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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

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Related Experiment Video

Updated: May 7, 2026

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

Published on: May 10, 2012

Evaluation of motion correction methods in human brain PET imaging--a simulation study based on human motion data.

Xiao Jin¹, Tim Mulnix, Jean-Dominique Gallezot

¹Biomedical Engineering, Yale University, New Haven, Connecticut 06520.

Medical Physics

|October 5, 2013

Summary

Event-by-event motion correction accurately corrects head motion in PET scans. Frame-based methods are accurate for intraframe motion under 5 mm, ensuring reliable human brain PET imaging.

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Radiotracer Administration for High Temporal Resolution Positron Emission Tomography of the Human Brain: Application to FDG-fPET

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Published on: October 22, 2019

Area of Science:

Medical Imaging
Nuclear Medicine
Neuroscience

Background:

Improved PET system resolution necessitates advanced motion correction techniques.
Accurate motion correction is crucial for quantitative analysis in human brain PET imaging.

Purpose of the Study:

To evaluate the accuracy of event-by-event and frame-based motion correction (MC) methods.
To compare these methods in human brain PET imaging scenarios.

Main Methods:

Simulated high-resolution PET data with varying head motion were used.
Motion-compensated reconstructions were performed using both event-by-event and frame-based MC.
Accuracy was assessed by analyzing region of interest (ROI) intensities and kinetic modeling parameters (VT, BPND).

Main Results:

Event-by-event MC accurately corrects head motion when motion data is precise.
Frame-based MC methods achieve comparable accuracy with correctly aligned attenuation maps.
Large intraframe motion (>5 mm) introduced bias in ROI intensities (≈9%), VT (≈5%), and BPND (≈10%) with frame-based methods.

Conclusions:

Frame-based MC is accurate for intraframe motion <5 mm and precise attenuation map alignment.
Event-by-event MC reliably corrects head motion in human brain PET studies with accurate motion data.
Normalized weighted residual sum of squared difference can serve as a metric for evaluating MC accuracy in real studies.