Respiration-phase-matched digital tomosynthesis imaging for moving target verification: a feasibility study

Prior frequency guided diffusion model for limited angle (LA)-CBCT reconstruction

Objective.Cone-beam computed tomography (CBCT) is widely used in image-guided radiotherapy. Reconstructing CBCTs from limited-angle acquisitions (LA-CBCT) is highly desired for improved imaging efficiency, dose reduction, and better mechanical clearance. LA-CBCT reconstruction, however, suffers from severe under-sampling artifacts, making it a highly ill-posed inverse problem. Diffusion models can generate data/images by reversing a data-noising process through learned data distributions; and can be incorporated as a denoiser/regularizer in LA-CBCT reconstruction. In this study, we developed a diffusion model-based framework, prior frequency-guided diffusion model (PFGDM), for robust and structure-preserving LA-CBCT reconstruction.Approach.PFGDM uses a conditioned diffusion model as a regularizer for LA-CBCT reconstruction, and the condition is based on high-frequency information extracted from patient-specific prior CT scans which provides a strong anatomical prior for LA-CBCT reconstruction. Specifically, we developed two variants of PFGDM (PFGDM-A and PFGDM-B) with different conditioning schemes. PFGDM-A applies the high-frequency CT information condition until a pre-optimized iteration step, and drops it afterwards to enable both similar and differing CT/CBCT anatomies to be reconstructed. PFGDM-B, on the other hand, continuously applies the prior CT information condition in every reconstruction step, while with a decaying mechanism, to gradually phase out the reconstruction guidance from the prior CT scans. The two variants of PFGDM were tested and compared with current available LA-CBCT reconstruction solutions, via metrics including peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM).Main results.PFGDM outperformed all traditional and diffusion model-based methods. The mean(s.d.) PSNR/SSIM were 27.97(3.10)/0.949(0.027), 26.63(2.79)/0.937(0.029), and 23.81(2.25)/0.896(0.036) for PFGDM-A, and 28.20(1.28)/0.954(0.011), 26.68(1.04)/0.941(0.014), and 23.72(1.19)/0.894(0.034) for PFGDM-B, based on 120°, 90°, and 30° orthogonal-view scan angles respectively. In contrast, the PSNR/SSIM was 19.61(2.47)/0.807(0.048) for 30° for DiffusionMBIR, a diffusion-based method without prior CT conditioning.Significance. PFGDM reconstructs high-quality LA-CBCTs under very-limited gantry angles, allowing faster and more flexible CBCT scans with dose reductions.

An unsupervised 2D-3D deformable registration network (2D3D-RegNet) for cone-beam CT estimation

Acquiring CBCTs from a limited scan angle can help to reduce the imaging time, save the imaging dose, and allow continuous target localizations through arc-based treatments with high temporal resolution. However, insufficient scan angle sampling leads to severe distortions and artifacts in the reconstructed CBCT images, limiting their clinical applicability. 2D-3D deformable registration can map a prior fully-sampled CT/CBCT volume to estimate a new CBCT, based on limited-angle on-board cone-beam projections. The resulting CBCT images estimated by 2D-3D deformable registration can successfully suppress the distortions and artifacts, and reflect up-to-date patient anatomy. However, traditional iterative 2D-3D deformable registration algorithm is very computationally expensive and time-consuming, which takes hours to generate a high quality deformation vector field (DVF) and the CBCT. In this work, we developed an unsupervised, end-to-end, 2D-3D deformable registration framework using convolutional neural networks (2D3D-RegNet) to address the speed bottleneck of the conventional iterative 2D-3D deformable registration algorithm. The 2D3D-RegNet was able to solve the DVFs within 5 seconds for 90 orthogonally-arranged projections covering a combined 90° scan angle, with DVF accuracy superior to 3D-3D deformable registration, and on par with the conventional 2D-3D deformable registration algorithm. We also performed a preliminary robustness analysis of 2D3D-RegNet towards projection angular sampling frequency variations, as well as scan angle offsets. The synergy of 2D3D-RegNet with biomechanical modeling was also evaluated, and demonstrated that 2D3D-RegNet can function as a fast DVF solution core for further DVF refinement.

4D digital tomosynthesis image reconstruction using brute force-based adaptive total variation (BF-ATV) in a prototype LINAC system

Respiratory-correlated cone-beam CT (CBCT) not only inhibits rapid scanning due to the slow speed of the LINAC head gantry rotation, but its implementation for routine patient imaging is impractical because of the high radiation dose delivered during the process. Digital tomosynthesis (DTS) is a potentially faster technique that delivers a much lower radiation dose by reducing the number of projections in a limited angular range. Unfortunately, 4D-DTS introduces strong aliasing artifacts in the reconstructed images due to the sparsely sampled projections in each respiratory phase bin. The authors hereby suggest a novel low-dose 4D-DTS image reconstruction method that achieves a compromise between the occurrence of aliasing artifacts and image smoothing using a brute force-based adaptive weighting parameter searching technique. We used a prototype LINAC system mounted with a flat-panel detector to acquire tomosynthesis projections of respiratory motion in a phantom in the anterior-posterior (AP) and lateral views. Three different 4D-DTS image reconstruction schemes that included conventional filtered back-projection (FBP), adaptive steepest descent projection onto convex sets (ASD-POCS), and the proposed brute force-based adaptive total variation (BF-ATV) were implemented in four different respiratory phase bins for both AP and lateral views. All reconstructions were accelerated using a single GPU card to reduce the computation time. To study the performance of the algorithm under various sparse conditions, we operated the prototype system in three different gantry sweep modes. The results indicate that the proposed BF-ATV method yields the largest structural similarities in the differenced image between the ground-truth dataset acquired using the slow gantry sweep mode and the sparse dataset from both moderate and fast sweep modes. In addition, the proposed method maintained the object sharpness with less streaking lines and small loss of sharpness compared to the conventional FBP and ASD-POCS methods. In conclusion, the proposed low-dose 4D-DTS reconstruction scheme may provide better performance due in part to its rapid scanning. Therefore, it is potentially applicable to practical 4D imaging for radiotherapy.

Model evaluation of rapid 4-dimensional lung tomosynthesis

Purpose

This is an investigation of a lung motion digital tomosynthesis (DTS) model using combined stationary detector and stationary cold cathode x-ray sources at projection acquisition rates that exceed the present norms. The intent is to reduce anatomical uncertainties from artifacts inherent in thoracic 4-dimensional computed tomography (CT).

Methods and materials

Parameters necessary to perform rapid lung 4-dimensional DTS were studied using a conventional radiographic system with linear motion of the x-ray source and a simple hypothetical hardware performance model. Hypothetical rapid imaging parameters of sweep duration, projections per second, pulse duration, and tube current (mA) were derived on the basis of 0.5 mm and 1 mm motion captures per phase, 10 and 15 breaths per minute (bpm), 10 to 40 mm breathing amplitude, and 2 signal-to-noise ratio (SNR) levels. Anterior-posterior and lateral projection images of a normal size anthropomorphic thorax phantom with iodine contrast inserts were collected and reconstructed with an algebraic algorithm to study the effects of reduced x-ray output associated with field emission cold cathodes composed of carbon nanotubes or metal Spindt-type. Radiographic projections were collected at 3 SNR levels that were set at standard clinical DTS milliampere-seconds (mAs) and reduced corresponding to 50% and 25% standard DTS mAs to simulate a reduced x-ray output.

Results

The DTS SNR of the inserts was superior in all reconstructions at clinical mAs versus automatic exposure-control radiographs and superior in 3 of 4 at the 50% and 25% mAs levels. The most demanding performance parameters corresponding to 40 mm amplitude, 15 bpm, 0.5 mm motion capture limit, and 61 projections were sweep duration (10.4 msec), projection rate (5862 projections per second), pulse duration (0.161 msec), current 189 mA anterior-posterior, and 653 mA lateral.

Conclusions

Feasibility depends on the output performance of stationary cold cathode hardware being developed for DTS. Present image receptor technology can accommodate frame acquisition rates.

Clinical Study of Orthogonal-View Phase-Matched Digital Tomosynthesis for Lung Tumor Localization

Background and Purpose:

Compared to cone-beam computed tomography, digital tomosynthesis imaging has the benefits of shorter scanning time, less imaging dose, and better mechanical clearance for tumor localization in radiation therapy. However, for lung tumors, the localization accuracy of the conventional digital tomosynthesis technique is affected by the lack of depth information and the existence of lung tumor motion. This study investigates the clinical feasibility of using an orthogonal-view phase-matched digital tomosynthesis technique to improve the accuracy of lung tumor localization.

Materials and Methods:

The proposed orthogonal-view phase-matched digital tomosynthesis technique benefits from 2 major features: (1) it acquires orthogonal-view projections to improve the depth information in reconstructed digital tomosynthesis images and (2) it applies respiratory phase-matching to incorporate patient motion information into the synthesized reference digital tomosynthesis sets, which helps to improve the localization accuracy of moving lung tumors. A retrospective study enrolling 14 patients was performed to evaluate the accuracy of the orthogonal-view phase-matched digital tomosynthesis technique. Phantom studies were also performed using an anthropomorphic phantom to investigate the feasibility of using intratreatment aggregated kV and beams’ eye view cine MV projections for orthogonal-view phase-matched digital tomosynthesis imaging. The localization accuracy of the orthogonal-view phase-matched digital tomosynthesis technique was compared to that of the single-view digital tomosynthesis techniques and the digital tomosynthesis techniques without phase-matching.

Results:

The orthogonal-view phase-matched digital tomosynthesis technique outperforms the other digital tomosynthesis techniques in tumor localization accuracy for both the patient study and the phantom study. For the patient study, the orthogonal-view phase-matched digital tomosynthesis technique localizes the tumor to an average (± standard deviation) error of 1.8 (0.7) mm for a 30° total scan angle. For the phantom study using aggregated kV–MV projections, the orthogonal-view phase-matched digital tomosynthesis localizes the tumor to an average error within 1 mm for varying magnitudes of scan angles.

Conclusion:

The pilot clinical study shows that the orthogonal-view phase-matched digital tomosynthesis technique enables fast and accurate localization of moving lung tumors.

Reducing scan angle using adaptive prior knowledge for a limited-angle intrafraction verification (LIVE) system for conformal arc radiotherapy

The purpose of this study is to develop an adaptive prior knowledge guided image estimation technique to reduce the scan angle needed in the limited-angle intrafraction verification (LIVE) system for 4D-CBCT reconstruction. The LIVE system has been previously developed to reconstruct 4D volumetric images on-the-fly during arc treatment for intrafraction target verification and dose calculation. In this study, we developed an adaptive constrained free-form deformation reconstruction technique in LIVE to further reduce the scanning angle needed to reconstruct the 4D-CBCT images for faster intrafraction verification. This technique uses free form deformation with energy minimization to deform prior images to estimate 4D-CBCT based on kV-MV projections acquired in extremely limited angle (orthogonal 3°) during the treatment. Note that the prior images are adaptively updated using the latest CBCT images reconstructed by LIVE during treatment to utilize the continuity of the respiratory motion. The 4D digital extended-cardiac-torso (XCAT) phantom and a CIRS 008A dynamic thoracic phantom were used to evaluate the effectiveness of this technique. The reconstruction accuracy of the technique was evaluated by calculating both the center-of-mass-shift (COMS) and 3D volume-percentage-difference (VPD) of the tumor in reconstructed images and the true on-board images. The performance of the technique was also assessed with varied breathing signals against scanning angle, lesion size, lesion location, projection sampling interval, and scanning direction. In the XCAT study, using orthogonal-view of 3° kV and portal MV projections, this technique achieved an average tumor COMS/VPD of 0.4  ±  0.1 mm/5.5  ±  2.2%, 0.6  ±  0.3 mm/7.2  ±  2.8%, 0.5  ±  0.2 mm/7.1  ±  2.6%, 0.6  ±  0.2 mm/8.3  ±  2.4%, for baseline drift, amplitude variation, phase shift, and patient breathing signal variation, respectively. In the CIRS phantom study, this technique achieved an average tumor COMS/VPD of 0.7  ±  0.1 mm/7.5  ±  1.3% for a 3 cm lesion and 0.6  ±  0.2 mm/11.4  ±  1.5% for a 2 cm lesion in the baseline drift case. The average tumor COMS/VPD were 0.5  ±  0.2 mm/10.8  ±  1.4%, 0.4  ±  0.3 mm/7.3  ±  2.9%, 0.4  ±  0.2 mm/7.4  ±  2.5%, 0.4  ±  0.2 mm/7.3  ±  2.8% for the four real patient breathing signals, respectively. Results demonstrated that the adaptive prior knowledge guided image estimation technique with LIVE system is robust against scanning angle, lesion size, location and scanning direction. It can estimate on-board images accurately with as little as 6 projections in orthogonal-view 3° angle. In conclusion, adaptive prior knowledge guided image reconstruction technique accurately estimates 4D-CBCT images using extremely-limited angle and projections. This technique greatly improves the efficiency and accuracy of LIVE system for ultrafast 4D intrafraction verification of lung SBRT treatments.

Preliminary clinical evaluation of a 4D-CBCT estimation technique using prior information and limited-angle projections

BACKGROUND AND PURPOSE

A technique has been previously reported to estimate high-quality 4D-CBCT using prior information and limited-angle projections. This study is to investigate its clinical feasibility through both phantom and patient studies.

MATERIALS AND METHODS

The new technique used to estimate 4D-CBCT is called MMFD-NCC. It is based on the previously reported motion modeling and free-form deformation (MMFD) method, with the introduction of normalized-cross-correlation (NCC) as a new similarity metric. The clinical feasibility of this technique was evaluated by assessing the accuracy of estimated anatomical structures in comparison to those in the 'ground-truth' reference 4D-CBCTs, using data obtained from a physical phantom and three lung cancer patients. Both volume percentage error (VPE) and center-of-mass error (COME) of the estimated tumor volume were used as the evaluation metrics.

RESULTS

The average VPE/COME of the tumor in the prior image was 257.1%/10.1 mm for the phantom study and 55.6%/3.8 mm for the patient study. Using only orthogonal-view 30° projections, the MMFD-NCC has reduced the corresponding values to 7.7%/1.2 mm and 9.6%/1.1 mm, respectively.

CONCLUSION

The MMFD-NCC technique is able to estimate 4D-CBCT images with geometrical accuracy of the tumor within 10% VPE and 2 mm COME, which can be used to improve the localization accuracy of radiotherapy.