Tumor detection and isolation in MAGNETIC RESONANCE imaging (MRI) is a significant consideration, but when done manually by people, it is very time consuming and may not be accurate. Also, the appearance of the tumor tissue varies from patient to patient, and there are similarities between the tumor and the natural tissue of the brain. In this paper, we have tried to provide an automated method for diagnosing and displaying brain tumors in MRI IMAGES. IMAGES of patients with glioblastoma were used after applying pre-processing and removing areas that have no useful information (such as eyes, scalp, etc.). We used a bounding box algorithm, to create a projection for to determining the initial range of the tumor in the next step, an artificial bee colony algorithm, to determine an initial point of the tumor area and then the Grow cut algorithm for, the exact boundary of the tumor area. Our method is automatic and extensively independent of the operator. comparison between results of 12 patients in our method with other similar methods indicate a high accuracy of the proposed method (about 98%) in comparison s.

Background and Aims: Disc displacement is the most common temporomandibular joint disorder and MAGNETIC RESONANCE imaging (MRI) is the gold standard in its diagnosis. This disorder can lead to changes in signal intensity of MAGNETIC RESONANCE (MR). The purpose of this study was evaluation of correlation between relative signal intensity of MR IMAGES of retrodiscal tissue, superior and inferior head of lateral ptrygoid muscle with type of anterior disk displacement and condylar head flattening in patients with temporomandibular disorder (TMD).Materials and Methods: In this retrospective study, 31 MR IMAGES of patients who had anterior disc displacement were evaluated. After relative signal intensity measurement for retrodiscal tissue, superior and inferior head of lateral ptrygoid muscle, the correlation between relative signal intensity and type of anterior disc displacement was evaluated with repeated measure ANOVA test. In each of these 3 areas, t-test was used to compare the groups with and without condylar head flattening.Results: The correlation between relative signal intensity of MR IMAGES and type of anterior disc displacement in retrodiscal tissue, superior and inferior head of lateral ptrygoid muscle was not significant. There was also no statistically significant correlation between relative signal intensity of MR IMAGES and flattening of condylar head in retrodiscal tissue, superior and inferior head of lateral ptrygoid muscle (P>0.05).Conclusion: According to findings of this study, relative signal intensity of MR IMAGES in retrodiscal tissue, superior and inferior head of ptrygoid muscle is not a good predictor for type of anterior disc displacement and flattening of condylar head. It seems that this cannot be used as a diagnostic marker for TMD progression.

Purpose: MAGNETIC RESONANCE Imaging (MRI) can guide the surgical strategy to identify brain tumors and monitor treatment response. It is possible to use transcranial Ultrasound (US) for periodical follow-ups. Ultrasound waves pass through the delicate areas of the skull called acoustic windows. In this study, the efficiency of ultrasound imaging was performed to diagnose glioblastoma brain tumors and the results were compared with MR IMAGES. Materials and Methods: Male Wistar rats were anesthetized by intraperitoneal injection of Ketamine and Xylazine. A stereotaxic device was used to determine the injection coordinates. C6 GBM cell lines were injected into the brains of rats. After two weeks, the formation of a glioblastoma tumor was confirmed histopathologically. The brain of animals was imaged by B-mode ultrasound and MRI. The section with the largest tumor dimensions was selected and the dimensions of the skull and tumor were measured based on the pixel size of each of the imaging methods. Pearson coefficient of correlation and Limits Of Agreement (LOA) were calculated for comparisons of the skull and tumor dimensions. Results: The skull and the tumor dimensions showed a significant correlation between the B-mode ultrasound and the MRI measurements (R=0.99 and p<0.05). According to the Bland-Altman analysis, the mean difference was 0.31 mm (SD=0.20) for skull and tumor dimensions. The exact shape of the tumor is not completely clear in the ultrasound IMAGES, but it can be useful to detect the presence of the tumor and its approximate dimensions. Conclusion: In conclusion, a glioblastoma tumor was produced in the male Wistar rat. The tumor dimensions were properly assessed by B-mode ultrasound image processing and compared with MR imaging.

It is important to have an accurate and reliable brain tumor segmentation for cancer diagnosis and treatment planning. There are few unsupervised approaches for brain tumor segmentation. In this paper, a new unsupervised approach based on graph coloring for brain tumor segmentation is introduced. In this study, a graph coloring approach is used for brain tumor segmentation. For this aim, each pixel of brain image assumed as a node of graph and difference between brightness of a couple of pixels considered as edge. This method was applied on T1-enhanced MAGNETIC RESONANCE IMAGES of low-grade and high-grade patients. Since a rigid graph was needed for graph coloring, edges must be divided into existing or nonexisting edge using a threshold. The value of this threshold has affected the accuracy of image segmentation, so the choice of the optimal threshold was important. The optimal value for this threshold was 0. 42 of maximum value of difference of brightness between pixels that caused the 83. 62% of correlation accuracy. The results showed that graph coloring approach can be a reliable unsupervised approach for brain tumor segmentation. This approach, as an unsupervised approach, shows better accuracy in comparison with neural networks and neuro-fuzzy networks. However, as a limitation, the accuracy of this approach is dependent on the threshold of edges.

Background: The MAGNETIC RESONANCE imaging (MRI)-based radiotherapy planning method have been considered in recent years because of the advantages of MRI and the problems of planning with two IMAGES modality. The first step in MRI-based radiotherapy is to evaluate MAGNETIC RESONANCE (MR) IMAGES geometric distortion. Therefore, the present study aimed to evaluate system related geometric distortion by a new large field of view phantom. Materials and Methods: A homemade phantom with Perspex sheets and plastic pipes containing water was built for evaluating MR IMAGES distortion. The phantom size is 48×48×37 cm3 and includes 325 water pipes. The study evaluated four different protocols from a 60 cm bore MAGNETOM® Symphony Syngo 1. 5 T (Siemens). Results: It was found that the amount of distortion for all protocols is under 2 mm for the radial distances less than 10 cm (field of view (FOV) = 20 cm), but distortion increased in radial distances greater than 10 cm, and reached about 5 cm for radial distances greater than 25 cm. Conclusion: Geometric distortion of each MR scanner has been shown to be dependent on the radio frequency (RF) sequence pulse (Spin echo or Gradient echo) and image parameters (echo time (TE), repetition time (TR), and receiver band-width)). The geometric distortion could be ignored for the FOV<20 cm (for the head region), and must be evaluated and corrected for more FOVs before the MR only radiotherapy.

Background: Myocardial infarction remains a leading cause of morbidity and mortality among cardiac disease. Cardiac wall thickening in patients withmyocardial infarction is less than healthy individuals. Accurate measurement of cardiac wall fractional thickening and path-length of myocardium points in healthy data and patients with myocardial infarction can help physicians in diagnosing the affected area. Patients and Methods: Epi/Endocardium of all slices in end-diastole frame were segmented, then more than 150 points in each slice were selected to track by weighted normalized mutual information algorithm over all frames. Weighted normalized mutual information was computed between two three-dimensional masks sized 3 3 3, pixel that were located in end-diastole and subsequent frames centroid of the selected points. Finally, by computing the distance between endocardium and epicardium in each slice over all frames, cardiac wall thickness and fractional thickening was measured. Moreover, the path-length of each data point during cardiac period was calculated and sketched in bulls-eye format. Evaluation of the method was done by ten healthy and twenty patients with myocardial infarction. Results: Cardiac wall kinesis was evaluated by normalized path length, which was presented in standard 17-segment bull’ s-eye format. Wall thickness and fractional wall thickening for all slices over all frames were extracted in order to determine the infarct region. Infarct regions had minimal fractional thickening and normalized path length. All evaluations demonstrated hypo-kinesis in the damaged region. Conclusion: Evaluation of obtained results showed significant difference between local parameters of healthy and infarcted myocardium. In all patients, the process was able to precisely determine the affected region that was all well matched with clinical evidence.

Introduction: Quantification of the neonatal brain development has a significant role in understanding, prevention, diagnosis and treatment of nervous system diseases during infancy. Since brain development and the its corresponding morphological changes are very fast during the first days after birth, an age-related brain atlas representing fine anatomical features of a neonatal brain is deemed necessary.Method: We constructed two neonatal brain atlases for the age ranges of 39-40 and 41-42 weeks of gestation, using 16 T1-weighted MAGNETIC RESONANCE IMAGES (MRI) through an improved group-wise registration paradigm.Results: Neonatal IMAGES were normalized to the newly created and previously available neonatal atlases. The similarity between these atlases and normalized IMAGES were calculated via mutual information.The mean mutual information between normalized IMAGES and the new atlases using proposed algorithm was considered optimal.Conclusion: This result confirms the greater similarity between normalized IMAGES and the atlases created through group-wise registrations of features retrieved from MRI.

This report presents the "penumbra sign" of a Brodie's abscess in a 69-year-old male patient. The lesion was located in the proximal metaphysis of the left tibia. Histopathology confirmed the diagnosis of subacute osteomyelitis. The penumbra sign on MAGNETIC RESONANCE (MR) IMAGES is a helpful sign for the diagnosis of Brodie's abscess.