scholarly journals Novel Method for Evaluating Spatial Resolution of Magnetic Resonance Images

2021 ◽  
Author(s):  
tomokazu takeuchi ◽  
Norio Hayashi ◽  
Yuta Asai ◽  
Yuka Kayaoka ◽  
Kiichi Yoshida
Keyword(s):  
Magnetic Resonance ◽  
Spatial Resolution ◽  
Nickel Sulfate ◽  
Region Of Interest ◽  
Volume Effect ◽  
Magnetic Resonance Images ◽  
Periodic Pattern ◽  
International Electrotechnical Commission ◽  
Modulation Transfer ◽  
Edge Image

Abstract Purpose Recently, several methods for evaluating the spatial resolution of magnetic resonance imaging (MRI) have been reported. However, these methods are not simple and can only be used for specific devices. The International Electrotechnical Commission (IEC) 62464-1 recommends a method that uses a periodic array pattern to evaluate the spatial resolution of an MRI device. In this study, we develop a new method (the ladder method) and evaluate its measurement accuracy by adapting the IEC method to evaluate the spatial resolution. Methods First, the adaptation of the IEC method is analyzed by simulating the ladder method using a phantom with a periodic pattern, which is constructed using acrylic plates and a nickel sulfate aqueous solution. Subsequently, the ladder method is evaluated in terms of spatial resolution by dividing the standard deviation (SD) by the average signal in the region of interest (ROI) on the ladder phantom image. To evaluate the precision of the ladder method, it is compared with the modulation transfer function (MTF) using a magnitude image with the partial volume effect of the edge image. Results The simulation result shows that the evaluation of the spatial resolution using the ladder method is viable, in which a coefficient of correlation of 0.90 or higher is obtained for all evaluations using the ladder and MTF methods. ConclusionsThe ladder method can be assessed using only the signal mean value and SD in the ROI on the target image. Therefore, the ladder method is a promising method as a substitute for the MTF.

scholarly journals The effect of varying spectral resolution on the quality of high spectral and spatial resolution magnetic resonance images of the breast

2003 ◽  
Vol 18 (4) ◽  
pp. 442-448 ◽  
Author(s):  
Milica Medved ◽  
Weiliang Du ◽  
Marta A. Zamora ◽  
Xiaobing Fan ◽  
Olufunmilayo I. Olopade ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Spatial Resolution ◽  
Spectral Resolution ◽  
Magnetic Resonance Images

Computer Aided Interpretation of Fibrous Texture in Hepatic Magnetic Resonance Images

2013 ◽  
Vol 647 ◽  
pp. 325-330 ◽  
Author(s):  
Yu Fan Zeng ◽  
Xue Jun Zhang ◽  
Wen Yan ◽  
Li Ling Long ◽  
Yu Kun Huang ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Hepatic Fibrosis ◽  
Region Of Interest ◽  
Texture Features ◽  
Magnetic Resonance Images ◽  
Optimal Number ◽  
Support Vector ◽  
Mr Images ◽  
Accuracy Rate ◽  
Occurrence Matrix

The fibrous texture in liver is one of important signs for interpreting the chronic liver diseases in radiologists’ routines. In order to investigate the usefulness of various texture features calculated by computer algorithm on hepatic magnetic resonance (MR) images, 15 texture features were calculated from the gray level co-occurrence matrix (GLCM) within a region of interest (ROI) which was selected from the MR images with 6 stages of hepatic fibrosis. By different combination of 15 features as input vectors, the classifier had different performance in staging the hepatic fibrosis. Each combination of texture features was tested by Support Vector Machine (SVM) with leave one case out method. 173 patients’ MR images including 6 stages of hepatic fibrosis were scanned within recent two years. The result showed that optimal number of features was confirmed from 3 to 7 by investigating the classified accuracy rate between each stage/group. It is evident that angular second moment, entropy, sum average and sum entropy played the most significant role in classification.

Measurement of tumor resection volumes from computerized images

1992 ◽  
Vol 77 (1) ◽  
pp. 151-154 ◽  
Author(s):  
Duc H. Duong ◽  
Robert C. Rostomily ◽  
David R. Haynor ◽  
G. Evren Keles ◽  
Mitchel S. Berger
Keyword(s):  
Magnetic Resonance ◽  
Brain Tumors ◽  
Tumor Resection ◽  
Region Of Interest ◽  
Magnetic Resonance Images ◽  
Content Type ◽  
Slide Scanner ◽  
Program Error ◽  
Fine Print ◽  
Area And Volume

✓ The authors describe a method for quantitation of the area and volume of the resection cavity in patients who have undergone surgery for brain tumors. Using a slide scanner and Image 1.27, a public domain program for the Apple Macintosh II computer, computerized tomography scans and magnetic resonance images can be digitized and analyzed for a particular region of interest, such as the area and volume of tumor on preoperative and postresection scans. Phantom scans were used to analyze the accuracy of the program and the program users. User error was estimated at 2%, program error was 4.5%. This methodology is proposed as a means of retrospectively calculating the extent of tumor resection.

scholarly journals Semi-automated cardiac segmentation on cine magnetic resonance images using GVF-Snake deformable models

2009 ◽  
Author(s):  
Constantin Constantinides ◽  
Yasmina Chenoune ◽  
Nadjia Kachenoura ◽  
Elodie Roullot ◽  
Elie Mousseaux ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Ejection Fraction ◽  
Region Of Interest ◽  
Left Ventricular ◽  
Magnetic Resonance Images ◽  
Deformable Model ◽  
Papillary Muscles ◽  
Myocardial Mass ◽  
Endocardial Border ◽  
Cardiac Segmentation

The segmentation of left ventricular structures is necessary for the evaluation of the ejection fraction (EF) and the myocardial mass (LVM). A semi-automated 2D algorithm using connected filters and a deformable model allowing an accurate endocardial detection was proposed. The epicardial border was deduced using a deformable model restricted inside a region of interest defined from the endocardial border. Papillary muscles were detected using a fuzzy k-means algorithm. The method was applied to the challenge training and validation databases, consisting of 15 subjects each. The evaluation was performed using the tools provided by the challenge. For both datasets, results show a mean Dice metric of 0.89 for endocardial borders (0.92 for epicardial borders). Overall average perpendicular distance was 2.2 mm. Very good correlation was obtained for the EF and LVM parameters. Visual overall rating given by the challenge’s cardiologist was 1.2. Segmentation was robust and performed successfully on both datasets.

INTEGRATING EDGE DETECTION AND THRESHOLDING APPROACHES TO SEGMENTING FEMORA AND PATELLAE FROM MAGNETIC RESONANCE IMAGES

2005 ◽  
Vol 17 (01) ◽  
pp. 1-11 ◽  
Author(s):  
JIANN-SHU LEE ◽  
YI-NUNG CHUNG
Keyword(s):  
Magnetic Resonance ◽  
Edge Detection ◽  
Knee Pain ◽  
Pathological Condition ◽  
Region Of Interest ◽  
Magnetic Resonance Images ◽  
Initial Guess ◽  
Joint Analysis ◽  
Edge Based ◽  
The Moment

Anterior knee pain (AKP) is a common pathological condition. The most obvious problem causing knee pain is the abnormal patellar tracking mechanism. For computerized knee joint analysis, how to successfully segment the knee bones is an import issue. This paper presents a simple while effective algorithm for fully automatic femur and patella segmentation for magnetic resonance (MR) knee images through integrating edge detection and thresholding approaches. Based on consideration of computational complexity and accuracy, we develop a compound approach to segment the MR knee images. The moment preserving thresholding is first utilized to gather the bone-outline information employed to estimate the region of interest (ROI). An ROI based wavelet enhancement is proposed to restrict the contrast improvement only around the bone edges. The restriction makes both the adhesion separation of bone and surrounding tissues and the bone contour conservation become possible and avoid harsh thresholding resulting from the global based wavelet enhancement. Cooperating with the morphology operation, stable initial guess of the bone regions can be achieved. To overwhelm the main drawback of the existing edge based segmentation methods, i.e. the necessity of complicated post-processing, a new approach - FLoG is proposed to provide a feasible solution. It converts the edge detection results using LoG into a region-based format through the flow fill operation. The developed onion-growing algorithm can properly combine the initial guess of bone regions with the FLoG outputs in an efficient way. The experimental study shows our method is superior to the conventional ones in meeting the requirement of physicians. This is because our method can perform well in dealing with the tougher conditions, i.e. the partial volume and the soft tissue adhesion conditions. Because of the integration of the thresholding approach with the FLoG edge detector, our algorithm is even robust to unsatisfactory imaging conditions. Hence, our method lends itself to assisting the clinical diagnosis of knee functions.

scholarly journals Region of Interest Identification for Brain Tumors in Magnetic Resonance Images

2020 ◽  
Author(s):  
Fateme Mostafaie ◽  
Reihaneh Teimouri ◽  
Zahra Nabizadeh Shahre Babak ◽  
Nader Karimi ◽  
Shadrokh Samavi
Keyword(s):  
Magnetic Resonance ◽  
Brain Tumors ◽  
Region Of Interest ◽  
Magnetic Resonance Images
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