2-Deoxy-2-[18F]fluoro-D-glucose positron emission tomography demonstrates target inhibition with the potential to predict anti-tumour activity following treatment with the AKT inhibitor AZD5363.

PURPOSE: The phosphatidyl inositol 3 kinase, AKT and mammalian target of rapamycin are frequently deregulated in human cancer and are among one of the most promising targets for cancer therapy. AZD5363 (AstraZeneca) is an AKT inhibitor in phase 1 clinical trials. Given its utility in assessing glucose metabolism, we investigated the role of 2-Deoxy-2-[18F]fluoro-D-glucose (18F-FDG) positron emission tomography (PET) as a biomarker to demonstrate target inhibition and its potential to predict and demonstrate the anti-tumour activity of AZD5363. METHODS: 18F-FDG PETscans were performed in nude mice in a number of xenograft models (U87-MG glioblastoma, BT474C breast carcinoma and Calu-6 lung). Mice were fasted prior to imaging, and either static or dynamic 18F-FDG PET imaging was performed. RESULTS: We have shown that 18F-FDG uptake in tumour xenografts was reduced by 39% reduction compared to vehicle after a single dose of AZD5363, demonstrating activation of the AKT pathway after only 4 h of dosing. Multiple doses of AZD5363 showed an anti-tumour volume effect and a reduction in 18F-FDG uptake (28% reduction compared to vehicle), highlighting the potential of 18F-FDG PET as an efficacy biomarker. Furthermore, the degree of inhibition of 18F-FDG uptake corresponded with the sensitivity of the tumour model to AZD5363. The use of dynamic 18F-FDG PET and a two-compartmental analysis identified the mechanism of this change to be due to a change in cellular uptake of 18F-FDG following administration of AZD5363. CONCLUSIONS: We conclude that 18F-FDG PET is a promising pharmacodynamic biomarker of AKT pathway inhibition, with potential to predict and demonstrate anti-tumour activity. It is a biomarker that may stop ineffective drug schedules, helping to make early stop decisions and identify responding subsets of patients, resulting in improved clinical decision making both during drug development and patient management.

High-performance GPU-based rendering for real-time, rigid 2D/3D-image registration and motion prediction in radiation oncology.

A common problem in image-guided radiation therapy (IGRT) of lung cancer as well as other malignant diseases is the compensation of periodic and aperiodic motion during dose delivery. Modern systems for image-guided radiation oncology allow for the acquisition of cone-beam computed tomography data in the treatment room as well as the acquisition of planar radiographs during the treatment. A mid-term research goal is the compensation of tumor target volume motion by 2D/3D Registration. In 2D/3D registration, spatial information on organ location is derived by an iterative comparison of perspective volume renderings, so-called digitally rendered radiographs (DRR) from computed tomography volume data, and planar reference x-rays. Currently, this rendering process is very time consuming, and real-time registration, which should at least provide data on organ position in less than a second, has not come into existence. We present two GPU-based rendering algorithms which generate a DRR of 512×512 pixels size from a CT dataset of 53 MB size at a pace of almost 100 Hz. This rendering rate is feasible by applying a number of algorithmic simplifications which range from alternative volume-driven rendering approaches - namely so-called wobbled splatting - to sub-sampling of the DRR-image by means of specialized raycasting techniques. Furthermore, general purpose graphics processing unit (GPGPU) programming paradigms were consequently utilized. Rendering quality and performance as well as the influence on the quality and performance of the overall registration process were measured and analyzed in detail. The results show that both methods are competitive and pave the way for fast motion compensation by rigid and possibly even non-rigid 2D/3D registration and, beyond that, adaptive filtering of motion models in IGRT.

Monitoring tumor motion by real time 2D/3D registration during radiotherapy.

BACKGROUND AND PURPOSE: In this paper, we investigate the possibility to use X-ray based real time 2D/3D registration for non-invasive tumor motion monitoring during radiotherapy. MATERIALS AND METHODS: The 2D/3D registration scheme is implemented using general purpose computation on graphics hardware (GPGPU) programming techniques and several algorithmic refinements in the registration process. Validation is conducted off-line using a phantom and five clinical patient data sets. The registration is performed on a region of interest (ROI) centered around the planned target volume (PTV). RESULTS: The phantom motion is measured with an rms error of 2.56 mm. For the patient data sets, a sinusoidal movement that clearly correlates to the breathing cycle is shown. Videos show a good match between X-ray and digitally reconstructed radiographs (DRR) displacement. Mean registration time is 0.5 s. CONCLUSIONS: We have demonstrated that real-time organ motion monitoring using image based markerless registration is feasible.

Validation for 2D/3D registration. II: The comparison of intensity- and gradient-based merit functions using a new gold standard data set.

PURPOSE: A new gold standard data set for validation of 2D/3D registration based on a porcine cadaver head with attached fiducial markers was presented in the first part of this article. The advantage of this new phantom is the large amount of soft tissue, which simulates realistic conditions for registration. This article tests the performance of intensity- and gradient-based algorithms for 2D/3D registration using the new phantom data set. METHODS: Intensity-based methods with four merit functions, namely, cross correlation, rank correlation, correlation ratio, and mutual information (MI), and two gradient-based algorithms, the backprojection gradient-based (BGB) registration method and the reconstruction gradient-based (RGB) registration method, were compared. Four volumes consisting of CBCT with two fields of view, 64 slice multidetector CT, and magnetic resonance-T1 weighted images were registered to a pair of kV x-ray images and a pair of MV images. A standardized evaluation methodology was employed. Targets were evenly spread over the volumes and 250 starting positions of the 3D volumes with initial displacements of up to 25 mm from the gold standard position were calculated. After the registration, the displacement from the gold standard was retrieved and the root mean square (RMS), mean, and standard deviation mean target registration errors (mTREs) over 250 registrations were derived. Additionally, the following merit properties were computed: Accuracy, capture range, number of minima, risk of nonconvergence, and distinctiveness of optimum for better comparison of the robustness of each merit. RESULTS: Among the merit functions used for the intensity-based method, MI reached the best accuracy with an RMS mTRE down to 1.30 mm. Furthermore, it was the only merit function that could accurately register the CT to the kV x rays with the presence of tissue deformation. As for the gradient-based methods, BGB and RGB methods achieved subvoxel accuracy (RMS mTRE down to 0.56 and 0.70 mm, respectively). Overall, gradient-based similarity measures were found to be substantially more accurate than intensity-based methods and could cope with soft tissue deformation and enabled also accurate registrations of the MR-T1 volume to the kV x-ray image. CONCLUSIONS: In this article, the authors demonstrate the usefulness of a new phantom image data set for the evaluation of 2D/3D registration methods, which featured soft tissue deformation. The author's evaluation shows that gradient-based methods are more accurate than intensity-based methods, especially when soft tissue deformation is present. However, the current nonoptimized implementations make them prohibitively slow for practical applications. On the other hand, the speed of the intensity-based method renders these more suitable for clinical use, while the accuracy is still competitive.

Stochastic rank correlation: a robust merit function for 2D/3D registration of image data obtained at different energies.

In this article, the authors evaluate a merit function for 2D/3D registration called stochastic rank correlation (SRC). SRC is characterized by the fact that differences in image intensity do not influence the registration result; it therefore combines the numerical advantages of cross correlation (CC)-type merit functions with the flexibility of mutual-information-type merit functions. The basic idea is that registration is achieved on a random subset of the image, which allows for an efficient computation of Spearman's rank correlation coefficient. This measure is, by nature, invariant to monotonic intensity transforms in the images under comparison, which renders it an ideal solution for intramodal images acquired at different energy levels as encountered in intrafractional kV imaging in image-guided radiotherapy. Initial evaluation was undertaken using a 2D/3D registration reference image dataset of a cadaver spine. Even with no radiometric calibration, SRC shows a significant improvement in robustness and stability compared to CC. Pattern intensity, another merit function that was evaluated for comparison, gave rather poor results due to its limited convergence range. The time required for SRC with 5% image content compares well to the other merit functions; increasing the image content does not significantly influence the algorithm accuracy. The authors conclude that SRC is a promising measure for 2D/3D registration in IGRT and image-guided therapy in general.

Content uniformity of pharmaceutical solid dosage forms by near infrared hyperspectral imaging: A feasibility study.

Near-infrared imaging systems simultaneously record spectral and spatial information. Each measurement generates a data cube containing several thousand spectra. Chemometric methods are therefore required to extract qualitative and quantitative information. The aim of this study was to determine the feasibility of quantifying active pharmaceutical ingredient (API) and excipient content in pharmaceutical formulations using hyperspectral imaging. Two kinds of tablets with a range of API content were analysed: a binary mixture of API and cellulose, and a pharmaceutical formulation with seven different compounds. Two pixel sizes, 10mum/pixel and 40mum/pixel, were compared, together with two types of spectral pretreatment: standard normal variate (SNV) normalization and Savitzky-Golay smoothing. Two methods of extracting concentrations were compared: the partial least squares 2 (PLS2) algorithm, which predicts the content of several compounds simultaneously, and the multivariate classical least squares (CLS) algorithm based on pure compound reference spectra without calibration. Best content predictions were achieved using 40mum/pixel resolution and the PLS2 method with SNV normalized spectra. However, the CLS method extracted distribution maps with higher contrast and was less sensitive to noisy spectra and outliers; its API predictions were also highly correlated to real content, indicating the feasibility of predicting API content using hyperspectral imaging without calibration.