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Updated: Feb 1, 2026

MRI and PET in Mouse Models of Myocardial Infarction
Published on: December 19, 2013
Optimization of a depth of interaction encoding PET block detector for a PET/MRI insert
Aaron R Selfridge1,2, Simon R Cherry1,3, Martin S Judenhofer1,3
1Department of Biomedical Engineering, UC Davis, Davis, California, United States of America.
This study developed a high-sensitivity preclinical PET/MRI insert for improved dynamic imaging. The new detector design enhances PET sensitivity, enabling more accurate quantitative studies in small animals.
Area of Science:
- Medical Imaging
- Biophysics
- Instrumentation
Background:
- Preclinical positron emission tomography/magnetic resonance imaging (PET/MRI) is vital for in vivo functional characterization.
- Low PET detection sensitivity limits quantitative accuracy in dynamic imaging studies.
- Emerging applications require higher sensitivity for precise biological process analysis.
Purpose of the Study:
- To develop a preclinical PET/MRI insert with >15% detection sensitivity for enhanced quantitative dynamic PET imaging.
- To improve accuracy in multifunctional dynamic imaging studies.
- To create a system suitable for small animal imaging (mice, rats).
Main Methods:
- Designed a detector module with a 2 cm thick crystal block and 8 cm axial FOV.
- Incorporated dual-ended depth-of-interaction (DOI) encoding using silicon photomultiplier (SiPM) arrays.
- Optimized SiPM geometry, crystal array materials (LYSO), surface treatments, and reflectors.
Main Results:
- Achieved 14.3% ± 2.9% energy resolution and 3.57 mm ± 0.88 mm DOI resolution.
- The detector module successfully resolved all elements in the 1 mm pitch LYSO crystal array.
- The prototype design demonstrates sufficient performance for a high-sensitivity PET/MRI insert.
Conclusions:
- The developed detector module provides high sensitivity and resolution for preclinical PET/MRI.
- This advancement enables more accurate quantitative dynamic imaging in small animal research.
- The new PET/MRI insert design is a significant step towards improved in vivo biological process characterization.
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