A Respiratory Motion Analysis for Guiding Stereotactic Arrhythmia Radiotherapy Motion Management
Yuhao Wang, Yao Hao, Hongyu An, H Michael Gach, Clifford Robinson, Phillip S Cuculich, Deshan Yang

TL;DR
This study introduces a novel groupwise surface-to-surface deformable image registration algorithm, gCGF, to accurately analyze respiratory heart motion in radiotherapy, effectively reducing cardiac motion artifacts in digital phantom validation and clinical data.
Contribution
The paper presents gCGF, a new DIR method with principal component filtering and spatial smoothing, improving cardiac motion artifact removal and respiratory motion analysis in STAR patients.
Findings
gCGF achieved a mean registration error of 0.63 mm on digital phantoms.
Respiratory motion of the heart ranged from 1.0 to 7.9 mm in patients.
Significant principal component dominance in motion direction was observed.
Abstract
Stereotactic Arrhythmia Radiotherapy (STAR) treats ventricular tachycardia (VT) but requires internal target volume (ITV) expansions to compensate for cardiorespiratory motion. Current clinical r4DCT imaging methods are limited, and the reconstructed r4DCTs suffer from unmanaged cardiac motion artifacts that affect the quantitative assessment of respiratory motion. A groupwise surface-to-surface deformable image registration (DIR) algorithm, named gCGF, was developed. A novel principal component filtering (PCF) mechanism and a spatial smoothing mechanism were developed and incorporated into gCGF to iteratively register heart contours from an average respiratory-phase CT to ten r4DCT phases while removing random cardiac motion from the cyclic respiratory motion. The performance of the groupwise DIR was quantitatively validated using 8 digital phantoms with simulated cardiac artifacts. An…
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Taxonomy
TopicsAdvanced Radiotherapy Techniques · Hepatocellular Carcinoma Treatment and Prognosis · Radiation Therapy and Dosimetry
