Technical innovations and developments in areas such as disease localization, dose calculation algorithms, motion management and dose delivery technologies have revolutionized radiation therapy resulting in improved patient care with superior outcomes. A consequence of the ability to design and accurately deliver complex radiation fields is the need for improved target visualization through imaging. While CT imaging has been the standard of care for more than three decades, the superior soft tissue contrast afforded by MR has resulted in the adoption of this technology in radiation therapy. With the development of real time MR imaging techniques, the problem of real time motion management is enticing. Currently, the integration of an MR imaging and megavoltage radiation therapy treatment delivery system (MR-linac or MRL) is a reality that has the potential to provide improved target localization and real time motion management during treatment. Higher magnetic field strengths provide improved image quality potentially providing the backbone for future work related to image texture analysis - a field known as Radiomics – thereby providing meaningful information on the selection of future patients for radiation dose escalation, motion-managed treatment techniques and ultimately better patient care. On-going advances in MRL technologies promise improved real time soft tissue visualization, treatment margin reductions, beam optimization, inhomogeneity corrected dose calculation, fast multileaf collimators and volumetric arc radiation therapy. This review article provides rationale, advantages and disadvantages as well as ideas for future research in MRI related to radiation therapy mainly in adoption of MRL.

Advances in image-guided radiotherapy have brought about improved oncologic outcomes and reduced toxicity. The next generation of image guidance in the form of magnetic resonance imaging (MRI) will improve visualization of tumors and make radiation treatment adaptation possible. In this review, we discuss the role that MRI plays in radiotherapy, with a focus on the integration of MRI with the linear accelerator. The MR linear accelerator (MR-Linac) will provide real-time imaging, help assess motion management, and provide online adaptive therapy. Potential advantages and the current state of these MR-Linacs are highlighted, with a discussion of six different clinical scenarios, leading into a discussion on the future role of these machines in clinical workflows.

This study aims to investigate the feasibility of using convolutional neural networks to predict an accurate and high resolution dose distribution from an approximated and low resolution input dose. Sixty-six patients were treated for prostate cancer with VMAT. We created the treatment plans using the Acuros XB algorithm with 2 mm grid size, followed by the dose calculated using the anisotropic analytical algorithm with 5 mm grid with the same plan parameters. U-net model was used to predict 2 mm grid dose from 5 mm grid dose. We investigated the two models differing for the training data used as input, one used just the low resolution dose (D model) and the other combined the low resolution dose with CT data (DC model). Dice similarity coefficient (DSC) was calculated to ascertain how well the shape of the dose-volume is matched. We conducted gamma analysis for the following: DVH from the two models and the reference DVH for all prostate structures. The DSC values in the DC model were significantly higher than those in the D model (p < 0.01). For the CTV, PTV, and bladder, the gamma passing rates in the DC model were significantly higher than those in the D model (p < 0.002-0.02). The mean doses in the CTV and PTV for the DC model were significantly better matched to those in the reference dose (p < 0.0001). The proposed U-net model with dose and CT image used as input predicted more accurate dose.

Background: Plastic scintillating detectors (PSD) have gained popularity due to small size and are ideally suited in small‐field dosimetry due to no correction needed and hence detector reading can be compared to dose. Likewise, these detectors are active and water equivalent. A new PSD from Blue Physics is characterized in photon beam.Purpose: Innovation in small‐field dosimetry detector has led us to examine Blue Physics PSD (BP‐PSD) for use in photon beams from linear accelerator.Methods: BP‐PSD was acquired and its characteristics were evaluated in photon beams from a Varian TrueBeam. Data were collected in a 3D water tank. Standard parameters; dose, dose rate, energy, angular dependence and temperature dependence were studied. Depth dose, profiles and output in a reference condition as well as small fields were measured.Results: BP‐PSD is versatile and provides data very similar to an ion chamber when Cerenkov radiation is properly accounted. This device measures data pulse by pulse which very few detectors can perform. The differences between ion chamber data and PSD are < 2% in most cases. The angular dependence is a bit pronounces to 1.5% which is due to PSD housing. Depth dose and profiles are comparable within < 1% to an ion chamber. For small fields this detector provides suitable field output factor compared to other detectors and Monte Carlo (MC) simulated data without any added correction factor.Conclusions: The characteristics of Blue Physics PSD is uniquely suitable in photon beam and more so in small fields. The data are reproducible compared to ion chamber for most parameters and ideally suitable for small‐field dosimetry without any correction factor.