Impact of lung density on isolated lung tumor dose in VMAT using inline MR-Linac

PurposeThis study investigated the impact of lung density on the isolated lung tumor dose for volumetric modulated arc therapy (VMAT) in an inline magnetic resonance linear accelerator (MR-Linac) using the Monte Carlo (MC) simulation.MethodsCT images of the thorax phantoms with lung tumors of 1, 2, and 3 cm diameters were converted into voxel-base phantoms with lung densities of 0.1, 0.2, and 0.3 g/cm³, respectively. The dose distributions were calculated for partial-arc VMAT. The dose distributions were compared using dose differences, dose volume histograms, and dose volume indices.ResultsIn all cases, the inline magnetic field significantly enhanced the lung tumor dose compared to that at 0 T. For the 1 cm lung tumor, the inline magnetic field of 1 T increased the minimum dose of 95% of the Planning target volume (PTV D₉₅) by 14.0% in 0.1 g/cm³ lung density as compared to that in 0.3 g/cm³ at 0 T. In contrast, at 0 and 0.5 T, the PTV D₉₅ in 0.3 g/cm³ lung density was larger than that in lung density of 0.1 g/cm³. For the 2 cm lung tumor, a similar tendency to 1 cm was observed, whereas the dose impact of lung density was smaller than that for 1 cm. For the 3 cm lung tumor, the lung tumor dose was independent of lung density at 0.5 T and 1.0 T.ConclusionThe inline MR-Linac with the magnetic field over 1 T can enhance the PTV D₉₅ for VMAT regardless of the lung density.     

Artificial Intelligence in magnetic Resonance guided Radiotherapy: Medical and physical considerations on state of art and future perspectives

Over the last years, technological innovation in Radiotherapy (RT) led to the introduction of Magnetic Resonance-guided RT (MRgRT) systems.Due to the higher soft tissue contrast compared to on-board CT-based systems, MRgRT is expected to significantly improve the treatment in many situations. MRgRT systems may extend the management of inter- and intra-fraction anatomical changes, offering the possibility of online adaptation of the dose distribution according to daily patient anatomy and to directly monitor tumor motion during treatment delivery by means of a continuous cine MR acquisition.Online adaptive treatments require a multidisciplinary and well-trained team, able to perform a series of operations in a safe, precise and fast manner while the patient is waiting on the treatment couch.Artificial Intelligence (AI) is expected to rapidly contribute to MRgRT, primarily by safely and efficiently automatising the various manual operations characterizing online adaptive treatments. Furthermore, AI is finding relevant applications in MRgRT in the fields of image segmentation, synthetic CT reconstruction, automatic (on-line) planning and the development of predictive models based on daily MRI.This review provides a comprehensive overview of the current AI integration in MRgRT from a medical physicist’s perspective. Medical physicists are expected to be major actors in solving new tasks and in taking new responsibilities: their traditional role of guardians of the new technology implementation will change with increasing emphasis on the managing of AI tools, processes and advanced systems for imaging and data analysis, gradually replacing many repetitive manual tasks.     

Monte Carlo study of dosimetric impact of gadolinium contrast medium in transverse field MR-Linac system

The aim of this study is to evaluate the dosimetric impact of gadolinium contrast medium (Gadovist) in a transverse MR-Linac system using Monte Carlo methods. The dose distributions were calculated using two heterogeneous multi-layer phantoms consisting of Gadovist, water, bone, and lung. The photon beam was irradiated with a filed size of 5 × 5 cm², and a transverse magnetic field of 0–3.0 T was applied perpendicular to the incident photon beam. Next, dose distributions for brain, head and neck (H&N), and lung cancer patients were calculated using a patient voxel-based phantom with and without replacing the patient’s GTV with Gadovist. The dose at the water-Gadovist interface increased by 8% without a magnetic field. A similar dose increment was observed at 0.35 T. In contrast, the dose increment at the water-Gadovist interface was small at 1.5 T and a dose decrement of 5% was observed at 3.0 T. The dose variation at the lung-Gadovist interface was larger than that at the water-Gadovist interface. The mass collision stopping power ratio for Gadovist was 7% lower than that for water, whereas, the electron fluence spectra at the water-Gadovist interface increased by 17.5%. In a patient study, Gadovist increased the D_mean for brain, H&N, and lung cancer patients by 0.65–8.9%. The dose variation due to Gadovist grew large in the low-dose region in H&N and lung cancer. The GTV dose variation due to Gadovist in all treatment site was below 2% at 0–3 T if the Gadovist concentration was lower than 0.2 mmol/ml⁻¹.     

Impact of the cavity on sinus wall dose in magnetic resonance image-guided radiation therapy

PurposeThis study aims to investigate the impact of the cavity on the sinus wall dose by comparing dose distributions with and without the sinus under magnetic fields using Monte Carlo calculations.MethodsA water phantom containing a sinus cavity (Empty) was created, and dose distributions were calculated for 1, 2, and 4 irradiation fields with 6 MV photons. The sinus in the phantom was then filled with water (Full), and the dose distributions were calculated again. The sinus was set to cubes of 2 cm and 4 cm. The magnetic field was applied to the transverse and inline direction under the magnetic flux densities of 0 T, 0.35 T, 0.5 T, 1.0 T, and 1.5 T. The dose distributions were analyzed by the dose difference, dose volume histogram, and D₂ with sinus wall thicknesses of 1 and 5 mm.ResultsD₂ in the “Empty” sinus wall under transverse magnetic fields for the 1-field and 4-field cases was 51.9% higher and 3.7% lower than that in the “Full” sinus wall at 1.5 T, respectively. Meanwhile, D₂ in the Empty sinus wall under inline magnetic fields for 1-field and 4-fields was 2.3% and 2.6% lower than that in the “Full” sinus at B = 0 T, respectively, whereas D₂ was 0.9% and 0.7% larger at 1.0 T, respectively.ConclusionsThe impact of the cavity on the sinus wall dose depends on the magnetic flux density, direction of the magnetic field and irradiation beam, and number of irradiation fields.     

A Deep Learning-based correction to EPID dosimetry for attenuation and scatter in the Unity MR-Linac system

PurposeEPID dosimetry in the Unity MR-Linac system allows for reconstruction of absolute dose distributions within the patient geometry. Dose reconstruction is accurate for the parts of the beam arriving at the EPID through the MRI central unattenuated region, free of gradient coils, resulting in a maximum field size of ~10 × 22 cm² at isocentre. The purpose of this study is to develop a Deep Learning-based method to improve the accuracy of 2D EPID reconstructed dose distributions outside this central region, accounting for the effects of the extra attenuation and scatter.MethodsA U-Net was trained to correct EPID dose images calculated at the isocenter inside a cylindrical phantom using the corresponding TPS dose images as ground truth for training. The model was evaluated using a 5-fold cross validation procedure. The clinical validity of the U-Net corrected dose images (the so-called DEEPID dose images) was assessed with in vivo verification data of 45 large rectum IMRT fields. The sensitivity of DEEPID to leaf bank position errors (±1.5 mm) and ±5% MU delivery errors was also tested.ResultsCompared to the TPS, in vivo 2D DEEPID dose images showed an average γ-pass rate of 90.2% (72.6%–99.4%) outside the central unattenuated region. Without DEEPID correction, this number was 44.5% (4.0%–78.4%). DEEPID correctly detected the introduced delivery errors.ConclusionsDEEPID allows for accurate dose reconstruction using the entire EPID image, thus enabling dosimetric verification for field sizes up to ~19 × 22 cm² at isocentre. The method can be used to detect clinically relevant errors.