PET/MR motion correction
In oncologic imaging, simultaneous Positron-Emission-Tomography/Magnetic Resonance (PET/MR) scanners offer a great potential for improving diagnostic accuracy. An accurate diagnosis requires a high PET image quality reflecting in long PET examination times under free movement conditions (respiration and heartbeat).
Hence, to ensure this high image quality one has to overcome the motion-induced artifacts. We utilize the simultaneously acquired MR for performing a retrospective MR-based non-rigid motion correction of the PET data in a clinical feasible way.
Overview
The following figure outlines the system overview:
Acquisition
For a short scan time of 1-2min, a reliable motion model can be generated, freeing time for additional diagnostic MR sequences. A 3D Cartesian time-independent variable-density subsampling with possible ESPReSSo mask and a self-navigation approach is applied. With the help of external sensor signals (ECG, camera signal, ...) and a sensor fusion approach, a respiratory surrogate signal for the complete scan time can be derived.
Reconstruction
After extracting the self-navigated MR signal and sensor signals, a sensor mapping is trained and all signals are fused, yielding a respiratory surrogate for the complete scan time. Based on the respiratory and cardiac surrogate, the acquired samples are gated, resulting in a 6D subsampled k-space (3D + tresp + tcard + NRx,channels). A joint Compressed Sensing reconstruction and motion estimation procedure returns a motion-resolved MR image and a motion model.
The motion model together with an attenuation map derived from the reconstructed MR images enables to perform the PET motion correction.
All reconstruction steps are carried out in Gadgetron.
Download
Acquisition
The precompiled acquisition sequence, termed CS_Retro (Siemens, VB20P) can be downloaded:
https://github.com/thomaskuestner/CS_MoCo_LAB/acquisition/CS-Retro
or
https://github.com/thomaskuestner/4DMRImaging/acquisition
Reconstruction
A motion-resolved MR image can be retrieved via
- Matlab: https://github.com/thomaskuestner/4DMRImaging/reconstruction
- Gadgetron: https://github.com/thomaskuestner/CS_LAB/reconstruction/gadgetron/CS_LAB_Gadget
- Data emitter and injector functors: https://github.com/thomaskuestner/CS_LAB/reconstruction/gadgetron/CS_LAB_Gadget/src/GADGET_TOOLS
- Please follow the guideline for setting up Gadgetron on a Siemens Biograph mMR.
Please cite one of the papers, if you use it in a scientific publication.