Image Enhancement and Edge Detection Techniques Applied to Renal Magnetic Resonance ImagingSara AlfordUniversity of Wisconsin - MadisonECE 533 ProjectDecember 12, 2003Image Processing TechniquesAppendix A: Matlab Code MRA ImagesAppendix B: Matlab Code for Edge DetectionImage Enhancement andEdge Detection TechniquesApplied to Renal MagneticResonance ImagingSara AlfordUniversity of Wisconsin - MadisonECE 533 ProjectDecember 12, 2003Problem Statement: To apply image enhancement techniques to magnetic resonance angiography(MRA) and blood oxygen level dependent (BOLD) magnetic resonance (MR) images in order to improvecontrast and aid in post processing.BackgroundResearch StudyIn radiology, a highly trained physician examines images of the human body in order to diagnose and treatpatients. The quality of these images should be at a high enough level so that they can easily performtheir dictation without much thought to the imaging techniques and formation process. My currentresearch uses a type of magnetic resonance imaging, blood oxygen level dependent (BOLD) to determinefunctional information about a specific organ of interest. A trained individual is needed to post processthese images. MRA images are used to determine the anatomy and perfusion of the kidney. Betterdefinition or contrast in the kidneys would be helpful in the diagnosis of ischemia. An image thatprovided guidelines for the placement of medulla through the location of edges between the medulla andcortex could also improve image processing. This would provide a check to ensure proper placement ofthe medulla.MRA is an imaging technique that captures the vasculature of the human body through the use of aGadolinium based contrast agent, Gd-DTPA [1]. A patient is injected with contrast during scanning, andimages are captured during the arterial phase. Arteries will appear bright on the image whereas otherstructures without the contrast will appear darker. These images can then be used to diagnose the variousvasculature diseases and conditions such as ischemia and stenosis. BOLD MR imaging is typically used to image the brain, but this project investigates its application to thekidneys. From BOLD MR imaging, functional information regarding the renal oxygenation is extractedthrough the calculation of R2* maps from a series of sixteen T2* weighted images. This could potentially2lead to a noninvasive method to diagnose the clinical problem of acute renal ischemia. The techniquewas validated by Prasad et al [2] and has been used in medical studies investigating the effects ofpharmacological agents [3] and water diuresis [4] on the kidney. Our current study’s objective is to assessthe potential of BOLD MR imaging to detect acute renal ischemia [5]. Image PhysiologyThe kidney is divided into three main regions: the cortex, medulla and collection system. This study wasconcerned with specifically determining oxygenation values for the cortex and medulla separately.Medulla and cortex, shown in Figure 1, differ in the location of the kidney. With T2* weighted MRimaging, the medulla will appear darker in intensity while cortex will appear white on the region. Thiscontrast has not been as good as hoped, and has led to more difficult placement of the medulla.Figure 1: Kidney Anatomy. Medullary pyramids are shown in the mid region of the kidney for acoronal slice. The collecting system is in the interior, and the cortex is the outer region surrounding thepyramids. (Image taken from Brenner and Rector’s The Kidney online edition [6]).Image AcquisitionFive medium sized swine were studied under a protocol approved by the University of WisconsinResearch Animal Resources Center. Artificial ventilation and general anesthesia were maintainedthroughout the study. Guided by x-ray fluoroscopy, a balloon catheter was placed in the renal artery.3Magnetic resonance (MR) imaging was performed on a 1.5 T whole-body scanner (Signa LX, GEMedical Systems, Milwaukee, WI) using a torso phased array or cardiac coil. Heart rate, respiration rate,and blood pressure were monitored throughout the study. A 3D-MRA confirmed anatomy and reperfusionto the kidney. A multi-gradient echo (mGRE) sequence was used to acquire T2* weighted images(TR/TE/Flip = 87ms/8.0-44.8ms/40). Three axial and three coronal slices (Figure 2) were prescribedper kidney with a FOV of 26 cm, matrix of 256x128, NEX of 1, and slice thickness of 10 mm.Breathing was suspended for a scan time of fifteen seconds per slice. Baseline and inflated ballooncatheter measurements were obtained. Figure 2: BOLD MR Images. Coronal (left) and axial (right) mGRE images were taken. Each setcontained 16 images.Image Post ProcessingAfter the acquisition of mGRE images, images are transferred to a SUN workstation to process. MRimages were 1024x1024 in size and 24-bit true color. A R2* map is calculated based on the change inintensity at each pixel (Figure 3). Correct placement on the anatomical structure is imperative formeaningful R2* values specific for the cortex and medulla. Based on the scanning parameters chosen,the cortex will appear bright on the image and is typically found near the outer rim of the kidney. Themedulla is harder to distinguish due to partial cortical volume averaging and a skewed cross-sectional4from a non-orthogonal slice acquisition. The medulla regions appear darker due to the physiologic natureof the medulla. This corresponds to a higher R2* [7-8]. Once regions of interest (ROIs) are determined,the R2* value is calculated by taking an average of the interior pixel’s R2* values. Images are then storedas jpeg files using Huffman sequential coding to compress the image. Figure 3: Corresponding R2* Maps calculated from coronal (left) and axial (right) mGRE images. Motivation for ProjectCurrently, the quality of the images analyzed is not perfect. Contrast in the MRA clearly shows the mainartery such as the aorta and its main branches, but renal arteries are not clearly defined. The imagetypically does not use the full range of pixel values, thereby limiting the contrast. An imaging methodsuch as histogram equalization would take advantage of these neglected pixel values and provide betterdefinition and
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