A comparison between adaptive kernel density estimation and Gaussian Mixture Regression for real-time tumour motion prediction from external surface motion

In this present study, tumour (3D) locations are predicted via external surface motion, extracted from abdomen/ thoracic surface measurements that can be used to enhance dose targeting in external beam radiotherapy. Canonical Correlation Analysis (CCA) is applied to the surface and tumour motion data to maximise the correlation between them. This correlation is exploited for motion prediction [1]. Nine dynamic CT datasets were used to extract the surface and tumour motion and to create the Canonical Correlation model (CCM). Gaussian Mixture Regression (GMR) and Adaptive Kernel Density Estimation (AKDE) were trained on these nine datasets to predict the respiratory signal by updating the surface motion and CCM. A leave-one-out method was used to evaluate and compare the performance of GMR and AKDE in predicting the tumour motion. © 2012 IEEE

Regression Analysis of Rectal Cancer and Possible Application of Artificial Intelligence (AI) Utilization in Radiotherapy

Artificial Intelligence (AI) has been widely employed in the medical field in recent years in such areas as image segmentation, medical image registration, and computer-aided detection. This study explores one application of using AI in adaptive radiation therapy treatment planning by predicting the tumor volume reduction rate (TVRR). Cone beam computed tomography (CBCT) scans of twenty rectal cancer patients were collected to observe the change in tumor volume over the course of a standard five-week radiotherapy treatment. In addition to treatment volume, patient data including patient age, gender, weight, number of treatment fractions, and dose per fraction were also collected. Application of a stepwise regression model showed that age, dose per fraction and weight were the best predictors for tumor volume reduction rate

Inter- and intra-subject variation of abdominal vs. thoracic respiratory motion using kernel density estimation

In nuclear medicine, there is a significant research focus in developing a new approach in monitoring, tracking and compensating respiratory motion during image acquisition. We address this by attempting to model the respiratory cycle pattern and finding a method that describes the configuration of the anterior surface which then correlates with the internal position/configuration of the internal organ as a foundation for motion compensation. This paper presents novel work in parameterizing external respiratory motion using a method based on the variation of abdominal vs. thoracic surface markers to investigate inter- and intra-subject variation. The dominant mode of variation of the Abdominal and Thoracic surfaces during respiration using Principle Component Analysis (PCA) is studied. This demonstrates that pattern of TS vs AS motion appears temporally at a global level stable. Thus although breathing style is consistent within a given subject, we there observe temporal changes in the amplitude and density of the PDF in intra-subject data

Improving MVCBCT image quality using a Cu target with flattening filter-free LINAC

Abstract: Megavoltage Cone Beam Computed Tomography MVCBCT is an image guided radiotherapy imaging tool used for everyday patient repositioning. Present work studies the effect on MVCBCT image quality in using a copper target in place of the original target. Monte Carlo (MC) simulations using FLUKA were carried out for the original target with flattened and unflattened 6 MV beams for different target materials and thicknesses, calculating the photon spectra incident on the phantom surface. MC simulations were also performed for the original and copper targets to calculate the local contrast (LC) in a simple phantom. Reduction is observed in the mean energy of the photon spectrum and a large increase is obtained in the low energy photons ratio when the copper and carbon targets are used in place of the original target, leading to an improvement in the quality of MVCBCT images. Further, the LC was improved by 31% when the copper target was used. The reduction in mean energy and the increase in low energy photons ratio for the carbon target was found to be higher than that for the copper target, noting that the copper target is already available in the head of most Varian LINACs for treatments requiring a higher photon energy mode (> 6MV). It can be concluded that with simple modification, using a copper target with an unflattened beam will improve the MVCBCT image quality. [F. A. Abolaban, M. A. Najem, Ahmad Hussain, Majdi Alnowami, David Bradley. Improving MVCBCT image quality using a Cu target with flattening filter-free LINAC, Life Sci

Adaptive Modelling and Prediction of Respiratory Motion in External Beam Radiotherapy.

The latter two decades of the last century saw significant improvements in External Beam Radiotherapy (EBRT), moved primarily by the advances in imaging modalities and computer-based treatment planning. These advances led to introducing the addition of a fourth dimension, time, to the three-dimensional EBRT arena. This new era in EBRT brings with it challenges and opportunities, in particular to compensate for the effect of respiratory-induced target motion and enhancing treatment delivery. Thus, characterising and modelling respiratory motion is of major importance in this research area. This thesis aims to enhance the understanding and control the effect of respiratory motion. As part of this work, the first principal component analysis (PCA) of respiratory motion is presented, as a basis for compactly and visually representing respiratory style and variation. These studies can be divided into two main aspects: firstly, understanding and characterising respiratory motion as the basis of any further steps towards compensating respiratory motion and secondly, utilising this knowledge in predicting and correlating internal and external respiratory motion in the abdominal thoracic region. This work has been developed starting with a piecewise sinusoidal model in an Eigenspace for modelling. Adaptive kernel density estimation (AKDE) for prediction and finally Canonical Correlation Analysis (CCA) for external-internal target correlation. A comparative study between these proposed approaches and state-of-the-art prior works showed promising results in terms of accuracy and computational efficiency: 20% error reduction compared to support vector regression (SVR) and kernel density estimation (KDE) and a significant reduction in computation speed during training stage. This journey into modelling and predicting respiratory behaviour has naturally raised questions of how best to track external motion. The need to track the surface with more than one marker, established within the aforementioned PCA analysis, motivates the desire for markerless tracking. Therefore, two different markerless systems have been studied, as potential solutions for this area, combined with a mesh model of the anterior surface. This suggests that the Microsoft Kinect camera is a promising low-cost technology for makerless respiratory tracking with less than 3. 1 +- 0. 6 mm accuracy

An Observation Model for Motion Correction in Nuclear Medicine

This paper describes a method of using a tracking system to track the upper part of the anterior surface during scanning for developing patient-specific models of respiration. In the experimental analysis, the natural variation in the anterior surface during breathing will be modeled to reveal the dominant pattern in the breathing cycle. The main target is to produce a patient-specific set of parameters that describes the configuration of the anterior surface for all respiration phases. These data then will be linked to internal organ motion to identify the effect of the morphology of each on motion using particle filter to account for previously unseen patterns of motion. In this initial study, a set of volunteers were imaged using the Codamotion infrared marker-based system. In the marker-based system, the temporal variation of the respiratory motion was studied. This showed that for the 12 volunteer cohort, the mean displacement of the thorax surface TS (abdomen surface AS) region is 10.7±5.6 mm (16.0±9.5mm). Finally, PCA was shown to capture the redundancy in the data set with the first principal component (PC) accounting for more than 96% of the overall variance in both AS and TS datasets. A fitting to the dominant modes of variation using a simple piecewise sinusoid has suggested a maximum error of about 1.1mm across the complete cohort dataset

Assessment of Microsoft Kinect technology (Kinect for Xbox and Kinect for windows) for patient monitoring during external beam radiotherapy

In external beam radiotherapy, patient misalignment during set-up and motion during treatment may result in lost dose to target tissue and increased dose to normal tissues, reducing therapeutic benefit. The most common method for initial patient setup uses room mounted lasers and surface marks on the skin. We propose to use the Microsoft Kinect which can capture a complete patient skin surface representing a multiplicity of 3D points in a fast reproducible, marker-less manner. Our first experiments quantitatively assess the technical performance of Kinect technology using a planar test object and a precision motion platform to compare the performance of Kinect for Xbox and Kinect for Windows. Further experiments were undertaken to investigate the likely performance of using the Kinect during treatment to detect respiratory motion, both in supine and prone positions. The Windows version of the Kinect produces superior performance of less than 2mm mean error at 80-100 cm distance. © 2013 IEEE