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Published on: August 16, 2017
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Quantifying the loss of information from binning list-mode data.
Summary
Binning list-mode data in medical imaging like PET and SPECT can lose valuable Fisher information. This study quantifies information loss, showing it depends on data smoothness, the imaged object, and the binning method.
Area of Science:
- Medical Imaging
- Nuclear Medicine
- Data Analysis
Background:
- List-mode data is increasingly utilized in single photon emission computed tomography (SPECT) and positron emission tomography (PET) imaging.
- Many current imaging systems bin list-mode data prior to image reconstruction, potentially leading to information loss.
Purpose of the Study:
- To demonstrate that binning list-mode data results in information loss.
- To develop a computational method for quantifying this information loss in SPECT and PET imaging.
Main Methods:
- Analysis of Fisher information in list-mode data.
- Development and application of a computational method to quantify information loss due to data binning.
Main Results:
- Binning list-mode data demonstrably leads to a loss of Fisher information.
- The extent of information loss is contingent upon the smoothness of the mean data function.
- Information loss is also influenced by the characteristics of the imaged object and the specific binning strategy employed.
Conclusions:
- Data binning in SPECT and PET imaging is not information-preserving.
- Quantifying information loss is crucial for optimizing imaging protocols.
- Future research should consider the interplay between data properties, object characteristics, and binning schemes to minimize information loss.
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