Accuracy and consistency of intensity-based deformable image registration in 4DCT for tumor motion estimation in liver radiotherapy planning

A vessel bifurcation liver CT landmark pair dataset for evaluating deformable image registration algorithms

PURPOSE

Evaluating deformable image registration (DIR) algorithms is vital for enhancing algorithm performance and gaining clinical acceptance. However, there is a notable lack of dependable DIR benchmark datasets for assessing DIR performance except for lung images. To address this gap, we aim to introduce our comprehensive liver computed tomography (CT) DIR landmark dataset library. This dataset is designed for efficient and quantitative evaluation of various DIR methods for liver CTs, paving the way for more accurate and reliable image registration techniques.

ACQUISITION AND VALIDATION METHODS

Forty CT liver image pairs were acquired from several publicly available image archives and authors' institutions under institutional review board (IRB) approval. The images were processed with a semi-automatic procedure to generate landmark pairs: (1) for each case, liver vessels were automatically segmented on one image; (2) landmarks were automatically detected at vessel bifurcations; (3) corresponding landmarks in the second image were placed using two deformable image registration methods to avoid algorithm-specific biases; (4) a comprehensive validation process based on quantitative evaluation and manual assessment was applied to reject outliers and ensure the landmarks' positional accuracy. This workflow resulted in an average of ∼56 landmark pairs per image pair, comprising a total of 2220 landmarks for 40 cases. The general landmarking accuracy of this procedure was evaluated using digital phantoms and manual landmark placement. The landmark pair target registration errors (TRE) on digital phantoms were 0.37 ± 0.26 and 0.55 ± 0.34 mm respectively for the two selected DIR algorithms used in our workflow, with 97% of landmark pairs having TREs below 1.5 mm. The distances from the calculated landmarks to the averaged manual placement were 1.27 ± 0.79 mm.

DATA FORMAT AND USAGE NOTES

All data, including image files and landmark information, are publicly available at Zenodo (https://zenodo.org/records/13738577). Instructions for using our data can be found on our GitHub page at https://github.com/deshanyang/Liver-DIR-QA.

POTENTIAL APPLICATIONS

The landmark dataset generated in this work is the first collection of large-scale liver CT DIR landmarks prepared on real patient images. This dataset can provide researchers with a dense set of ground truth benchmarks for the quantitative evaluation of DIR algorithms within the liver.

Development of respiratory motion-resolved hepatobiliary phase cine-magnetic resonance imaging for stereotactic body radiotherapy in liver tumor

Cine-magnetic resonance imaging (MRI) has been used to track respiratory-induced motion of the liver and tumor and assist in the accurate delineation of tumor volume. Recent developments in compressed sensitivity encoding (SENSE; CS) have accelerated temporal resolution while maintaining contrast resolution. This study aimed to develop and assess hepatobiliary phase (HBP) cine-MRI scans using CS. Phantom was imaged using cine-MRI and signal intensity (SI) and contrast ratio (CR) measured to determine the optimal flip-angle turbo field echo (TFE) prepulse delay. We performed cine-MRI in 20 patients for one minute, with images taken every 0.5 s after administration of gadoxetic acid contrast agent. Acquired images had three different acceleration factors (SENSE, CS without denoising [CS-no], and CS with strong denoising [CS-strong]). The image quality of the HBP cine MRI was quantitatively and qualitatively analyzed. In the phantom study, a flip angle of 30 °and TFE prepulse delay of 150 ms were optimal for clinical imaging. In a clinical study, CS-strong showed the highest signal-to-noise ratio and comparable contrast ratio among the three sequences. The CS-strong group showed a significantly higher image quality (P < 0.01), except for motion smoothness (P = 0.11). CS with denoising improved the tumor-to-liver contrast and image quality in high-temporal-resolution HBP cine MRI.

Comparing the outcomes of MR-based versus CT-based tumor delineation in locally advanced non-small cell lung cancer treated with hypo-fractionated radiotherapy and concurrent chemotherapy

Background

Delineating gross tumor volume (GTV) using computed tomography (CT) imaging is the standard for lung cancer contouring, but discrepancies among observers compromise accuracy and reliability. Magnetic resonance imaging (MRI) provides superior soft-tissue resolution compared to CT, thus, we design this retrospective study to compare the treatment outcomes of magnetic resonance-based (MR-based) and CT-based tumor delineation in locally advanced non-small cell lung cancer (LA-NSCLC) patients treated with hypo-fractionated concurrent chemoradiotherapy (hypo-CCRT).

Methods

A total of 293 LA-NSCLC patients treated with hypo-CCRT from three trials between October 2015 and October 2020 were screened. Ninety patients with each MR-based delineation and CT-based delineation of the primary tumor were selected for analysis. In the MR-based delineation group, T1-enhanced MR images was rigidly registered with 10 respiratory phases of planning CT images, respectively. The primary tumors were contoured on each respiratory phase based on co-registered MRI. The locoregional progression-free survival (LPFS), progression-free survival (PFS), overall survival (OS) and toxicities in both groups were analyzed.

Results

The 2-year LPFS rate was 69.2% [95% confidence interval (CI): 59.6-80.2%] in the MR-based delineation group and 61.0% (95% CI: 50.9-73.0%) in the CT-based delineation group (P=0.37). There was no significant difference in median PFS (P=0.45) or OS (P=0.69) between the two groups. The MR-based delineation group had smaller planning target volume (186.1 vs. 315.3 cm3, P<0.001), lower incidences of ≥G2 pneumonitis (10% vs. 24.4%, P=0.001) and ≥G3 esophagitis (2.2% vs. 15.6%, P<0.001). In evaluating the patterns of recurrence, in-field recurrences were the dominant type in both groups (21 out of 27 patients in MR-based delineation group, 24 out of 32 patients in CT-based delineation group).

Conclusions

MR-based delineation in hypo-CCRT was feasible and achieved similar treatment efficacy to CT-based delineation. The use of MR imaging to reduce the target volume resulted in promising local control and lower incidence of radiation-induced toxicities.

Review and recommendations on deformable image registration uncertainties for radiotherapy applications

Deformable image registration (DIR) is a versatile tool used in many applications in radiotherapy (RT). DIR algorithms have been implemented in many commercial treatment planning systems providing accessible and easy-to-use solutions. However, the geometric uncertainty of DIR can be large and difficult to quantify, resulting in barriers to clinical practice. Currently, there is no agreement in the RT community on how to quantify these uncertainties and determine thresholds that distinguish a good DIR result from a poor one. This review summarises the current literature on sources of DIR uncertainties and their impact on RT applications. Recommendations are provided on how to handle these uncertainties for patient-specific use, commissioning, and research. Recommendations are also provided for developers and vendors to help users to understand DIR uncertainties and make the application of DIR in RT safer and more reliable.

ORRN: An ODE-Based Recursive Registration Network for Deformable Respiratory Motion Estimation With Lung 4DCT Images

OBJECTIVE

Deformable Image Registration (DIR) plays a significant role in quantifying deformation in medical data. Recent Deep Learning methods have shown promising accuracy and speedup for registering a pair of medical images. However, in 4D (3D + time) medical data, organ motion, such as respiratory motion and heart beating, can not be effectively modeled by pair-wise methods as they were optimized for image pairs but did not consider the organ motion patterns necessary when considering 4D data.

METHODS

This article presents ORRN, an Ordinary Differential Equations (ODE)-based recursive image registration network. Our network learns to estimate time-varying voxel velocities for an ODE that models deformation in 4D image data. It adopts a recursive registration strategy to progressively estimate a deformation field through ODE integration of voxel velocities.

RESULTS

We evaluate the proposed method on two publicly available lung 4DCT datasets, DIRLab and CREATIS, for two tasks: 1) registering all images to the extreme inhale image for 3D+t deformation tracking and 2) registering extreme exhale to inhale phase images. Our method outperforms other learning-based methods in both tasks, producing the smallest Target Registration Error of 1.24 mm and 1.26 mm, respectively. Additionally, it produces less than 0.001% unrealistic image folding, and the computation speed is less than 1 s for each CT volume.

CONCLUSION

ORRN demonstrates promising registration accuracy, deformation plausibility, and computation efficiency on group-wise and pair-wise registration tasks.

SIGNIFICANCE

It has significant implications in enabling fast and accurate respiratory motion estimation for treatment planning in radiation therapy or robot motion planning in thoracic needle insertion.

Online prediction for respiratory movement compensation: a patient-specific gating control for MRI-guided radiotherapy

BACKGROUND

This study aims to validate the effectiveness of linear regression for motion prediction of internal organs or tumors on 2D cine-MR and to present an online gating signal prediction scheme that can improve the accuracy of MR-guided radiotherapy for liver and lung cancer.

MATERIALS AND METHODS

We collected 2D cine-MR sequences of 21 liver cancer patients and 10 lung cancer patients to develop a binary gating signal prediction algorithm that forecasts the crossing-time of tumor motion traces relative to the target threshold. Both 0.4 s and 0.6 s prediction windows were tested using three linear predictors and three recurrent neural networks (RNNs), given the system delay of 0.5 s. Furthermore, an adaptive linear regression model was evaluated using only the first 30 s as the burn-in period, during which the model parameters were adapted during the online prediction process. The accuracy of the predicted traces was measured using amplitude metrics (MAE, RMSE, and R2), and in addition, we proposed three temporal metrics, namely crossing error, gating error, and gating accuracy, which are more relevant to the nature of the gating signals.

RESULTS

In both 0.6 s and 0.4 s prediction cases, linear regression outperformed other methods, demonstrating significantly smaller amplitude errors compared to the RNNs (P < 0.05). The proposed algorithm with adaptive linear regression had the best performance with an average gating accuracy of 98.3% and 98.0%, a gating error of 44 ms and 45 ms, for liver cancer and lung cancer patients, respectively.

CONCLUSION

A functional online gating control scheme was developed with an adaptive linear regression that is both more cost-efficient and accurate than sophisticated RNN based methods in all studied metrics.