Facilitating MR-Guided Adaptive Proton Therapy in Children Using Deep Learning-Based Synthetic CT

The emerging role of Artificial Intelligence in proton therapy: A review

Artificial intelligence (AI) has made a tremendous impact in the space of healthcare, and proton therapy is not an exception. Proton therapy has witnessed growing popularity in oncology over recent decades, and researchers are increasingly looking to develop AI and machine learning tools to aid in various steps of the treatment planning and delivery processes. This review delves into the emergent role of AI in proton therapy, evaluating its development, advantages, intended clinical contexts, and areas of application. Through the analysis of 76 studies, we aim to underscore the importance of AI applications in advancing proton therapy and to highlight their prospective influence on clinical practices.

A systematic literature review: deep learning techniques for synthetic medical image generation and their applications in radiotherapy

The aim of this systematic review is to determine whether Deep Learning (DL) algorithms can provide a clinically feasible alternative to classic algorithms for synthetic Computer Tomography (sCT). The following categories are presented in this study: ∙ MR-based treatment planning and synthetic CT generation techniques. ∙ Generation of synthetic CT images based on Cone Beam CT images. ∙ Low-dose CT to High-dose CT generation. ∙ Attenuation correction for PET images. To perform appropriate database searches, we reviewed journal articles published between January 2018 and June 2023. Current methodology, study strategies, and results with relevant clinical applications were analyzed as we outlined the state-of-the-art of deep learning based approaches to inter-modality and intra-modality image synthesis. This was accomplished by contrasting the provided methodologies with traditional research approaches. The key contributions of each category were highlighted, specific challenges were identified, and accomplishments were summarized. As a final step, the statistics of all the cited works from various aspects were analyzed, which revealed that DL-based sCTs have achieved considerable popularity, while also showing the potential of this technology. In order to assess the clinical readiness of the presented methods, we examined the current status of DL-based sCT generation.

Generative Adversarial Network (Generative Artificial Intelligence) in Pediatric Radiology: A Systematic Review

Generative artificial intelligence, especially with regard to the generative adversarial network (GAN), is an important research area in radiology as evidenced by a number of literature reviews on the role of GAN in radiology published in the last few years. However, no review article about GAN in pediatric radiology has been published yet. The purpose of this paper is to systematically review applications of GAN in pediatric radiology, their performances, and methods for their performance evaluation. Electronic databases were used for a literature search on 6 April 2023. Thirty-seven papers met the selection criteria and were included. This review reveals that the GAN can be applied to magnetic resonance imaging, X-ray, computed tomography, ultrasound and positron emission tomography for image translation, segmentation, reconstruction, quality assessment, synthesis and data augmentation, and disease diagnosis. About 80% of the included studies compared their GAN model performances with those of other approaches and indicated that their GAN models outperformed the others by 0.1-158.6%. However, these study findings should be used with caution because of a number of methodological weaknesses. For future GAN studies, more robust methods will be essential for addressing these issues. Otherwise, this would affect the clinical adoption of the GAN-based applications in pediatric radiology and the potential advantages of GAN could not be realized widely.

Deep Learning in MRI-guided Radiation Therapy: A Systematic Review

MRI-guided radiation therapy (MRgRT) offers a precise and adaptive approach to treatment planning. Deep learning applications which augment the capabilities of MRgRT are systematically reviewed. MRI-guided radiation therapy offers a precise, adaptive approach to treatment planning. Deep learning applications which augment the capabilities of MRgRT are systematically reviewed with emphasis placed on underlying methods. Studies are further categorized into the areas of segmentation, synthesis, radiomics, and real time MRI. Finally, clinical implications, current challenges, and future directions are discussed.

Adaptive Proton Therapy of Pediatric Head and Neck Cases Using MRI-Based Synthetic CTs: Initial Experience of the Prospective KiAPT Study

BACKGROUND AND PURPOSE

Interfractional anatomical changes might affect the outcome of proton therapy (PT). We aimed to prospectively evaluate the role of Magnetic Resonance Imaging (MRI) based adaptive PT for children with tumors of the head and neck and base of skull.

METHODS

MRI verification images were acquired at half of the treatment course. A synthetic computed tomography (CT) image was created using this MRI and a deformable image registration (DIR) to the reference MRI. The methodology was verified with in-silico phantoms and validated using a clinical case with a shrinking cystic hygroma on the basis of dosimetric quantities of contoured structures. The dose distributions on the verification X-ray CT and on the synthetic CT were compared with a gamma-index test using global 2 mm/2% criteria.

RESULTS

Regarding the clinical validation case, the gamma-index pass rate was 98.3%. Eleven patients were included in the clinical study. The most common diagnosis was rhabdomyosarcoma (73%). Craniofacial tumor site was predominant in 64% of patients, followed by base of skull (18%). For one individual case the synthetic CT showed an increase in the median D2 and Dmax dose on the spinal cord from 20.5 GyRBE to 24.8 GyRBE and 14.7 GyRBE to 25.1 GyRBE, respectively. Otherwise, doses received by OARs remained relatively stable. Similarly, the target volume coverage seen by D95% and V95% remained unchanged.

CONCLUSIONS

The method of transferring anatomical changes from MRIs to a synthetic CTs was successfully implemented and validated with simple, commonly available tools. In the frame of our early results on a small cohort, no clinical relevant deterioration for neither PTV coverage nor an increased dose burden to OARs occurred. However, the study will be continued to identify a pediatric patient cohort, which benefits from adaptive treatment planning.