1. PET data preprocessing and alternative image reconstruction strategies

2. The DoPET Scanner PET scanner for hadron therapy monitoring: Made by 8 Modules (4 vs 4) Each module is PMT H8500 coupled to 23x23 crystals LYSO matrix MLEM reconstruction Reconstructed FOV is 100x100x100 mm 3 Voxel size 1x1x1 mm 3 4 Million of Line Of Responses Sketch of the DoPET scanner

3. The IRIS PET scanner Pre-clinical PET scanner: Made of two octagonal rings (16 modules) Each module is a made of a PMT H8500 coupled to 27x26 crystals LYSO matrix Reconstructed FOV is 86x86x102 mm 3 Two multi-ray System Response Matrices (SRM) available: Small pixel: 0.42x0.42x0.855 mm 3 Big pixel: 0.855x0.855x0.855 mm 3 Offers the unique capability of performing both rotational and static acquisitions 23 Million of Lines Of Responses Sketch of the IRIS PET scanner

4. PET Calibration/Preprocessing

5. PET Reconstruction software We develop a framework for LOR based MLEM/OSEM reconstruction Insight ToolKit (ITK) based software (Image processing/handling) C++ implementation CMake build system Cross platform (Linux, Windows and OSX) Based on a pre-computed System Response Matrix (SRM) The SRMs is implemented using a Siddon based multi- ray approach Multi-thread implemented with Threading Build Block (TBB) Intel Massive use of symmetries Component based normalization

6. Automatic symmetries exploitation from a pre-computed SRM

7. System Response Matrix representation (SRM) The SRM P(j,i), gives the probability that a photon pair emitted in the voxel i of the FOV is detected in the LOR j The SRM is the fundamental part of the MLEM/OSEM reconstruction In our representation each LOR of the SRM is represented as a list of entries The i-th entry is composed by the voxel Cartesian coordinates (x,y,z) vx i and a probability p i

8. Automatic exploitation of SRM symmetries For modern scanners, the SRM can easily exceed tents of GB One way of reducing the memory footprint of the SRM is by implementing symmetries Symmetries are an error prone task Typically implemented manually Our idea: –Would be good not to rely on any scanner/geometry based assumption –Find an algorithm to extract symmetries from a pre- computed SRM

9. Definition of symmetry The algorithm considers two LORs L,M to be symmetric within a threshold T if the following three conditions are met: A.L and M intersect the same number of voxels B.The probabilities of the entries L,M are the same within the percentage threshold T C.It is possible to find a voxel space transformation to transform L into M

10. Implementation in practice Loop over all the SRM LORs Conditions A and B are easy to be verified Condition C can be verified by restricting the transformations to reflections and translations Given two LORs L,M we can express translation and reflection in voxel space: Translation (A=-1) : difference of coordinates is a constant Reflection (A=1) : sum of the coordinates is a constant 8 (2 3 ) Symmetry types are allowed This relation cannot be used directly as depends on the way L and M are arranged ( voxel ordering)

11. To solve this issue…

12. Long story short…

13. Results on the IRIS/PET scanner

14. Two slices of the same NEMA IQ phantom reconstructed with original SRM, SRM 1%, SRM 5%

15. Dividing voxel wise images obtained with SRM compressed at different threshold

16. T vs SRM size

17. Normalization

18. Stepwise normalization procedure with a planar source (IRIS PET) 1/2 In this position the coincidences involving this head are not used for normalization 18 FDG filled planar source This procedure is repeated twice over 360 degrees View duration 10 min Total normalization duration is 160 min (16 views) Decay correction is needed

19. Stepwise normalization procedure with a planar source (IRIS PET) 2/2 The same procedure is “simulated” using the SRM, i.e. by evaluating the forward projection of all the LORs onto a uniform planar phantom posed in different positions With this data a Defrise like [1] component based normalization is evaluated [1] “A normalization technique for 3D PET data”,Defrise et al., Phys. Med. Biol 1991

20. Conclusions The Pisa group can take care of: –Implementing PET data preprocessing –Providing the data in the format needed by the reconstruction –Implementing (if needed) some of the tools contained in our Preprocessing and Reconstruction framework