonance Imaging

SDMS CME Credit – The Acute Female Nongravid Pelvis: A Review of Appropriate Clinical Applications of Sonography, Computed Tomography, and Magnetic Resonance Imaging

Recent Advances in Explainable Artificial Intelligence for Magnetic Resonance Imaging.

Advances in artificial intelligence (AI), especially deep learning (DL), have facilitated magnetic resonance imaging (MRI) data analysis, enabling AI-assisted medical image diagnoses and prognoses. However, most of the DL models are considered as "black boxes". There is an unmet need to demystify DL models so domain experts can trust these high-performance DL models. This has resulted in a sub-domain of AI research called explainable artificial intelligence (XAI). In the last decade, many experts have dedicated their efforts to developing novel XAI methods that are competent at visualizing and explaining the logic behind data-driven DL models. However, XAI techniques are still in their infancy for medical MRI image analysis. This study aims to outline the XAI applications that are able to interpret DL models for MRI data analysis. We first introduce several common MRI data modalities. Then, a brief history of DL models is discussed. Next, we highlight XAI frameworks and elaborate on the principles of multiple popular XAI methods. Moreover, studies on XAI applications in MRI image analysis are reviewed across the tissues/organs of the human body. A quantitative analysis is conducted to reveal the insights of MRI researchers on these XAI techniques. Finally, evaluations of XAI methods are discussed. This survey presents recent advances in the XAI domain for explaining the DL models that have been utilized in MRI applications.

Multislice B1 Mapping Method Using Magnetic Resonance Composite Spin Echo Sequences and Simultaneous Echo Refocusing

Radiofrequency (RF) transmit field (B1) mapping is a promising method in mitigating the B1 inhomogeneity in various magnetic resonance imaging (MRI) applications. Although several phase- or magnitude-based B1 mapping methods have been proposed, these methods often require complex modeling, long acquisition time, or specialized MRI sequences. A recently introduced simultaneous echo refocusing (SER) technique can be applied in the B1 mapping method to extend the three-dimensional (3D) spatial coverage only without long data acquisition. Therefore, in this study, a multislice B1 mapping method using composite spin echo sequences and SER techniques is proposed to obtain more accurate B1 mapping with short data acquisition time. To evaluate the performance of the proposed B1 mapping method, computational simulations were performed and compared with Morrell’s method, double angle method, and Yarnykh’s method. These results showed that the angle-to-noise ratio of the proposed B1 mapping method has wider B1 range compared to that of other B1 mapping methods. In addition, the proposed B1 mapping methods were compared to the multislice iterative signal intensity mapping method in both phantom and in vivo human experiments, and there was no remarkable difference between the two methods regarding the flip angle distribution in these experiments. Based on these results, this study demonstrated that the proposed B1 mapping method is suitable for accurately measuring B1 propagation under the condition providing reduced scan time and wider 3D coverage of B1 mapping by applying composite RF pulse and SER techniques into the phase-sensitive method.

Preoperative prediction of macrotrabecular-massive hepatocellular carcinoma through dynamic contrast-enhanced magnetic resonance imaging-based radiomics.

Macrotrabecular-massive hepatocellular carcinoma (MTM-HCC) is closely related to aggressive phenotype, gene mutation, carcinogenic pathway, and immunohistochemical markers and is a strong independent predictor of early recurrence and poor prognosis. With the development of imaging technology, successful applications of contrast-enhanced magnetic resonance imaging (MRI) have been reported in identifying the MTM-HCC subtype. Radiomics, as an objective and beneficial method for tumour evaluation, is used to convert medical images into high-throughput quantification features that greatly push the development of precision medicine. To establish and verify a nomogram for preoperatively identifying MTM-HCC by comparing different machine learning algorithms. This retrospective study enrolled 232 (training set, 162; test set, 70) hepatocellular carcinoma patients from April 2018 to September 2021. A total of 3111 radiomics features were extracted from dynamic contrast-enhanced MRI, followed by dimension reduction of these features. Logistic regression (LR), K-nearest neighbour (KNN), Bayes, Tree, and support vector machine (SVM) algorithms were used to select the best radiomics signature. We used the relative standard deviation (RSD) and bootstrap methods to quantify the stability of these five algorithms. The algorithm with the lowest RSD represented the best stability, and it was used to construct the best radiomics model. Multivariable logistic analysis was used to select the useful clinical and radiological features, and different predictive models were established. Finally, the predictive performances of the different models were assessed by evaluating the area under the curve (AUC). The RSD values based on LR, KNN, Bayes, Tree, and SVM were 3.8%, 8.6%, 4.3%, 17.7%, and 17.4%, respectively. Therefore, the LR machine learning algorithm was selected to construct the best radiomics signature, which performed well with AUCs of 0.766 and 0.739 in the training and test sets, respectively. In the multivariable analysis, age [odds ratio (OR) = 0.956, P = 0.034], alpha-fetoprotein (OR = 10.066, P < 0.001), tumour size (OR = 3.316, P = 0.002), tumour-to-liver apparent diffusion coefficient (ADC) ratio (OR = 0.156, P = 0.037), and radiomics score (OR = 2.923, P < 0.001) were independent predictors of MTM-HCC. Among the different models, the predictive performances of the clinical-radiomics model and radiological-radiomics model were significantly improved compared to those of the clinical model (AUCs: 0.888 vs 0.836, P = 0.046) and radiological model (AUCs: 0.796 vs 0.688, P = 0.012), respectively, in the training set, highlighting the improved predictive performance of radiomics. The nomogram performed best, with AUCs of 0.896 and 0.805 in the training and test sets, respectively. The nomogram containing radiomics, age, alpha-fetoprotein, tumour size, and tumour-to-liver ADC ratio revealed excellent predictive ability in preoperatively identifying the MTM-HCC subtype.

Clinical applicability of arterial spin labeling magnetic resonance imaging in patients with possible idiopathic normal pressure hydrocephalus: A prospective preliminary study

idiopathic Normal Pressure Hydrocephalus (iNPH) patients have a global reduction of cerebral blood flow (CBF) and Arterial Spin Label (ASL) MRI allows a global evaluation of CBF without the injection of contrast agents. This work aims to assess the qualitative evaluation agreement of ASL CBF colored maps between different neuroradiologists and by correlating these data to the Tap Test. Thirty - seven patients with the diagnosis of possible iNPH were consecutively submitted to a diagnostic MRI on a 1.5 Tesla Magnet before and after the lumbar infusion test and the Tap Test. Twenty - seven patients improved after the Tap Test and were addressed to surgery while 10 patients did not improve. All the MRI examinations included a 3D-Pulsed ASL sequence. Two different neuroradiologists independently reviewed all ASL images. They were asked to give a score (0 not improved; 1 improved) to global perfusion image quality by comparing ASL images obtained after the Tap Test to those obtained before. Comparison between inter- and intra-reader qualitative scores were performed with Cohen's kappa. Inter-reader agreement between the two neuroradiologists showed that qualitative scores were attributed similarly by two readers (k= 0.83). This technique has a good PPV (90.5 %; CI 95 %, 72.7-97.1 %), NPV (50 %; CI 95 %, 34.1-65.6 %), SN (70.37 %; CI 95 %, 49.8-86.2 %) SP (80 %; CI 95 %, 44.4-97.5 %) and accuracy (73 %; CI 95 %, 55.9-86.2 %) when considered in the setting of possible iNPH patients. ASL-MRI seems to be a promising non-invasive technique in the preoperative selection of patients affected by possible iNPH.

Evaluating semi-supervision methods for medical image segmentation: applications in cardiac magnetic resonance imaging.

Neural networks have potential to automate medical image segmentation but require expensive labeling efforts. While methods have been proposed to reduce the labeling burden, most have not been thoroughly evaluated on large, clinical datasets or clinical tasks. We propose a method to train segmentation networks with limited labeled data and focus on thorough network evaluation. We propose a semi-supervised method that leverages data augmentation, consistency regularization, and pseudolabeling and train four cardiac magnetic resonance (MR) segmentation networks. We evaluate the models on multiinstitutional, multiscanner, multidisease cardiac MR datasets using five cardiac functional biomarkers, which are compared to an expert's measurements using Lin's concordance correlation coefficient (CCC), the within-subject coefficient of variation (CV), and the Dice coefficient. The semi-supervised networks achieve strong agreement using Lin's CCC ( ), CV similar to an expert, and strong generalization performance. We compare the error modes of the semi-supervised networks against fully supervised networks. We evaluate semi-supervised model performance as a function of labeled training data and with different types of model supervision, showing that a model trained with 100 labeled image slices can achieve a Dice coefficient within 1.10% of a network trained with 16,000+ labeled image slices. We evaluate semi-supervision for medical image segmentation using heterogeneous datasets and clinical metrics. As methods for training models with little labeled data become more common, knowledge about how they perform on clinical tasks, how they fail, and how they perform with different amounts of labeled data is useful to model developers and users.

Inhalable Microparticles Embedding Biocompatible Magnetic Iron-Doped Hydroxyapatite Nanoparticles.

Recently, there has been increasing interest in developing biocompatible inhalable nanoparticle formulations, as they have enormous potential for treating and diagnosing lung disease. In this respect, here, we have studied superparamagnetic iron-doped calcium phosphate (in the form of hydroxyapatite) nanoparticles (FeCaP NPs) which were previously proved to be excellent materials for magnetic resonance imaging, drug delivery and hyperthermia-related applications. We have established that FeCaP NPs are not cytotoxic towards human lung alveolar epithelial type 1 (AT1) cells even at high doses, thus proving their safety for inhalation administration. Then, D-mannitol spray-dried microparticles embedding FeCaP NPs have been formulated, obtaining respirable dry powders. These microparticles were designed to achieve the best aerodynamic particle size distribution which is a critical condition for successful inhalation and deposition. The nanoparticle-in-microparticle approach resulted in the protection of FeCaP NPs, allowing their release upon microparticle dissolution, with dimensions and surface charge close to the original values. This work demonstrates the use of spray drying to provide an inhalable dry powder platform for the lung delivery of safe FeCaP NPs for magnetically driven applications.

Spin-lattice relaxation time in water/graphene-oxide dispersion.

We present the results of the calculations of the spin-lattice relaxation time of water in contact with graphene oxide by means of all-atom molecular dynamics simulations. We fully characterized the water-graphene oxide interaction through the calculation of the relaxation properties of bulk water and of the contact angle as a function of graphene oxide oxidation state and comparing them with the available experimental data. We then extended the calculation to investigate how graphene oxide alters the dynamical and relaxation properties of water in different conditions and concentrations. We show that, despite the diamagnetic nature of the graphene oxide, the confining effects of the bilayers strongly affect the longitudinal relaxation properties of interfacial water, which presents a reduced dynamics due to hydrogen bonds with oxygen groups on graphene oxide. This property makes graphene oxide an interesting platform to investigate water dynamics in confined geometries and an alternative contrast-agent for magnetic resonance imaging applications, especially in view of the possibility to functionalize graphene oxide from theranostic perspectives.

A hybrid FDTD/MoM algorithm with a conformal Huygens' equivalent surface for MRI RF coil design and analysis

Accurate design and analysis of radiofrequency (RF) coils are crucial for ultra-high field (UHF) magnetic resonance imaging (MRI) applications. To improve the numerical accuracy of electromagnetic (EM) simulations, we propose a hybrid finite difference time domain (FDTD)/method of moments (MoM) method. Unlike conventional cuboid-like Huygens' equivalent surfaces (HES), we proposed to use a conformal HES to interface the EM data of the FDTD and MoM zone. The shape and size of the conformal surface can be adjusted to fit different RF coil models, thus broadening the application range of the hybrid FDTD/MoM method. Two numerical models: an 8-channel ellipse array, and an 8-channel bent dipole array, are simulated and compared with the conventional HES counterpart. Numerical results demonstrate the capability of the conformal HES method in the analysis of RF coils.

A Novel One-Pot Synthesis of PVP-Coated Iron Oxide Nanoparticles as Biocompatible Contrast Agents for Enhanced T2-Weighted MRI

A contrast agent with specific characteristics is essential for high-quality magnetic resonance imaging (MRI). It plays a crucial role in enhancing the visibility of certain tissues and structures, making it imperative for diagnostic procedures. Superparamagnetic iron oxide nanoparticles (SPIONs) have emerged as a promising alternative to traditional contrast agents for MRI due to their non-toxicity and superior magnetic properties. However, a suitable surface coating strategy is needed to produce polymer-coated SPIONs with controllable sizes in order to enhance their stability and biocompatibility. This study presents a novel one-pot synthesis method for the production of highly stable polyvinylpyrrolidone (PVP)-coated SPIONs. By systematically manipulating the physicochemical properties of SPIONs, the effect of different molecular weights of PVP was studied. The results showed that SPIONs coated with PVP with molecular wight 40,000 g/mol (40 K) exhibited a high magnetization (Ms = 48.4 emu/g), an average size distribution (11.61 nm), and excellent stability. The relaxivity of coated and uncoated SPIONs was investigated using MRI images. The results revealed that the (r2/r1) ratio of PVP40K-SPIONs was 72.55, compared to 55.72 for the bare SPIONs, making them a highly promising T2-contrast agent for future development of MRI applications. This study opens new avenues for the development of biocompatible and stable SPIONs for improved medical diagnostic and imaging.

Hemodialysis-Related Acute Brain Injury Demonstrated by Application of Intradialytic Magnetic Resonance Imaging and Spectroscopy.

Hemodialysis (HD) results in reduced brain blood flow, and HD-related circulatory stress and regional ischemia are associated with brain injury over time. However, studies to date have not provided definitive direct evidence of acute brain injury during a HD treatment session. Using intradialytic magnetic resonance imaging (MRI) and spectroscopy to examine HD-associated changes in brain structure and neurochemistry, the authors found that multiple white (WM) tracts had diffusion imaging changes characteristic of cytotoxic edema, a consequence of ischemic insult and a precursor to fixed structural WM injury. Spectroscopy showed decreases in prefrontal N -acetyl aspartate (NAA) and choline concentrations consistent with energy deficit and perfusion anomaly. This suggests that one HD session can cause brain injury and that studies of interventions that mitigate this treatment's effects on the brain are warranted. Hemodialysis (HD) treatment-related hemodynamic stress results in recurrent ischemic injury to organs such as the heart and brain. Short-term reduction in brain blood flow and long-term white matter changes have been reported, but the basis of HD-induced brain injury is neither well-recognized nor understood, although progressive cognitive impairment is common. We used neurocognitive assessments, intradialytic anatomical magnetic resonance imaging, diffusion tensor imaging, and proton magnetic resonance spectroscopy to examine the nature of acute HD-associated brain injury and associated changes in brain structure and neurochemistry relevant to ischemia. Data acquired before HD and during the last 60 minutes of HD (during maximal circulatory stress) were analyzed to assess the acute effects of HD on the brain. We studied 17 patients (mean age 63±13 years; 58.8% were male, 76.5% were White, 17.6% were Black, and 5.9% were of Indigenous ethnicity). We found intradialytic changes, including the development of multiple regions of white matter exhibiting increased fractional anisotropy with associated decreases in mean diffusivity and radial diffusivity-characteristic features of cytotoxic edema (with increase in global brain volumes). We also observed decreases in proton magnetic resonance spectroscopy-measured N -acetyl aspartate and choline concentrations during HD, indicative of regional ischemia. This study demonstrates for the first time that significant intradialytic changes in brain tissue volume, diffusion metrics, and brain metabolite concentrations consistent with ischemic injury occur in a single dialysis session. These findings raise the possibility that HD might have long-term neurological consequences. Further study is needed to establish an association between intradialytic magnetic resonance imaging findings of brain injury and cognitive impairment and to understand the chronic effects of HD-induced brain injury. NCT03342183 .

Engineered Graphene Quantum Dots as a Magnetic Resonance Signal Amplifier for Biomedical Imaging.

The application of magnetic resonance imaging (MRI) nano-contrast agents (nano-CAs) has increasingly attracted scholarly interest owing to their size, surface chemistry, and stability. Herein, a novel T1 nano-CA (Gd(DTPA)-GQDs) was successfully prepared through the functionalization of graphene quantum dots with poly(ethylene glycol) bis(amine) and their subsequent incorporation into Gd-DTPA. Remarkably, the resultant as-prepared nano-CA displayed an exceptionally high longitudinal proton relaxivity (r1) of 10.90 mM-1 s-1 (R2 = 0.998), which was significantly higher than that of commercial Gd-DTPA (4.18 mM-1 s-1, R2 = 0.996). The cytotoxicity studies indicated that the Gd(DTPA)-GQDs were not cytotoxic by themselves. The results of the hemolysis assay and the in vivo safety evaluation demonstrate the outstanding biocompatibility of Gd(DTPA)-GQDs. The in vivo MRI study provides evidence that Gd(DTPA)-GQDs exhibit exceptional performance as T1-CAs. This research constitutes a viable approach for the development of multiple potential nano-CAs with high-performance MR imaging capabilities.

Advanced MR Techniques for Preoperative Glioma Characterization: Part 1.

Preoperative clinical magnetic resonance imaging (MRI) protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of advanced MRI techniques in gliomas and their possible clinical translation or lack thereof. This review describes current methods, limits, and applications of advanced MRI for the preoperative assessment of glioma, summarizing the level of clinical validation of different techniques. In this first part, we discuss dynamic susceptibility contrast and dynamic contrast-enhanced MRI, arterial spin labeling, diffusion-weighted MRI, vessel imaging, and magnetic resonance fingerprinting. The second part of this review addresses magnetic resonance spectroscopy, chemical exchange saturation transfer, susceptibility-weighted imaging, MRI-PET, MR elastography, and MR-based radiomics applications. Evidence Level: 3 Technical Efficacy: Stage 2.

New 3D phase-unwrapping method based on voxel clustering and local polynomial modeling: application to quantitative susceptibility mapping.

To develop an accurate and robust 3-dimensional (3D) phase-unwrapping method that works effectively in the presence of severe noise, disconnected regions, rapid phase changes, and open-ended lines for quantitative susceptibility mapping (QSM). We developed a 3D phase-unwrapping method based on voxel clustering and local polynomial modeling named CLOSE3D, which firstly explores the 26-neighborhood to calculate local variation of the phasor and the phase, and then according to the local variation of the phasor, clusters the phase data into easy-to-unwrap blocks and difficult-to-unwrap residual voxels. Next, CLOSE3D sequentially performs intrablock, interblock, and residual-voxel unwrapping by using the region-growing local polynomial modeling method. CLOSED3D was evaluated in simulation and using in vivo brain QSM data, and was compared with classical region-growing and region-expanding labeling for unwrapping estimates methods. The simulation experiments showed that CLOSE3D achieved accurate phase-unwrapping results with a mean error ratio <0.39%, even in the presence of serious noise, disconnected regions, and rapid phase changes. The error ratios of region-growing (P=0.000 and P=0.000) and region-expanding labeling for unwrapping estimates (P=0.007, P=0.014) methods were both significantly higher than that of CLOSE3D, when the noise level was ≥60%. The results of the in vivo brain QSM experiments showed that CLOSE3D unwrapped the phase data and accurately reconstructed quantitative susceptibility data, even with serious noise, rapid-varying phase, or an open-ended cutline. CLOSE3D achieves phase unwrapping with high accuracy and robustness, which will help phase-related 3D magnetic resonance imaging (MRI) applications such as QSM and susceptibility weighted imaging.

What is new about cardiovascular magnetic resonance imaging: indications, contraindications, and clinical applications

Enrika Mačiulaitytė1, Gabija Balandytė1, Gabrielė Žebrauskaitė2 1Lithuanian University of Health Sciences, Faculty of Medicine, Kaunas, Lithuania 2Kaunas Hospital of Lithuanian University of Health Sciences, Department of Cardiology, Kaunas, Lithuania Abstract Background. Cardiovascular magnetic resonance imaging (MRI) plays an established role in the diagnosis and risk assessment of people with heart disease. The possibilities of using magnetic resonance imaging in clinical practice are expanding along with new MRI sequences, research methods, and the application of artificial intelligence. These technologies raise the possibility of getting a broader range of information, and better image quality to diagnose heart disease. Aim: to review indications, contraindications, and clinical applications of cardiovascular magnetic resonance imaging. Methodology. The literature used for this review was selected using “Pubmed” and “Web of science” databases. Following dates from 2013 to 2022, using keywords: “cardiac magnetic resonance imaging“, “heart diseases“, “magnetic resonance imaging”. Results. MRI is a secondary choice of research method in the diagnosis of heart diseases. Cardiovascular MRI is used to diagnose ischemic heart disease, myocarditis, non-ischemic cardiomyopathy, valvular pathology, pericardial, and congenital heart diseases, cardiac tumours, and aneurysm. Cardiac implanted electronic devices are no longer considered as absolute contraindication for performing MRI. During MRI, the absolute contraindication to using gadolinium contrast is acute kidney disease, glomerular filtration rate <30 ml/min/1.73m2 and pregnancy. Conclusions. Cardiovascular MRI plays an established role in the diagnosis of heart diseases. MRI provides useful information in assessment of myocardial perfusion, vitality, infiltrations, valvular and coronary vascular pathologies. Absolute contraindications to cardiovascular MRI include metal, electrical devices, or other implants that are not available for this examination. Keywords: cardiovascular magnetic resonance imaging; heart diseases; magnetic resonance imaging. Full article https://doi.org/10.53453/ms.2023.2.13