Recent Advancements of Artificial Intelligence in Particle Therapy

Peng, Hao; Wu, Chao; Nguyen, Dan; Schuemann, Jan; Mairani, A.; Pu, Yunjiao; Jiang, Steve B

doi:10.1109/trpms.2023.3241102

Cited by 5 publications

(8 citation statements)

References 133 publications

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“…The obtained values are then compared with those available in the literature. We particularly refer to other works regarding AI-driven sCT generation in proton or carbon ion therapy, starting from other imaging modalities, such as CBCT (Kurz et , since to our knowledge this is the first attempt at using in-beam PET data for this task (Peng et al 2023). In table 3, we summarise the results from the literature used in the comparison.…”

Section: Image Quality Assessmentmentioning

confidence: 99%

“…This project aims to enhance image interpretability by generating sCT via neural network (NN) algorithms. NNs have exhibited substantial success in various imaging tasks and have recently gained traction in multi-modal imaging for diagnostics (Yoo 2015, Spadea et al 2021, Man and Chahl 2022, Peng et al 2023. In proton therapy, NNs offer a unique opportunity to integrate multiple data sources, including the planning CT, to reconstruct control CT images.…”

Section: Introductionmentioning

confidence: 99%

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Synthetic CT imaging for PET monitoring in proton therapy: a simulation study

Moglioni,

Carra,

Arezzini

et al. 2024

Phys. Med. Biol.

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Objective. This study addresses a fundamental limitation of In-beam Positron Emission Tomography (IB-PET) in proton therapy: the lack of direct anatomical representation in the images it produces. We aim to overcome this shortcoming by pioneering the application of deep learning techniques to create synthetic control CT images (sCT) from combining IB-PET and planning CT scan data.Approach. We conducted simulations involving six patients who underwent irradiation with proton beams. Leveraging the architecture of a Visual Transformer (ViT) Neural Network (NN), we developed a model to generate sCT images of these patients using the planning CT scans and the inter-fractional simulated PET activity maps during irradiation. To evaluate the model's performance, a comparison was conducted between the sCT images produced by the ViT model and the authentic control CT images – serving as the benchmark.Main Results. The Structural Similarity Index (SSIM) was computed at a mean value across all patients of 0.91, while the Mean Absolute Error (MAE) measured 22 Hounsfield Units (HU). Root Mean Squared Error (RMSE) and Peak Signal-to-Noise Ratio (PSNR) values were 56 HU and 30 dB, respectively. The Dice Similarity Coefficient (DSC) exhibited a value of 0.98. These values are comparable to or exceed those found in the literature. More than 70% of the synthetic morphological changes were found to be geometrically compatible with the ones reported in the real control CT scan.Significance. Our study presents an innovative approach to surface the hidden anatomical information of IB-PET in proton therapy. Our ViT-based model successfully generates sCT images from inter-fractional PET data and planning CT scans. Our model's performance stands on par with existing models relying on input from Cone Beam CT (CBCT) or Magnetic Resonance Imaging (MRI), which contain more anatomical information than activity maps.

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Section: Image Quality Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Synthetic CT imaging for PET monitoring in proton therapy: a simulation study

Moglioni,

Carra,

Arezzini

et al. 2024

Phys. Med. Biol.

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“…• Production of β + emitting nuclei from nuclear interactions between the primary particles and the medium, used for range monitoring purposes (see Section 6.1.1). General purpose MC codes are able to predict the yields and spatial distributions of the most abundantly produced species as 11 C or 15 O with sufficient accuracy, but more efforts are needed to predict spatial distributions of nuclides with short lifetimes such as 10 C or 12 N [10]. Figure 9 shows the cross section data from the EXFOR library for the production of 11 [31][32][33][34][35][36][37][38][39][40][41].…”

Section: Nuclear Physics Modeling In MC Codesmentioning

confidence: 99%

“…Various recent detailed reviews about particle therapy hardware technologies are available [3][4][5][6], as well as overviews of radiobiological advances [7][8][9], Monte Carlo simulations [10,11], treatment planning [12,13], treatment verification techniques [14], and applications of artificial intelligence in particle therapy [15]. Future directions in proton and particle therapy are discussed by Mohan [16] and Graeff et al [17], respectively.…”

Section: Introductionmentioning

confidence: 99%

Technological Developments and Future Perspectives in Particle Therapy: A Topical Review

Kraan,

Del Guerra

2024

IEEE Trans. Radiat. Plasma Med. Sci.

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In the last decades, important technological progress has been made to enhance the quality and efficiency of particle therapy treatments. Continuous improvements in dose delivery, treatment planning and verification techniques have lead to higher dose conformity and better sparing of healthy tissue. At the same time, particle therapy treatments are complex and much more expensive than conventional radiotherapy, and only highly specialized facilities can offer these treatments. Cost reduction is thus a strong drive behind technological developments in the field. The number of treatment facilities offering proton and carbon therapy has strongly grown in the last decades, and the amount of research efforts and innovations have increased continuously.From a technological perspective, advances in hardware are often accompanied by innovations in software and computation, and vice versa. In this review we will present a basic overview of technological advances in particle therapy hardware (accelerators, gantries, applications of superconductivity, treatment verification techniques), software (Monte Carlo simulations, treatment planning calculations), and studies towards clinical applications. By combining a broad selection of topics into a single review and by covering both proton and carbon therapy, we aim at providing the reader a unique overview of the evolution of various technologies developed for particle therapy.

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“…These and many other developments promise increased process efficiency and decreased facility operational costs, coupled with improving accuracy and personalisation of PT 22,27,31,144,145,148 . They include accelerator control efficiency, higher dose rates, faster energy and beamline switching, flexible beam direction selection, and beam verification and measurement equipment and methods, as well as treatment planning algorithms and software systems, online imaging systems and image‐guidance, 22,150–152 motion management methods, 24,153,154 adaptive CIRT 149,155 and the application of machine learning/artificial intelligence to many aspects of PT 156,157 …”

Section: Costs and Cost Effectiveness?mentioning

confidence: 99%

The rationale for a carbon ion radiation therapy facility in Australia

Thwaites,

Prokopovich,

Garrett

et al. 2023

J of Medical Radiation Sci

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Australia has taken a collaborative nationally networked approach to achieve particle therapy capability. This supports the under‐construction proton therapy facility in Adelaide, other potential proton centres and an under‐evaluation proposal for a hybrid carbon ion and proton centre in western Sydney. A wide‐ranging overview is presented of the rationale for carbon ion radiation therapy, applying observations to the case for an Australian facility and to the clinical and research potential from such a national centre.

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Recent Advancements of Artificial Intelligence in Particle Therapy

Cited by 5 publications

References 133 publications

Synthetic CT imaging for PET monitoring in proton therapy: a simulation study

Synthetic CT imaging for PET monitoring in proton therapy: a simulation study

Technological Developments and Future Perspectives in Particle Therapy: A Topical Review

The rationale for a carbon ion radiation therapy facility in Australia

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