Patient motion during treatment is well understood as a prime factor limiting radiotherapy success, with the risks most pronounced in modern safety critical therapies promising the greatest benefit. In this paper we describe a real-time visual feedback device designed to help patients to actively manage their body position, pose and motion. In addition to technical device details, we present preliminary trial results showing that its use enables volunteers to successfully manage their respiratory motion. The device enables patients to view their live body surface measurements relative to a prior reference, operating on the concept that co-operative engagement with patients will both improve geometric conformance and remove their perception of isolation, in turn easing stress related motion. The device is driven by a real-time wide field optical sensor system developed at The Christie. Feedback is delivered through three intuitive visualization modes of hierarchically increasing display complexity. The device can be used with any suitable display technology; in the presented study we use both personal video glasses and a standard LCD projector. The performance characteristics of the system were measured, with the frame rate, throughput and latency of the feedback device being 22.4 fps, 47.0 Mbps, 109.8 ms, and 13.7 fps, 86.4 Mbps, 119.1 ms for single and three-channel modes respectively. The pilot study, using ten healthy volunteers over three sessions, shows that the use of visual feedback resulted in both a reduction in the participants’ respiratory amplitude, and a decrease in their overall body motion variability.
In image guided radiotherapy (IGRT) two of the most promising recent developments are four dimensional
cone beam CT (4D CBCT) and dynamic optical metrology of patient surfaces. 4D CBCT is now becoming
commercially available and finds use in treatment planning and verification, and whilst optical monitoring
is a young technology, its ability to measure during treatment delivery without dose consequences has led
to its uptake in many institutes. In this paper, we demonstrate the use of dynamic patient surfaces,
simultaneously captured during CBCT acquisition using an optical sensor, to phase sort projection images
for 4D CBCT volume reconstruction.
The dual modality approach we describe means that in addition to 4D volumetric data, the system provides
correlated wide field measurements of the patient's skin surface with high spatial and temporal resolution.
As well as the value of such complementary data in verification and motion analysis studies, it introduces
flexibility into the acquisition of the signal required for phase sorting. The specific technique used may be
varied according to individual patient circumstances and the imaging target. We give details of three
different methods of obtaining a suitable signal from the optical surfaces: simply following the motion of
triangulation spots used to calibrate the surfaces' absolute height; monitoring the surface height in a single,
arbitrarily selected, camera pixel; and tracking, in three dimensions, the movement of a surface feature. In
addition to describing the system and methodology, we present initial results from a case study oesophageal
cancer patient.
Optical imaging is becoming more prevalent in image guided radiotherapy as a complementary technology to traditional
ionizing radiation based modalities. We present a novel structured light based device that can capture a patient's body
surface topology with a large field of view and high spatial and temporal resolution. The system is composed of three
cross-calibrated sensor heads that enable 'wrap around' imaging previously unavailable with similar line of sight optical
techniques. The system has been installed in a treatment bunker at the Christie Hospital alongside an Elekta linear
accelerator equipped with cone beam CT (CBCT) on-board imaging. In this paper we describe the system, focussing on
the methodologies required to create a robust and practical device. We show examples of measurements made to
ascertain its repeatability and accuracy, and present some initial experiences in using the device for pre-treatment patient
set-up.
Image segmentation and delineation is at the heart of modern radiotherapy, where the aim is to deliver as high a radiation
dose as possible to a cancerous target whilst sparing the surrounding healthy tissues. This, of course, requires that a
radiation oncologist dictates both where the tumour and any nearby critical organs are located. As well as in treatment
planning, delineation is of vital importance in image guided radiotherapy (IGRT): organ motion studies demand that
features across image databases are accurately segmented, whilst if on-line adaptive IGRT is to become a reality, speedy
and correct target identification is a necessity.
Recently, much work has been put into the development of automatic and semi-automatic segmentation tools, often
using prior knowledge to constrain some grey level, or derivative thereof, interrogation algorithm. It is hoped that such
techniques can be applied to organ at risk and tumour segmentation in radiotherapy.
In this work, however, we make the assumption that grey levels do not necessarily determine a tumour's extent,
especially in CT where the attenuation coefficient can often vary little between cancerous and normal tissue. In this
context we present an algorithm that generates a discontinuity free delineation surface driven by user placed, evidence
based support points. In regions of sparse user supplied information, prior knowledge, in the form of a statistical shape
model, provides guidance.
A small case study is used to illustrate the method. Multiple observers (between 3 and 7) used both the presented tool
and a commercial manual contouring package to delineate the bladder on a serially imaged (10 cone beam CT volumes )
prostate patient. A previously presented shape analysis technique is used to quantitatively compare the observer
variability.
There has been an influx of imaging and treatment technologies into cancer radiotherapy over the past fifteen years. The result is that radiation fields can now be accurately shaped to target disease delineated on pre-treatment planning scans whilst sparing critical healthy structures. Two well known problems remain causes for concern. The first is inter- and intra-observer variability in planning scan delineations, the second is the motion and deformation of a tumour and interacting adjacent organs during the course of radiotherapy which compromise the planned targeting regime. To be able to properly address these problems, and hence accurately shape the margins of error used to account for them, an intuitive and quantitative system of describing this variability must be used. This paper discusses a method of automatically creating correspondence points over similar non-polar delineation volumes, via spherical parameterisation, so that their shape variability can be analysed as a set of independent one dimensional statistical problems. The importance of 'pole' selection to initial parameterisation and hence ease of optimisation is highlighted, the use of sparse anatomical landmarks rather than spherical harmonic expansion for establishing point correspondence discussed, and point variability mapping introduced. A case study is presented to illustrate the method. A group of observers were asked to delineate a rectum on a series of time-of-treatment Cone Beam CT scans over a patient's fractionation schedule. The overall observer variability was calculated using the above method and the significance of the organ motion over time evaluated.
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