Table of contents

To report on a novel technique for providing artifact-free quantitative four-dimensional computed tomography (4DCT) image datasets for breathing motion modeling. Commercial clinical 4DCT methods have difficulty managing irregular breathing. The resulting images contain motion-induced artifacts that can distort structures and inaccurately characterize breathing motion. We have developed a novel scanning and analysis method for motion-correlated CT that utilizes standard repeated fast helical acquisitions, a simultaneous breathing surrogate measurement, deformable image registration, and a published breathing motion model. The motion model differs from the CT-measured motion by an average of 0.65 mm, indicating the precision of the motion model. The integral of the divergence of one of the motion model parameters is predicted to be a constant 1.11 and is found in this case to be 1.09, indicating the accuracy of the motion model. The proposed technique shows promise for providing motion-artifact free images at user-selected breathing phases, accurate Hounsfield units, and noise characteristics similar to non-4D CT techniques, at a patient dose similar to or less than current 4DCT techniques.

A review of reported tissue optical properties summarizes the wavelength-dependent behavior of scattering and absorption. Formulae are presented for generating the optical properties of a generic tissue with variable amounts of absorbing chromophores (blood, water, melanin, fat, yellow pigments) and a variable balance between small-scale scatterers and large-scale scatterers in the ultrastructures of cells and tissues.

We consider multicriteria radiation therapy treatment planning by navigation over the Pareto surface, implemented by interpolation between discrete treatment plans. Current state of the art for calculation of a discrete representation of the Pareto surface is to sandwich this set between inner and outer approximations that are updated one point at a time. In this paper, we generalize this sequential method to an algorithm that permits parallelization. The principle of the generalization is to apply the sequential method to an approximation of an inexpensive model of the Pareto surface. The information gathered from the model is sub-sequently used for the calculation of points from the exact Pareto surface, which are processed in parallel. The model is constructed according to the current inner and outer approximations, and given a shape that is difficult to approximate, in order to avoid that parts of the Pareto surface are incorrectly disregarded. Approximations of comparable quality to those generated by the sequential method are demonstrated when the degree of parallelization is up to twice the number of dimensions of the objective space. For practical applications, the number of dimensions is typically at least five, so that a speed-up of one order of magnitude is obtained.

This study aims to quantify how filter choice affects several fluoro-deoxy-glucose (FDG)-positron emission tomography (PET) segmentation methods and present the use of model fitting via generalized estimating equations (GEEs) to appropriately account for the properties of a common segmentation quality metric (Dice similarity coefficient). Spherical and irregularly shaped 'hot' objects filled with 18F-FDG were placed in a medium with background activity and imaged for 1, 2 and 5 min durations at low and high contrasts. Images were filtered with Gaussian and bilateral filters of 5 and 7 mm full-width half maximum (FWHM), with and without 3 mm FWHM Gaussian pre-smoothing. Four segmentation methods were used: 40% thresholding, adaptive thresholding, k-means clustering and seeded region-growing. Segmentation accuracy was quantified by overlap (using Dice similarity coefficient (DSC)) and distance between surfaces (using symmetric-mean-absolute-surface-distance (SMASD)) of the ground truth and segmented volumes. All segmentation methods showed mean DSC values between 0.71–0.87 and mean SMASD values between 0.72–2.10 mm across filters. The bilateral filter with 3 mm FWHM Gaussian pre-smoothing had mean DSC 0.80 ± 0.17 and mean SMASD 1.17 ± 1.51 mm displaying approximately equal performance to a 5 mm Gaussian filter with mean DSC 0.79 ± 0.18 and mean SMASD 1.27 ± 1.52 mm. Results from models fit using GEE with a binomial distribution and exchangeable correlation structure estimated the correlation between DSC values as 0.118 and 0.290 for spheres and irregular objects, respectively. The GEE approach accounts for several factors specific to the DSC metric that simpler statistical approaches do not, providing more accurate estimations of experimental effects commonly associated with nuclear medicine segmentation studies.

The aim of this work is to describe and validate a new general research tool that performs Monte Carlo (MC) simulations for volumetric modulated arc therapy (VMAT) and dynamic intensity modulated radiation therapy (DIMRT), simultaneously tracking dose deposition in both the patient CT geometry and an arbitrary planar detector system. The tool is generalized to handle either entrance or exit detectors and provides the simulated dose for the individual control-points of the time-dependent VMAT and DIMRT deliveries. The MC simulation tool was developed with the EGSnrc radiation transport. For the individual control point simulation, we rotate the patient/phantom volume only (i.e. independent of the gantry and planar detector geometries) using the gantry angle in the treatment planning system (TPS) DICOM RP file such that each control point has its own unique phantom file. After MC simulation, we obtained the total dose to the phantom by summing dose contributions for all control points. Scored dose to the sensitive layer of the planar detector is available for each control point. To validate the tool, three clinical treatment plans were used including VMAT plans for a prostate case and a head-and-neck case, and a DIMRT plan for a head-and-neck case. An electronic portal imaging device operated in 'movie' mode was used with the VMAT plans delivered to cylindrical and anthropomorphic phantoms to validate the code using an exit detector. The DIMRT plan was delivered to a novel transmission detector, to validate the code using an entrance detector. The total MC 3D absolute doses in patient/phantom were compared with the TPS doses, while 2D MC doses were compared with planar detector doses for all individual control points, using the gamma evaluation test with 3%/3 mm criteria. The MC 3D absolute doses demonstrated excellent agreement with the TPS doses for all the tested plans, with about 95% of voxels having γ <1 for the plans. For planar dosimetry image comparisons, we defined an acceptable pass rate of >90% of percentage pixels with γ <1. We found that over 90% of control points in the plans passed this criterion. In general, our results indicate that the simulation tool is suitable for accurately calculating both patient/phantom doses and planar doses for VMAT dose delivery. The tool will be valuable to check performance and advance the development of in vivo planar detectors for use in measurement-based VMAT dose verification. In addition, the tool can be useful as an independent research tool for VMAT commissioning of the TPS and delivery system.

In this paper, we establish the mathematical framework of a novel imaging technique, namely photo-magnetic imaging (PMI). PMI uses a laser to illuminate biological tissues and measure the induced temperature variations using magnetic resonance imaging (MRI). PMI overcomes the limitation of conventional optical imaging and allows imaging of the optical contrast at MRI spatial resolution. The image reconstruction for PMI, using a finite-element-based algorithm with an iterative approach, is presented in this paper. The quantitative accuracy of PMI is investigated for various inclusion sizes, depths and absorption values. Then, a comparison between conventional diffuse optical tomography (DOT) and PMI is carried out to illustrate the superior performance of PMI. An example is presented showing that two 2 mm diameter inclusions embedded 4.5 mm deep and located side by side in a 25 mm diameter circular geometry medium are recovered as a single 6 mm diameter object with DOT. However, these two objects are not only effectively resolved with PMI, but their true concentrations are also recovered successfully.

We present a method to implement probabilistic treatment planning of intensity-modulated radiation therapy using custom software plugins in a commercial treatment planning system. Our method avoids the definition of safety-margins by directly including the effect of geometrical uncertainties during optimization when objective functions are evaluated. Because the shape of the resulting dose distribution implicitly defines the robustness of the plan, the optimizer has much more flexibility than with a margin-based approach. We expect that this added flexibility helps to automatically strike a better balance between target coverage and dose reduction for surrounding healthy tissue, especially for cases where the planning target volume overlaps organs at risk. Prostate cancer treatment planning was chosen to develop our method, including a novel technique to include rotational uncertainties. Based on population statistics, translations and rotations are simulated independently following a marker-based IGRT correction strategy. The effects of random and systematic errors are incorporated by first blurring and then shifting the dose distribution with respect to the clinical target volume. For simplicity and efficiency, dose-shift invariance and a rigid-body approximation are assumed. Three prostate cases were replanned using our probabilistic objective functions. To compare clinical and probabilistic plans, an evaluation tool was used that explicitly incorporates geometric uncertainties using Monte-Carlo methods. The new plans achieved similar or better dose distributions than the original clinical plans in terms of expected target coverage and rectum wall sparing. Plan optimization times were only about a factor of two higher than in the original clinical system. In conclusion, we have developed a practical planning tool that enables margin-less probability-based treatment planning with acceptable planning times, achieving the first system that is feasible for clinical implementation.

We have developed a Monte-Carlo photon-tracking and readout simulator called SCOUT to study the stochastic behavior of signals output from a simplified rectangular scintillation-camera design. SCOUT models the salient processes affecting signal generation, transport, and readout of a scintillation camera. In this work, we compare output signal statistics from SCOUT to experimental results for both a discrete and a monolithic camera. We also benchmark the speed of this simulation tool and compare it to existing simulation tools. We find this modeling tool to be relatively fast and predictive of experimental results. Depending on the modeled camera geometry, we found SCOUT to be 4 to 140 times faster than other modeling tools.

The electrical conductivity of small samples of mouse cortex (in vitro) has been measured at 10 kHz through the four-electrode method of van der Pauw. Brain slices from three mice were prepared under seizing and non-seizing conditions by changing the concentration of magnesium in the artificial cerebrospinal fluid used to maintain the tissue. These slices provided 121 square samples of cortical tissue; the conductivity of these samples was measured with an Agilent E4980A four-point impedance monitor. Of these, 73 samples were considered acceptable on the grounds of having good electrical contact between electrodes and tissue excluding outlier measurements. Results show that there is a significant difference (p = 0.03) in the conductivities of the samples under the two conditions. The seizing and non-seizing samples have mean conductivities of 0.33 and 0.36 S m⁻¹, respectively; however, these quantitative values should be used with caution as they are both subject to similar systematic uncertainties due to non-ideal temperature conditions and non-ideal placement of electrodes. We hypothesize that the difference between them, which is more robust to uncertainty, is due to the changing gap junction connectivity during seizures.

Real-time knowledge of tumor position during radiation therapy is essential to overcome the adverse effect of intra-fractional organ motion. The goal of this work is to develop a tumor tracking strategy by effectively utilizing the inherent image features of stereoscopic x-ray images acquired during dose delivery. In stereoscopic x-ray image guided radiation delivery, two orthogonal x-ray images are acquired either simultaneously or sequentially. The essence of markerless tumor tracking is the reliable identification of inherent points with distinct tissue features on each projection image and their association between two images. The identification of the feature points on a planar x-ray image is realized by searching for points with high intensity gradient. The feature points are associated by using the scale invariance features transform descriptor. The performance of the proposed technique is evaluated by using images of a motion phantom and four archived clinical cases acquired using either a CyberKnife equipped with a stereoscopic x-ray imaging system, or a LINAC equipped with an onboard kV imager and an electronic portal imaging device. In the phantom study, the results obtained using the proposed method agree with the measurements to within 2 mm in all three directions. In the clinical study, the mean error is 0.48 ± 0.46 mm for four patient data with 144 sequential images. In this work, a tissue feature-based tracking method for stereoscopic x-ray image guided radiation therapy is developed. The technique avoids the invasive procedure of fiducial implantation and may greatly facilitate the clinical workflow.

In radiopharmaceutical therapy, an understanding of the dose distribution in normal and target tissues is important for optimizing treatment. Three-dimensional (3D) dosimetry takes into account patient anatomy and the nonuniform uptake of radiopharmaceuticals in tissues. Dose–volume histograms (DVHs) provide a useful summary representation of the 3D dose distribution and have been widely used for external beam treatment planning. Reliable 3D dosimetry requires an accurate 3D radioactivity distribution as the input. However, activity distribution estimates from SPECT are corrupted by noise and partial volume effects (PVEs). In this work, we systematically investigated OS-EM based quantitative SPECT (QSPECT) image reconstruction in terms of its effect on DVHs estimates. A modified 3D NURBS-based Cardiac-Torso (NCAT) phantom that incorporated a non-uniform kidney model and clinically realistic organ activities and biokinetics was used. Projections were generated using a Monte Carlo (MC) simulation; noise effects were studied using 50 noise realizations with clinical count levels. Activity images were reconstructed using QSPECT with compensation for attenuation, scatter and collimator–detector response (CDR). Dose rate distributions were estimated by convolution of the activity image with a voxel S kernel. Cumulative DVHs were calculated from the phantom and QSPECT images and compared both qualitatively and quantitatively. We found that noise, PVEs, and ringing artifacts due to CDR compensation all degraded histogram estimates. Low-pass filtering and early termination of the iterative process were needed to reduce the effects of noise and ringing artifacts on DVHs, but resulted in increased degradations due to PVEs. Large objects with few features, such as the liver, had more accurate histogram estimates and required fewer iterations and more smoothing for optimal results. Smaller objects with fine details, such as the kidneys, required more iterations and less smoothing at early time points post-radiopharmaceutical administration but more smoothing and fewer iterations at later time points when the total organ activity was lower. The results of this study demonstrate the importance of using optimal reconstruction and regularization parameters. Optimal results were obtained with different parameters at each time point, but using a single set of parameters for all time points produced near-optimal dose–volume histograms.

The simultaneous PET data reconstruction of emission activity and attenuation coefficient distribution is presented, where the attenuation image is constrained by exploiting an external transmission source. Data are acquired in time-of-flight (TOF) mode, allowing in principle for separation of emission and transmission data. Nevertheless, here all data are reconstructed at once, eliminating the need to trace the position of the transmission source in sinogram space. Contamination of emission data by the transmission source and vice versa is naturally modeled. Attenuated emission activity data also provide additional information about object attenuation coefficient values. The algorithm alternates between attenuation and emission activity image updates. We also proposed a method of estimation of spatial scatter distribution from the transmission source by incorporating knowledge about the expected range of attenuation map values. The reconstruction of experimental data from the Siemens mCT scanner suggests that simultaneous reconstruction improves attenuation map image quality, as compared to when data are separated. In the presented example, the attenuation map image noise was reduced and non-uniformity artifacts that occurred due to scatter estimation were suppressed. On the other hand, the use of transmission data stabilizes attenuation coefficient distribution reconstruction from TOF emission data alone. The example of improving emission images by refining a CT-based patient attenuation map is presented, revealing potential benefits of simultaneous CT and PET data reconstruction.

This paper describes a framework for vascular image segmentation evaluation. Since the size of vessel wall and plaque burden is defined by the lumen and wall boundaries in vascular segmentation, these two boundaries should be considered as a pair in statistical evaluation of a segmentation algorithm. This work proposed statistical metrics to evaluate the difference of local vessel wall thickness (VWT) produced by manual and algorithm-based semi-automatic segmentation methods (ΔT) with the local segmentation standard deviation of the wall and lumen boundaries considered. ΔT was further approximately decomposed into the local wall and lumen boundary differences (ΔW and ΔL respectively) in order to provide information regarding which of the wall and lumen segmentation errors contribute more to the VWT difference. In this study, the lumen and wall boundaries in 3D carotid ultrasound images acquired for 21 subjects were each segmented five times manually and by a level-set segmentation algorithm. The (absolute) difference measures (i.e., ΔT, ΔW, ΔL and their absolute values) and the pooled local standard deviation of manually and algorithmically segmented wall and lumen boundaries were computed for each subject and represented in a 2D standardized map. The local accuracy and variability of the segmentation algorithm at each point can be quantified by the average of these metrics for the whole group of subjects and visualized on the 2D standardized map. Based on the results shown on the 2D standardized map, a variety of strategies, such as adding anchor points and adjusting weights of different forces in the algorithm, can be introduced to improve the accuracy and variability of the algorithm.

Intensity modulated treatment plan optimization is a computationally expensive task. The feasibility of advanced applications in intensity modulated radiation therapy as every day treatment planning, frequent re-planning for adaptive radiation therapy and large-scale planning research severely depends on the runtime of the plan optimization implementation. Modern computational systems are built as parallel architectures to yield high performance. The use of GPUs, as one class of parallel systems, has become very popular in the field of medical physics. In contrast we utilize the multi-core central processing unit (CPU), which is the heart of every modern computer and does not have to be purchased additionally. In this work we present an ultra-fast, high precision implementation of the inverse plan optimization problem using a quasi-Newton method on pre-calculated dose influence data sets. We redefined the classical optimization algorithm to achieve a minimal runtime and high scalability on CPUs. Using the proposed methods in this work, a total plan optimization process can be carried out in only a few seconds on a low-cost CPU-based desktop computer at clinical resolution and quality. We have shown that our implementation uses the CPU hardware resources efficiently with runtimes comparable to GPU implementations, at lower costs.

Three-dimensional reconstruction of cardiac vasculature from angiographic C-arm CT (rotational angiography) data is a major challenge. Motion artefacts corrupt image quality, reducing usability for diagnosis and guidance. Many state-of-the-art approaches depend on retrospective ECG-gating of projection data for image reconstruction. A trade-off has to be made regarding the size of the ECG-gating window. A large temporal window is desirable to avoid undersampling. However, residual motion will occur in a large window, causing motion artefacts. We present an algorithm to correct for residual motion. Our approach is based on a deformable 2D–2D registration between the forward projection of an initial, ECG-gated reconstruction, and the original projection data. The approach is fully automatic and does not require any complex segmentation of vasculature, or landmarks. The estimated motion is compensated for during the backprojection step of a subsequent reconstruction. We evaluated the method using the publicly available CAVAREV platform and on six human clinical datasets. We found a better visibility of structure, reduced motion artefacts, and increased sharpness of the vessels in the compensated reconstructions compared to the initial reconstructions. At the time of writing, our algorithm outperforms the leading result of the CAVAREV ranking list. For the clinical datasets, we found an average reduction of motion artefacts by 13 ± 6%. Vessel sharpness was improved by 25 ± 12% on average.

A beta camera has been developed that allows planar imaging of the spatial and temporal distribution of beta particles using a 14 × 14 mm² position sensitive avalanche photodiode (PSAPD). This camera system, which we call Betabox, can be directly coupled to microfluidic chips designed for cell incubation or other biological applications. Betabox allows for imaging the cellular uptake of molecular imaging probes labeled with charged particle emitters such as ¹⁸F inside these chips. In this work, we investigate the quantitative imaging capabilities of Betabox for ¹⁸F beta particles, in terms of background rate, efficiency, spatial resolution, and count rate. Measurements of background and spatial resolution are considered both at room temperature (21 °C ± 1 °C) and at an elevated operating temperature (37 °C ± 1 °C), as is often required for biological assays. The background rate measured with a 4 keV energy cutoff is below 2 cph mm⁻² at both 21 and 37 °C. The absolute efficiency of Betabox for the detection of ¹⁸F positron sources in contact with a PSAPD with the surface passivated from ambient light and damage is 46% ± 1%. The lower detection limit is estimated using the Rose Criterion to be 0.2 cps mm⁻² for 1 min acquisitions and a 62 × 62 µm² pixel size. The upper detection limit is approximately 21 000 cps. The spatial resolution at both 21 and 37 °C ranges from 0.4 mm FWHM at the center of the field of view (FOV), and degrades to 1 mm at a distance of 5 mm away from center yielding a useful FOV of approximately 10 × 10 mm². We also investigate the effects on spatial resolution and sensitivity that result from the use of a polymer based microfluidic chip. For these studies we place varying layers of low-density polyethylene (LDPE) between the detector and the source and find that the spatial resolution degrades by ∼180 µm for every 100 µm of LDPE film. Sensitivity is reduced by half with the inclusion of ∼200 µm of additional LDPE film. Lastly, we demonstrate the practical utilization of Betabox, with an imaging test of its linearity, when coupled to a polydimethylsiloxane microfluidic chip designed for cell based assays.

Radiotherapy with narrow scanned carbon ion beams enables a highly accurate treatment of tumours while sparing the surrounding healthy tissue. Changes in the patient's geometry can alter the actual ion range in tissue and result in unfavourable changes in the dose distribution. Consequently, it is desired to verify the actual beam delivery within the patient. Real-time and non-invasive measurement methods are preferable. Currently, the only technically feasible method to monitor the delivered dose distribution within the patient is based on tissue activation measurements by means of positron emission tomography (PET). An alternative monitoring method based on tracking of prompt secondary ions leaving a patient irradiated with carbon ion beams has been previously suggested. It is expected to help in overcoming the limitations of the PET-based technique like physiological washout of the beam induced activity, low signal and to allow for real-time measurements. In this paper, measurements of secondary charged particle tracks around a head-sized homogeneous PMMA phantom irradiated with pencil-like carbon ion beams are presented. The investigated energies and beam widths are within the therapeutically used range. The aim of the study is to deduce properties of the primary beam from the distribution of the secondary charged particles. Experiments were performed at the Heidelberg Ion Beam Therapy Center, Germany. The directions of secondary charged particles emerging from the PMMA phantom were measured using an arrangement of two parallel pixelated silicon detectors (Timepix). The distribution of the registered particle tracks was analysed to deduce its dependence on clinically important beam parameters: beam range, width and position. Distinct dependencies of the secondary particle tracks on the properties of the primary carbon ion beam were observed. In the particular experimental set-up used, beam range differences of 1.3 mm were detectable. In addition, variations in the beam width could be measured with a precision of 0.9 mm. Furthermore, shifts of the lateral beam position could be monitored with a sub-millimetre precision. The presented investigations demonstrate experimentally that the non-invasive measurement and analysis of secondary ion distributions around head-sized homogeneous objects provide information on the actual beam delivery. Beam range, width and position could be monitored with a precision attractive for therapeutic situations.

A novel method is proposed for the measurement of signal-to-noise ratio (SNR) for the purpose of quality assurance (QA) in MRI. A boxcar filtering technique is applied which allows estimation of signal and noise from a single image. The method has been used to estimate SNR in a large set of images acquired in a consistent manner using various scanner models, coils and phantoms. Performance is evaluated by comparison with the double-image subtraction technique incorporating temporal instability correction. The limits of agreement between the techniques are comparable to typical variability in daily SNR, and significantly superior to the performance of other single-image methods published to date. Single-image methods are preferable as they halve the image acquisition time of the recommended double-image approach. Major inaccuracies are identified in methods of SNR measurement currently used for QA in MRI.

PETbox4 is a new, fully tomographic bench top PET scanner dedicated to high sensitivity and high resolution imaging of mice. This manuscript characterizes the performance of the prototype system using the National Electrical Manufacturers Association NU 4-2008 standards, including studies of sensitivity, spatial resolution, energy resolution, scatter fraction, count-rate performance and image quality. The PETbox4 performance is also compared with the performance of PETbox, a previous generation limited angle tomography system. PETbox4 consists of four opposing flat-panel type detectors arranged in a box-like geometry. Each panel is made by a 24 × 50 pixelated array of 1.82 × 1.82 × 7 mm bismuth germanate scintillation crystals with a crystal pitch of 1.90 mm. Each of these scintillation arrays is coupled to two Hamamatsu H8500 photomultiplier tubes via a glass light guide. Volumetric images for a 45 × 45 × 95 mm field of view (FOV) are reconstructed with a maximum likelihood expectation maximization algorithm incorporating a system model based on a parameterized detector response. With an energy window of 150–650 keV, the peak absolute sensitivity is approximately 18% at the center of FOV. The measured crystal energy resolution ranges from 13.5% to 48.3% full width at half maximum (FWHM), with a mean of 18.0%. The intrinsic detector spatial resolution is 1.5 mm FWHM in both transverse and axial directions. The reconstructed image spatial resolution for different locations in the FOV ranges from 1.32 to 1.93 mm, with an average of 1.46 mm. The peak noise equivalent count rate for the mouse-sized phantom is 35 kcps for a total activity of 1.5 MBq (40 µCi) and the scatter fraction is 28%. The standard deviation in the uniform region of the image quality phantom is 5.7%. The recovery coefficients range from 0.10 to 0.93. In comparison to the first generation two panel PETbox system, PETbox4 achieves substantial improvements on sensitivity and spatial resolution. The overall performance demonstrates that the PETbox4 scanner is suitable for producing high quality images for molecular imaging based biomedical research.

Positron emission tomography (PET) has been suggested as an imaging technique for in vivo proton dose and range verification after proton induced-tissue activation. During proton treatment, irradiated tissue is activated and decays while emitting positrons. In this paper, we assessed the feasibility of using PET imaging after proton treatment to determine tissue elemental composition by evaluating the resultant composite decay curve of activated tissue. A phantom consisting of sections composed of different combinations of ¹H, ¹²C, ¹⁴N, and ¹⁶O was irradiated using a pristine Bragg peak and a 6 cm spread-out Bragg-peak (SOBP) proton beam. The beam ranges defined at 90% distal dose were 10 cm; the delivered dose was 1.6 Gy for the near monoenergetic beam and 2 Gy for the SOBP beam. After irradiation, activated phantom decay was measured using an in-room PET scanner for 30 min in list mode. Decay curves from the activated ¹²C and ¹⁶O sections were first decomposed into multiple simple exponential decay curves, each curve corresponding to a constituent radioisotope, using a least-squares method. The relative radioisotope fractions from each section were determined. These fractions were used to guide the decay curve decomposition from the section consisting mainly of ¹²C + ¹⁶O and calculate the relative elemental composition of ¹²C and ¹⁶O. A Monte Carlo simulation was also used to determine the elemental composition of the ¹²C + ¹⁶O section. The calculated compositions of the ¹²C + ¹⁶O section using both approaches (PET and Monte Carlo) were compared with the true known phantom composition. Finally, two patients were imaged using an in-room PET scanner after proton therapy of the head. Their PET data and the technique described above were used to construct elemental composition (¹²C and ¹⁶O) maps that corresponded to the proton-activated regions. We compared the ¹²C and ¹⁶O compositions of seven ROIs that corresponded to the vitreous humor, adipose/face mask, adipose tissue, and brain tissue with ICRU 46 elemental composition data. The ¹²C and ¹⁶O compositions of the ¹²C + ¹⁶O phantom section were estimated to within a maximum difference of 3.6% for the near monoenergetic and SOBP beams over an 8 cm depth range. On the other hand, the Monte Carlo simulation estimated the corresponding ¹²C and ¹⁶O compositions in the ¹²C + ¹⁶O section to within a maximum difference of 3.4%. For the patients, the ¹²C and ¹⁶O compositions in the seven ROIs agreed with the ICRU elemental composition data, with a mean (maximum) difference of 9.4% (15.2%). The ¹²C and ¹⁶O compositions of the phantom and patients were estimated with relatively small differences. PET imaging may be useful for determining the tissue elemental composition and thereby improving proton treatment planning and verification.

During one year of clinical activity at the Italian National Center for Oncological Hadron Therapy 31 patients were treated with actively scanned proton beams. Results of patient-specific quality assurance procedures are presented here which assess the accuracy of a three-dimensional dose verification technique with the simultaneous use of multiple small-volume ionization chambers. To investigate critical cases of major deviations between treatment planning system (TPS) calculated and measured data points, a Monte Carlo (MC) simulation tool was implemented for plan verification in water. Starting from MC results, the impact of dose calculation, dose delivery and measurement set-up uncertainties on plan verification results was analyzed. All resulting patient-specific quality checks were within the acceptance threshold, which was set at 5% for both mean deviation between measured and calculated doses and standard deviation. The mean deviation between TPS dose calculation and measurement was less than ±3% in 86% of the cases. When all three sources of uncertainty were accounted for, simulated data sets showed a high level of agreement, with mean and maximum absolute deviation lower than 2.5% and 5%, respectively.

Iterative reconstructions in positron emission tomography (PET) need a model relating the recorded data to the object/patient being imaged, called the system matrix (SM). The more realistic this model, the better the spatial resolution in the reconstructed images. However, a serious concern when using a SM that accurately models the resolution properties of the PET system is the undesirable edge artefact, visible through oscillations near sharp discontinuities in the reconstructed images. This artefact is a natural consequence of solving an ill-conditioned inverse problem, where the recorded data are band-limited. In this paper, we focus on practical aspects when considering image-based point-spread function (PSF) reconstructions. To remove the edge artefact, we propose to use a particular case of the method of sieves (Grenander 1981 Abstract Inference New York: Wiley), which simply consists in performing a standard PSF reconstruction, followed by a post-smoothing using the PSF as the convolution kernel. Using analytical simulations, we investigate the impact of different reconstruction and PSF modelling parameters on the edge artefact and its suppression, in the case of noise-free data and an exactly known PSF. Using Monte-Carlo simulations, we assess the proposed method of sieves with respect to the choice of the geometric projector and the PSF model used in the reconstruction. When the PSF model is accurately known, we show that the proposed method of sieves succeeds in completely suppressing the edge artefact, though after a number of iterations higher than typically used in practice. When applying the method to realistic data (i.e. unknown true SM and noisy data), we show that the choice of the geometric projector and the PSF model does not impact the results in terms of noise and contrast recovery, as long as the PSF has a width close to the true PSF one. Equivalent results were obtained using either blobs or voxels in the same conditions (i.e. the blob's density function being the same as the voxel-based PSF). From a practical point-of-view, the method can be used to perform fast reconstructions based on very simple models (compared to sinogram-based PSF modelling), producing artefact-free images with a better compromise between noise and spatial resolution than images reconstructed without or with under-estimated PSF. Besides, the method inherently limits the spatial resolution in the reconstructed images to the intrinsic one of the PET system.

We present a method for adapting a biologically optimized treatment planning for particle beams to a spatially inhomogeneous tumor sensitivity due to hypoxia, and detected e.g., by PET functional imaging. The TRiP98 code, established treatment planning system for particles, has been extended for including explicitly the oxygen enhancement ratio (OER) in the biological effect calculation, providing the first set up of a dedicated ion beam treatment planning approach directed to hypoxic tumors, TRiP-OER, here reported together with experimental tests. A simple semi-empirical model for calculating the OER as a function of oxygen concentration and dose averaged linear energy transfer, generating input tables for the program is introduced. The code is then extended in order to import such tables coming from the present or alternative models, accordingly and to perform forward and inverse planning, i.e., predicting the survival response of differently oxygenated areas as well as optimizing the required dose for restoring a uniform survival effect in the whole irradiated target. The multiple field optimization results show how the program selects the best beam components for treating the hypoxic regions. The calculations performed for different ions, provide indications for the possible clinical advantages of a multi-ion treatment. Finally the predictivity of the code is tested through dedicated cell culture experiments on extended targets irradiation using specially designed hypoxic chambers, providing a qualitative agreement, despite some limits in full survival calculations arising from the RBE assessment. The comparison of the predictions resulting by using different model tables are also reported.

As radio frequency (RF) catheter ablation becomes increasingly prevalent in the management of ventricular arrhythmia in patients, an accurate and rapid determination of the arrhythmogenic site is of important clinical interest. The aim of this study was to test the hypothesis that the inversely reconstructed ventricular endocardial current density distribution from body surface potential maps (BSPMs) can localize the regions critical for maintenance of a ventricular ectopic activity. Patients with isolated and monomorphic premature ventricular contractions (PVCs) were investigated by noninvasive BSPMs and subsequent invasive catheter mapping and ablation. Equivalent current density (CD) reconstruction (CDR) during symptomatic PVCs was obtained on the endocardial ventricular surface in six patients (four men, two women, years 23–77), and the origin of the spontaneous ectopic activity was localized at the location of the maximum CD value. Compared with the last (successful) ablation site (LAS), the mean and standard deviation of localization error of the CDR approach were 13.8 and 1.3 mm, respectively. In comparison, the distance between the LASs and the estimated locations of an equivalent single moving dipole in the heart was 25.5 ± 5.5 mm. The obtained CD distribution of activated sources extending from the catheter ablation site also showed a high consistency with the invasively recorded electroanatomical maps. The noninvasively reconstructed endocardial CD distribution is suitable to predict a region of interest containing or close to arrhythmia source, which may have the potential to guide RF catheter ablation.

In robotic radiosurgery, it is necessary to compensate for systematic latencies arising from target tracking and mechanical constraints. This compensation is usually achieved by means of an algorithm which computes the future target position. In most scientific works on respiratory motion prediction, only one or two algorithms are evaluated on a limited amount of very short motion traces. The purpose of this work is to gain more insight into the real world capabilities of respiratory motion prediction methods by evaluating many algorithms on an unprecedented amount of data. We have evaluated six algorithms, the normalized least mean squares (nLMS), recursive least squares (RLS), multi-step linear methods (MULIN), wavelet-based multiscale autoregression (wLMS), extended Kalman filtering, and ε-support vector regression (SVRpred) methods, on an extensive database of 304 respiratory motion traces. The traces were collected during treatment with the CyberKnife (Accuray, Inc., Sunnyvale, CA, USA) and feature an average length of 71 min. Evaluation was done using a graphical prediction toolkit, which is available to the general public, as is the data we used. The experiments show that the nLMS algorithm—which is one of the algorithms currently used in the CyberKnife—is outperformed by all other methods. This is especially true in the case of the wLMS, the SVRpred, and the MULIN algorithms, which perform much better. The nLMS algorithm produces a relative root mean square (RMS) error of 75% or less (i.e., a reduction in error of 25% or more when compared to not doing prediction) in only 38% of the test cases, whereas the MULIN and SVRpred methods reach this level in more than 77%, the wLMS algorithm in more than 84% of the test cases. Our work shows that the wLMS algorithm is the most accurate algorithm and does not require parameter tuning, making it an ideal candidate for clinical implementation. Additionally, we have seen that the structure of a patient's respiratory motion trace has strong influence on the outcome of prediction. Further work is needed to determine a priori the suitability of an individual's respiratory behaviour to motion prediction.

In this treatment planning study, the potential benefits of a rotating shield brachytherapy (RSBT) technique based on a partially-shielded electronic brachytherapy source were assessed for treating cervical cancer. Conventional intracavitary brachytherapy (ICBT), intracavitary plus supplementary interstitial (IS+ICBT), and RSBT treatment plans for azimuthal emission angles of 180° (RSBT-180) and 45° (RSBT-45) were generated for five patients. For each patient, high-risk clinical target volume (HR-CTV) equivalent dose in 2 Gy fractions (EQD2) (α/β = 10 Gy) was escalated until bladder, rectum, or sigmoid colon tolerance EQD2 values were reached. External beam radiotherapy dose (1.8 Gy × 25) was accounted for, and brachytherapy was assumed to have been delivered in 5 fractions. IS+ICBT provided a greater HR-CTV D₉₀ (minimum EQD2 to the hottest 90%) than ICBT. D₉₀ was greater for RSBT-45 than IS+ICBT for all five patients, and greater for RSBT-180 than IS+ICBT for two patients. When the RSBT-45/180 plan with the lowest HR-CTV D₉₀ that was greater than the D₉₀ the ICBT or IS+ICBT plan was selected, the average (range) of D₉₀ increases for RSBT over ICBT and IS+ICBT were 16.2 (6.3–27.2)and 8.5 (0.03–20.16) Gy, respectively. The average (range) treatment time increase per fraction of RSBT was 34.56 (3.68–70.41) min over ICBT and 34.59 (3.57–70.13) min over IS+ICBT. RSBT can increase D₉₀ over ICBT and IS+ICBT without compromising organ-at-risk sparing. The D₉₀ and treatment time improvements from RSBT depend on the patient and shield emission angle.

Reducing the number of output channels in pixelated positron emission tomography (PET) detectors is an effective way to minimize cost and complexity while minimizing the impact on detector performance. This paper compares the system performance of two multiplexing schemes by using both simulation and experimental studies, with respect to spatial, time and energy resolutions. Simulations were performed using the SPICE environment to investigate differences in resulting flood histograms and rising edge slopes. Experiments were performed using lutetium-yttrium oxyorthosilicate (LYSO) crystals coupled to a SensL ArraySL-4 silicon photomultiplier (SiPM) connected to interchangeable circuit boards containing the two multiplexing schemes of interest. Three crystal configurations were tested: a single crystal element (3×3×20 mm³), 2×2 array (crystal pitch: 3×3×20 mm³) and 6×6 array (crystal pitch: 2.1×2.1×20 mm³). Good agreement was found between the simulations and experiment results. It is found that the capacitive multiplexing is able to achieve an improved time resolution of good uniformity (average of 1.11 ± 0.01 ns and 1.90 ± 0.03 ns for the arrays, respectively) and crystal separation, compared to the resistive multiplexing (average of 1.95 ± 0.03 ns and 3.33 ± 0.10 ns). On the other hand, the resistive multiplexing demonstrates slightly improved energy resolution (11 ± 0.1% and 22 ± 0.6%, compared to 12 ± 0.1% and 24 ± 0.4% for the capacitive array), which is believed to be caused by the RC circuit formed between the splitting capacitors and the input impedance of amplifiers. The relevancy of this work to the PET block detector design using SiPM arrays is also discussed, including light sharing, edge compression and gain variation among SiPM pixels.

The configuration of a treatment planning system (TPS) for double-scattering-based proton therapy requires many user inputs. Most of these are either gathered during the routine collection of commissioning data, or can be supplied by the equipment vendor; however, this is not true of all. In this study we developed a technique both to (a) expedite the extraction of those undetermined TPS parameters related to the range modulator wheels that can only otherwise be obtained by the time-consuming process of trial-and-error, and (b) demonstrate how, for a commonly-employed, commercially-available TPS, the judicious determination of such parameters can be used to optimize the resultant modelling of longitudinal dose distributions delivered by a double scattering proton therapy system. Our technique is simple to implement, robust in nature and also provides insight allowing parameters that must be contrived in that model to be related directly to physical aspects of the beam delivery system.