Quantification with a dedicated breast PET/CT scanner
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Updated: May 22, 2026

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
Published on: December 15, 2014
Spencer L Bowen1, Andrea Ferrero, Ramsey D Badawi
1Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129, USA. slbowen@nmr.mgh.harvard.edu
Purpose:
Dedicated breast PET/CT is expected to have utility in local staging, surgical planning, monitoring of therapy response, and detection of residual disease for breast cancer. Quantitative metrics will be integral to several such applications. The authors present a validation of fully 3D data correction schemes for a custom built dedicated breast PET/CT (DbPET/CT) scanner via (18)F-FDG phantom scans.
Methods:
A component-based normalization was implemented, live-time was estimated with a multicomponent model, and a variance reduced randoms estimate was computed from delayed coincidences. Attenuation factors were calculated by using a CT based segmentation scheme while scatter was computed using a Monte Carlo (MC) simulation method. As no performance standard currently exists for breast PET systems, custom performance tests were created based on prior patient imaging results. Count-rate linearity for live-time and randoms corrections was measured with a decay experiment for a solid polyethylene cylinder phantom with an offset line source. A MC simulation was used to validate attenuation correction, a multicompartment phantom with asymmetric activity distribution provided an assessment of scatter correction, and image uniformity after geometric and detector normalization was measured from a high count scan of a uniform cylinder phantom. Raw data were reconstructed with filtered back projection (FBP) after Fourier rebinning. To quantify performance absolute activity concentrations, contrast recovery coefficients and image uniformity were calculated through region of interest analysis.
Results:
The most significant source of error was attributed to mispositioning of events due to pile-up, presenting in count-related axial and transaxial nonuniformities that were not corrected for with the normalization method used here. Within the range of singles counts observed during clinical trials residual error after applying all corrections was comparable to that of a commercial whole body PET/CT system.
Conclusions:
The results suggest that DbPET/CT is capable of producing quantitative images under the operating conditions expected during patient imaging.
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