Radiotherapy (RT) aims to deliver a spatially conformal dose of radiation to tumours while maximizing the dose sparing to healthy tissues. However, the internal patient anatomy is constantly moving due to respiratory, cardiac, gastrointestinal and urinary activity. The long term goal of the RT community to ‘see what we treat, as we treat’ and to act on this information instantaneously has resulted in rapid technological innovation. Specialized treatment machines, such as robotic or gimbal-steered linear accelerators (linac) with in-room imaging suites, have been developed specifically for real-time treatment adaptation. Additional equipment, such as stereoscopic kilovoltage (kV) imaging, ultrasound transducers and electromagnetic transponders, has been developed for intrafraction motion monitoring on conventional linacs. Magnetic resonance imaging (MRI) has been integrated with cobalt treatment units and more recently with linacs. In addition to hardware innovation, software development has played a substantial role in the development of motion monitoring methods based on respiratory motion surrogates and planar kV or Megavoltage (MV) imaging that is available on standard equipped linacs. In this paper, we review and compare the different intrafraction motion monitoring methods proposed in the literature and demonstrated in real-time on clinical data as well as their possible future developments. We then discuss general considerations on validation and quality assurance for clinical implementation. Besides photon RT, particle therapy is increasingly used to treat moving targets. However, transferring motion monitoring technologies from linacs to particle beam lines presents substantial challenges. Lessons learned from the implementation of real-time intrafraction monitoring for photon RT will be used as a basis to discuss the implementation of these methods for particle RT.

In this paper we present spatially mapped point-spread function (PSF) measurements of an optical coherence tomography (OCT) instrument and subsequent spatial deconvolution. The OCT B-scan image plane was divided into 2400 subimages, for which PSFs were determined from OCT measurements of a specially designed phantom. Each PSF was deconvolved from its corresponding subimage of the phantom using the Lucy-Richardson algorithm. Following deconvolution, all of the subimages were reassembled to form a final deconvolved image, from which the resolution improvement was quantitatively assessed. The lateral resolution was found to improve by 3.1 microm compared to an axial resolution enhancement of 4.5 microm. The spatial uniformity of both axial and lateral resolution was also observed to increase following deconvolution, demonstrating the advantage of deconvolving local PSFs from their associated subimages.

PurposeOur purpose was to perform an in vivo validation of ultrasound imaging for intrafraction motion estimation using the Elekta Clarity Autoscan system during prostate radiation therapy. The study was conducted as part of the Clarity-Pro trial (NCT02388308).Methods and MaterialsInitial locations of intraprostatic fiducial markers were identified from cone beam computed tomography scans. Marker positions were translated according to Clarity intrafraction 3-dimensional prostate motion estimates. The updated locations were projected onto the 2-dimensional electronic portal imager plane. These Clarity-based estimates were compared with the actual portal-imaged 2-dimensional marker positions. Images from 16 patients encompassing 80 fractions were analyzed. To investigate the influence of intraprostatic markers and image quality on ultrasound motion estimation, 3 observers rated image quality, and the marker visibility on ultrasound images was assessed.ResultsThe median difference between Clarity-defined intrafraction marker locations and portal-imaged marker locations was 0.6 mm (with 95% limit of agreement at 2.5 mm). Markers were identified on ultrasound in only 3 of a possible 240 instances. No linear relationship between image quality and Clarity motion estimation confidence was identified. The difference between Clarity-based motion estimates and electronic portal–imaged marker location was also independent of image quality. Clarity estimation confidence was degraded in a single fraction owing to poor probe placement.ConclusionsThe accuracy of Clarity intrafraction prostate motion estimation is comparable with that of other motion-monitoring systems in radiation therapy. The effect of fiducial markers in the study was deemed negligible as they were rarely visible on ultrasound images compared with intrinsic anatomic features. Clarity motion estimation confidence was robust to variations in image quality and the number of ultrasound-imaged anatomic features; however, it was degraded as a result of poor probe placement.