Basic Principles Of MR Image Analysis Terminology Of FMRI
Basic principles of MR image analysisBasic principles of MR image analysis Terminology of fMRI Brain extraction RegistrationJulien MillesLeiden University Medical Center Linear registration-linear registration NonNonNon-linearThanks to:Prof. Mooijaart, Dr. Rombouts and Dr. Crone (course organization)Dr. Grol (course organization and help with the slides)2Basic principles of MR image analysis Terminology of fMRI Brain extraction RegistrationTerminology of fMRIStructural (T1) images- high resolution- to distinguish different types of tissueFunctional (T2*) images:- lower spatial resolution- to relate changes in MR signal (BOLD)to an experimental manipulation Linear registration-linear registration NonNonNon-linear326/02/2008fMRI coursefMRI courseTime seriesA large number of imagesacquired in temporalorder at a specific rate26/02/20084fMRI coursenCodoi ti26/02/2008nAtnCodoi tinB
Terminology of fMRITerminology of fMRIsubjectsVolume:Field of View(FOV),e.g. 192x192 mmsessionsMatrix Sizee.g., 64 x 64single runvolumeslices5voxelfMRI courseTerminology of fMRI Brain extraction 3 mm3 mmIn-plane resolution192 mm / 64 3 mm26/02/2008Basic principles of MR image analysis Slice thicknesse.g., 3 mmAxial slicesruns63 mmVoxel Size(volumetric pixel)fMRI course26/02/2008Brain extraction: Why? Surrounding tissues and acquisition artefacts can hampersubsequent data processing Structural images present most type of tissues Functional image present little non-brain tissuenonnon-brainRegistration Linear registration-linear registration NonNonNon-linear High chance of mismatch between function and anatomy7fMRI course26/02/20088fMRI course26/02/2008
Mismatch between function and anatomyMismatch between function and anatomyJenkinson and Smith, Medical Image Analysis, 20019fMRI course26/02/2008Mismatch between function and anatomy11fMRI course26/02/200810fMRI course26/02/2008fMRI course26/02/2008Brain extraction: How?12
Brain extractionIn practice: Brain extraction using FSL Pre-process structural data (with BET)PrePre-process Process functional data (included in FEAT and MELODIC) Always double-check your results!doubledouble-check13fMRI course26/02/2008In practice: Brain extraction using FSL15fMRI course14fMRI course26/02/2008In practice: Brain extraction using FSL26/02/200816fMRI course26/02/2008
Basic principles of MR image analysis Terminology of fMRI Brain extraction Registration (re-alignement)(realignement)(re-alignement)Image registration: Why? Linear registration-linear registration NonNonNon-linear17fMRI course26/02/2008Image registration: Why?19fMRI course18fMRI course26/02/2008Registration uses26/02/2008 Single-subject studies:SingleSingle-subject Group studies: Compensate for movement of the subject during an acquisition-up studies Compensate for displacement of the head in followfollowfollow-up-subject anatomical differences Compensate interinterinter-subject20fMRI course26/02/2008
Registration uses Registration usesSingle-subject studies:SingleSingle-subject Compensate for movement of the subject during an acquisition-up studies Compensate for displacement of the head in followfollowfollow-up Single-subject studies:SingleSingle-subject Group studies: Compensate for movement of the subject during an acquisition-up studies Compensate for displacement of the head in followfollowfollow-upLinear registration Group studies:-subject anatomical differences Compensate interinterinter-subject-subject anatomical differences Compensate interinterinter-subjectNonNon-linear registration21fMRI course26/02/2008During an acquisition, prevention is the best medicine22fMRI course26/02/2008PostPost-acquisition motion correction: Linear registration Assumes that all movements are those of a rigid body, i.e. theshape of the brain does not change Registration optimises a number of parameters that describe atransformation between the source and a reference image How to measure the goodness of fit?fit? What transformation to use? Always constrain the subject’s head Instruct him/her to remain as calm as possible and to talk andswallow as little as possible Do not scan for too long – everybody moves after a while23fMRI course26/02/2008 Resampling consists in applying the estimated transformation24fMRI course26/02/2008
Registration: Evaluating goodness of fit3D linear transformations available in FSLDifferent measures can be used: Sum of squared differences: If the two images are aligned, their difference is (close to) zerozero Similar spatial representation of the anatomical structures Similar contrast needed Ex: MRI with T1 contrast vs MRI with T1 contrast Correlation: If the two images are aligned, their spatial variations are similarsimilar Similar spatial representation of the anatomical structure Ex: MRI with T1 contrast vs MRI with T2* contrast Mutual information: If the two images are aligned, their joint p.d.f. is well localizedlocalized No need for similar spatial representation or contrast Ex: MRI with PET/CT25fMRI course26/02/20083D linear transformations available in FSL Translation only (3 parameters) Rigid body (6 parameters) Global rescale (7 parameters) Traditional (9 parameters) Affine (12 parameters) Translation only (3 parameters) Rigid body (6 parameters) Global rescale (7 parameters) Traditional (9 parameters) Affine (12 parameters)26fMRI course26/02/2008Rigid bodyPossible object movement:TranslationsRotations (around the origin)In 2 dimensions, translations and rotations:x1 cos(θ) x0 sin(θ) y0 txy1 -sin(θ) x0 cos(θ) y0 tyIn 3 dimensions:Translations by tx, ty and tzRotations by θ, Φ and Ω6 parameters: tx, ty, tz and θ, Φ, ΩUsed for intraintra-subject registration27fMRI course26/02/200828fMRI course26/02/2008
AffineAffinePossible object movement:TranslationsRotations (around the origin)Scalings (zooms)ShearsPossible object movement:TranslationsRotations (around the origin)Scalings (zooms)ShearsIn 2 dimensions, shear is defined by:x1 x0 hy.y0y1 y0In 2 dimensions, shear is defined by:x1 x0 hx.y0y1 y0Orx1 x0y1 hy.x0 y029fMRI course26/02/2008Affine30fMRI course26/02/2008NonNon-linear registrationPossible object movement:TranslationsRotations (around the origin)Scalings (zooms)ShearsIn 3 dimensions:Translations by tx, ty and tzRotations by θ, Φ and ΩScaling by sx, sy and szShears by hx, hy and hz12 parameters: tx, ty, tz, θ, Φ, Ω, sx, sy, sz and hx, hy, hz More than 12 parameters Can be purely local Based on different constraints Piecewise rigidspline,, etc) Basis functions (spline((spline, FluidUsed for interinter-subject registrationSecond step of normalisationUsed as a first step in normalisation31fMRI course26/02/200832fMRI course26/02/2008
NonNon-linear registrationInterpolation More than 12 parameters Can be purely local Based on different constraints Piecewise rigidBasis functions (splinespline,, etc)((spline,FluidUsed for interinter-subject registrationSecond step of normalisationLess robust than linear registration33fMRI course26/02/2008Interpolation3426/02/2008fMRI courseInterpolationVarious types of interpolation: Local Global 35fMRI course26/02/200836fMRI courseNearest urier-based26/02/2008
Interpolation37InterpolationVarious types of interpolation:Various types of interpolation: Local Local Global Global Nearest neighbour Trilinear Sinc Spline-based FourierFourierFourier-based26/02/2008fMRI courseInterpolation3938 Nearest neighbour Trilinear Sinc Spline-based FourierFourierFourier-based26/02/2008fMRI courseInterpolationVarious types of interpolationVarious types of interpolation Various resultsVarious resultsfMRI course26/02/200840fMRI course26/02/2008
RegistrationIn practice: Registration using FSL Registration implies in the choice of Intra-subject registration:IntraIntra-subject Inter-subject registration:InterInter-subject Double-check the registration results!DoubleDouble-check A similarity measure A type of transformation Rigid body (6 parameters) transform 1st step: affine transform-linear registration 2nd step: nonnonnon-linear41fMRI course26/02/2008In practice: Registration using FSL43fMRI course42fMRI course26/02/2008In practice: Registration using FSL26/02/200844fMRI course26/02/2008
In practice: Registration using FSL45fMRI courseIn practice: Registration using FSL26/02/2008Thank you for your attentionQuestions?47fMRI course26/02/200846fMRI course26/02/2008
Terminology of fMRI 6 26/02/2008 Slice thickness e.g., 3 mm Volume: Field of View (FOV), e.g. 192x192 mm Axial slices 3 mm 3 mm 3 mm Voxel Size (volumetric pixel) Matrix Size e.g., 64 x 64 In-plane resolution 192 mm / 64 3 mm Terminology of fMRI 7 fMRI course26/02/2008 Basic principles of MR image analysis
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facile. POCHOIR MONOCHROME SUR PHOTOSHOP Étape 1. Ouvrez l’image. Allez dans Image Image size (Image Taille de l’image), et assurez-vous que la résolution est bien de 300 dpi (ppp). Autre-ment l’image sera pixe-lisée quand vous allez l’éditer. Étape 2. Passez l’image en noir et blanc en choisissant Image Mode Grays-
Image Deblurring with Blurred/Noisy Image Pairs Lu Yuan1 Jian Sun2 Long Quan2 Heung-Yeung Shum2 1The Hong Kong University of Science and Technology 2Microsoft Research Asia (a) blurred image (b) noisy image (c) enhanced noisy image (d) our deblurred result Figure 1: Photographs in a low light environment. (a) Blurred image (with shutter speed of 1 second, and ISO 100) due to camera shake.
Digital Image Fundamentals Titipong Keawlek Department of Radiological Technology Naresuan University Digital Image Structure and Characteristics Image Types Analog Images Digital Images Digital Image Structure Pixels Pixel Bit Depth Digital Image Detail Pixel Size Matrix size Image size (Field of view) The imaging modalities Image Compression .
The odd-even image tree and DCT tree are also ideal for parallel computing. We use Matlab function Our Image Compression and Denoising Algorithm Input: Image Output: Compressed and denoised image 4 Decompressed and denoised image 4 Part One: Encoding 1.1 Transform the image 7 into an odd-even image tree where
The input for image processing is an image, such as a photograph or frame of video. The output can be an image or a set of characteristics or parameters related to the image. Most of the image processing techniques treat the image as a two-dimensional signal and applies the standard signal processing techniques to it. Image processing usually .
the workspace variable. To use the Crop Image tool, follow this procedure: 1) View an image in the Image Viewer. imtool(A); 2) Start the Crop Image tool by clicking Crop Image in the Image Viewer toolbar or selecting Crop Image from the Image Viewer Tools menu. (Another option is to open a figure
