Decompose kV projection using neural network for improved motion tracking in paraspinal SBRT

He, Xiuxiu; Cai, Weixing; Li, Feifei; Fan, Qilin; Zhang, Pengpeng; Cuaron, John; Cerviño, Laura; Li, Xiang; Li, Tianfang

doi:10.1002/mp.15295

Cited by 12 publications

(14 citation statements)

References 37 publications

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“…We evaluate the performance of the proposed patient-specific prior approach using PCAT, and compare it with ResNetGAN. 11 The side-by-side comparison is shown, with visualization of the images in Figure 3a and corresponding line profiles in Figure 3b. Four examples are randomly chosen from varied x-ray beam angles of the testing patients.…”

Section: Resultsmentioning

confidence: 99%

“…The PCAT incorporated the patient-specific prior knowledge by selectively amplifying the transmission of the projection image features that correlate with features of the object prior. We benchmarked the PCAT approach with the ResNetGAN 11 . The ResNetGAN has a network structure similar to the PCAT, except for incorporating latent features of patient-specific prior.…”

Section: Discussionmentioning

confidence: 99%

“…To elaborate on the viability and effectiveness of patientspecific prior in kV image decomposition, we compare the model performance of the proposed PCAT and the recently published ResNetGAN. 11 For comparison, we keep the same structure for the encoder, decoder, and DFD, for PCAT and ResNetGAN. The PCAT can degenerate to ResNetGAN.…”

Section: Model Comparison Studymentioning

confidence: 99%

“…[8][9][10] Previously, we developed a deep learning-based medical image decomposition approach using the ResNetGAN. 11 However, what remains challenging is that deep-learning-based image generative models tend to generate artifacts when the training data distribution is limited, and downstream applications are susceptible to adversarial attacks. 12 Consequently, it reduces confidence in applying the deep-learning model in medicine.…”

Section: Introduction and Purposementioning

confidence: 99%

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Patient specific prior cross attention for kV decomposition in paraspinal motion tracking

Cai

et al. 2023

Medical Physics

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BackgroundX‐ray image quality is critical for accurate intrafraction motion tracking in radiation therapy.Purpose: This study aims to develop a deep‐learning algorithm to improve kV image contrast by decomposing the image into bony and soft tissue components. In particular, we designed a priori attention mechanism in the neural network framework for optimal decomposition. We show that a patient‐specific prior cross‐attention (PCAT) mechanism can boost the performance of kV image decomposition. We demonstrate its use in paraspinal SBRT motion tracking with online kV imaging.MethodsOnline 2D kV projections were acquired during paraspinal SBRT for patient motion monitoring. The patient‐specific prior images were generated by randomly shifting and rotating spine‐only DRR created from the setup CBCT, simulating potential motions. The latent features of the prior images were incorporated into the PCAT using multi‐head cross attention. The neural network aimed to learn to selectively amplify the transmission of the projection image features that correlate with features of the priori. The PCAT network structure consisted of (1) a dual‐branch generator that separates the spine and soft tissue component of the kV projection image and (2) a dual‐function discriminator (DFD) that provides the realness score of the predicted images. For supervision, we used a loss combining mean absolute error loss, discriminator loss, perceptual loss, total variation, and mean squared error loss for soft tissues. The proposed PCAT approach was benchmarked against previous work using the ResNet generative adversarial network (ResNetGAN) without prior information.ResultsThe trained PCAT had improved performance in effectively retaining and preserving the spine structure and texture information while suppressing the soft tissues from the kV projection images. The decomposed spine‐only x‐ray images had the submillimeter matching accuracy at all beam angles. The decomposed spine‐only x‐ray significantly reduced the maximum errors to 0.44 mm (<2 pixels) in comparison to 0.92 mm (∼4 pixels) of ResNetGAN. The PCAT decomposed spine images also had higher PSNR and SSIM (p‐value < 0.001).ConclusionThe PCAT selectively learned the important latent features by incorporating the patient‐specific prior knowledge into the deep learning algorithm, significantly improving the robustness of the kV projection image decomposition, and leading to improved motion tracking accuracy in paraspinal SBRT.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Model Comparison Studymentioning

confidence: 99%

Section: Introduction and Purposementioning

confidence: 99%

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Patient specific prior cross attention for kV decomposition in paraspinal motion tracking

Cai

et al. 2023

Medical Physics

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“…Figure 7 further shows the proposed PAUnet had improvement in steering clear of misidentifying other high‐frequency objects, wire, and surgical clips, for example, and achieving high accuracy despite overlap with spine or low contrast. As researchers are applying deep learning methods to a broad array of medical technologies, 27 the re‐formularization of these methods could further inspire how the problems in medical imaging are approached, 28 the image synthesis–based image decomposition, 29 for example, the rapid progress of the past decade might be the taxiing phase.…”

Section: Discussionmentioning

confidence: 99%

Automatic stent recognition using perceptual attention U‐net for quantitative intrafraction motion monitoring in pancreatic cancer radiotherapy

Cai

et al. 2022

Medical Physics

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Purpose Stent has often been used as an internal surrogate to monitor intrafraction tumor motion during pancreatic cancer radiotherapy. Based on the stent contours generated from planning CT images, the current intrafraction motion review (IMR) system on Varian TrueBeam only provides a tool to verify the stent motion visually but lacks quantitative information. The purpose of this study is to develop an automatic stent recognition method for quantitative intrafraction tumor motion monitoring in pancreatic cancer treatment. Methods A total of 535 IMR images from 14 pancreatic cancer patients were retrospectively selected in this study, with the manual contour of the stent on each image serving as the ground truth. We developed a deep learning–based approach that integrates two mechanisms that focus on the features of the segmentation target. The objective attention modeling was integrated into the U‐net framework to deal with the optimization difficulties when training a deep network with 2D IMR images and limited training data. A perceptual loss was combined with the binary cross‐entropy loss and a Dice loss for supervision. The deep neural network was trained to capture more contextual information to predict binary stent masks. A random‐split test was performed, with images of ten patients (71%, 380 images) randomly selected for training, whereas the rest of four patients (29%, 155 images) were used for testing. Sevenfold cross‐validation of the proposed PAUnet on the 14 patients was performed for further evaluation. Results Our stent segmentation results were compared with the manually segmented contours. For the random‐split test, the trained model achieved a mean (±standard deviation) stent Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), the center‐of‐mass distance (CMD), and volume difference Voldiff$Vo{l_{diff}}$ were 0.96 (±0.01), 1.01 (±0.55) mm, 0.66 (±0.46) mm, and 3.07% (±2.37%), respectively. The sevenfold cross‐validation of the proposed PAUnet had the mean (±standard deviation) of 0.96 (±0.02), 0.72 (±0.49) mm, 0.85 (±0.96) mm, and 3.47% (±3.27%) for the DSC, HD95, CMD, and Voldiff$Vo{l_{diff}}$. Conclusion We developed a novel deep learning–based approach to automatically segment the stent from IMR images, demonstrated its clinical feasibility, and validated its accuracy compared to manual segmentation. The proposed technique could be a useful tool for quantitative intrafraction motion monitoring in pancreatic cancer radiotherapy.

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Artificial intelligence‐based motion tracking in cancer radiotherapy: A review

Salari,

Wang,

Wynne

et al. 2024

J Applied Clin Med Phys

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Radiotherapy aims to deliver a prescribed dose to the tumor while sparing neighboring organs at risk (OARs). Increasingly complex treatment techniques such as volumetric modulated arc therapy (VMAT), stereotactic radiosurgery (SRS), stereotactic body radiotherapy (SBRT), and proton therapy have been developed to deliver doses more precisely to the target. While such technologies have improved dose delivery, the implementation of intra‐fraction motion management to verify tumor position at the time of treatment has become increasingly relevant. Artificial intelligence (AI) has recently demonstrated great potential for real‐time tracking of tumors during treatment. However, AI‐based motion management faces several challenges, including bias in training data, poor transparency, difficult data collection, complex workflows and quality assurance, and limited sample sizes. This review presents the AI algorithms used for chest, abdomen, and pelvic tumor motion management/tracking for radiotherapy and provides a literature summary on the topic. We will also discuss the limitations of these AI‐based studies and propose potential improvements.

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Decompose kV projection using neural network for improved motion tracking in paraspinal SBRT

Cited by 12 publications

References 37 publications

Patient specific prior cross attention for kV decomposition in paraspinal motion tracking

Patient specific prior cross attention for kV decomposition in paraspinal motion tracking

Automatic stent recognition using perceptual attention U‐net for quantitative intrafraction motion monitoring in pancreatic cancer radiotherapy

Artificial intelligence‐based motion tracking in cancer radiotherapy: A review

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