Magnetic Resonance Imaging Image Quality Research Articles

Brain tumor detection using a MobileNetV2-SSD model with modified feature pyramid network levels

Brain tumors, a subset of these malignancies, demand accurate and efficient diagnosis. Traditional methods use non-invasive medical imaging like magnetic resonance imaging (MRI) and computed tomography (CT). Although necessary for diagnosis, manual brain MRI picture segmentation is tedious and time-consuming. Using deep learning is a promising solution. This study proposes an innovative approach for brain tumor detection, focusing on meningioma tumors. Utilizing threshold-based segmentation, the MobileNetV2 architecture, a modified feature pyramid network (FPN), and single shot MultiBox detector (SSD), our model achieves precise localization and object detection. Pre-processing techniques such as grayscale conversion, histogram equalization, and Gaussian filtering enhance the MRI image quality. Morphological operations and thresholding facilitate tumor segmentation. Data augmentation and a meticulous dataset division aid in model generalization. The architecture combines MobileNetV2 as a feature extractor, SSD for object detection, and FPN for detecting small objects. Modifications, including lowering the minimum FPN level, enhance small object detection accuracy. The proposed model achieved a recall value of around 98% and a precision value of around 89%. Additionally, the proposed model achieved approximately 93% on both the dice similarity coefficient (DSC) value and the index of similarity. Based on the promising results, our research holds significant advancements for the field of medical imaging and tumor detection.

RAD-IQMRI: A benchmark for MRI image quality assessment

Magnetic resonance imaging (MRI) is susceptible to visual artifacts that can degrade the perceptual image quality, potentially leading to inaccurate or inefficient diagnoses in clinical practice. It is critical to evaluate the perceptual image quality and build this technique into clinical solutions. In a previous study, an MRI database was created for image quality assessment (IQA), where various types of MRI artifacts with different degrees of degradation were simulated. Application specialists assessed the image quality; however, radiologists’ perception of MRI image quality remains unknown. To make IQA clinically relevant, in this paper we conduct a new subjective experiment where 13 radiologists rated the quality of images contained in the MRI database. Based on this subjective IQA benchmark named RAD-IQMRI, we evaluate the performance of state-of-the-art objective IQA models, providing insights into their application for MRI image quality assessment in clinical settings.

Impact of prostate MRI image quality on diagnostic performance for clinically significant prostate cancer (csPCa).

With the widespread clinical application of prostate magnetic resonance imaging (MRI), there has been an increasing demand for lesion detection and accurate diagnosis in prostate MR, which relies heavily on satisfactory image quality. Focusing on the primary sequences involved in Prostate Imaging Reporting and Data System (PI-RADS), this study have evaluated common quality issues in clinical practice (such as signal-to-noise ratio (SNR), artifacts, boundaries, and enhancement). The aim of the study was to determine the impact of image quality on clinically significant prostate cancer (csPCa) detection, positive predictive value (PPV) and radiologist's diagnosis in different sequences and prostate zones. This retrospective study included 306 patients who underwent prostate MRI with definitive pathological reports from February 2021 to December 2022. All histopathological specimens were evaluated according to the recommendations of the International Society of Urological Pathology (ISUP). An ISUP Grade Group ≥ 2 was considered as csPCa. Three radiologists from different centers respectively performed a binary classification assessment of image quality in the following ten aspects: (1) T2WI in the axial plane: SNR, prostate boundary conditions, the presence of artifacts; (2) T2WI in the sagittal or coronal plane: prostate boundary conditions; (3) DWI: SNR, delineation between the peripheral and transition zone, the presence of artifacts, the matching of DWI and T2WI images; (4) DCE: the evaluation of obturator artery enhancement, the evaluation of dynamic contrast enhancement. Fleiss' Kappa was used to determine the inter-reader agreement. Wilson's 95% confidence interval (95% CI) was used to calculate PPV. Chi-square test was used to calculate statistical significance. A p-value < 0.05 was considered statistically significant. High-quality images had a higher csPCa detection rate (56.5% to 64.3%) in axial T2WI, DWI, and DCE, with significant statistical differences in SNR in axial T2WI (p 0.002), the presence of artifacts in axial T2WI (p 0.044), the presence of artifacts in DWI (p < 0.001), and the matching of DWI and T2WI images (p < 0.001). High-quality images had a higher PPV (72.5% to 78.8%) and showed significant statistical significance in axial T2WI, DWI, and DCE. Additionally, we found that PI-RADS 3 (24.0% to 52.9%) contained more low-quality images compared to PI-RADS 4-5 (20.6% to 39.3%), with significant statistical differences in the prostate boundary conditions in axial T2WI (p 0.048) and the presence of artifacts in DWI (p 0.001). Regarding the relationship between csPCa detection and image quality in different prostate zones, this study found that significant statistical differences were only observed between high- (63.5% to 75.7%) and low-quality (30.0% to 50.0%) images in the peripheral zone (PZ). Prostate MRI quality may have an impact on the diagnostic performance. The poorer image quality is associated with lower csPCa detection rates and PPV, which can lead to an increase in radiologist's ambiguous diagnosis (PI-RADS 3), especially for the lesions located at PZ.

Inter-scanner super-resolution of 3D cine MRI using a transfer-learning network for MRgRT

Objective. Deep-learning networks for super-resolution (SR) reconstruction enhance the spatial-resolution of 3D magnetic resonance imaging (MRI) for MR-guided radiotherapy (MRgRT). However, variations between MRI scanners and patients impact the quality of SR for real-time 3D low-resolution (LR) cine MRI. In this study, we present a personalized super-resolution (psSR) network that incorporates transfer-learning to overcome the challenges in inter-scanner SR of 3D cine MRI. Approach: Development of the proposed psSR network comprises two-stages: (1) a cohort-specific SR (csSR) network using clinical patient datasets, and (2) a psSR network using transfer-learning to target datasets. The csSR network was developed by training on breath-hold and respiratory-gated high-resolution (HR) 3D MRIs and their k-space down-sampled LR MRIs from 53 thoracoabdominal patients scanned at 1.5 T. The psSR network was developed through transfer-learning to retrain the csSR network using a single breath-hold HR MRI and a corresponding 3D cine MRI from 5 healthy volunteers scanned at 0.55 T. Image quality was evaluated using the peak-signal-noise-ratio (PSNR) and the structure-similarity-index-measure (SSIM). The clinical feasibility was assessed by liver contouring on the psSR MRI using an auto-segmentation network and quantified using the dice-similarity-coefficient (DSC). Results. Mean PSNR and SSIM values of psSR MRIs were increased by 57.2% (13.8–21.7) and 94.7% (0.38–0.74) compared to cine MRIs, with the reference 0.55 T breath-hold HR MRI. In the contour evaluation, DSC was increased by 15% (0.79–0.91). Average time consumed for transfer-learning was 90 s, psSR was 4.51 ms per volume, and auto-segmentation was 210 ms, respectively. Significance. The proposed psSR reconstruction substantially increased image and segmentation quality of cine MRI in an average of 215 ms across the scanners and patients with less than 2 min of prerequisite transfer-learning. This approach would be effective in overcoming cohort- and scanner-dependency of deep-learning for MRgRT.

Improved Hybrid K-Nearest Neighbours Techniques in Segmentation of Low-Grade Tumor and Cerebrospinal Fluid

In image segmentation, identifying information or object detection in medical im-ages is crucial, particularly information that is harder to spot in magnetic resonance imaging (MRI) of low-grade tumors or cerebrospinal fluid (CSF). To address the aforementioned problems associated with missing data in MRI images and the low quality of MRI images that required longer processing times, this research is to seg-ment brain tumors or detect CSF in four-dimensional MRI images. A new hybrid k-nearest neighbors (k-NN) framework is also proposed, which consists of three tech-niques: correlation matrices of discrete Fourier transform (CM-DFT), Laplace Eigen maps of locally preserving projection (LELPP), and a hybrid GrabCut hidden Markov model of k-mean clustering (GCHMkC). The combination of the Hidden Markov Model (HMM) and the k-mean clustering technique is known as the Hidden Markov Model of the k-mean clustering method (HMMkC). To begin with, the Graph Cut and Support Vector Machine (GCSVM) and the GCHMkC approach are combined. The method increased the quality of the images suggested by the methodology, achieving an accuracy of 99.83%, a sensitivity of 99.99%, a specificity of 99.8%, and a computa-tional execution time of 14.9 seconds. Second, a technique called CM-DFT is sug-gested to improve MRI images while resolving the issue of missing imputation data. The accuracy of the MRI image datasets was improved to 99.84%, the time-lag in the hybrid k-NN algorithm was reduced to 99%, the missing data ratio was reduced to 0.9%, 10%, and 12%, and the correctness of the imputed data was improved to 1.533 seconds with computational execution. Thirdly, the nonlinear data is reduced and unnecessary features are eliminated using the Laplace Eigen maps of locally pre-serving projection (LELPP) approach. The hybrid k-NN algorithm used by the tech-nique yields results with 99% accuracy and an execution duration of 2.42 seconds.

Effect of Breath Training on Image Quality of Chest Magnetic Resonance Free-breathing Sequence.

Magnetic Resonance Imaging (MRI) plays a role in demonstrating substantial utility in lung lesion imaging, detection, diagnosis, and evaluation. Previous studies have found that free-breathing star VIBE sequences not only have high image quality but also have a high ability to detect and display nodules. However, in our routine clinical practice, we have encountered suboptimal image quality in the free-breathing sequences of certain patients. This study aims to assess the impact of breath training on the quality of chest magnetic resonance imaging obtained during free-breathing sequences. A total of 68 patients with lung lesions, such as nodules or masses detected via Computed Tomography (CT) examination, were prospectively gathered. They were then randomly divided into two groups: an observation group and a control group. Standard preparation was performed for all patients in both groups before the examination. The observation group underwent 30 minutes of breath training prior to the MRI examination additionally, followed by the acquisition of MRI free-breathing sequence images. The signal intensity (SI) and standard deviation (SD) of the lesion and adjacent normal lung tissue were measured, and the image signal-to-noise ratio (SNR) and contrast signal-to-noise ratio (CNR) of the lesion were calculated for objective image quality evaluation. The subjective image quality of the two groups of images was also evaluated using a 5-point method. MRI examinations were completed in both groups. Significantly better subjective image quality (edge and internal structure clarity, vascular clarity, breathing and cardiac artifacts, and overall image quality) was achieved in the observation group compared to the control group (P<0.05). In addition, higher SNR and CNR values for disease lesions were observed in the observation group compared to the control group (t=4.35, P<0.05; t=5.35, P<0.05). It is concluded that the image quality of free-breathing sequences MRI can be improved through breath training before examination.

Positive Effect of Super-Resolved Structural Magnetic Resonance Imaging for Mild Cognitive Impairment Detection.

This paper presents a novel approach to improving the detection of mild cognitive impairment (MCI) through the use of super-resolved structural magnetic resonance imaging (MRI) and optimized deep learning models. The study introduces enhancements to the perceptual quality of super-resolved 2D structural MRI images using advanced loss functions, modifications to the upscaler part of the generator, and experiments with various discriminators within a generative adversarial training setting. It empirically demonstrates the effectiveness of super-resolution in the MCI detection task, showcasing performance improvements across different state-of-the-art classification models. The paper also addresses the challenge of accurately capturing perceptual image quality, particularly when images contain checkerboard artifacts, and proposes a methodology that incorporates hyperparameter optimization through a Pareto optimal Markov blanket (POMB). This approach systematically explores the hyperparameter space, focusing on reducing overfitting and enhancing model generalizability. The research findings contribute to the field by demonstrating that super-resolution can significantly improve the quality of MRI images for MCI detection, highlighting the importance of choosing an adequate discriminator and the potential of super-resolution as a preprocessing step to boost classification model performance.

Artifacts Caused by Orthodontic Appliances on Magnetic Resonance Imaging: Awareness and Knowledge Level of Maxillofacial Radiologists and Orthodontists

Objective: Artifacts caused by orthodontic appliances on Magnetic Resonance Imaging (MRI) can affect the image quality and make diagnosis difficult. The debate is ongoing on whether orthodontic appliances should be removed to eliminate these problems. This study aimed to evaluate the awareness and knowledge level of dentists working in maxillofacial radiology and orthodontics about this subject through a questionnaire.&#x0D; Methods: The questionnaire consisted of 20 items and four parts: A) Personal information, B) Awareness level about the artifacts and complications caused by orthodontic appliances on MRI, C) Knowledge level about the factors related to artifacts and complications caused by orthodontic appliances on MRI, and D) Preventive approaches. The questionnaire was prepared on Google Forms and sent to potential participants via e-mail. The Chi-square (χ2) test was used for the statistical analysis of the variables. &#x0D; Results: Most participants were aware of the artifacts caused by orthodontic appliances on MRI (90.8%) (93.5% of maxillofacial radiologists and 88.5% of orthodontists) and thought that material type influenced the artifact formation (98.1%) (100% of maxillofacial radiologists and 96.3% of orthodontists). The percentage of participants with 1-5 years of experience who were aware of artifacts was less than those with more experience (p = .033). The percentage of orthodontists who referred patients for orthodontic appliance removal was higher than maxillofacial radiologists who requested the orthodontic appliance removal (93.5%&gt;15%).&#x0D; Conclusion: Simultaneously increasing demands for both orthodontic treatment and MRI in the society cause concerns about the MRI image quality due to artifacts on images of the head and neck region. The main output of this study is that dentists working in orthodontics and maxillofacial radiology have a high awareness and knowledge about the artifacts and complications caused by orthodontic appliances on MRI.

To Assess the Factors Effecting Image Quality in Magnetic Resonance Imaging at 1.5T

Background: Good image quality is essential for accurate diagnosis in magnetic resonance imaging (MRI) examinations. This study aimed to investigate how image quality can be improved by implementing improvement strategies on factors that effects the MRI parameters instead of relying solely on conventional techniques. Method: This research employs a Comparative study design to compare the image quality in routine brain Magnetic Resonance Imaging (MRI) scans at 1.5T before and after implementing image quality improvement strategies. The study involved 37 individuals participant’s data (20 females and 17 males) aged 15-70 years who underwent MRI scans. Participants with any systemic disease, acute trauma, pregnancy and participants with absolute contraindications were excluded from the study. Result: The study found that several factors can effects MRI image quality and can be improved by implementing improvement strategies on these factors by manual adjustments. By identifying factors affecting image quality and evaluating improvement strategies, we seek to optimize MRI protocols and enhance the diagnostic utility of routine brain imaging. For instance, increasing the number of Excitation (NEX) values improved image quality. By increasing the echo time (TE) value a 50% images shows improvement in image quality. The volume of shimming showed better image quality than the other two modes (difficult mode and auto mode). Decreasing the flip angle and voxel size also improved image quality Conclusion: In conclusion, image quality can be improved by implementing improvement strategies on factors that effects the MRI parameters in comparison with the conventional techniques.

Pseudo-Computed Tomography Generation from Noisy Magnetic Resonance Imaging with Deep Learning Algorithm

Purpose: Magnetic Resonance Imaging (MRI) applications offer superior soft tissue contrast compared with Computed Tomography (CT) for accurate radiotherapy planning although MRI images suffer from poor image quality and lack electron density for radiation dose calculation. The present study aims to use the Deep Learning (DL) approach to 1) enhance the quality of MRI images and 2) generate synthetic CT images using MRI images for more accurate radiotherapy planning.&#x0D; Materials and Methods: In this paper, the pix2pix Generative Adversarial Network was utilized to synthesize CT images from noisy MRI images of 20 arbitrarily patients with brain disease. The standard statistical measurements investigated the accuracy comparison of the modeled Hounsfield Unit (HU) value from MRI images and referenced CT of each patient. The famous quality metrics that were used to compare synthetic CTs and referenced CTs were the Mean Absolute eError (MAE), the structural similarity index (SSIM), and the Peak Signal-to-Noise Ratio (PSNR).&#x0D; Results: The higher quality measurements between the synthetic pseudo-CT and the referenced CT images as PSNR and SSIM should correlate with the lower MAE value. For the overall brain among blind test data, the measured peak signal-to-noise ratio, mean absolute error, and structural similarity index values were about 16.5, 28.13, and 93.46, respectively.&#x0D; Conclusion: The proposed method provides an acceptable level of statistical measurements computed on the Pseudo-CT and referenced CT, and it could be concluded that the p-CT can be implemented in radiotherapy treatment planning with acceptable accuracy.

Endograft position and endoleak detection after endovascular abdominal aortic repair with low-field tiltable MRI: a feasibility study

BackgroundAbdominal aortic endoleaks after endovascular aneurysm repair might be position-dependent, therefore undetectable using supine imaging. We aimed to determine the feasibility and benefit of using a low-field tiltable magnetic resonance imaging (MRI) scanner allowing to study patients who can be imaged in both supine and upright positions of endoleaks.MethodsTen EVAR patients suspected of endoleak based on ultrasound examination were prospectively included. MRI in upright and supine positions was compared with routine supine computed tomography angiography (CTA). Analysis was performed through (1) subjective image quality assessment by three observers, (2) landmark registration between MRI and CTA scans, (3) Euclidean distances between renal and endograft landmarks, and (4) evaluation of endoleak detection on MRI by a consensus panel. Statistical analysis was performed by one-way repeated measures analysis of variance.ResultsThe image quality of upright/supine MRI was inferior compared to CTA. Median differences in both renal and endograft landmarks were approximately 6–7 mm between upright and supine MRI and 5–6 mm between supine MRI and CTA. In the proximal sealing zone of the endograft, no differences were found among all three scan types (p = 0.264). Endoleak detection showed agreement between MRI and CTA in 50% of the cases, with potential added value in only one patient.ConclusionsThe benefit of low-field upright MRI for endoleak detection was limited. While MRI assessment was non-inferior to standard CTA in detecting endoleaks in selected cases, improved hardware and sequences are needed to explore the potential of upright MRI in patients with endoleaks.Relevance statementUpright low-field MRI has limited clinical value in detecting position-dependent endoleaks; improvements are required to fulfil its potential as a complementary modality in this clinical setting.Key points• Upright MRI shows potential for imaging endoleaks in aortic aneurysm patients in different positions.• The image quality of upright MRI is inferior to current techniques.• Upright MRI complements CTA, but lacks accurate deformation measurements for clinical use.• Advancements in hardware and imaging sequences are needed to fully utilise upright MRI capabilities.Graphical

Improving portable low-field MRI image quality through image-to-image translation using paired low- and high-field images

Low-field portable magnetic resonance imaging (MRI) scanners are more accessible, cost-effective, sustainable with lower carbon emissions than superconducting high-field MRI scanners. However, the images produced have relatively poor image quality, lower signal-to-noise ratio, and limited spatial resolution. This study develops and investigates an image-to-image translation deep learning model, LoHiResGAN, to enhance the quality of low-field (64mT) MRI scans and generate synthetic high-field (3T) MRI scans. We employed a paired dataset comprising T1- and T2-weighted MRI sequences from the 64mT and 3T and compared the performance of the LoHiResGAN model with other state-of-the-art models, including GANs, CycleGAN, U-Net, and cGAN. Our proposed method demonstrates superior performance in terms of image quality metrics, such as normalized root-mean-squared error, structural similarity index measure, peak signal-to-noise ratio, and perception-based image quality evaluator. Additionally, we evaluated the accuracy of brain morphometry measurements for 33 brain regions across the original 3T, 64mT, and synthetic 3T images. The results indicate that the synthetic 3T images created using our proposed LoHiResGAN model significantly improve the image quality of low-field MRI data compared to other methods (GANs, CycleGAN, U-Net, cGAN) and provide more consistent brain morphometry measurements across various brain regions in reference to 3T. Synthetic images generated by our method demonstrated high quality both quantitatively and qualitatively. However, additional research, involving diverse datasets and clinical validation, is necessary to fully understand its applicability for clinical diagnostics, especially in settings where high-field MRI scanners are less accessible.

Non-sedated fast spine magnetic resonance imaging in pediatric patients.

Traditional spine magnetic resonance imaging (MRI) protocols require sedation in young children and uncooperative patients. There is an increased interest in non-sedated pediatric MRI protocols to reduce risks associated with anesthetic agents and improve MRI access. To evaluate the image quality of pediatric non-sedated fast spine MRI. We retrospectively reviewed 69 pediatric non-sedated fast spine MRI exams performed in 57 patients. Two blinded readers provided image quality ratings for the evaluation of bones, cranio-cervical junction, cerebrospinal fluid (CSF) spaces, spinal cord, soft tissues, ligaments, and overall diagnostic quality on a 1-5 scale, and determined whether there was evidence of syringomyelia, abnormal conus medullaris position, or filum terminale abnormality. Mean patient age was 7.2years (age range ≤ 1-17). Indications included syringomyelia (n=25), spinal dysraphism (n=4), combination of both syringomyelia and spinal dysraphism (n=8), and other miscellaneous indications (n=32). The inter-observer agreement ranged between moderate and very good for each variable (Cohen's weighted kappa] range=0.45-0.69). The highest image quality ratings were given to CSF spaces (mean image quality=3.5/5 ± 0.8) and cranio-cervical junction evaluations (3.5/5 ± 0.9). Overall diagnostic quality was worst in the <5years group (P=0.006). Readers independently identified a cervical spinal cord syrinx in 6 cases, and 1mm spinal cord central canal dilation in one case. Readers agreed on the position of the conus medullaris in 92% of cases (23/25 cases). Non-sedated pediatric spine MRI can be an effective diagnostic test to evaluate for spine pathology, especially syringomyelia, Chiari malformation, and conus medullaris anatomy.

Panoramic Magnetic Resonance Imaging of the Breast With a Wearable Coil Vest.

Breast cancer, the most common malignant cancer in women worldwide, is typically diagnosed by x-ray mammography, which is an unpleasant procedure, has low sensitivity in women with dense breasts, and involves ionizing radiation. Breast magnetic resonance imaging (MRI) is the most sensitive imaging modality and works without ionizing radiation, but is currently constrained to the prone imaging position due to suboptimal hardware, therefore hampering the clinical workflow. The aim of this work is to improve image quality in breast MRI, to simplify the clinical workflow, shorten measurement time, and achieve consistency in breast shape with other procedures such as ultrasound, surgery, and radiation therapy. To this end, we propose "panoramic breast MRI"-an approach combining a wearable radiofrequency coil for 3 T breast MRI (the "BraCoil"), acquisition in the supine position, and a panoramic visualization of the images. We demonstrate the potential of panoramic breast MRI in a pilot study on 12 healthy volunteers and 1 patient, and compare it to the state of the art. With the BraCoil, we demonstrate up to 3-fold signal-to-noise ratio compared with clinical standard coils and acceleration factors up to 6 × 4. Panoramic visualization of supine breast images reduces the number of slices to be viewed by a factor of 2-4. Panoramic breast MRI allows for high-quality diagnostic imaging and facilitated correlation to other diagnostic and interventional procedures. The developed wearable radiofrequency coil in combination with dedicated image processing has the potential to improve patient comfort while enabling more time-efficient breast MRI compared with clinical coils.

Aluminum Thin Film Nanostructure Traces in Pediatric EEG Net for MRI and CT Artifact Reduction.

Magnetic resonance imaging (MRI) and continuous electroencephalogram (EEG) monitoring are essential in the clinical management of neonatal seizures. EEG electrodes, however, can significantly degrade the image quality of both MRI and CT due to substantial metallic artifacts and distortions. Thus, we developed a novel thin film trace EEG net ("NeoNet") for improved MRI and CT image quality without compromising the EEG signal quality. The aluminum thin film traces were fabricated with an ultra-high-aspect ratio (up to 17,000:1, with dimensions 30 nm × 50.8 cm × 100 µm), resulting in a low density for reducing CT artifacts and a low conductivity for reducing MRI artifacts. We also used numerical simulation to investigate the effects of EEG nets on the B1 transmit field distortion in 3 T MRI. Specifically, the simulations predicted a 65% and 138% B1 transmit field distortion higher for the commercially available copper-based EEG net ("CuNet", with and without current limiting resistors, respectively) than with NeoNet. Additionally, two board-certified neuroradiologists, blinded to the presence or absence of NeoNet, compared the image quality of MRI images obtained in an adult and two children with and without the NeoNet device and found no significant difference in the degree of artifact or image distortion. Additionally, the use of NeoNet did not cause either: (i) CT scan artifacts or (ii) impact the quality of EEG recording. Finally, MRI safety testing confirmed a maximum temperature rise associated with the NeoNet device in a child head-phantom to be 0.84 °C after 30 min of high-power scanning, which is within the acceptance criteria for the temperature for 1 h of normal operating mode scanning as per the FDA guidelines. Therefore, the proposed NeoNet device has the potential to allow for concurrent EEG acquisition and MRI or CT scanning without significant image artifacts, facilitating clinical care and EEG/fMRI pediatric research.

Effect of Laryngeal Mask Airway on Image Quality in Pediatric Patients Undergoing Brain Magnetic Resonance Imaging: A Randomized Controlled Trial.

Magnetic resonance imaging (MRI) is highly sensitive to motion, resulting in artifacts and lowering image quality. Laryngeal mask airway (LMA) provides numerous advantages over endotracheal tubes as it reduces laryngospasm, coughing, and the risk of postoperative desaturation. We aimed to compare LMA with oral airway for airway management during brain MRI in terms of reducing motion artifacts, which can improve image quality. This randomized, controlled, double-blind trial was carried out on 40 pediatrics aged 1 - 18 years, American Society of Anesthesiologists (ASA) physical status І and П undergoing brain MRI. Patients were randomized into two equal groups according to the airway method, the control (Guedel oral airway) group and the LMA group. A compatible anesthesia machine was used to provide O2 and sevoflurane 2% - 4%. The mean MRI image quality score was significantly higher in the LMA group than in the control group (26.10 ± 3.97 versus 18.60 ± 5.30, P < 0.001). Mean arterial blood pressure and heart rate were significantly lower in the LMA group than in the control group at all study times except at baseline and immediate post-extubation (P < 0.05). Cough was significantly lower in LMA than in the control group (15% vs. 50%, P = 0.040). Airway complications (sore throat, laryngeal spasm, and bronchospasm), nausea, and vomiting did not have a significantly different between the two groups. Compared to Guedel oral airway, using LMA for airway management in pediatrics undergoing MRI scans improved the image quality with less cough and better hemodynamics.

The Effect of Oral Laxatives on Rectal Distension and Image Quality in Magnetic Resonance Imaging of the Prostate.

Introduction Increasing rectal size is associated with increased artefacts on magnetic resonance imaging (MRI) of the prostate and has the potential to degrade image quality. The objective of this study was to analyse the effect of oral laxative medication on rectal distension and image quality in prostate MRI. Methods Eighty patients prospectively received either 15 mg of oral senna (laxative group) or no medication (control group). Patients underwent prostate MRI according to standard local protocol and seven rectal dimensions on axial and sagittal images were measured. A subjective assessment of rectal distension was also made using a five-point Likert scale.Finally, artefacts on diffusion-weighted sequences were assessed using a four-point Likert scale. Results There was a small reduction in rectal diameter on sagittal images in the laxative group compared to the control group, with mean diameters of 27.1 mm and 30.0 mm respectively, p=0.02. There was no significant difference in rectal measurements of anteroposterior diameter, transverse diameter, or rectal circumference on axial imaging.Subjective scoring also demonstrated no significant difference in diffusion-weighted imaging quality between the laxative group and control group, p=0.82. Conclusion Bowel preparation with the oral laxative, senna, provided only a marginal decrease in rectal distension on one measure and no reduction in artefacts on diffusion-weighted sequences. The findings of this study do not support the routine use of this medication in patients undergoing prostate MRI.

4D lung MRI with high-isotropic-resolution using half-spoke (UTE) and full-spoke 3D radial acquisition and temporal compressed sensing reconstruction

Objective. To develop a respiratory motion-resolved four-dimensional (4D) magnetic resonance imaging (MRI) technique with high-isotropic-resolution (1.1 mm) using 3D radial sampling, camera-based respiratory motion sensing, and temporal compressed sensing reconstruction for lung cancer imaging. Approach. Free-breathing half- and full-spoke 3D golden-angle radial acquisitions were performed on eight healthy volunteers and eight patients with lung tumors of varying size. A back-and-forth k-space ordering between consecutive interleaves of the 3D radial acquisition was performed to minimize eddy current-related artifacts. Data were sorted into respiratory motion states using camera-based motion navigation and 4D images were reconstructed using temporal compressed sensing to reduce scan time. Normalized sharpness indices of the diaphragm, apparent signal-to-noise ratio (aSNR) and contrast-to-noise ratio (CNR) of the lung tumor (patients only), liver, and aortic arch were compared between half- and full-spoke 4D MRI images to evaluate the impact of respiratory motion and image contrast on 4D MRI image quality. Respiration-induced changes in lung volumes and center of mass shifts were compared between half- and full-spoke 4D MRI measurements. In addition, the motion measurements from 4D MRI and the same-day 4D CT were presented in one of the lung tumor patients. Main results. Half-spoke 4D MRI provides better visualization of the lung parenchyma, while full-spoke 4D MRI presents sharper diaphragm images and higher aSNR and CNR in the lung tumor, liver, and aortic arch. Lung volume changes and center of mass shifts measured by half- and full-spoke 4D MRI were not statistically different. For the patient with 4D MRI and same-day 4D CT, lung volume changes and center of mass shifts were generally comparable. Significance. This work demonstrates the feasibility of a motion-resolved 4D MRI technique with high-isotropic-resolution using 3D radial acquisition, camera-based respiratory motion sensing, and temporal compressed sensing reconstruction for treatment planning and motion monitoring in radiotherapy of lung cancer.

Intelligent MRI Room Design Using Visible Light Communication with Range Augmentation

Radio waves and strong magnetic fields are used by Magnetic Resonance Imaging (MRI) scanners to detect tumours, wounds and visualize detailed images of the human body. Wi-Fi and other medical devices placed in the MRI procedure room produces RF noise in MRI Images. The RF noise is the result of electromagnetic emissions produced by Wi-Fi and other medical devices that interfere with the operation of the MRI scanner. Existing techniques for RF noise mitigation involve RF shielding techniques which induce eddy currents that affect the MRI image quality. RF shielding techniques are complex and lead to RF leakage. VLC (Visible light Communication) is an emerging and efficient technology to avoid RF interference near MRI scanners. Range augmentation with power conservation of the LED is a big challenge in existing VLC systems. The major objective of the proposed work is to develop an intelligent-MRI room design without RF interference using visible light communication and enhance the distance between VLC transmitter and VLC receiver. In this paper, it is proposed to implement VLC using On-Off keying modulation and enhance distance using large active area photodiodes with Automatic Gain Control Circuit (AGC) using software and hardware. The performance of the proposed intelligent MRI-VLC system is analyzed by calculating Bit Error Rate at an inclined distance of 50 cm away from line of sight of the LED. The Experimental results showed that the maximum distance achieved was 400 cm at Bit Error Rate (BER) of 1.5 × 10−5.

New Contrast Enhancement Method for Multiple Sclerosis Lesion Detection.

Multiple sclerosis (MS) is oneof the most serious neurological diseases. It is the most frequent reason of non-traumatic disability among young adults. MS is an autoimmune disease wherein the central nervous system wrongly destructs the myelin sheath surrounding and protecting axons of nerve cells of the brain and the spinal cord which results in presence of lesions called plaques. The damage of myelin sheath alters the normal transmission of nerve flow at the plaqueslevel, consequently, a loss of communication between the brain and other organs. The consequence of this poor transmission of nerve impulses is the occurrence of various neurological symptoms. MS lesions cause mobility, vision, cognitive, and memory disorders. Indeed, early detection of lesions provides an accurate MS diagnosis. Consequently, and with the adequate treatment, clinicians will be able to deal effectively with the disease and reduce the number of relapses. Therefore, the use of magnetic resonance imaging (MRI) is primordial which is proven as the relevant imaging tool for early diagnosis of MS patients. But, low contrast MRI images can hide important objects in the image such lesions. In this paper, we propose a new automated contrast enhancement (CE) method to ameliorate the low contrast of MRI images for a better enhancement of MS lesions. This step is very important as it helps radiologists in confirming their diagnosis. The developed algorithm called BDS is based on Brightness Preserving Dynamic Fuzzy Histogram Equalization (BPDFHE) and Singular Value Decomposition with Discrete Wavelet Transform (SVD-DWT) techniques. BDS is dedicated to improve the low quality of MRI images with preservation of the brightness level and the edge details from degradation and without added artifacts or noise. These features are essential in CE approaches for a better lesion recognition. A modified version of BDS called MBDS is also implemented in the second part of this paper wherein we have proposed a new method for computing the correction factor. Indeed, with the use of the new correction factor, the entropy has been increased and the contrast is greatly enhanced. MBDS is specially dedicated for very low contrast MRI images.The experimental results proved the effectiveness of developed methods in improving low contrast of MRI images with preservation of brightness level and edge information. Moreover, performances of both proposed BDS and MBDS algorithms exceeded conventional CE methods.