Useful diagnostic biometabolic data obtained by PET/CT and MR fusion imaging using open source software

Area of Science:

• Medical Imaging
• Radiology
• Nuclear Medicine

Background:

• Integrating Positron Emission Tomography (PET) and Magnetic Resonance (MR) imaging offers enhanced diagnostic capabilities.
• Existing PET/MR scanners are costly, and their diagnostic protocols are still under investigation.
• Nuclear Medicine provides functional and metabolic data, while MR offers superior anatomical detail.

Purpose of the Study:

• To develop and evaluate an inexpensive, reproducible offline method for fusing PET and MR data acquired from different scanners on separate days.
• To assess the global quality, alignment accuracy, and diagnostic confidence of the fused PET-MR images.
• To enable data exchange over networks for collaborative medical staff review.

Main Methods:

• Utilized open-source software (Osirix 5.7) for offline fusion of PET/CT and MR studies.
• Co-registered CT slices with MR-T1 sequences, using CT as a reference, and fused with PET data.
• Evaluated 100 fused PET-MR studies (head, thorax, abdomen, pelvis, whole body) by experienced radiologists and nuclear medicine physicians using a five-point scoring scheme.

Main Results:

• The fusion method demonstrated high global quality, alignment accuracy, and diagnostic confidence for head, thorax, and pelvic regions.
• Poorer alignment and global quality were observed in abdominal and pelvic fusions due to organ movement and time differences between scans.
• The method successfully combined time-of-flight reconstructed PET/CT with detailed MR anatomical images without additional software costs.

Conclusions:

• The proposed offline PET-MR fusion method is cost-effective, reproducible, and facilitates data sharing among medical staff.
• This approach avoids the need for expensive, dedicated PET/MR scanners and specialized personnel.
• The technique is adaptable to various MR sequences and applicable to virtually any DICOM study, enhancing disease characterization and discrimination.