PET data preprocessing and alternative image

reconstruction strategies

Niccolò Camarlinghi, Dipartimento di Fisica dell’università di Pisa

Contact: niccolo.camarlinghi@df.unipi.it

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 mm3

• Voxel size 1x1x1 mm3

• 4 Million of Line Of Responses

Sketch of the DoPET scanner

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 mm3

• Two multi-ray System Response Matrices (SRM) available: • Small pixel: 0.42x0.42x0.855

mm3

• Big pixel: 0.855x0.855x0.855 mm3

• Offers the unique capability of performing both rotational and static acquisitions

• 23 Million of Lines Of Responses

Sketch of the IRIS PET scanner

PET Calibration/Preprocessing

Calibration :– Pixel identification– Energy calibration

Preprocessing tasks: – Estimation of random events distribution – Dead time correction– Decay correction…– LOR data for the reconstruction

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

Automatic symmetries exploitation from a pre-computed SRM

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) vxi and a probability pi

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

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

voxelsB. 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

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 (23) Symmetry types are allowed• This relation cannot be used directly as depends on

the way L and M are arranged ( voxel ordering)

To solve this issue…

• If a symmetry exists between L,M then the following relation holds

• This gives 8 k values that can be tested directly

• If at least of component of k is not integer you can discard the k value

Long story short…

• Given two symmetric LORs L,M and (A,k) it is possible to recover L from M within the max percentage diff T by using

• This transformation can be performed before projections and retro-projections operations

• No tolerance in coordinates mismatch• Percentage tolerance T is allowed in the

recovered probability values

Results on the IRIS/PET scanner

• For this study an SRM reconstruction a FOV of 101x101x60 pixel was implemented

• The size of the SRM is 52 Gb • SRM is computed using 4x4x8 rays per

crystal, i.e. 16384 rays per LOR• Uncompressed SRM computation time 2-3

hrs • SRM compressed with

T=1%,2%,3%,4%,5%

Two slices of the same NEMA IQ phantom reconstructed with original SRM, SRM1%,SRM5%

Dividing voxel wise images obtained with SRM compressed at different threshold

T vs SRM size• The IRIS SRM was reduced by a

factor 29 using T=5%

Normalization

Stepwise normalization procedure with a planar source (IRIS PET) 1/2

In this position the coincidences involving this head are not used for normalization

• 18FDG 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

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

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