A review of the clinical introduction of 4D particle therapy research concepts

Physics and Imaging in Radiation Oncology, Journal Year: 2024, Volume and Issue: 29, P. 100535 - 100535

Published: Jan. 1, 2024

Background and purposeMany 4D particle therapy research concepts have been recently translated into clinics, however, remaining substantial differences depend on the indication institute-related aspects. This work aims to summarize current state-of-the-art technology outline a roadmap for future developments.Material methodsThis review focused clinical implementation of approaches imaging, treatment planning, delivery evaluation based 2021 2022 Treatment Workshops Particle Therapy as well most recent surveys, guidelines scientific papers dedicated this topic.ResultsAvailable technological capabilities motion surveillance compensation determined course each treatment. management, techniques strategies including imaging were diverse depended many factors. These included aspects amplitude, tumour location, accelerator driving necessity centre-specific dosimetric validation. Novel methodologies X-ray image processing MRI real-time tracking management shown large potential online offline adaptation schemes compensating anatomical changes over course.The latest developments dominated by artificial intelligence methods, FLASH adding another level complexity but also opportunities in context treatments.ConclusionThis showed that rapid advances radiation oncology together with available intrafractional adaptive paved way towards implementation.

Language: Английский

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Gradient system characterization of a 1.5 T MR‐Linac with application to 4D UTE imaging for adaptive MR‐guided radiotherapy of lung cancer

Magnetic Resonance in Medicine, Journal Year: 2025, Volume and Issue: unknown

Published: March 19, 2025

To measure the gradient system transfer function (GSTF) of an MR-Linac (Elekta Unity, Stockholm, Sweden) using accessible phantom-based method and to apply trajectory corrections for UTE image reconstruction in context MR-guided radiotherapy lung cancer. The first-order GSTF a 1.5 T, split Elekta Unity was measured thin-slice technique characterize imperfections each physical axis (X, Y, Z). Repeatability measurements were performed 48 h apart. applied correction multi-echo (TEs = 0.176, 1.85, 3.52 ms) allow UTE-Dixon inputs generation synthetic CT. Images acquired anthropomorphic phantom two free-breathing cancer patients. For patient scans, respiratory-correlated 4D-MR images reconstructed self-navigation iterative compressed-sensing algorithm. magnitude similar across X/Y/Z axes up ˜6 kHz. phase between components ˜3 demonstrated minimal variations corresponding delay difference 0.06 μs. Corrected spokes are shifted approximately 1 m-1 compared nominal k-space location. exhibited improved signal uniformity contrast reduced halo loss artifacts. Trajectory later TE did not improve overall quality. proposed measurement standard hardware enables successful imaging reconstruction, with applications CT radiotherapy.

Language: Английский

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The role of 4D particle therapy in daily patient care and research

Physics and Imaging in Radiation Oncology, Journal Year: 2024, Volume and Issue: 29, P. 100560 - 100560

Published: Jan. 1, 2024

Language: Английский

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Multi-Device Parallel MRI Reconstruction: Efficient Partitioning for Undersampled 5D Cardiac CINE

Sensors, Journal Year: 2024, Volume and Issue: 24(4), P. 1313 - 1313

Published: Feb. 18, 2024

Cardiac CINE, a form of dynamic cardiac MRI, is indispensable in the diagnosis and treatment heart conditions, offering detailed visualization essential for early detection diseases. As demand higher-resolution images increases, so does volume data requiring processing, presenting significant computational challenges that can impede efficiency diagnostic imaging. Our research presents an approach takes advantage power multiple Graphics Processing Units (GPUs) to address these challenges. GPUs are devices capable performing large volumes computations short period, have significantly improved MRI reconstruction process, allowing be produced faster. The innovation our work resides utilizing multi-device system processing substantial demanded by high-resolution, five-dimensional MRI. This surpasses memory capacity limitations single partitioning datasets into smaller, manageable segments parallel thereby preserving image integrity accelerating times. Utilizing OpenCL technology, offers adaptability cross-platform functionality, ensuring wider applicability. proposed advancement medical imaging, process facilitating faster more effective health assessment.

Language: Английский

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Accelerating MRI Uncertainty Estimation with Mask-Based Bayesian Neural Network

Published: July 24, 2024

Language: Английский

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