• 2022-06
  • 2022-05
  • 2022-04
  • 2021-03
  • 2020-08
  • 2020-07
  • 2018-07
  • Simulation and delivery was executed


    Simulation and delivery was executed in supine position with the use of coils and head phones for noise reduction. Patient positioning was performed on an MR-compatible positioning board (Macromedics, Waddinxveen, The Netherlands), including foot, knee and arm support. The acquired CT (with dummy coils) was non-rigidly co-registered with the simulation MR, with the fusion centered on the area of interest, i.e. the prostate. When gas in rectum was variable between the CT and MR simulation, special care was taken to obtain a good agreement between the anatomies reflected in both scans after non-rigid registration, especially in the area of the CTV (prostate). Patients were instructed to empty their bladder two hours before treatment, followed by intake of 500 ml of water. No specific rectal preparations such as endorectal balloons or pre-treatment enemas were required.
    2.3. Target definition and radiation dose fractionation
    For MRgRT of prostate cancer, target definition was basically identical to other techniques delivering SBRT. Briefly, the clinical target volume (CTV) was delineated on the simulation MR-scan. For ‘low risk’ patients (cT1c-T2a, Gleason < 7 and PSA < 10 µg/L), the CTV con-sisted of the prostate gland. For ‘high’ and ‘intermediate risk’ patients [22], the base of the seminal vesicles was also included in the CTV. As a result of daily MR-based setup, low spatial distortion, online plan Amiloride HCL and real-time prostate monitoring during treatment, only a 3 mm CTV to PTV uniform margin was used for MRgRT. For baseline planning, relevant OAR, i.e. the bladder, rectum, urethra and femora were contoured on the MR-scan. A good discrimination between the posterior border of the prostate and the anterior rectal wall was ob-tained with current MR True FISP sequence. Although not standard in SBRT for prostate cancer, in an attempt to decrease acute and late ur-inary toxicity, integrated urethral sparing was used by generating an
    urethral PTVurethra with a margin of 2–3 mm around the delineated urethra (Fig. 1). Most patients were treated with 5 fractions of 7.25 Gy per fraction delivered on the prostate with a simultaneous integrated sparing (SIS) of the urethra with a dose of 32.5 Gy in 5 fractions (6.5 Gy per fraction). In some cases (n = 10) with tumour near the urethra, the SBRT was delivered in fractions of 7 Gy up to a total dose of 35 Gy without urethral sparing. The majority of OAR constraints were ex-pressed in absolute volume (cc), which allows partial contour deli-neation during the adaptive workflow (see also below).
    2.4. Online contour generation
    At each fraction, online new contours were generated for prostate, OARs and structures needed for treatment planning. Firstly, the CTV was rigidly copied from the pre-treatment MR scan to the MR volu-metric scan of the day and both scans were rigidly registered on the target. The CTV is then edited by the physician when needed, ac-counting for rotations and deformations of the prostate and/or seminal vesicles. After that, a new PTV (CTV + 3 mm) was automatically gen-erated to account for delineation uncertainties, intra-fraction motion and random spatial distortions on the MR-scan (less than 1 mm in a 20 cm DSV). A second non-rigid registration of both MR-scans was thereafter performed and the deformation field map was also applied to the OAR contours to generate structures reflecting the anatomy-of-the-
    Fig. 1. Contouring for MRgRT: CTV consisting of prostate and base of vesicles (green contour), PTV (CTV + 3 mm; red contour) visualized in an axial, sagittal and coronal plane. The urethral contour (cyan contour) and urethral PRV (urethra + 2 mm) can be best seen in the sagittal plane. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
    day. The deformable registration algorithm implemented on the MRIdian and employed for online adaptive minimizes a cost function that measures the similarity between the images. It also uses a reg-ularization term to obtain smoother deformation fields and prevents sharp discontinuities. The optimization method relies on a simple gra-dient descend performing the registration firstly on a down sampled version of the image serving the results as initial guesses for each upper level.
    The electron density map generated from the CT for dose calculation underwent the same deformation applied to the OAR contours. The newly generated electron density map for that particular fraction was briefly checked by the radiation technologist and physicist for the presence of missing tissue densities and mismatch for air pockets in rectum. In case of mismatch, structure densities were overridden and corrected online before dose calculation and plan adaptation.