Spring 2027

Pharmacokinetic Modeling in Biomedical Imaging

Explores pharmacokinetic modeling in biomedical imaging, covering PET and MRI applications in neurology and oncology.

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Recorded instructors · Spring 2027 No instructors listed

Summary

What you’ll be able to do

  • Understand fundamental principles of pharmacokinetic modeling within the context of biomedical imaging

  • Implement appropriate mathematical models for given biological tracer systems

  • Apply these skills to biomedical research questions

  • Analyze biomedical imaging data to investigate pharmacokinetic properties

  • Create a publication quality research paper

Source: UW Guide

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Prerequisites

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Graduate/professional standing

  • Graduate/professional standing
MEDPHYS 780

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  • Graduate/professional standing

Professors

Spring 2027

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Calendar & sections

Spring 2027

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Student experience

the class

Concepts and techniques of pharmacokinetic modeling will be presented in the context of biomedical imaging. Examine applications in various specialties, e.g. neurology and oncology, using different imaging tools, e.g. positron emission tomography (PET) and magnetic resonance imaging (MRI).

difficulty & workload

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Topics

  • Pharmacokinetic modeling
  • Biomedical imaging applications in neurology and oncology
  • PET and MRI technologies

Skills

  • Pharmacokinetic modeling concepts and techniques
  • Mathematical model implementation for biological tracers
  • Analysis of biomedical imaging data
  • Research paper writing

Grades

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Sources & history

UW–Madison

Catalog & offerings

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Dataset & model

Recorded 2026-10-14

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Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
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Full model traces

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Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "a65cf0f124130ca41917b15f1baa119fc4c6dc75730c9e04ea07361177e9de9a",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}