Patient set-up misalignment/motion can be a significant source of error within external beam radiotherapy, leading to unwanted dose to healthy tissues and sub-optimal dose to the target tissue. Such inadvertent displacement or motion of the target volume may be caused by treatment set-up error, respiratory motion or an involuntary movement potentially decreasing therapeutic benefit. The conventional approach to managing abdominal-thoracic patient set-up is via skin markers (tattoos) and laser-based alignment. Alignment of the internal target volume with its position in the treatment plan can be achieved using Deep Inspiration Breath Hold (DIBH) in conjunction with marker-based respiratory motion monitoring.
We propose a marker-less single system solution for patient set-up and respiratory motion management based on low cost 3D depth camera technology (such as the Microsoft Kinect). In this new work we assess this approach in a study group of six volunteer subjects. Separate simulated treatment mimic treatment "fractions" or set-ups are compared for each subject, undertaken using conventional laser-based alignment and with intrinsic depth images produced by Kinect. Microsoft Kinect is also compared with the well-known RPM system for respiratory motion management in terms of monitoring free-breathing and DIBH. Preliminary results suggest that Kinect is able to produce mm-level surface alignment and a comparable DIBH respiratory motion management when compared to the popular RPM system. Such an approach may also yield significant benefits in terms of patient throughput as marker alignment and respiratory motion can be automated in a single system.
Accurate, Respiratory Motion Modelling of the abdominal-thoracic organs serves as a pre-requisite for motion correction of Nuclear Medicine (NM) Images. Many respiratory motion models to date build a static correspondence between a parametrized external surrogate signal and internal motion. Mean drifts in respiratory motion, changes in respiratory style and noise conditions of the external surrogate signal motivates a more adaptive approach to capture non-stationary behavior. To this effect we utilize the application of our novel Kalman model with an incorporated expectation maximization step to allow adaptive learning of model parameters with changing respiratory observations. A comparison is made with a popular total least squares (PCA) based approach. It is demonstrated that in the presence of noisy observations the Kalman framework outperforms the static PCA model, however, both methods correct for respiratory motion in the computational anthropomorphic phantom to < 2mm. Motion correction performed on 3 dynamic MRI patient datasets using the Kalman model results in correction of respiratory motion to ≈ 3mm.
Patient respiratory motion is a major problem during external beam radiotherapy of the thoracic and abdominal regions due to the associated organ and target motion. In addition, such motion introduces uncertainty in both radiotherapy planning and delivery and may potentially vary between the planning and delivery sessions. The aim of this work is to examine subject-specific external respiratory motion and its associated drift from an assumed average cycle which is the basis for many respiratory motion compensated applications including radiotherapy treatment planning and delivery. External respiratory motion data were acquired from a group of
20 volunteers using a marker-less 3D depth camera, Kinect for Windows. The anterior surface encompassing
thoracic and abdominal regions were subject to principal component analysis (PCA) to investigate dominant variations. The first principal component typically describes more than 70% of the motion data variance in the thoracic and abdominal surfaces. Across all of the subjects used in this study, 58% of subjects demonstrate largely abdominal breathing and 33% exhibited largely thoracic dominated breathing. In most cases there is observable drift in respiratory motion during the 300s capture period, which is visually demonstrated using Kernel Density Estimation. This study demonstrates that for this cohort of apparently healthy volunteers, there is significant respiratory motion drift in most cases, in terms of amplitude and relative displacement between the thoracic and abdominal respiratory components. This has implications for the development of effective motion compensation methodology.
This paper describes a quantitative assessment of the Microsoft Kinect for X-box360TM for potential application
in tracking respiratory and body motion in diagnostic imaging and external beam radiotherapy. However, the
results can also be used in many other biomedical applications. We consider the performance of the Kinect in
controlled conditions and find mm precision at depths of 0.8-1.5m. We also demonstrate the use of the Kinect for
monitoring respiratory motion of the anterior surface. To improve the performance of respiratory monitoring,
we fit a spline model of the chest surface through the depth data as a method of a marker-less monitoring of
a respiratory motion. In addition, a comparison between the Kinect camera with and without zoom lens and
a marker-based system was used to evaluate the accuracy of using the Kinect camera as a respiratory tracking