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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: Jul 9, 2026

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

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Published on: October 24, 2012

Modeling PET Data Acquired During Nonsteady Conditions: What If Brain Conditions Change During the Scan?

Evan D Morris^1,2,3, Gaelle M Emvalomenos⁴, Jocelyn Hoye³

¹Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut; evan.morris@yale.edu.

Journal of Nuclear Medicine : Official Publication, Society of Nuclear Medicine

|October 24, 2024

Summary

Researchers developed advanced time-variant kinetic models for dynamic positron emission tomography (PET) imaging. These models improve the detection and characterization of transient brain processes, offering new insights into neurochemistry.

Keywords:

lp-ntPET neurotransmitter radiotracer tissue kinetics time-varying models tracer kinetics transient signal

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Last Updated: Jul 9, 2026

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Area of Science:

Neuroscience
Medical Imaging
Biophysics

Background:

Dynamic PET imaging uses tracer molecules to study molecular processes in the brain.
Traditional kinetic models assume steady-state conditions, limiting their use for transient events.
Non-steady brain processes, like neurotransmitter fluctuations, require specialized modeling.

Purpose of the Study:

To review the historical development and advancements of time-variant kinetic models in PET imaging.
To provide context for the evolution of models designed to capture transient neurochemical phenomena.
To highlight the impact of mathematical, computational, and statistical innovations on PET modeling.

Main Methods:

Review of time-variant kinetic modeling techniques in dynamic PET.
Classification of models into first, second, and third generations based on innovation.
Analysis of advancements leveraging mathematics, image processing, AI, and statistics.

Main Results:

Development of increasingly sophisticated time-variant models since the 1990s.
Improved sensitivity and performance in detecting and describing non-steady brain phenomena.
Enhanced ability to extract transient signals from dynamic PET data.

Conclusions:

Time-variant models are crucial for studying dynamic, non-steady brain processes with PET.
Ongoing model refinement accelerates understanding of neurotransmitter fluctuations and neurochemical events.
These advanced models enhance the characterization of molecular processes in the brain.