# Deformable motion compensation for 3D image-guided interventional radiology

> **NIH NIH R01** · JOHNS HOPKINS UNIVERSITY · 2021 · $368,438

## Abstract

PROJECT SUMMARY / ABSTRACT
C-arm cone-beam CT (CBCT) plays an increasing role in guidance of interventional radiology (IR) procedures in the abdo-
men, with special emphasis in embolization procedures, such as transarterial chemoembolization (TACE) for treatment of
hepatocellular carcinoma (HCC) or transarterial embolization (TAE) for control of internal hemorrhage. However, relatively
long scan time of CBCT results in artifacts arising from organ motion (respiratory and cardiac motion and peristalsis). This
poses a significant challenge to guidance in interventional radiology: for example, motion artifacts were found to render
up to 25% of CBCT images un-interpretable in image-guided TACE, and 18% in CBCT-guided emergency TAE. The impact
of motion is most significant in cases of single or isolated lesions treated with selective embolization that requires visual-
ization of very small vascular structures. Existing motion correction methods often invoke assumption of periodicity, lim-
iting their applicability outside of cardiac and respiratory motions, or rely on fiducial tracking or gated acquisition that
disrupt IR workflow and/or increase radiation dose. Therefore, the application of CBCT in image-guided interventional
procedures in the abdomen would significantly benefit from new methods that estimate complex deformable motion
directly from image data. “Autofocus” techniques based on maximization of a regularized image sharpness criterion were
shown to yield effective patient motion compensation in extremity, head and cardiac CBCT. However, current applications
of such methods are limited to rigid motions. We hypothesize that deformable organ motion compensation in interven-
tional soft-tissue CBCT can be achieved with advanced autofocus techniques using multiple locally rigid regions of in-
terest, preconditioned with basic motion characteristics obtained through a machine learning decision framework. The
following aims will be pursued: 1) Develop a joint multi-region autofocus optimization method to compensate deforma-
ble organ motion. This includes incorporation into a comprehensive artifacts correction and image reconstruction pipe-
line, design of multi-stage optimization schedules for convergence acceleration, and performance evaluation in deforma-
ble phantoms, and cadaver and animal experiments. 2) Develop a decision framework for preconditioning of the motion
compensation method through a combination of projection-based approaches for physiological signal estimations (res-
piratory cycle) and a multi-input, multi-branch, deep learning architecture trained on extremely realistic simulated data
that will estimate basic properties of motion (spatial distribution of amplitude, direction, and frequency) from an initial
motion-contaminated image and its associated raw projection data. 3) Evaluate deformable motion compensation in
animal experiments and in a clinical study in 50 cases of CBCT-guided TACE and assess image quality via expert...

## Key facts

- **NIH application ID:** 10100337
- **Project number:** 1R01EB030547-01
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Alejandro Sisniega Crespo
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $368,438
- **Award type:** 1
- **Project period:** 2021-04-01 → 2024-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10100337

## Citation

> US National Institutes of Health, RePORTER application 10100337, Deformable motion compensation for 3D image-guided interventional radiology (1R01EB030547-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10100337. Licensed CC0.

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