PRaVDA: The first solid-state system for proton computed tomography.

PURPOSE: Proton CT is widely recognised as a beneficial alternative to conventional X-ray CT for treatment planning in proton beam radiotherapy. A novel proton CT imaging system, based entirely on solid-state detector technology, is presented. Compared to conventional scintillator-based calorimeters, positional sensitive detectors allow for multiple protons to be tracked per read out cycle, leading to a potential reduction in proton CT scan time. Design and characterisation of its components are discussed. An early proton CT image obtained with a fully solid-state imaging system is shown and accuracy (as defined in Section IV) in Relative Stopping Power to water (RSP) quantified. METHOD: A solid-state imaging system for proton CT, based on silicon strip detectors, has been developed by the PRaVDA collaboration. The system comprises a tracking system that infers individual proton trajectories through an imaging phantom, and a Range Telescope (RT) which records the corresponding residual energy (range) for each proton. A back-projection-then-filtering algorithm is used for CT reconstruction of an experimentally acquired proton CT scan. RESULTS: An initial experimental result for proton CT imaging with a fully solid-state system is shown for an imaging phantom, namely a 75 mm diameter PMMA sphere containing tissue substitute inserts, imaged with a passively-scattered 125 MeV beam. Accuracy in RSP is measured to be ⩽1.6% for all the inserts shown. CONCLUSIONS: A fully solid-state imaging system for proton CT has been shown capable of imaging a phantom with protons and successfully improving RSP accuracy. These promising results, together with system the capability to cope with high proton fluences (2×108 protons/s), suggests that this research platform could improve current standards in treatment planning for proton beam radiotherapy.

Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels.

BACKGROUND: Accurate segmentation of brain tumour in magnetic resonance images (MRI) is a difficult task due to various tumour types. Using information and features from multimodal MRI including structural MRI and isotropic (p) and anisotropic (q) components derived from the diffusion tensor imaging (DTI) may result in a more accurate analysis of brain images. METHODS: We propose a novel 3D supervoxel based learning method for segmentation of tumour in multimodal MRI brain images (conventional MRI and DTI). Supervoxels are generated using the information across the multimodal MRI dataset. For each supervoxel, a variety of features including histograms of texton descriptor, calculated using a set of Gabor filters with different sizes and orientations, and first order intensity statistical features are extracted. Those features are fed into a random forests (RF) classifier to classify each supervoxel into tumour core, oedema or healthy brain tissue. RESULTS: The method is evaluated on two datasets: 1) Our clinical dataset: 11 multimodal images of patients and 2) BRATS 2013 clinical dataset: 30 multimodal images. For our clinical dataset, the average detection sensitivity of tumour (including tumour core and oedema) using multimodal MRI is 86% with balanced error rate (BER) 7%; while the Dice score for automatic tumour segmentation against ground truth is 0.84. The corresponding results of the BRATS 2013 dataset are 96%, 2% and 0.89, respectively. CONCLUSION: The method demonstrates promising results in the segmentation of brain tumour. Adding features from multimodal MRI images can largely increase the segmentation accuracy. The method provides a close match to expert delineation across all tumour grades, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management.

Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI.

PURPOSE: We propose a fully automated method for detection and segmentation of the abnormal tissue associated with brain tumour (tumour core and oedema) from Fluid- Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Imaging (MRI). METHODS: The method is based on superpixel technique and classification of each superpixel. A number of novel image features including intensity-based, Gabor textons, fractal analysis and curvatures are calculated from each superpixel within the entire brain area in FLAIR MRI to ensure a robust classification. Extremely randomized trees (ERT) classifier is compared with support vector machine (SVM) to classify each superpixel into tumour and non-tumour. RESULTS: The proposed method is evaluated on two datasets: (1) Our own clinical dataset: 19 MRI FLAIR images of patients with gliomas of grade II to IV, and (2) BRATS 2012 dataset: 30 FLAIR images with 10 low-grade and 20 high-grade gliomas. The experimental results demonstrate the high detection and segmentation performance of the proposed method using ERT classifier. For our own cohort, the average detection sensitivity, balanced error rate and the Dice overlap measure for the segmented tumour against the ground truth are 89.48 %, 6 % and 0.91, respectively, while, for the BRATS dataset, the corresponding evaluation results are 88.09 %, 6 % and 0.88, respectively. CONCLUSIONS: This provides a close match to expert delineation across all grades of glioma, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management.

Towards real-time VMAT verification using a prototype, high-speed CMOS active pixel sensor.

This work investigates the feasibility of using a prototype complementary metal oxide semiconductor active pixel sensor (CMOS APS) for real-time verification of volumetric modulated arc therapy (VMAT) treatment. The prototype CMOS APS used region of interest read out on the chip to allow fast imaging of up to 403.6 frames per second (f/s). The sensor was made larger (5.4 cm × 5.4 cm) using recent advances in photolithographic technique but retains fast imaging speed with the sensor's regional read out. There is a paradigm shift in radiotherapy treatment verification with the advent of advanced treatment techniques such as VMAT. This work has demonstrated that the APS can track multi leaf collimator (MLC) leaves moving at 18 mm s(-1) with an automatic edge tracking algorithm at accuracy better than 1.0 mm even at the fastest imaging speed. Evaluation of the measured fluence distribution for an example VMAT delivery sampled at 50.4 f/s was shown to agree well with the planned fluence distribution, with an average gamma pass rate of 96% at 3%/3 mm. The MLC leaves motion and linac pulse rate variation delivered throughout the VMAT treatment can also be measured. The results demonstrate the potential of CMOS APS technology as a real-time radiotherapy dosimeter for delivery of complex treatments such as VMAT.

Imaging of moving fiducial markers during radiotherapy using a fast, efficient active pixel sensor based EPID.

PURPOSE: The purpose of this work was to investigate the use of an experimental complementary metal-oxide-semiconductor (CMOS) active pixel sensor (APS) for tracking of moving fiducial markers during radiotherapy. METHODS: The APS has an active area of 5.4 × 5.4 cm and maximum full frame read-out rate of 20 frame s(-1), with the option to read out a region-of-interest (ROI) at an increased rate. It was coupled to a 4 mm thick ZnWO4 scintillator which provided a quantum efficiency (QE) of 8% for a 6 MV x-ray treatment beam. The APS was compared with a standard iViewGT flat panel amorphous Silicon (a-Si) electronic portal imaging device (EPID), with a QE of 0.34% and a frame-rate of 2.5 frame s(-1). To investigate the ability of the two systems to image markers, four gold cylinders of length 8 mm and diameter 0.8, 1.2, 1.6, and 2 mm were placed on a motion-platform. Images of the stationary markers were acquired using the APS at a frame-rate of 20 frame s(-1), and a dose-rate of 143 MU min(-1) to avoid saturation. EPID images were acquired at the maximum frame-rate of 2.5 frame s(-1), and a reduced dose-rate of 19 MU min(-1) to provide a similar dose per frame to the APS. Signal-to-noise ratio (SNR) of the background signal and contrast-to-noise ratio (CNR) of the marker signal relative to the background were evaluated for both imagers at doses of 0.125 to 2 MU. RESULTS: Image quality and marker visibility was found to be greater in the APS with SNR ∼5 times greater than in the EPID and CNR up to an order of magnitude greater for all four markers. To investigate the ability to image and track moving markers the motion-platform was moved to simulate a breathing cycle with period 6 s, amplitude 20 mm and maximum speed 13.2 mm s(-1). At the minimum integration time of 50 ms a tracking algorithm applied to the APS data found all four markers with a success rate of ≥92% and positional error ≤90 µm. At an integration time of 400 ms the smallest marker became difficult to detect when moving. The detection of moving markers using the a-Si EPID was difficult even at the maximum dose-rate of 592 MU min(-1) due to the lower QE and longer integration time of 400 ms. CONCLUSIONS: This work demonstrates that a fast read-out, high QE APS may be useful in the tracking of moving fiducial markers during radiotherapy. Further study is required to investigate the tracking of markers moving in 3D in a treatment beam attenuated by moving patient anatomy. This will require a larger sensor with ROI read-out to maintain speed and a manageable data-rate.

Estimation of protein secondary structure from synchrotron radiation circular dichroism spectra.

A new approach to estimate the fraction of secondary structures fractions from synchrotron radiation circular dichroism (SRCD) spectra is presented. The protein SRCD spectra are first approximated using radial basis function networks (RBFN) and the resulting set is used to train a self-organising map (SOM). Thus the data are arranged in a two-dimensional map in such a way that most similar proteins are close to each other and vice versa. Estimation of the parallel and antiparallel beta sheets is discussed. The number of spectra in the training set is twenty four proteins and the protein under examination is also included in the set. Estimation results shows improvements compared with previous methods such as K2D and SOMCD.