# Quantifying the Loss of Information from Binning List-Mode Data

## TL;DR

This paper investigates how binning list-mode data in imaging modalities like SPECT and PET causes information loss, quantifies this loss using Fisher information, and identifies three key factors influencing it.

## Contribution

It introduces a computational method to quantify Fisher information loss due to binning in list-mode data, considering data smoothness, object characteristics, and binning scheme.

## Key findings

- Information loss depends on data smoothness, object, and binning scheme.
- Fisher information decreases with more aggressive binning.
- The method enables optimization of binning strategies for minimal information loss.

## Abstract

List-mode data is increasingly being uesd in SPECT and PET imaging, among other imaging modalities. However, there are still many imaging designs that effectively bin list-mode data before image reconstruction or other estimation tasks are performed. Intuitively, the binning operation should result in a loss of information. In this work we show that this is true for Fisher information and provide a computational method for quantifying the information loss. In the end we find that the information loss depends on three factors. The first factor is related to the smoothness of the mean data function for the list-mode data. The second factor is the actual object being imaged. Finally, the third factor is the binning scheme in relation to the other two factors.