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Enhancement of Long-Term External-Internal Correlation by Phase-Shift Detection and Correction Based on Concurrent External Bellows and Internal Navigator Signals.

ABSTRACT: Purpose:The purpose of this study was to enhance the correlation between external and internal respiratory motions by dynamically determining and correcting the patient-specific phase shift between external and internal respiratory waveforms acquired concurrently during respiratory-correlated 4-dimensional magnetic resonance imaging scans. Methods and Materials:Internal-navigator and external-bellows waveforms were acquired simultaneously during 6- to 15-minute respiratory-correlated 4-dimensional magnetic resonance imaging scans in 10 healthy participants under an institutional review board-approved protocol. The navigator was placed at the right lung-diaphragm interface, and the bellows were placed ?5 cm inferior to the sternum. Three segments of each respiratory waveform, at the beginning, middle, and end of a scan, were analyzed. Three phase-domain methods were employed to estimate the phase shift, including analytical signal analysis, phase-space oval fitting, and principal component analysis. A robust strategy for estimating the phase shift was realized by combining these methods in a weighted average and by eliminating outliers (>2 ?) caused by breathing irregularities. Whether phase-shift correction affects the external-internal correlation was evaluated. The cross-correlation between the 2 waveforms in the time domain provided an independent check of the correlation enhancement. Results:Phase-shift correction significantly enhanced the external-internal correlation in all participants across the entire 6- to 15-minute scans. On average, the correlation increased from 0.45 ± 0.28 to 0.85 ± 0.15 for the combined method. The combined method exhibited a 99.5% success rate and revealed that the phase of the external waveform leads that of the internal waveform in all 10 participants by 57 o ± 17o (1.6 ± 0.5 bins) on average. Seven participants exhibited highly reproducible phase shifts over time, evidenced by standard deviations (?) < 4o, whereas 8o < ? < 12o in the remaining 3 participants. Regardless, phase-shift correction significantly improved the correlation in all participants. Conclusions:Correcting the phase shift estimated by the phase-domain methods provides a new approach for enhancing the correlation between external and internal respiratory motions. This strategy holds promise for improving the accuracy of respiratory-gated radiation therapy.

SUBMITTER: Milewski AR

PROVIDER: S-EPMC6460238 | biostudies-literature | 2019 Apr-Jun

REPOSITORIES: biostudies-literature

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Project description:Objectives: The complexity and specificity of lung tumor motion render it necessary to determine the external and internal correlation individually before applying indirect tumor tracking. However, the correlation cannot be determined from patient respiratory and tumor clinical characteristics before treatment. The purpose of this study is to present a machine learning model for an external/internal correlation prediction that is based on computed tomography (CT) radiomic features. Methods: 4-dimensional computed tomography (4DCT) images of 67 patients were collected retrospectively, and the external/internal correlation of lung tumor was calculated based on Spearman's rank correlation coefficient. Radiomic features were extracted from average intensity projection and the light gradient boosting machine (LightGBM)-based cross-validation (the recursive elimination method) was used for feature selection. The LightGBM framework forecasting models with classification thresholds 0.7, 0.8, and 0.9 are established using stratified 5-fold cross-validation. Model performance was assessed using receiver operating characteristics, sensitivity, and specificity. Results: There were 16, 18, and 13 features selected for models 0.7, 0.8, and 0.9, respectively. Texture features are of great importance in external/internal correlation prediction compared to other features in all models. The sensitivities of the predictions in models 0.7, 0.8, and 0.9 were 0.800 ± 0.126, 0.829 ± 0.140, and 0.864 ± 0.086, respectively. The specificities were 0.771 ± 0.114, 0.936 ± 0.0581, and 0.839 ± 0.101, whereas the area under the curve (AUC) was 0.837, 0.946, and 0.877, respectively. Conclusions: Our findings indicate that radiomics is an effective tool for respiratory motion correlation prediction, which can extract tumor motion characteristics. We proposed a machine learning framework for correlation prediction in the motion management strategy for lung tumor patients.

Project description:Intraoperative tissue deformation, known as brain shift, decreases the benefit of using preoperative images to guide neurosurgery. Non-rigid registration of preoperative magnetic resonance (MR) to intraoperative ultrasound (iUS) has been proposed as a means to compensate for brain shift. We focus on the initial registration from MR to predurotomy iUS. We present a method that builds on previous work to address the need for accuracy and generality of MR-iUS registration algorithms in multi-site clinical data. High-dimensional texture attributes were used instead of image intensities for image registration and the standard difference-based attribute matching was replaced with correlation-based attribute matching. A strategy that deals explicitly with the large field-of-view mismatch between MR and iUS images was proposed. Key parameters were optimized across independent MR-iUS brain tumor datasets acquired at 3 institutions, with a total of 43 tumor patients and 758 reference landmarks for evaluating the accuracy of the proposed algorithm. Despite differences in imaging protocols, patient demographics and landmark distributions, the algorithm is able to reduce landmark errors prior to registration in three data sets (5.37±4.27, 4.18±1.97 and 6.18±3.38?mm, respectively) to a consistently low level (2.28±0.71, 2.08±0.37 and 2.24±0.78?mm, respectively). This algorithm was tested against 15 other algorithms and it is competitive with the state-of-the-art on multiple datasets. We show that the algorithm has one of the lowest errors in all datasets (accuracy), and this is achieved while sticking to a fixed set of parameters for multi-site data (generality). In contrast, other algorithms/tools of similar performance need per-dataset parameter tuning (high accuracy but lower generality), and those that stick to fixed parameters have larger errors or inconsistent performance (generality but not the top accuracy). Landmark errors were further characterized according to brain regions and tumor types, a topic so far missing in the literature.

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Enhancement of Long-Term External-Internal Correlation by Phase-Shift Detection and Correction Based on Concurrent External Bellows and Internal Navigator Signals.

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