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ST-NeRP: Spatial-temporal neural representation learning with prior embedding for patient-specific imaging study.

Liang Qiu1, Liyue Shen2, Lianli Liu1

  • 1Department of Radiation Oncology, Stanford University, Stanford, 94305, CA, USA.

Computers in Biology and Medicine
|November 6, 2025
PubMed
Summary

This study introduces Spatial-Temporal Neural Representation learning with Prior embedding (ST-NeRP) to track patient-specific anatomical changes during therapy using medical imaging. The novel computational framework accurately predicts spatial-temporal deformations for improved treatment monitoring.

Keywords:
Deformable registrationDisease progression monitoringImplicit neural representationPatient-specific imaging study

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Area of Science:

  • Medical imaging analysis
  • Computational anatomy
  • Artificial intelligence in healthcare

Background:

  • Medical imaging is crucial for monitoring disease progression and treatment response.
  • Predicting spatial-temporal anatomical changes from image sequences is challenging.
  • A computational framework is needed for patient-specific imaging studies.

Purpose of the Study:

  • To develop a novel computational framework for patient-specific imaging studies.
  • To enable accurate prediction of spatial-temporal anatomical changes.
  • To enhance the monitoring of anatomical changes during therapeutic journeys.

Main Methods:

  • Proposed a strategy named Spatial-Temporal Neural Representation learning with Prior embedding (ST-NeRP).
  • Utilized Implicit Neural Representation (INR) networks to encode reference images and learn deformation functions.
  • Trained the model on patient-specific image sequences for predicting deformation fields.

Main Results:

  • Demonstrated the efficacy of ST-NeRP on diverse sequential image series (4D CT, longitudinal CT).
  • Successfully applied the model to thoracic and abdominal imaging datasets.
  • Showcased the model's ability to predict deformation fields at various time points.

Conclusions:

  • The ST-NeRP model shows significant potential for patient-specific imaging analysis.
  • It enables effective monitoring of anatomical changes throughout therapy.
  • This framework advances computational anatomy for clinical applications.