In previous work we demonstrated the potential of positron emission tomography (PET) detectors with depth-of-interaction (DOI) encoding capability based on phosphor-coated crystals. A DOI resolution of 8 mm full-width at half-maximum was obtained for 20 mm long scintillator crystals using a delayed charge integration linear regression method (DCI-LR). Phosphor-coated crystals modify the pulse shape to allow continuous DOI information determination, but the relationship between pulse shape and DOI is complex. We are therefore interested in developing a sensitive and robust method to estimate the DOI. Here, linear discriminant analysis (LDA) was implemented to classify the events based on information extracted from the pulse shape. Pulses were acquired with 2 × 2 × 20 mm3 phosphor-coated crystals at five irradiation depths and characterized by their DCI values or Laguerre coefficients. These coefficients were obtained by expanding the pulses on a Laguerre basis set and constituted a unique signature for each pulse. The DOI of individual events was predicted using LDA based on Laguerre coefficients (Laguerre-LDA) or DCI values (DCI-LDA) as discriminant features. Predicted DOIs were compared to true irradiation depths. Laguerre-LDA showed higher sensitivity and accuracy than DCI-LDA and DCI-LR and was also more robust to predict the DOI of pulses with higher statistical noise due to low light levels (interaction depths further from the photodetector face). This indicates that Laguerre-LDA may be more suitable to DOI estimation in smaller crystals where lower collected light levels are expected. This novel approach is promising for calculating DOI using pulse shape discrimination in single-ended readout depth-encoding PET detectors.
Positron emission tomography (PET); Depth of interaction (DOI); Phosphor-coated scintillators; PET detectors; Laguerre expansion; Pulse shape discrimination; Linear discriminant analysis; Delayed Charge Integration (DCI)
The quantification of tumor molecular expression in vivo could have a significant impact for informing and monitoring immerging targeted therapies in oncology. Molecular imaging of targeted tracers can be used to quantify receptor expression in the form of a binding potential (BP) if the arterial input curve or a surrogate of it is also measured. However, the assumptions of the most common approaches (reference tissue models) may not be valid for use in tumors. In this study, the validity of reference tissue models is investigated for use in tumors experimentally and in simulations. Three different tumor lines were grown subcutaneously in athymic mice and the mice were injected with a mixture of an epidermal growth factor receptor- (EGFR-) targeted fluorescent tracer and an untargeted fluorescent tracer. A one-compartment plasma input model demonstrated that the transport kinetics of both tracers were significantly different between tumors and all potential reference tissues, and using the reference tissue model resulted in a theoretical underestimation in BP of 50 ± 37%. On the other hand, the targeted and untargeted tracers demonstrated similar transport kinetics, allowing a dual-tracer approach to be employed to accurately estimate binding potential (with a theoretical error of 0.23 ± 9.07%). These findings highlight the potential for using a dual-tracer approach to quantify receptor expression in tumors with abnormal hemodynamics, possibly to inform the choice or progress of molecular cancer therapies.
tracer kinetics; receptor density; compartment modelling; epidermal growth factor receptor (EGFR); mouse; human xenographs
Several different Monte Carlo codes are currently being used at proton therapy centers to improve upon dose predictions over standard methods using analytical or semi-empirical dose algorithms. There is a need to better ascertain the differences between proton dose predictions from different available Monte Carlo codes. In this investigation Geant4 and MCNPX, the two most-utilized Monte Carlo codes for proton therapy applications, were used to predict energy deposition distributions in a variety of geometries, comprising simple water phantoms, water phantoms with complex inserts and in a voxelized geometry based on clinical CT data. The gamma analysis was used to evaluate the differences of the predictions between the codes. The results show that in the all cases the agreement was better than clinical acceptance criteria.
Monte Carlo validation; Geant4; MCNPX
Intravascular near-infrared fluorescence (iNIRF) imaging can enable the in vivo visualization of biomarkers of vascular pathology, including high-risk plaques. The technique resolves the bio-distribution of systemically administered fluorescent probes with molecular specificity in the vessel wall. However, the geometrical variations that may occur in the distance between fibre-tip and vessel wall can lead to signal intensity variations and challenge quantification. Herein we examined whether the use of anatomical information of the cross-section vessel morphology, obtained from co-registered intravascular ultrasound (IVUS), can lead to quantification improvements when fibre-tip and vessel wall distance variations are present. The algorithm developed employs a photon propagation model derived from phantom experiments that is used to calculate the relative attenuation of fluorescence signals as they are collected over 360 degrees along the vessel wall, and utilizes it to restore accurate fluorescence readings. The findings herein point to quantification improvements when employing hybrid iNIRF, with possible implications to the clinical detection of high-risk plaques or blood vessel theranostics.
This paper introduces a mouse atlas registration system (MARS), composed of a stationary top-view x-ray projector and a side-view optical camera, coupled to a mouse atlas registration algorithm. This system is using the x-ray and optical images to guide a fully automatic co-registration of a mouse atlas with each subject, in order to provide anatomical reference for small animal molecular imaging systems such as Positron Emission Tomography (PET). To facilitate the registration, a statistical atlas that accounts for inter-subject anatomical variations was constructed based on 83 organ-labeled mouse micro-CT images. The statistical shape model and conditional Gaussian model techniques were used to register the atlas with the x-ray image and optical photo. The accuracy of the atlas registration was evaluated by comparing the registered atlas with the organ-labeled micro-CT images of the test subjects. The results showed excellent registration accuracy of the whole-body region, and good accuracy for the brain, liver, heart, lungs and kidneys. In its implementation, the MARS system was integrated with a preclinical PET scanner to deliver combined PET/MARS imaging, and to facilitate atlas-assisted analysis of the preclinical PET images.
Ultrasound Current Source Density Imaging (UCSDI) exploits the acoustoelectric (AE) effect, an interaction between ultrasound pressure and electrical resistivity, to map electrical conduction in the heart. The conversion efficiency for UCSDI is determined by the AE interaction constant K, a fundamental property of all materials; K directly affects the magnitude of the detected voltage signal in UCSDI. This paper describes a technique for measuring K in biological tissue, and reports its value for the first time in cadaver hearts. A custom chamber was designed and fabricated to control the geometry for estimating K, which was measured in different ionic salt solutions and 7 cadaver rabbit hearts. We found K to be strongly dependent on concentration for the divalent salt CuSO4, but not for the monovalent salt NaCl, consistent with their different chemical properties. In the rabbit heart, K was determined to be 0.041±0.012 %/MPa, similar to the measurement of K in physiologic saline (0.034±0.003 %/MPa). This study provides a baseline estimate of K for modeling and experimental studies that involve UCSDI to map cardiac conduction and reentry currents associated with arrhythmias.
arrhythmia; cardiac ablation; acoustoelectric effect; interaction constant
Many scientists in the field of x-ray imaging rely on the simulation of x-ray images. As the phantom models become more and more realistic, their projection requires high computational effort. Since x-ray images are based on transmission, many standard graphics acceleration algorithms cannot be applied to this task. However, if adapted properly, simulation speed can be increased dramatically using state-of-the-art graphics hardware.
A custom graphics pipeline that simulates transmission projections for tomographic reconstruction was implemented based on moving spline surface models. All steps from tessellation of the splines, projection onto the detector, and drawing are implemented in OpenCL. We introduced a special append buffer for increased performance in order to store the intersections with the scene for every ray. Intersections are then sorted and resolved to materials. Lastly, an absorption model is evaluated to yield an absorption value for each projection pixel.
Projection of a moving spline structure is fast and accurate. Projections of size 640×480 can be generated within 254 ms. Reconstructions using the projections show errors below 1 HU with a sharp reconstruction kernel. Traditional GPU-based acceleration schemes are not suitable for our reconstruction task. Even in the absence of noise, they result in errors up to 9 HU on average, although projection images appear to be correct under visual examination.
Projections generated with our new method are suitable for the validation of novel CT reconstruction algorithms. For complex simulations, such as the evaluation of motion-compensated reconstruction algorithms, this kind of x-ray simulation will reduce the computation time dramatically. Source code is available at http://conrad.stanford.edu/
x-ray imaging; simulation; hardware acceleration; GPU; digitally reconstructed radiographs
A mathematical model which reconstructs the structure of existing vasculature using patient-specific anatomical, functional and molecular imaging as input was developed. The vessel structure is modelled according to empirical vascular parameters, such as the mean vessel branching angle. The model is calibrated such that the resultant oxygen map modelled from the simulated microvasculature stochastically matches the input oxygen map to a high degree of accuracy (R2 ≈ 1). The calibrated model was successfully applied to preclinical imaging data. Starting from the anatomical vasculature image (obtained from contrast-enhanced computed tomography), a representative map of the complete vasculature was stochastically simulated as determined by the oxygen map (obtained from hypoxia [64Cu]Cu-ATSM positron emission tomography). The simulated microscopic vasculature and the calculated oxygenation map successfully represent the imaged hypoxia distribution (R2 = 0.94). The model elicits the parameters required to simulate vasculature consistent with imaging and provides a key mathematical relationship relating the vessel volume to the tissue oxygen tension. Apart from providing an excellent framework for visualizing the imaging gap between the microscopic and macroscopic imagings, the model has the potential to be extended as a tool to study the dynamics between the tumour and the vasculature in a patient-specific manner and has an application in the simulation of anti-angiogenic therapies.
Micro-CT can play an important role in preclinical studies of cardiovascular disease because of its high spatial and temporal resolution. Quantitative analysis of 4D cardiac images requires segmentation of the cardiac chambers at each time point, an extremely time consuming process if done manually. To improve throughput this study proposes a pipeline for registration-based segmentation and functional analysis of 4D cardiac micro-CT data in the mouse. Following optimization and validation using simulations, the pipeline was applied to in vivo cardiac micro-CT data corresponding to 10 cardiac phases acquired in C57BL/6 mice (n = 5). After edge-preserving smoothing with a novel adaptation of 4D bilateral filtration, one phase within each cardiac sequence was manually segmented. Deformable registration was used to propagate these labels to all other cardiac phases for segmentation. The volumes of each cardiac chamber were calculated and used to derive stroke volume, ejection fraction, cardiac output, and cardiac index. Dice coefficients and volume accuracies were used to compare manual segmentations of two additional phases with their corresponding propagated labels. Both measures were, on average, >0.90 for the left ventricle and >0.80 for the myocardium, the right ventricle, and the right atrium, consistent with trends in inter- and intra-segmenter variability. Segmentation of the left atrium was less reliable. On average, the functional metrics of interest were underestimated by 6.76% or more due to systematic label propagation errors around atrioventricular valves; however, execution of the pipeline was 80% faster than performing analogous manual segmentation of each phase.
Computational tumour models have emerged as powerful tools for the optimization of cancer therapies; ideally, these models should incorporate patient-specific imaging data indicative of therapeutic response. The purpose of this study was to develop a tumour modelling framework in order to simulate the therapeutic effects of anti-angiogenic agents based upon clinical molecular imaging data. The model was applied to positron emission tomography (PET) data of cellular proliferation and hypoxia from a phase I clinical trial of bevacizumab, an antibody that neutralizes the vascular endothelial growth factor (VEGF). When using pre-therapy PET data in combination with literature-based dose response parameters, simulated follow-up hypoxia data yielded good qualitative agreement with imaged hypoxia levels. Improving the quantitative agreement with follow-up hypoxia and proliferation PET data required tuning of the maximum vascular growth fraction (VGFmax) and the tumour cell cycle time to patient-specific values. VGFmax was found to be the most sensitive model parameter (CV=22%). Assuming availability of patient-specific, intratumoural VEGF levels, we show how bevacizumab dose levels can potentially be ‘tailored’ to improve levels of tumour hypoxia while maintaining proliferative response, both of which are critically important in the context of combination therapy. Our results suggest that, upon further validation, the application of image-driven computational models may afford opportunities to optimize dosing regimens and combination therapies in a patient-specific manner.
computational modelling; in silico oncology; patient-specific modelling; tumour growth; tumour modelling; angiogenesis; anti-angiogenic therapy; therapy response; bevacizumab
Glioblastoma multiforme (GBM) is the most malignant form of primary brain tumors known as gliomas. They proliferate and invade extensively and yield short life expectancies despite aggressive treatment. Response to treatment is usually measured in terms of survival of groups of patients treated similarly but this statistical approach misses the subgroups that may have responded to or may have been injured by treatment. Such statistics offer scant reassurance to individual patients who have suffered through these treatments. Furthermore, current imaging-based treatment response metrics in individual patients ignore patient-specific differences in tumor growth kinetics, which have been shown to vary widely across patients even within the same histological diagnosis and, unfortunately, these metrics have shown only minimal success in predicting patient outcome.
We consider nine newly diagnosed GBM patients receiving diagnostic biopsy followed by standard of care external beam radiation therapy (XRT). We present and apply a patient-specific, biologically-based mathematical model for glioma growth that quantifies response to XRT in individual patients in vivo. The mathematical model uses net rates of proliferation and migration of malignant tumor cells to characterize the tumor’s growth and invasion along with the linear-quadratic model for the response to radiation therapy. Using only routinely available pre-treatment MRIs to inform the patient-specific bio-mathematical model simulations, we find that radiation response in these patients, quantified by both clinical and model-generated measures, could have been predicted prior to treatment with high accuracy. Specifically, we find the net proliferation rate is correlated with the radiation response parameter (r = 0.89, p = 0.0007), resulting in a predictive relationship that is tested with a leave-one-out cross validation technique. This relationship predicts the tumor size post therapy to within inter-observer tumor volume uncertainty.
The results of this study suggest that a mathematical model can create a virtual in silico tumor with the same growth kinetics as a particular patient and can not only predict treatment response in individual patients in vivo but also provide a basis for evaluation of response in each patient to any given therapy.
The axial field of view (AFOV) of the current generation of clinical whole-body PET scanners range from 15–22 cm, which limits sensitivity and renders applications such as whole-body dynamic imaging, or imaging of very low activities in whole-body cellular tracking studies, almost impossible. Generally, extending the AFOV significantly increases the sensitivity and count-rate performance. However, extending the AFOV while maintaining detector thickness has significant cost implications. In addition, random coincidences, detector dead time, and object attenuation may reduce scanner performance as the AFOV increases. In this paper, we use Monte Carlo simulations to find the optimal scanner geometry (i.e. AFOV, detector thickness and acceptance angle) based on count-rate performance for a range of scintillator volumes ranging from 10 to 90 l with detector thickness varying from 5 to 20 mm. We compare the results to the performance of a scanner based on the current Siemens Biograph mCT geometry and electronics. Our simulation models were developed based on individual components of the Siemens Biograph mCT and were validated against experimental data using the NEMA NU-2 2007 count-rate protocol. In the study, noise-equivalent count rate (NECR) was computed as a function of maximum ring difference (i.e. acceptance angle) and activity concentration using a 27 cm diameter, 200 cm uniformly filled cylindrical phantom for each scanner configuration. To reduce the effect of random coincidences, we implemented a variable coincidence time window based on the length of the lines of response, which increased NECR performance up to 10% compared to using a static coincidence time window for scanners with large maximum ring difference values. For a given scintillator volume, the optimal configuration results in modest count-rate performance gains of up to 16% compared to the shortest AFOV scanner with the thickest detectors. However, the longest AFOV of approximately 2 m with 20 mm thick detectors resulted in performance gains of 25–31 times higher NECR relative to the current Siemens Biograph mCT scanner configuration.
Focusing heat delivery while minimizing collateral damage to normal tissues is essential for successful nanoparticle-mediated laser-induced thermal cancer therapy. We present thermal maps obtained via magnetic resonance imaging (MRI) characterizing laser heating of a phantom tissue containing a multiwalled carbon nanotube inclusion. The data demonstrate that heating continuously over tens of seconds leads to poor localization (~ 0.5 cm) of the elevated temperature region. By contrast, for the same energy input, heat localization can be reduced to the millimeter rather than centimeter range by increasing the laser power and shortening the pulse duration. The experimental data can be well understood within a simple diffusive heat conduction model. Analysis of the model indicates that to achieve 1 mm or better resolution, heating pulses of ~ 2s or less need to be used with appropriately higher heating power. Modeling these data using a diffusive heat conduction analysis predicts parameters for optimal targeted delivery of heat for ablative therapy.
carbon nanotube; MR thermometry; photothermal therapy; heat localization; diffusive heat model; MR Imaging; proton resonance shift
Dynamic emission computed tomographic imaging with compartment modeling can quantify in vivo physiologic processes, eliciting more information regarding underlying molecular disease processes than is obtained from static imaging. However, estimation of kinetic rate parameters for multi-compartment models can be computationally demanding and problematic due to local minima. A number of techniques for kinetic parameter estimation have been studied and are in use today, generally offering a tradeoff between computation time, robustness of fit, and flexibility with differing sets of assumptions. This paper presents a means to eliminate all differential operations by using the integration-by-parts method to provide closed-form formulas, so that the mathematical model is less sensitive to data sampling and noise. A family of closed-form formulas can be obtained. Computer simulations show that the proposed method is robust without having to specify the initial condition.
A cone beam micro-CT has previously been utilized along with a pressure-tracking respiration sensor to acquire prospectively gated images of both wild-type mice and various adult murine disease models. While the pressure applied to the abdomen of the subject by this sensor is small and is generally without physiological effect, certain disease models of interest, as well as very young animals, are prone to atelectasis with added pressure, or they generate too weak of a respiration signal with this method to achieve optimal prospective gating. In this work we present a new fiber-optic displacement sensor which monitors respiratory motion of a subject without requiring physical contact. The sensor outputs an analog signal which can be used for prospective respiration gating in micro-CT imaging. The device was characterized and compared against a pneumatic air chamber pressure sensor for the imaging of adult wild-type mice. The resulting images were found to be of similar quality with respect to physiological motion blur; the quality of the respiration signal trace obtained using the non-contact sensor was comparable to that of the pressure sensor and was superior for gating purposes due to its better signal-to-noise ratio. The non-contact sensor was then used to acquire in-vivo micro-CT images of a murine model for congenital diaphragmatic hernia and of 11-day-old mouse pups. In both cases, quality CT images were successfully acquired using this new respiration sensor. Despite the presence of beam hardening artifact arising from the presence of a fiber-optic cable in the imaging field, we believe this new technique for respiration monitoring and gating presents an opportunity for in-vivo imaging of disease models which were previously considered too delicate for established animal handling methods.
MRI-guided prostate biopsy in conventional closed-bore scanners requires transferring the patient outside the bore during needle insertion due to the constrained in-bore space, causing a safety hazard and limiting image feedback. To address this issue, we present our custom-made in-bore setup and software to support MRI-guided transperineal prostate biopsy in a wide-bore 3 Tesla (T) MRI scanner. The setup consists of a specially designed tabletop and a needle-guiding template with Z-frame that give a physician access to the perineum of the patient at the imaging position and allow performance of MRI-guided transperineal biopsy without moving the patient out of the scanner. The software and Z-frame allow registration of the template, target planning, and biopsy guidance. Initially, we performed phantom experiments to assess the accuracy of template registration and needle placement in a controlled environment. Subsequently, we embarked on our clinical trial (N = 10). The phantom experiments showed that the translational errors of the template registration along the right-left (RP) and anterior-posterior (AP) axes were 1.1 ± 0.8 mm and 1.4 ± 1.1 mm respectively, while the rotational errors around the RL, AP, and superior-inferior axes were 0.8 ± 1.0 degrees, 1.7 ± 1.6 degrees, and 0.0 ± 0.0 degrees respectively. The 2D root-mean-square (RMS) needle placement error was 3.0 mm. The clinical biopsy procedures were safely carried out in all ten clinical cases with a needle placement error of 5.4 mm (2D RMS). In conclusion, transperineal prostate biopsy in a wide-bore 3T scanner is feasible using our custom-made tabletop set up and software, which supports manual needle placement without moving the patient out of the magnet.
MRI; prostate cancer; image-guided therapy; navigation; biopsy
To improve the delivery efficiency of VMAT by extending the recently published VMAT treatment planning algorithm vmerge to automatically generate optimal partial-arc plans.
Methods and materials
A high-quality initial plan is created by solving a convex multicriteria optimization problem using 180 equi-spaced beams. This initial plan is used to form a set of dose constraints, and a set of partial-arc plans is created by searching the space of all possible partial-arc plans that satisfy these constraints. For each partial-arc, an iterative fluence map merging and sequencing algorithm (vmerge) is used to improve the delivery efficiency. Merging continues as long as the dose quality is maintained above a user-defined threshold. The final plan is selected as the partial-arc with the lowest treatment time. The complete algorithm is called pmerge.
Partial-arc plans are created using pmerge for a lung, liver and prostate case, with final treatment times of 127, 245 and 147 seconds. Treatment times using full arcs with vmerge are 211, 357 and 178 seconds. The mean doses to the critical structures for the vmerge and pmerge plans are kept within 5% of those in the initial plan, and the target volume covered by the prescription isodose is maintained above 98% for the pmerge and vmerge plans. Additionally, we find that the angular distribution of fluence in the initial plans is predictive of the start and end angles of the optimal partial-arc.
The pmerge algorithm is an extension to vmerge that automatically finds the partial-arc plan that minimizes the treatment time. VMAT delivery efficiency can be improved by employing partial-arcs without compromising dose quality. Partial-arcs are most applicable to cases with non-centralized targets, where the time savings are greatest.
VMAT; treatment planning; partial-arcs; delivery efficiency
The mechanical model commonly used in magnetic resonance elastography (MRE) is linear elasticity. However, soft tissue may exhibit frequency-and direction-dependent (FDD) shear moduli in response to an induced excitation causing a purely linear elastic model to provide an inaccurate image reconstruction of its mechanical properties. The goal of this study was to characterize the effects of reconstructing FDD data using a linear elastic inversion (LEI) algorithm. Linear and FDD phantoms were manufactured and LEI images were obtained from time-harmonic MRE acquisitions with variations in frequency and driving signal amplitude. LEI responses to artificially imposed uniform phase shifts in the displacement data from both purely linear elastic and FDD phantoms were also evaluated. Of the variety of FDD phantoms considered, LEI appeared to tolerate viscoelastic data-model mismatch better than deviations caused by poroelastic and anisotropic mechanical properties in terms of visual image contrast. However, the estimated shear modulus values were substantially incorrect relative to independent mechanical measurements even in the successful viscoelastic cases and the variations in mean values with changes in experimental conditions associated with uniform phase shifts, driving signal frequency and amplitude were unpredictable. Overall, use of LEI to reconstruct data acquired in phantoms with FDD material properties provided biased results under the best conditions and significant artifacts in the worst cases. These findings suggest that the success with which LEI is applied to MRE data in tissue will depend on the underlying mechanical characteristics of the tissues and/or organs systems of clinical interest.
We have previously developed a model that provides relative dosimetry estimates for targeted radionuclide therapy (TRT) agents. The whole body and tumor pharmacokinetic (PK) parameters of this model can be noninvasively measured with molecular imaging, providing a means of comparing potential TRT agents. Parameter sensitivities and noise will affect the accuracy and precision of the estimated PK values and hence dosimetry estimates. The aim of this work is to apply a PK model for TRT to two agents with different magnitudes of clearance rates, NM404 and FLT, explore parameter sensitivity with respect to time and investigate the effect of noise on parameter precision and accuracy.
Twenty-three tumor bearing mice were injected with a ‘slow-clearing’ agent, 124I-NM404 (n=10), or a ‘fast-clearing agent, 18F-FLT (3′-deoxy-3′-fluorothymidine) (n=13), and imaged via microPET/CT pseudo-dynamically or dynamically, respectively. Regions-of-interest (ROI) were drawn within the heart and tumor to create time-concentration-curves (TCC) for blood pool and tumor. PK analysis was performed to estimate the mean and standard error of the central compartment efflux-to-influx ratio (k12/k21), central elimination rate constant (kel), and tumor influx-to-efflux ratio (k34/k43), as well as the mean and standard deviation of the dosimetry estimates. NM404 and FLT parameter estimation results were used to analyze model accuracy and parameter sensitivity. The accuracy of the experimental sampling schedule was compared to that of an optimal sampling schedule found using Cramer-Rao Lower Bounds (CRLB) theory. Accuracy was assessed using correlation coefficient, bias, and standard error of the estimate normalized to the mean (SEE/mean).
The PK parameter estimation of NM404 yielded a central clearance, kel (0.009 ± 0.003 hr−1), normal body retention, k12/k21 (0.69 ± 0.16), tumor retention, k34/k43 (1.44 ± 0.46) and predicted dosimetry, Dtumor (3.47 ± 1.24 Gy). The PK parameter estimation of FLT yielded a central elimination rate constant, kel (0.050 ± 0.025 min−1), normal body retention, k12/k21 (2.21 ± 0.62) and tumor retention, k34/k43 (0.65 ± 0.17), and predicted dosimetry, Dtumor (0.61 ± 0.20 Gy). Compared to experimental sampling, optimal sampling decreases the dosimetry bias and SEE/mean for NM404; however, it increases bias and decreases SEE/mean for FLT. For both NM404 and FLT, central compartment efflux rate constant, k12, and central compartment influx rate constant, k21, possess mirroring sensitivities at relatively early time points. The instantaneous concentration in the blood, C0, was most sensitive at early time points; central elimination, kel, and tumor efflux, k43, are most sensitive at later time points.
A PK model for TRT was applied to both a slow-clearing, NM404, and fast-clearing, FLT, agent in a xenograft murine model. NM404 possesses more favorable PK values according to the PK TRT model. The precise and accurate measurement of k12, k21, kel, k34, and k43 will translate into improved and precise dosimetry estimations. This work will guide the future use of this PK model for assessing the relative effectiveness of potential TRT agents.
A set of three cubic one-litre phantoms containing spherical simulated lesions was produced for use in comparing lesion detection performance of different elastography systems. The materials employed are known to be stable in heterogeneous configurations regarding geometry and elastic contrast ≡ (storage modulus of lesion material) ÷ (storage modulus of background material), and regarding ultrasound and NMR properties. The materials mimic soft tissues in terms of elastic, ultrasound and NMR properties. Each phantom has only one value of elastic contrast (3.3, 4.6 or 5.5) and contains arrays of 1.6 mm, 2 mm, 3 mm and 4 mm diameter spherical simulated lesions. All the spheres of a given diameter are arranged in a regular array with coplanar centres. Elastograms of an array made with ultrasound allow determination of the depth range over which lesions of that diameter and elastic contrast can be detected. Two phantoms are made from agar-plus-gelatin-based materials, and one is made from oil-in-gelatin dispersions. The methods for producing the phantoms are described in detail. Lesion detection performances for two ultrasound systems, both operating at about 7.5 MHz and focused at about 5 cm, were quantified with distinctions between the two systems demonstrated. Neither system was capable of detecting any of the 1.6 mm lesions. Phantoms such as these should be useful in research labs that are refining hardware and/or software for elastography.
Five 9 cm × 9 cm × 9 cm phantoms, each with a 2-cm-diameter cylindrical inclusion, were produced with various dry-weight concentrations of agar and gelatin. Elastic contrasts ranged from 1.5 to 4.6, and values of the storage modulus (real part of the complex Young’s modulus) were all in the soft tissue range. Additives assured immunity from bacterial invasion and can produce tissue-mimicking ultrasound and NMR properties. Monitoring of strain ratios over a 7 to 10 month period indicated that the mechanical properties of the phantoms were stable, allowing about 1 month for the phantom to reach chemical equilibrium. The only dependable method for determining the storage moduli of the inclusions is to make measurements on samples excised from the phantoms. If it is desired to produce and accurately characterize a phantom with small inclusions with other shapes, such as an array of small spheres, an auxiliary phantom with the geometry of the cylindrical inclusion phantoms or the equivalent should be made at the same time using the same materials. The elastic contrast can then be determined using samples excised from the auxiliary phantom. A small increase of about 10% in volume of the cylindrical inclusions occurred—a tolerable increase. Interestingly, the smallest increase (about 5%) occurred in the phantom with the largest elastic contrast.
Surgical targeting of the incorrect vertebral level (“wrong-level” surgery) is among the more common wrong-site surgical errors, attributed primarily to a lack of uniquely identifiable radiographic landmarks in the mid-thoracic spine. Conventional localization method involves manual counting of vertebral bodies under fluoroscopy, is prone to human error, and carries additional time and dose. We propose an image registration and visualization system (referred to as LevelCheck), for decision support in spine surgery by automatically labeling vertebral levels in fluoroscopy using a GPU-accelerated, intensity-based 3D-2D (viz., CT-to-fluoroscopy) registration. A gradient information (GI) similarity metric and CMA-ES optimizer were chosen due to their robustness and inherent suitability for parallelization. Simulation studies involved 10 patient CT datasets from which 50,000 simulated fluoroscopic images were generated from C-arm poses selected to approximate C-arm operator and positioning variability. Physical experiments used an anthropomorphic chest phantom imaged under real fluoroscopy. The registration accuracy was evaluated as the mean projection distance (mPD) between the estimated and true center of vertebral levels. Trials were defined as successful if the estimated position was within the projection of the vertebral body (viz., mPD < 5mm). Simulation studies showed a success rate of 99.998% (1 failure in 50,000 trials) and computation time of 4.7 sec on a midrange GPU. Analysis of failure modes identified cases of false local optima in the search space arising from longitudinal periodicity in vertebral structures. Physical experiments demonstrated robustness of the algorithm against quantum noise and x-ray scatter. The ability to automatically localize target anatomy in fluoroscopy in near-real-time could be valuable in reducing the occurrence of wrong-site surgery while helping to reduce radiation exposure. The method is applicable beyond the specific case of vertebral labeling, since any structure defined in pre-operative (or intra-operative) CT or cone-beam CT can be automatically registered to the fluoroscopic scene.
Wrong-site surgery; surgical planning; vertebral level localization; spine surgery; 3D-2D registration; GPU-acceleration; fluoroscopy; cone-beam CT
Voxelwise quantification of hepatic perfusion parameters from dynamic contrast enhanced (DCE) imaging greatly contributes to assessment of liver function in response to radiation therapy. However, the efficiency of the estimation of hepatic perfusion parameters voxel-by-voxel in the whole liver using a dual-input single-compartment model requires substantial improvement for routine clinical applications. In this paper, we utilize the parallel computation power of a graphics processing unit (GPU) to accelerate the computation, while maintaining the same accuracy as the conventional method. Using CUDA-GPU, the hepatic perfusion computations over multiple voxels are run across the GPU blocks concurrently but independently. At each voxel, non-linear least squares fitting the time series of the liver DCE data to the compartmental model is distributed to multiple threads in a block, and the computations of different time points are performed simultaneously and synchronically. An efficient fast Fourier transform in a block is also developed for the convolution computation in the model. The GPU computations of the voxel-by-voxel hepatic perfusion images are compared with ones by the CPU using the simulated DCE data and the experimental DCE MR images from patients. The computation speed is improved by 30 times using a NVIDIA Tesla C2050 GPU compared to a 2.67 GHz Intel Xeon CPU processor. To obtain liver perfusion maps with 626400 voxels in a patient’s liver, it takes 0.9 min with the GPU-accelerated voxelwise computation, compared to 110 min with the CPU, while both methods result in perfusion parameters differences less than 10−6. The method will be useful for generating liver perfusion images in clinical settings.
dynamic contrast enhanced imaging; hepatic perfusion; compartmental model; nonlinear least squares fitting; GPU; convolution
Heating induced near deep brain stimulation (DBS) lead electrodes during MRI with a 3T transceive head coil was measured, modeled, and imaged in three cadaveric porcine heads (mean body weight = 85.47±3.19 kg, mean head weight = 5.78±0.32 kg). The effect of the placement of the extra-cranial portion of the DBS lead on the heating was investigated by looping the extra-cranial lead on the top, side, and back of the head; and placing it parallel to the coil’s longitudinal axial direction. The heating was induced using a 641 s long turbo spin echo sequence with the mean whole head average SAR of 3.16 W/kg. Temperatures were measured using fluoroptic probes at the scalp, first and second electrodes from the distal lead tip, and 6 mm distal from electrode 1 (T6mm). The heating was modeled using the maximum T6mm and imaged using a proton resonance frequency shift based MR thermometry method. Results showed that the heating was significantly reduced when the extra-cranial lead was placed in the longitudinal direction compared to the other placements (peak temperature change = 1.5–3.2 °C vs 5.1–24.7 °C). Thermal modeling and MR thermometry may be used together to determine the heating and improve patient safety online.
MRI; Safety; DBS; Heating; 3T
To formulate and solve the fluence-map merging procedure of the recently-published VMAT treatment-plan optimization method, called VMERGE, as a bi-criteria optimization problem. Using an exact merging method rather than the previously-used heuristic, we are able to better characterize the trade-off between the delivery efficiency and dose quality.
VMERGE begins with a solution of the fluence-map optimization problem with 180 equi-spaced beams that yields the “ideal” dose distribution. Neighboring fluence maps are then successively merged, meaning that they are added together and delivered as a single map. The merging process improves the delivery efficiency at the expense of deviating from the initial high-quality dose distribution. We replace the original merging heuristic by considering the merging problem as a discrete bi-criteria optimization problem with the objectives of maximizing the treatment efficiency and minimizing the deviation from the ideal dose. We formulate this using a network-flow model that represents the merging problem. Since the problem is discrete and thus non-convex, we employ a customized box algorithm to characterize the Pareto frontier. The Pareto frontier is then used as a benchmark to evaluate the performance of the standard VMERGE algorithm as well as two other similar heuristics.
We test the exact and heuristic merging approaches on a pancreas and a prostate cancer case. For both cases, the shape of the Pareto frontier suggests that starting from a high-quality plan, we can obtain efficient VMAT plans through merging neighboring fluence maps without substantially deviating from the initial dose distribution. The trade-off curves obtained by the various heuristics are contrasted and shown to all be equally capable of initial plan simplifications, but to deviate in quality for more drastic efficiency improvements.
This work presents a network optimization approach to the merging problem. Contrasting the trade-off curves of the merging heuristics against the Pareto approximation validates that heuristic approaches are capable of achieving high-quality merged plans that lie close to the Pareto frontier.
VMAT Treatment Planning; Pareto Efficiency; Network Optimization; Discrete Bi-criteria Optimization