Image Reconstruction and Slide Acknowledgements Image Fusion (PET/CT) CTI PET Systems: Christian Michel, Thomas Bruckbauer, Jim Hamill
Jeffrey T. Yap, Ph.D K. U. Leuven, Belgium: Johan Nuyts, University of Pittsburgh School of Medicine University of Pittsburgh: David Townsend, Continuing Education Course David Brasse AAPM Annual Meeting July 17, 2002, Montreal, Canada
Part I: PET Image Reconstruction Randoms Corrections Methods Focus: Impact of clinically available reconstruction methods on image quality delayed window (128 ns), on-line subtraction 2D versus 3D Imaging
Scatter and Randoms Correction delayed window, off-line subtraction of smoothed delayeds (Casey) Attenuation Correction estimate from R = k x singles2 x (2τ) Filtered BackProjection
Statistical (Iterative) Reconstruction extrapolation from outside object
Scatter Correction Methods Attenuation Correction Methods
Energy window: Grootonk, Bendriem Calculation Cylindrical source Deconvolution: Bergstrom, McKee Ring sources Convolution Subtraction: Baily, Shao, Yanch, Lercher Rotating rod or point sources Rotating singles point sources Analytical Functions: Cherry, Sterns Segmentation Scatter Model: Barney, Ollinger, Watson CT-based correction factors
Monte Carlo estimate: Levin
1 Geometry of Projection Data Reconstruction Algorithms Geometry of Projection Data
2D algorithms Photons collected along a "tube" or line of response (LOR) Projection: p(xr,φ) Filtered BackProjection (FBP) xr Ordered Subset Expectation Maximization (OSEM) y 2.5D algorithms yr LOR Row Action ML Algorithm (RAMLA) Object: f(x,y) xr 3D algorithms φ x 3D ReProjection (3DRP) 3D Row Action ML Algorithm (3D RAMLA) Sinogram: s(xr,φ)
Approximation single projection SSRB (Single Slice Rebinning) (SSRB) φ sine wave traced FOurier REbinning (FORE) out by a point FORE+ 2D algorithms (FBP, OSEM) xr Active scanner surface FORE + AWOSEM
Analytic Reconstruction: Limitations of Analytic Reconstruction FBP Algorithm FBP = analytical inversion of Radon Transform 1. Fourier transform the projection: (pre-corrected for random, sensitivity, attenuation, scatter). PFpxν ,,φφ= ( xr) 1 { ( r )} FBP ignores noise : all projections have equal weight. Highly attenuated LOR’s are noisy & produce streak artifacts 2. Filter the projection in frequency space: => Bias in VOIs (correlated noise). F Pv( xr,,φ ) = vPv xr( xr φ )
3. Inverse Fourier transform the filtered projection: FF−1 px( rxr,,φ ) = F1 { Pv( φ )} Noise is controlled through linear filtering in sinogram space which is suboptimal.
4. Backproject the filter projections for allx r : F fxy( ,,) =+∆⋅ fxy( ) φ p( xr ,φ )
5. Repeat steps 1-4 for each φ :0≤<φπ
Limitations of Analytic Reconstruction Statistical Reconstruction Methods • Missing data or bad quality data (missing, dead or Minimize bias and variance through a better drifted LOR’s) are (generally) not handled properly. statistical model and physical model (positron range, gamma non- collinearity, pixel detection probability) Handle missing data naturally Spatial resolution limited by the line integral model. Allow constraints on activity distribution Spatial resolution is limited by physical model at Need for statistical methods to overcome these difficulties. high count (but by statistical noise at low count) Resolution recovery is possible PET data are Poisson => Maximum Likelihood (ML) based reconstruction is a natural approach Cost is reconstruction time (acceleration possible) and noise propagation (regularization)
2 Maximum Likelihood Expectation Part II: Image Fusion (PET/CT) Maximization (MLEM) Original update equation by Shepp and Vardi Software Image Registration assumes that corrected trues are Poisson distributed – False! PET/CT Prototype Development Better MLEM update equations progressively restore Poisson nature of data by un- doing corrections Commercial PET/CT Development convergence is slow and local (i.e. non uniform) PET/CT Applications Noise deterioration with increasing iteration: requires regularization (e.g. post- smoothing)
The need for multi-modality image fusion Software image registration Radiotracers are designed to image tumor Manual scaling, translation, and/or function rather than normal tissue anatomy rotation Point matching algorithms Functional imaging typically has poor image Internal landmarks quality (noise and spatial resolution) Fiducial markers compared to anatomical imaging Surface matching algorithms Image intensity-based methods Anatomical detail improves diagnostic Image correlation interpretation and is required for surgical or Mutual information RT planning Deformable models
Limitations of software image UPMC PET/CT clinical evaluation registration over 280 patients scanned on the PET/CT prototype: - head and neck 76 Non- brain studies may require non- rigi d -lung 21 transformations - esophageal 33 More specific radiotracers provide less - lymphoma 19 anatomical information - melanoma 15 Functional changes occur due to disease - ovarian 34 progression or treatment effects - colorectal 8 Multiple scans are acquired at different times • reduced scanning time or injected activity Patient inconvenience • accurate spatial localization of lesions Organ displacement and physiological changes can • identification of normal physiological uptake occur • assessment of response to therapy Requires expertise and can be labor intensive • management impact in ~ 20-30% of cases
3 The Role of PET/CT in RT PET/CT Operational Considerations
What is the “best” target volume Absolute positioning to correspond with treatment position Functional changes precede structural changes PET provides better tumor detection than anatomical External lasers imaging for many indications Flat pallet attachment for RT table position
Use of devices for patient immobilization and What is the optimal dose to deliver? repositioning PET can quantify basic tumor biological properties that are useful for determining target volumes and dose Coordination of positioning with RT physicist or These same properties can be followed during and dosimetrist after therapy to assess treatment response to a given dose delivery Coordination of interpretation and ROIs with radiologist, nucmed physician, and oncologist
PET/CT Vs. CT in Staging/Restaging PET/CT vs CT Staging/Restaging of Gynecological Malignancies Additional Change Change in • Cancer patients scanned with 18FDG PET/CT for Findings in Stage Treatment suspected disease recurrence (20 ovarian Ovarian 16/20 12/20 16/20 carcinoma, 18 cervical carcinoma, and 4 Ovarian 16/20 12/20 16/20 endometrial) (80%) (60%) (80%) Cervical 9/18 6/18 6/18 • Evaluated number and location of lesions in CT vs. (50%) (33%) (33%) PET/CT Endometrial 2/4 2/4 1/3 • Compared stage and treatment management (50%) (50%) (33%)
Blodgett et al, SNM Annual Meeting, 2002
PET/CT Impact on Therapy Benefits of PET/CT in RT
• Changed management in 23/51 (45%) patients Definition of Biological Target Volumes
Metabolism, blood flow, proliferation, hypoxia, tumor- • 3 patients changed from surgery to chemotherapy specific receptors, angiogenesis, apoptosis
• 13 patients changed from observation to salvage Improved Treatment Efficacy chemotherapy Increase radiation fields to include additional lesions
Boost tumor dose while sparing normal tissue • 1 patient changed to observation Avoid ineffective treatments (up-staging) • 6 patients had IMRT and/or extension of radiation Evaluate treatment response to continue, alter or terminate treatment fields to cover additional lesions
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