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Updated: Jun 24, 2025

Whole-body PET/MRI of Pediatric Patients: The Details That Matter
Published on: December 19, 2017
Total Body PET-CT Protocols in Oncology.
Antonia Dimitrakopoulou-Strauss1, Leyun Pan1, Christos Sachpekidis1
1Clinical Cooperation Unit Nuclear Medicine, German Cancer Research Center, Heidelberg, Germany.
Long axial field of view (LAFOV) PET-CT scanners offer higher sensitivity for improved lesion detection and faster whole-body scans. These advanced systems enable dynamic imaging and reduced radiotracer doses for oncological studies.
Area of Science:
- Nuclear Medicine
- Radiological Imaging
- Oncology
Background:
- Long axial field of view (LAFOV) PET-CT scanners, including total body systems, represent a significant advancement over standard axial field of view (SAFOV) scanners.
- LAFOV scanners offer up to 20-fold higher sensitivity, leading to enhanced lesion detectability and improved image quality.
Purpose of the Study:
- To review dedicated PET acquisition protocols for oncological studies using LAFOV scanners.
- To present current literature data on the application of LAFOV PET-CT in oncology.
Main Methods:
- Review of existing literature and technical specifications of LAFOV PET-CT scanners.
- Discussion of static, dynamic, and parametric whole-body PET acquisition protocols.
- Consideration of emerging AI-driven applications for image reconstruction and analysis.
Main Results:
- LAFOV PET-CT facilitates reduced acquisition times and lower radiotracer doses, enabling longitudinal studies and increased patient throughput.
- Improved sensitivity and time resolution allow for whole-body dynamic PET scanning and parametric imaging, crucial for characterizing novel radiopharmaceuticals and cancer biology.
- The ability to perform delayed scans with good image quality is beneficial for long half-life radionuclides and pharmaceuticals, such as 89Zr-labeled antibodies.
Conclusions:
- LAFOV PET-CT scanners provide substantial advantages in sensitivity, speed, and imaging capabilities for oncological applications.
- These advancements support detailed characterization of cancer, assessment of treatment response, and facilitate novel radiopharmaceutical development.
- Future developments, including AI, promise further enhancements in image quality and quantitative analysis for total metabolic tumor volume assessment.
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