Jürgen Hesser

Image Noise Level Estimation by Principal Component Analysis

The problem of blind noise level estimation arises in many image processing applications, such as denoising, compression, and segmentation. In this paper, we propose a new noise level estimation method on the basis of principal component analysis of image blocks. We show that the noise variance can be estimated as the smallest eigenvalue of the image block covariance matrix. Compared with 13 existing methods, the proposed approach shows a good compromise between speed and accuracy. It is at least 15 times faster than methods with similar accuracy, and it is at least two times more accurate than other methods. Our method does not assume the existence of homogeneous areas in the input image and, hence, can successfully process images containing only textures.

GMC: a GPU implementation of a Monte Carlo dose calculation based on Geant4.

We present a GPU implementation called GMC (GPU Monte Carlo) of the low energy (<100 GeV) electromagnetic part of the Geant4 Monte Carlo code using the NVIDIA® CUDA programming interface. The classes for electron and photon interactions as well as a new parallel particle transport engine were implemented. The way a particle is processed is not in a history by history manner but rather by an interaction by interaction method. Every history is divided into steps that are then calculated in parallel by different kernels. The geometry package is currently limited to voxelized geometries. A modified parallel Mersenne twister was used to generate random numbers and a random number repetition method on the GPU was introduced. All phantom results showed a very good agreement between GPU and CPU simulation with gamma indices of >97.5% for a 2%/2u2009mm gamma criteria. The mean acceleration on one GTX 580 for all cases compared to Geant4 on one CPU core was 4860. The mean number of histories per millisecond on the GPU for all cases was 658 leading to a total simulation time for one intensity-modulated radiation therapy dose distribution of 349 s. In conclusion, Geant4-based Monte Carlo dose calculations were significantly accelerated on the GPU.

Dependence of Monte Carlo microdosimetric computations on the simulation geometry of gold nanoparticles

Recently, interactions of x-rays with gold nanoparticles (GNPs) and the resulting dose enhancement have been studied using several Monte Carlo (MC) codes (Jones etxa0al 2010 Med. Phys. 37 3809-16, Lechtman etxa0al 2011 Phys. Med. Biol. 56 4631-47, McMahon etxa0al 2011 Sci. Rep. 1 1-9, Leung etxa0al 2011 Med. Phys. 38 624-31). These MC simulations were carried out in simplified geometries and provided encouraging preliminary data in support of GNP radiotherapy. As these studies showed, radiation transport computations of clinical beams to obtain dose enhancement from nanoparticles has several challenges, mostly arising from the requirement of high spatial resolution and from the approximations used at the interface between the macroscopic clinical beam transport and the nanoscopic electron transport originating in the nanoparticle or its vicinity. We investigate the impact of MC simulation geometry on the energy deposition due to the presence of GNPs, including the effects of particle clustering and morphology. Dose enhancement due to a single and multiple GNPs using various simulation geometries is computed using GEANT4 MC radiation transport code. Various approximations in the geometry and in the phase space transition from macro- to micro-beams incident on GNPs are analyzed. Simulations using GEANT4 are compared to a deterministic code CEPXS/ONEDANT for microscopic (nm-µm) geometry. Dependence on the following microscopic (µ) geometry parameters is investigated: µ-source-to-GNP distance (µSAD), µ-beam size (µS), and GNP size (µC). Because a micro-beam represents clinical beam properties at the microscopic scale, the effect of using different types of micro-beams is also investigated. In particular, a micro-beam with the phase space of a clinical beam versus a plane-parallel beam with an equivalent photon spectrum is characterized. Furthermore, the spatial anisotropy of energy deposition around a nanoparticle is analyzed. Finally, dependence of dose enhancement on the number of GNPs in a finite cluster of nanoparticles is determined. Simulations were performed for 100xa0nm GNPs irradiated in water phantom by various monoenergetic (11xa0keV-1 MeV) and spectral (50 kVp) sources. The dose enhancement ratio (DER) is very sensitive to the specific simulation geometry (µSAD, µS, µC parameters) and µ-source type. For a single GNP the spatial distribution of DER is found to be nearly isotropic with limited magnitude and relatively short range (∼100-200xa0nm for DER significantly greater than 1). For a cluster of GNPs both the magnitude and range are found much greater (∼1-2xa0µm). The relation between DER for a cluster of GNPs and a single GNP is strongly nonlinear. Relatively strong dependence of DER on the simulation micro-geometry cautions future studies and the interpretation of existing MC results obtained in different simulations geometries. The nonlinear relation between DER for a single and multiple GNPs suggests that parameters such as the number of adjacent nanoparticles per cell and the distances between the GNPs and the cellular target may be important in assessing the biological effectiveness associated with GNP.

Real-Time GPU-Based Ultrasound Simulation Using Deformable Mesh Models

This paper presents a real-time capable graphics processing unit (GPU)-based ultrasound simulator suitable for medical education. The main focus of the simulator is to synthesize realistic looking ultrasound images in real-time including artifacts, which are essential for the interpretation of this data. The simulation is based on a convolution-enhanced ray-tracing approach and uses a deformable mesh model. Deformations of the mesh model are calculated using the PhysX engine. Our method advances the state of the art for real-time capable ultrasound simulators by following the path of the ultrasound pulse, which enables better simulation of ultrasound-specific artifacts. An evaluation of our proposed method in comparison with recent generative slicing-based strategies as well as real ultrasound images is performed. Hereby, a gelatin ultrasound phantom containing syringes filled with different media is scanned with a real transducer. The obtained images are then compared to images which are simulated using a slicing-based technique and our proposed method. The particular benefit of our method is the accurate simulation of ultrasound-specific artifacts, like range distortion, refraction and acoustic shadowing. Several test scenarios are evaluated regarding simulation time, to show the performance and the bottleneck of our method. While being computationally more intensive than slicing techniques, our simulator is able to produce high-quality images in real-time, tracing over 5000 rays through mesh models with more than 2 000 000 triangles of which up to 200 000 may be deformed each frame.

Using immunotherapy to boost the abscopal effect

More than 60 years ago, the effect whereby radiotherapy at one site may lead to regression of metastatic cancer at distant sites that are not irradiated was described and called the abscopal effect (from ab scopus, that is, away from the target). The abscopal effect has been connected to mechanisms involving the immune system. However, the effect is rare because at the time of treatment, established immune-tolerance mechanisms may hamper the development of sufficiently robust abscopal responses. Today, the growing consensus is that combining radiotherapy with immunotherapy provides an opportunity to boost abscopal response rates, extending the use of radiotherapy to treatment of both local and metastatic disease. In this Opinion article, we review evidence for this growing consensus and highlight emerging limitations to boosting the abscopal effect using immunotherapy. This is followed by a perspective on current and potential cross-disciplinary approaches, including the use of smart materials to address these limitations.

Evaluation of robustness of maximum likelihood cone-beam CT reconstruction with total variation regularization

The objective of this paper is to evaluate an iterative maximum likelihood (ML) cone-beam computed tomography (CBCT) reconstruction with total variation (TV) regularization with respect to the robustness of the algorithm due to data inconsistencies. Three different and (for clinical application) typical classes of errors are considered for simulated phantom and measured projection data: quantum noise, defect detector pixels and projection matrix errors. To quantify those errors we apply error measures like mean square error, signal-to-noise ratio, contrast-to-noise ratio and streak indicator. These measures are derived from linear signal theory and generalized and applied for nonlinear signal reconstruction. For quality check, we focus on resolution and CT-number linearity based on a Catphan phantom. All comparisons are made versus the clinical standard, the filtered backprojection algorithm (FBP). In our results, we confirm and substantially extend previous results on iterative reconstruction such as massive undersampling of the number of projections. Errors of projection matrix parameters of up to 1° projection angle deviations are still in the tolerance level. Single defect pixels exhibit ring artifacts for each method. However using defect pixel compensation, allows up to 40% of defect pixels for passing the standard clinical quality check. Further, the iterative algorithm is extraordinarily robust in the low photon regime (down to 0.05 mAs) when compared to FPB, allowing for extremely low-dose image acquisitions, a substantial issue when considering daily CBCT imaging for position correction in radiotherapy. We conclude that the ML method studied herein is robust under clinical quality assurance conditions. Consequently, low-dose regime imaging, especially for daily patient localization in radiation therapy is possible without change of the current hardware of the imaging system.

A deconvolution approach for PET-based dose reconstruction in proton radiotherapy

Positron emitters are activated by proton beams in proton radiotherapy, and positron emission tomography (PET) images can thus be used for dose verification. Since a PET image is not directly proportional to the delivered radiation dose distribution, predicted PET images are compared to measured PET images and an agreement of both indicates a successful irradiation. Such predictions are given on the basis of Monte Carlo calculations or a filtering approach which uses a convolution of the planned dose with specific filter functions to estimate the PET activity. In this paper, we describe and evaluate a dose reconstruction method based on PET images which reverses the just mentioned convolution approach using appropriate deconvolution methods. Deconvolution is an ill-posed inverse problem, and suitable regularization techniques are required in order to guarantee a stable solution. The basic convolution approach is developed for homogeneous media and additional procedures are necessary to generalize the PET estimation to inhomogeneous media. This generalization formalism is used in our dose deconvolution approach as well. Various simulations demonstrate that the dose reconstruction method is able to reverse the PET estimation method both in homogeneous and inhomogeneous media. Measured PET images are however degraded by noise and artifacts and the dose reconstructions become more difficult and the results suffer from artifacts as well. Recently used in-room PET scanners allow a decreased delay time between irradiation and imaging, and thus the influence of short-lived positron emitters on the PET images increases considerably. We extended our dose reconstruction method to process PET images which contain several positron emitters and simulated results are shown.

Kilovoltage beam model for flat panel imaging system with bow-tie filter for scatter prediction and correction.

PURPOSEnKilovoltage flat-panel imaging systems are used for cone-beam Computed Tomography (CBCT) and digital Tomosynthesis (DTS). Hereby, the presence of scatter and relatively large dose from imaging are challenging factors. In this study a phenomenological beam model was developed to characterize imager response to imaging beams with a bow-tie filter (Varian OBI system).nnnMATERIALS AND METHODnThe kilovoltage beam model was based on dose ratio formalism and thus was using standard concepts of megavoltage dose calculation such as scatter factors, tissue maximum ratio and off-axis ratio. Primary and scatter (head and phantom scatter) were modeled with three Gaussian kernels. Parameters were based on measured transmission images for slabs of solid water of different total thickness and various jaw settings.nnnRESULTSnThe beam model was used to evaluate contributions from primary, secondary and tertiary contributions for different geometrical objects such as cylinders and step-like phantoms. Theoretical predictions of radiographs using the model for known objects are consistent with the measurements.nnnCONCLUSIONnSecondary and tertiary contributions were interpreted as scatter and can be subtracted from CBCT projections based on the analytical model. Therefore our model can provide a basis for improvement of image quality (less artifacts due to scatter, better contrast and resolution) in CBCT reconstruction.

Image Sensor Noise Parameter Estimation by Variance Stabilization and Normality Assessment

High-quality image denoising requires taking into account the dependence of the noise distribution on the original image. The parameters of this dependence are often unknown and we propose a new method to estimate them here. Using an optimization procedure, we find a variance-stabilizing transformation, which transforms the input image into an image with signal-independent noise. Principal component analysis of blocks of the transformed image allows estimation of the variance of the signal-independent noise so that the parameters of the original noise model can be computed. The image blocks for processing are selected in such a way that they have low stochastic texture strength but preserve the noise distribution. The algorithm does not require the original image to have homogeneous areas and can accurately process images with regular textures. It has high computational efficiency and smaller maximum estimation error compared with the state of the art. Our experiments have also shown that denoising with the noise parameters estimated by this method leads to the same results as denoising with the true noise parameters.

Phenotypic profiling of the human genome reveals gene products involved in plasma membrane targeting of SRC kinases

SRC proteins are non-receptor tyrosine kinases that play key roles in regulating signal transduction by a diverse set of cell surface receptors. They contain N-terminal SH4 domains that are modified by fatty acylation and are functioning as membrane anchors. Acylated SH4 domains are both necessary and sufficient to mediate specific targeting of SRC kinases to the inner leaflet of plasma membranes. Intracellular transport of SRC kinases to the plasma membrane depends on microdomains into which SRC kinases partition upon palmitoylation. In the present study, we established a live-cell imaging screening system to identify gene products involved in plasma membrane targeting of SRC kinases. Based on siRNA arrays and a human model cell line expressing two kinds of SH4 reporter molecules, we conducted a genome-wide analysis of SH4-dependent protein targeting using an automated microscopy platform. We identified and validated 54 gene products whose down-regulation causes intracellular retention of SH4 reporter molecules. To detect and quantify this phenotype, we developed a software-based image analysis tool. Among the identified gene products, we found factors involved in lipid metabolism, intracellular transport, and cellular signaling processes. Furthermore, we identified proteins that are either associated with SRC kinases or are related to various known functions of SRC kinases such as other kinases and phosphatases potentially involved in SRC-mediated signal transduction. Finally, we identified gene products whose function is less defined or entirely unknown. Our findings provide a major resource for future studies unraveling the molecular mechanisms that underlie proper targeting of SRC kinases to the inner leaflet of plasma membranes.