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Related Experiment Video

Updated: May 28, 2026

Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies

Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies

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Correcting fast irregular motion in PET: maximum-likelihood motion and activity (MLMA) reconstruction.

Rodrigo José Santo¹, Ethan Waterink², Cornelis A T van den Berg²

¹Department of Radiotherapy, Imaging & Oncology Division, UMC Utrecht, Utrecht, The Netherlands. r.josesanto@umcutrecht.nl.

|May 26, 2026

Summary

This study introduces Maximum-Likelihood Motion and Activity (MLMA) reconstruction, a novel PET imaging technique. MLMA accurately corrects for high-frequency motion, significantly improving image quality even with low counts.

Keywords:

Gateless motion-corrected PET High-frequency motion-corrected PET Motion correction irregular non-rigid motion correction

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Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

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Area of Science:

Medical Imaging
Nuclear Medicine
Image Reconstruction

Background:

Positron emission tomography (PET) imaging is susceptible to motion blur due to lengthy acquisition times.
Traditional motion correction methods struggle with irregular motion and high noise in short timeframes.
Compensating for motion is crucial for enhancing PET image quality.

Purpose of the Study:

To develop a novel method for motion compensation in PET imaging.
To address limitations of traditional motion correction techniques, particularly for irregular and high-frequency motion.
To introduce Maximum-Likelihood Motion and Activity (MLMA) reconstruction for improved PET image quality.

Main Methods:

Proposed a new alternating estimation and correction method for high-temporal-frequency motion in PET.
Utilized a cubic B-spline motion model and spatial regularization for motion compressibility and smoothness.
Configured MLMA at 2 Hz resolution and validated on digital phantoms, anthropomorphic torso phantom, and clinical patient data.

Main Results:

MLMA accurately corrected high-frequency motion (2 Hz) with subvoxel accuracy (up to 2.5 mm RMSE).
The method captured realistic breathing motion (14.7 mm amplitude, 4.5 s period).
Visually significant improvements in PET image quality were observed.

Conclusions:

The MLMA reconstruction method effectively resolves motion in short PET timeframes.
It overcomes challenges posed by low counts and inherent noise in PET projection data.
MLMA enables visualization of anatomical structures with temporal motion information.