1 Correction of Artifacts in MR Image Analysis Jayaram K. Udupa Medical Image Processing Group Department of Radiology University of Pennsylvania Philadelphia, PA Udupa 2 CAVA CAVA: Computer-Aided Visualization and Analysis The science underlying computerized methods of image processing, analysis, and visualization to facilitate new therapeutic strategies, basic clinical research, education, and training. 3 CAD vs CAVA CAD: Computer-Aided Diagnosis The science underlying computerized methods for the diagnosis of diseases via images 4 Purpose of CAVA In:Multiple multimodality multidimensional images of an object system. Out:Qualitative/quantitative information about objects in the object system. Object system a collection of rigid, deformable, static, or dynamic, physical or conceptual objects. 5 Sources of Multidimensional Images 2D:digitized/digital radiographic images. 3D:tomographic images of a static object system CT, MRI, PET, SPECT, US, and a host of other emerging new modalities such as Optical Imaging, MR Elastography, Magnetic Source Imaging, MR Spectroscopy,... 4D:tomographic images of a dynamic object system such as heart, joint, lung. 5D:parametric tomographic images of a dynamic object system. 6 CAVA Operations Img Processing: for enhancing information about and defining object system. Visualization:for viewing and comprehending object system. Manipulation:for altering object system (virtual surgery). Analysis:for quantifying information about object system. 7 Terminology voxels: Cuboidal elements into which body region is digitized by the imaging device. Scene:Multidimensional (2D, 3D, 4D,) image of the body region; S = (C, f ) Scene domain: Rectangular array of voxels on which the scene is defined; C. Scene intensity:Values assigned to voxels; f (c). 8 The CAVA Process Object System Scenes Structure System Renditions Quantitative Information Img process Scan Manipulate Analyze Visualize 9 abc: scanner coordinate system xyz: scene coordinate system uvw: structure coordinate system rst: display coordinate system body coordinate system s structure voxel y scene domain z x w u v r a b t c pixel 10 CAVA Operations Img processing: Volume of interest Filtering Interpolation Registration Segmentation Visualization Manipulation Analysis 11 Scale in CAVA Scale represents level of detail of object information in scenes. Scale is needed to handle variable object size in different parts of the scene. Global scale: Process the scene at each of various fixed scales and then combine the results scale space approach. Local scale: At each voxel, define largest homogeneous region, and treat these as fundamental units in the scene. 12 Global Scale Not clear how to combine results from multiple scales. 13 Local Scale At any voxel v in a scene, b-scale: largest homogeneous ball centered at v. t-scale: largest homogeneous ellipsoid centered at v. g-scale: largest connected homogeneous region containing v. 14 Local Scale brain PD slice tensor scale ball scale generalized scale b-, t-, and g-scales can be employed for controlling CAVA operation parameters locally adaptively. 15 Filtering Scene Purpose: To suppress unwanted (non-object) information. To enhance wanted (object) information. Suppressive: Mainly for suppressing random noise. Enhancive: For enhancing edges, regions. For correcting background variation. For intensity scale standardization. 16 Suppressive Filtering Gaussian, Median Gaussian: f F (v) is a Gaussian weighted average of f(v) in a neighborhood of v. This neighborhood may be a b-, t-, or g-scale region of v. Median: f F (v) is the median of the intensities f(u) in a neighborhood of v. This neighborhood may be a b-, t-, or g-scale region of v. 17 Suppressive Filtering - Diffusion Intensity at v diffuses to neighboring voxels iteratively, except at boundary interfaces, where diffusion is reduced considerably or halted. This modification of diffusion is controlled by the size, shape, and orientation of scale region. 18 Suppressive Filtering - Diffusion Conductance t (c, d) controlling flow V t from voxel c to voxel d at the t-th iteration is: s :large in the deep interior of large scale regions, large along boundaries, small near boundaries in orthogonal direction. 19 Suppressive Filtering - Diffusion The iterative process is defined as follows: A - constant (depends on adjacency) D(c, d) - unit vector from c to d. N c - neighborhood of c. 20 Suppressive Filtering - Diffusion: Examples original b-diffusion regular-diffusion g-diffusion 21 Suppressive Filtering - Diffusion: Examples originalROIregular diffusionb-diffusion original regular diffusion b-diffusiont-diffusion 22 FOC curve (200 iterations) gBDgBD bD NCD # iterations 23 Enhancive Filtering Enhancing edges: Edge detection. Enhancing regions: Histogram equalization. Intensity scale For MRI to make sure that intensity standardization: values have the same tissue specific meaning. Inhomogeneity For correcting background intensity correction: variation. 24 Enhancive Filtering: Intensity Standardization Problem: MRI intensities do not have a fixed meaning, even for the same protocol, body region, patient, scanner. Poses problems for image operations (segmentation). Simple linear scaling does not help. 25 Enhancive Filtering: Intensity Standardization Histograms of WM regions in 10 PD-weighted MRI scenes: Shown separately (left); and combined into one distribution (right). Before standardization After standardization 26 Enhancive Filtering: Intensity Standardization Approach consists of: Training Transformation Training: (1) Identify tissue specific landmarks LM 1,., LM n on each of a set of images. (2) Choose a standard scale, say [0, 4000]. (3) Map LM 1,., LM n from each input image on to standard scale. (4) Find average location on standard scale for each landmark. 27 Enhancive Filtering: Intensity Standardization image scale standard scale 28 Transformation: (1)Identify landmarks in image scale. (2)Map them to standard scale and determine transformation. (3)Map all intensities in image scale as per this transformation to standard scale. std scale input image scale Enhancive Filtering: Intensity Standardization 29 Enhancive Filtering: Intensity Standardization Choosing landmarks on intensity scale: (1)On image histogram median, mode, quartiles, deciles, (2) Using local scales largest b-scale or g-scale (3) Interactively paint regions corresponding to different tissues where mean intensities are used as LM i. 30 Enhancive Filtering: Intensity Standardization Histograms of WM regions in 10 PD-weighted MRI scenes: Shown separately (left); and combined into one distribution (right). Before standardization After standardization 31 Enhancive Filtering: Intensity Standardization Before After Data set 1 Data set 2 Data set 3 PD- weighted brain MRI scenes of three subjects 32 Original PD scenes with WM highlighted for fixed intensity range Standardized PD scenes with WM highlighted for fixed intensity range MTR scenes with WM highlighted for fixed intensity range Enhancive Filtering: Intensity Standardization 33 Enhancive Filtering: Intensity Standardization Before After 34 Enhancive Filtering: Intensity Standardization Data from Different Hospitals Before After 35 Mixed Training Enhancive Filtering: Intensity Standardization Mixed Training Before After 36 Enhancive Filtering: Intensity Standardization (1) 20 Patient scene data sets (2) Segment WM, GM, CSF (3) Determine % CV of mean intensity in tissue regions across patients. Scanner dependent inter-patient variations are considerably reduced after standardization. 37 Enhancive Filtering: Intensity Non Uniformity Correction Imperfections in the RF field cause background variations in MR images. Poses challenges in image segmentation and analysis. Problem: OriginalN3 (Sled et al.)SBC 38 Enhancive Filtering: Intensity Non Uniformity Correction Goal: To develop a general method for correcting the variations that fulfills: (R1) no need for user help per scene (R2) no need for accurate prior segmentation (R3) no need for prior knowledge of tissue intensity distribution A standardization Based Correction (SBC) method is described. 39 Non Uniformity Correction SBC Method Step 0: Set C c = C, the given scene. Step 1: Standardize C c to the standard intensity gray scale for the particular imaging protocol and body region under consideration and output scene C s ; Step 2: determine tissue regions C B1, C B2,..., C Bm by using fixed threshold intervals on C s ; Step 3: if C Bi determined in the previous iteration are insignificantly (