A patient-specific deep learning framework for 3D motion estimation and volumetric imaging during lung cancer radiotherapy.

Objective. Respiration introduces a constant source of irregular motion that poses a significant challenge for the precise irradiation of thoracic and abdominal cancers. Current real-time motion management strategies require dedicated systems that are not available in most radiotherapy centers. We sought to develop a system that estimates and visualises the impact of respiratory motion in 3D given the 2D images acquired on a standard linear accelerator.Approach. In this paper we introduceVoxelmap, a patient-specific deep learning framework that achieves 3D motion estimation and volumetric imaging using the data and resources available in standard clinical settings. Here we perform a simulation study of this framework using imaging data from two lung cancer patients.Main results. Using 2D images as input and 3D-3DElastixregistrations as ground-truth,Voxelmapwas able to continuously predict 3D tumor motion with mean errors of 0.1 ± 0.5, -0.6 ± 0.8, and 0.0 ± 0.2 mm along the left-right, superior-inferior, and anterior-posterior axes respectively.Voxelmapalso predicted 3D thoracoabdominal motion with mean errors of -0.1 ± 0.3, -0.1 ± 0.6, and -0.2 ± 0.2 mm respectively. Moreover, volumetric imaging was achieved with mean average error 0.0003, root-mean-squared error 0.0007, structural similarity 1.0 and peak-signal-to-noise ratio 65.8.Significance. The results of this study demonstrate the possibility of achieving 3D motion estimation and volumetric imaging during lung cancer treatments on a standard linear accelerator.

Real-time radial reconstruction with domain transform manifold learning for MRI-guided radiotherapy.

BACKGROUND: MRI-guidance techniques that dynamically adapt radiation beams to follow tumor motion in real time will lead to more accurate cancer treatments and reduced collateral healthy tissue damage. The gold-standard for reconstruction of undersampled MR data is compressed sensing (CS) which is computationally slow and limits the rate that images can be available for real-time adaptation. PURPOSE: Once trained, neural networks can be used to accurately reconstruct raw MRI data with minimal latency. Here, we test the suitability of deep-learning-based image reconstruction for real-time tracking applications on MRI-Linacs. METHODS: We use automated transform by manifold approximation (AUTOMAP), a generalized framework that maps raw MR signal to the target image domain, to rapidly reconstruct images from undersampled radial k-space data. The AUTOMAP neural network was trained to reconstruct images from a golden-angle radial acquisition, a benchmark for motion-sensitive imaging, on lung cancer patient data and generic images from ImageNet. Model training was subsequently augmented with motion-encoded k-space data derived from videos in the YouTube-8M dataset to encourage motion robust reconstruction. RESULTS: AUTOMAP models fine-tuned on retrospectively acquired lung cancer patient data reconstructed radial k-space with equivalent accuracy to CS but with much shorter processing times. Validation of motion-trained models with a virtual dynamic lung tumor phantom showed that the generalized motion properties learned from YouTube lead to improved target tracking accuracy. CONCLUSION: AUTOMAP can achieve real-time, accurate reconstruction of radial data. These findings imply that neural-network-based reconstruction is potentially superior to alternative approaches for real-time image guidance applications.

MRI-guided cardiac-induced target motion tracking for atrial fibrillation cardiac radioablation.

BACKGROUND AND PURPOSE: Atrial fibrillation (AF) cardiac radioablation (CR) challenges radiotherapy tracking: multiple small targets close to organs-at-risk undergo rapid differential cardiac contraction and respiratory motion. MR-guidance offers a real-time target tracking solution. This work develops and investigates MRI-guided tracking of AF CR targets with cardiac-induced motion. MATERIALS AND METHODS: A direct tracking method (Trackingdirect) and two indirect tracking methods leveraging population-based surrogacy relationships with the left atria (Trackingindirect_LA) or other target (Trackingindirect_target) were developed. Tracking performance was evaluated using transverse ECG-gated breathhold MRI images from 15 healthy and 10 AF participants. Geometric and volumetric tracking errors were calculated, defined as the difference between the ground-truth and tracked target centroids and volumes respectively. Transverse, breath-hold, noncardiac-gated cine images were acquired at 4 Hz in 5 healthy and 5 AF participants to qualitatively characterize tracking performance on images more comparable to MRILinac acquisitions. RESULTS: The average 3D geometric tracking errors for Trackingdirect, Trackingindirect_LA and Trackingindirect_target respectively were 1.7 ± 1.2 mm, 1.6 ± 1.1 mm and 1.9 ± 1.3 mm in healthy participants and 1.7 ± 1.3 mm, 1.5 ± 1.0 mm and 1.7 ± 1.2 mm in AF participants. For Trackingdirect, 88% of analyzed images had 3D geometric tracking errors <3 mm and the average volume tracking error was 1.7 ± 1.3 cc. For Trackingdirect on non-cardiac-gated cine images, tracked targets overlapped organsat-risk or completely missed the target area on 2.2% and 0.08% of the images respectively. CONCLUSION: The feasibility of non-invasive MRI-guided tracking of cardiac-induced AF CR target motion was demonstrated for the first time, showing potential for improving AF CR treatment efficacy.

Proof-of-concept for x-ray based real-time image guidance during cardiac radioablation.

Cardiac radioablation offers non-invasive treatments for refractory arrhythmias. However, treatment delivery for this technique remains challenging. In this paper, we introduce the first method for real-time image guidance during cardiac radioablation for refractory atrial fibrillation on a standard linear accelerator. Our proposed method utilizes direct diaphragm tracking on intrafraction images to estimate the respiratory component of cardiac substructure motion. We compare this method to treatment scenarios without real-time image guidance using the 4D-XCAT digital phantom. Pre-treatment and intrafraction imaging was simulated for 8 phantoms with unique anatomies programmed using cardiorespiratory motion from healthy volunteers. As every voxel in the 4D-XCAT phantom is labelled precisely according to the corresponding anatomical structure, this provided ground-truth for quantitative evaluation. Tracking performance was compared to the ground-truth for simulations with and without real-time image guidance using the left atrium as an exemplar target. Differences in target volume size, mean volumetric coverage, minimum volumetric coverage and geometric error were recorded for each simulation. We observed that differences in target volume size were statistically significant (p < 0.001) across treatment scenarios and that real-time image guidance enabled reductions in target volume size ranging from 11% to 24%. Differences in mean and minimum volumetric coverage were statistically insignificant (bothp = 0.35) while differences in geometric error were statistically significant (p = 0.039). The results of this study provide proof-of-concept for x-ray based real-time image guidance during cardiac radioablation.

Real-time direct diaphragm tracking using kV imaging on a standard linear accelerator.

PURPOSE: As the predominant driver of respiratory motion, the diaphragm represents a key surrogate for motion management during the irradiation of thoracic cancers. Existing approaches to diaphragm tracking often produce phase-based estimates, suffer from lateral side failures or are not executable in real-time. In this paper, we present an algorithm that continuously produces real-time estimates of three-dimensional (3D) diaphragm position using kV images acquired on a standard linear accelerator. METHODS: Patient-specific 3D diaphragm models were generated via automatic segmentation on end-exhale four-dimensional-computed tomography (4D-CT) images. The estimated trajectory of diaphragmatic motion, referred to as the principal motion vector, was obtained by registering end-exhale to end-inhale 4D-CT images. Two-dimensional (2D) diaphragm masks were generated by forward-projecting 3D models over the complement of angles spanned during kV image acquisition. For each kV image, diaphragm position was determined by shifting angle-matched 2D masks along the principal motion vector and selecting the position of highest contrast on a vertical difference image. Retrospective analysis was performed using 22 cone beam CT (CBCT) image sequences for six lung cancer patients across two datasets. Given the current lack of objective ground truth for diaphragm position, our algorithm was evaluated by examining its ability to track implanted markers. Simple linear regression was used to construct 3D marker motion models and estimation errors were computed as the difference between estimated and ground truth marker positions. Additionally, Pearson correlation coefficients were used to characterize diaphragm-marker correlation. RESULTS: The mean ± standard deviation of the estimation errors across all image sequences was -0.1 ± 0.7 mm, -0.1 ± 1.8 mm and 0.2 ± 1.4 mm in the LR, SI, and AP directions respectively. The 95th percentile of the absolute errors ranged over 0.5-3.1 mm, 1.6-6.7 mm, and 1.2-4.0 mm in the LR, SI, and AP directions, respectively. The mean ± standard deviation of diaphragm-marker correlations over all image sequences was -0.07 ± 0.57, 0.67 ± 0.49, and 0.29 ± 0.52 in the LR, SI, and AP directions, respectively. Diaphragm-marker correlation was observed to be highly dependent on marker position. Mean correlation along the SI axis ranged over 0.91-0.93 for markers situated in the lower lobes of the lung, while correlations ranging over -0.51-0.79 were observed for markers situated in the upper and middle lobes. CONCLUSION: This work advances a new approach to real-time direct diaphragm tracking in realistic treatment scenarios. By achieving continuous estimates of diaphragmatic motion, the proposed method has applications for both markerless tumor tracking and respiratory binning in 4D-CBCT.