Magnetic Resonance Imaging Image Sequences Research Articles

Role of magnetic resonance imaging in differentiating intrasellar cavernous hemangioma and pituitary adenoma: A case report-Decipit frons prima multos.

Cavernous hemangioma represents a rare vascular malformation usually located in the cavernous sinus that could be exceptionally found purely in the intrasellar region. The clinical presentation of intrasellar cavernous hemangioma (ICH), frequently variable and unspecific, poses the patient at risk for misdiagnosis and the clinical consequences of suboptimal treatment. We present a case of ICH and describe the advanced magnetic resonance imaging (MRI) features that should direct toward the clinical suspicion of ICH. An illustrative case of a 61-year-old man complaining of recurrent headaches and diagnosed with a sellar and parasellar lesion was reported and used as a cue to discuss MRI imaging sequences that may aid in the distinction of ICH from pituitary adenoma and other skull base lesions. Heterogeneous enhancement followed by intense homogeneous enhancement at the dynamic contrast-enhanced sequences ("fill-in" phenomenon), absence of blooming signs at the gradient recalled echo (GRE) T2*-weighted and/or susceptibility-weighted imaging (SWI) MRI sequences, and elevated apparent diffusion coefficient (ADC) values usually characterize ICH instead of pituitary adenoma. Advanced MRI imaging plays an invaluable role in the pre-operative characterization of skull base lesions. Although rare, skull base surgeons should be aware of the ICH in the differential diagnosis process in case of the intrasellar lesion, and a tailored MRI examination should be performed to direct the patient toward the safest and optimal treatment.

THE UTILIZATION OF MAGNETIC RESONANCE IMAGING (MRI) TO DIFFERENTIATE VARIOUS GRADES OF BRAIN TUMOURS

Introduction: Intracranial cancers structure a critical medical problem. The yearly frequency of essential and auxiliary focal sensory system neoplasms goes from 10 to 17 for every 100,000 people. Imaging assumes a significant part in administration and guess of intracranial growths. Magnetic Resonance (MR) imaging has arisen as the imaging methodology most often used to assess intracranial growths and it keeps on having a consistently extending, complex job. On conventional MR imaging Grade I tumors show no edema and enhancement, Grade II tumors show minimal edema and enhancement, Grade III tumors show significant edema and heterogenous enhancement where as Grade IV tumors show significant edema, heterogenous enhancement, hemorrhage and necrosis.
 Aim: Utility of advanced magnetic resonance imaging sequences in order to differentiate different grades of brain tumors and to evaluate the advance Magnetic Resonance imaging (MRI) Protocol in Gradingbrain tumors both intra axial and extra axial.
 Results: The present study disclosed that for high grade gliomas Cho/Cr ratios with mean average values 5.13±1.9 ranging from 1.6 to 8.1 and median value of 5.3 and Cho/NAA ratios with mean average values 5.29 ± 1.84 ranging from 1.2 to 7.2 and median value is 6.05. Similarly Cho/Cr ratios for low grade tumors range with mean average values 1.85 ±0.58 ranging from 1.58 to 4.2 and median value of 1.61 and Cho/NAA ratios with mean average values 1.58±0.84 ranging from 1.1 to 5.1 and median value is 1.3. In the study 37 (92.5%) patients show supratentorial location of tumor and 3(7.5%) patients show infratentorial location of tumor.
 Conclusion: We concluded that by utility of advance MRI imaging can effectively differentiate different grades of brain tumors. The advance MRI imaging methods perfusion study, spectroscopy and Diffuse tensor imaging have significant correlation with degree of malignancy, thus predicting different grades of tumors compared to conventional MRI imaging alone, aiding in management and follow up. So utility of advance MRI imaging sequences in brain tumors imaging provides significant insight regarding malignancy of the tumor apart from conventional imaging. Being these are non invasive techniques and easily available helps in prognostication of patient and thus avoiding unnecessary stereotactic biopsies for low grade gliomas which require follow up. Utility of advance MRI imaging sequences in brain tumor for differentiating different grades of tumors forms an integral part of patient management.

Cardiac magnetic resonance image segmentation based on convolutional neural network

ObjectiveIn cardiac medical imaging, the extraction and segmentation of the part of interest is the key to the diagnosis of heart disease. Due to irregular diastole and contraction, magnetic resonance imaging (MRI) images have poorly defined boundaries, and traditional segmentation algorithms have poor performance. In this paper, a cardiac MRI segmentation technique using convolutional neural network and image saliency is suggested. MethodsThe convolutional neural network is used for detecting target area, filter out the ribs, muscles and the other parts of the anatomy where the contrast is not clearly defined. It can also be used to extract the region of interest (ROI), and compute the contrast of the ROI in order to improve clarity of the heart tissue within the ROI. The cardiac image diagnosis is performed using the obtained saliency image and compared with the segmentation result of the region growth algorithm. Finally, the images of 85 patients were used to train and test the algorithm model. Here, 46 patients were randomly selected for training, and the remaining 39 were harnessed for further tests. ResultsSegmentation accuracy of our algorithm model in ventricles, septum and the apex of the heart segment reaches 93.14%, 92.58% and 96.21% respectively, which are better than the segmentation method based on the regional growth technique. ConclusionsThe segmentation method using convolutional neural network and image saliency can meet the needs of automatic heart segmentation tasks based on cardiac MRI image sequences. The segmented image is able to assist the doctor to observe the patient's heart health more effectively. As such, our proposed technique has strong potential in clinical applications.

Dilated 3D Convolutional Neural Networks for Brain MRI Data Classification

Benefiting from the research of machine learning (ML) and deep learning(DL), multivariate methods based on ML and DL have been the mainstream and successful analysis methods in Neural Engineering or Neuroimaging research, for example, assisting diagnosis based on brain Magnetic Resonance Imaging (MRI). However, many existing methods based on traditional ML methods cannot sufficiently extract discriminative features, especially feature patterns across long-distance brain areas, resulting in unsatisfactory classification performance. Designing an effective and robust classifier for different MRI images remains a challenge. In this paper, we introduced dilated 3D CNN method for classifying 3D MRI images combining CNN structure and dilated convolution with a small number of feature maps. We also presented a methodology framework based on dilated 3D CNN method, which can classify both single MRI images and image sequences. Our method and framework were evaluated on the structural MRI images of ADHD-200 dataset and fMRI images of a Schizophrenia dataset, demonstrating better performances than some other state-of-the-art methods.

4D flow MRI to assess right ventricular outflow tract in patients undergoing transcatheter pulmonary valve replacement for severe pulmonary regurgitation

Magnetic resonance imaging (MRI) is essential to assess right ventricular enlargement and function as well as the severity of pulmonary regurgitation before pulmonary valve replacement. Transcatheter pulmonary valve replacement (TPVR) has emerged as an alternative to surgical replacement but limited by the size of the right ventricular outflow tract (RVOT). The aim of the study was to compare the measurements of native RVOT between different MRI imaging sequences and catheterization findings. Single-center retrospective study of patients who underwent TPVI for severe pulmonary regurgitation assessed by 4D flow MRI, without significant residual RVOT obstruction. Balloon calibration was used as the reference. Nine adult with repaired tetralogy of Fallot and one patient with Ross procedure were included (mean age: 43.9 ± 12.9). One patient had a too large RVOT to achieve a TPVR, 1 patient had TPVR with Edwards XT 29 mm valve, 7 patients had Melody valve implantation procedure. Mean RVOT measured 25.1 ± 3.0 mm by balloon calibration, 25.0 ± 3.2 mm and 22.5 ± 3.0 mm 4D flow MRI, in respectively in systole and diastole, 22.7 ± 2.4 mm in 2D SSFP MRI. Interventional angiograms dimensions were 26.7 ± 3.3 mm and 24.4 ± 1.3 mm respectively in systole and diastole. No statistical differences were found comparing different measurements dimensions. Good correlation was found between balloon calibration and MRI 4D flow in systole ( r = 0.794, P = 0.01), while no correlation existed between balloon calibration, 2D MRI and 4D flow MRI in diastole. Balloon calibration was also well correlated to angiograms measurements ( r = 0.881; P = 0.0002 and r = 0.775; P = 0.024, respectively). 4D flow MRI sequence allowing 3D measurement through the cardiac cycle, appears as an interesting technique to measure RVOT in daily practice and therefore to plan TPVI.

An intelligent skull stripping algorithm for MRI image sequences using mathematical morphology

Brain tumor is a dreadful disease which occurs when abnormal cells form uncontrollably. The modality adopted to detect abnormalities is Magnetic Resonance Imaging (MRI). MRI brain images contain nonbrain tissues. One of the important preprocessing steps is the whole brain segmentation, the process of skull stripping which isolates brain tissue and non-brain tissue. Segmentation is tedious and consumes more time only well experienced radiologist or a clinical expert can perform it with best accuracy. In order to overcome these limitations, computer aided medical diagnosis is essential. In this work, an intelligent and a robust skull stripping algorithm using mathematical morphology suited for all types of MR sequences is proposed. The method was validated on the international database collected from whole brain Atlas. The performance was evaluated using the metrics Jaccard Similarity Coefficient (JSC), Dice Similarity Coefficient (DSC), False Positive Rate (FPR), False Negative Rate (FNR), sensitivity, specificity and accuracy. An average of 97.25% indicates better overlap between proposed skull stripping and manual stripping by radiologists as a gold standard. The simulation results proved high accuracy in comparison to the ground truth results which is evident from the similarity coefficient metrics.

An MRI-Compatible Robotic System With Hybrid Tracking for MRI-Guided Prostate Intervention

This paper reports the development, evaluation, and first clinical trials of the access to the prostate tissue (APT) II system-a scanner independent system for magnetic resonance imaging (MRI)-guided transrectal prostate interventions. The system utilizes novel manipulator mechanics employing a steerable needle channel and a novel six degree-of-freedom hybrid tracking method, comprising passive fiducial tracking for initial registration and subsequent incremental motion measurements. Targeting accuracy of the system in prostate phantom experiments and two clinical human-subject procedures is shown to compare favorably with existing systems using passive and active tracking methods. The portable design of the APT II system, using only standard MRI image sequences and minimal custom scanner interfacing, allows the system to be easily used on different MRI scanners.

Automatic Segmentation of the Skull in MRI Sequences Using Level Set Method

Magnetic Resonance Imaging (MRI) has been widely used in clinical diagnose. Segmentation of these images obtained by MRI is a necessary procedure in medical image processing. In this paper, an improved level set algorithm was proposed to optimize the segmentation of MRI image sequences based on article [1]. Firstly, we add an area term and the edge indicator function to the total energy function for single image segmentation. Secondly, we presented a new method which uses the circumscribed polygon of the previous segmentation result as the initial contour of the next image to achieve automatic segmentation of image sequences. The algorithm was tested on MRI image sequences provided by Chuiyanliu Hospital, Chaoyang District of Beijing; the results have indicated that the proposed algorithm can effectively enhance the segmentation speed and quality of MRI sequences.

Automatic tracing and segmentation of rat mammary fat pads in MRI image sequences based on cartoon-texture model

The growth patterns of mammary fat pads and glandular tissues inside the fat pads may be related with the risk factors of breast cancer. Quantitative measurements of this relationship are available after segmentation of mammary pads and glandular tissues. Rat fat pads may lose continuity along image sequences or adjoin similar intensity areas like epidermis and subcutaneous regions. A new approach for automatic tracing and segmentation of fat pads in magnetic resonance imaging (MRI) image sequences is presented, which does not require that the number of pads be constant or the spatial location of pads be adjacent among image slices. First, each image is decomposed into cartoon image and texture image based on cartoon-texture model. They will be used as smooth image and feature image for segmentation and for targeting pad seeds, respectively. Then, two-phase direct energy segmentation based on Chan-Vese active contour model is applied to partitioning the cartoon image into a set of regions, from which the pad boundary is traced iteratively from the pad seed. A tracing algorithm based on scanning order is proposed to accurately trace the pad boundary, which effectively removes the epidermis attached to the pad without any post processing as well as solves the problem of over-segmentation of some small holes inside the pad. The experimental results demonstrate the utility of this approach in accurate delineation of various numbers of mammary pads from several sets of MRI images.

The effect of macronutrients on gastric volume responses and gastric emptying in humans: a magnetic resonance imaging study

The effects of macronutrients on gastric volume changes, emptying, and gastrointestinal symptoms are incompletely understood. Three liquid meals of 500 ml (fat emulsion, 375 kcal; protein solution, 375 kcal; glucose solution, 400 kcal) were infused into the stomach of 12 healthy volunteers on three occasions. Studies were performed in seated body position using an open-configuration magnetic resonance imaging (MRI) system. MRI imaging sequences, assessing stomach and meal volumes, were performed prior to and at times t = 0, 3, 6, 9, 12, 15, 25, 35, 45, 60, 75, and 90 min after meal administration. Areas under the curve for the early emptying phase (0-15 and 0-45 min) were calculated, and characteristics of the volume curves were analyzed by a gastric emptying model. Gastrointestinal symptoms were assessed by a self-report scale. Initial (t = 0 min) and early postprandial gastric volumes were highest for glucose because of lower initial emptying. However, in the early emptying phase the characteristics of the volume curves for stomach and meal were uniform for all macronutrients. Perceptions of fullness and satiety were linearly associated with postprandial gastric volumes, but not with macronutrient composition. Isovolumic macronutrient meals modulate gastric volume response by initial meal emptying patterns. Macronutrient specific accommodation responses, as shown in barostat studies, are not reflected as gastric volume responses under noninvasive conditions.

The effects of 1.5T magnetic resonance imaging on early murine in-vitro embryo development.

Although no ionizing radiation is involved, patients undergoing magnetic resonance imaging (MRI) are exposed to powerful static magnetic fields, magnetic field gradients, and radio-frequency fields that may be potentially damaging. Our study aims to document the effect of MRI imaging sequences on early murine embryo development (two-cell to blastocyst stage) in vitro. Two-cell murine embryos were exposed to various lengths of MRI using pulse sequences employed in present day clinical imaging. Early murine embryo development was documented in vitro, and blastocyst development rates were computed for both the control and exposed groups. There were no significant differences detected in the rate of blastocyst formation between the control groups and the embryos exposed to MRI.