Evaluation of novel AI-based extended field-of-view CT reconstructions.

PURPOSE: Modern computed tomography (CT) scanners have an extended field-of-view (eFoV) for reconstructing images up to the bore size, which is relevant for patients with higher BMI or non-isocentric positioning due to fixation devices. However, the accuracy of the image reconstruction in eFoV is not well known since truncated data are used. This study introduces a new deep learning-based algorithm for extended field-of-view reconstruction and evaluates the accuracy of the eFoV reconstruction focusing on aspects relevant for radiotherapy. METHODS: A life-size three-dimensional (3D) printed thorax phantom, based on a patient CT for which eFoV was necessary, was manufactured and used as reference. The phantom has holes allowing the placement of tissue mimicking inserts used to evaluate the Hounsfield unit (HU) accuracy. CT images of the phantom were acquired using different configurations aiming to evaluate geometric and HU accuracy in the eFoV. Image reconstruction was performed using a state-of-the-art reconstruction algorithm (HDFoV), commercially available, and the novel deep learning-based approach (HDeepFoV). Five patient cases were selected to evaluate the performance of both algorithms on patient data. There is no ground truth for patients so the reconstructions were qualitatively evaluated by five physicians and five medical physicists. RESULTS: The phantom geometry reconstructed with HDFoV showed boundary deviations from 1.0 to 2.5 cm depending on the volume of the phantom outside the regular scan field of view. HDeepFoV showed a superior performance regardless of the volume of the phantom within eFOV with a maximum boundary deviation below 1.0 cm. The maximum HU (absolute) difference for soft issue inserts is below 79 and 41 HU for HDFoV and HDeepFoV, respectively. HDeepFoV has a maximum deviation of -18 HU for an inhaled lung insert while HDFoV reached a 229 HU difference. The qualitative evaluation of patient cases shows that the novel deep learning approach produces images that look more realistic and have fewer artifacts. CONCLUSION: To be able to reconstruct images outside the sFoV of the CT scanner there is no alternative than to use some kind of extrapolated data. In our study, we proposed and investigated a new deep learning-based algorithm and compared it to a commercial solution for eFoV reconstruction. The deep learning-based algorithm showed superior performance in quantitative evaluations based on phantom data and in qualitative assessments of patient data.

A phase 1 'window-of-opportunity' trial testing evofosfamide (TH-302), a tumour-selective hypoxia-activated cytotoxic prodrug, with preoperative chemoradiotherapy in oesophageal adenocarcinoma patients.

BACKGROUND: Neo-adjuvant chemoradiotherapy followed by surgery is the standard treatment with curative intent for oesophageal cancer patients, with 5-year overall survival rates up to 50 %. However, patients' quality of life is severely compromised by oesophagectomy, and eventually many patients die due to metastatic disease. Most solid tumours, including oesophageal cancer, contain hypoxic regions that are more resistant to chemoradiotherapy. The hypoxia-activated prodrug evofosfamide works as a DNA-alkylating agent under these hypoxic conditions, which directly kills hypoxic cancer cells and potentially minimizes resistance to conventional therapy. This drug has shown promising results in several clinical studies when combined with chemotherapy. Therefore, in this phase I study we investigate the safety of evofosfamide added to the chemoradiotherapy treatment of oesophageal cancer. METHODS/DESIGN: A phase I, non-randomized, single-centre, open-label, 3 + 3 trial with repeated hypoxia PET imaging, will test the safety of evofosfamide in combination with neo-adjuvant chemoradiotherapy in potentially resectable oesophageal adenocarcinoma patients. Investigated dose levels range from 120 mg/m2 to 340 mg/m2. Evofosfamide will be administered one week before the start of chemoradiotherapy (CROSS-regimen) and repeated weekly up to a total of six doses. PET/CT acquisitions with hypoxia tracer (18)F-HX4 will be made before and after the first administration of evofosfamide, allowing early assessment of changes in hypoxia, accompanied with blood sampling to measure hypoxia blood biomarkers. Oesophagectomy will be performed according to standard clinical practice. Higher grade and uncommon non-haematological, haematological, and post-operative toxicities are the primary endpoints according to the CTCAEv4.0 and Clavien-Dindo classifications. Secondary endpoints are reduction in hypoxic fraction based on (18)F-HX4 imaging, pathological complete response, histopathological negative circumferential resection margin (R0) rate, local and distant recurrence rate, and progression free and overall survival. DISCUSSION: This is the first clinical trial testing evofosfamide in combination with chemoradiotherapy. The primary objective is to determine the dose limiting toxicity of this combined treatment and herewith to define the maximum tolerated dose and recommended phase 2 dose for future clinical studies. The addition of non-invasive repeated hypoxia imaging ('window-of-opportunity') enables us to identify the biologically effective dose. We believe this approach could also be used for other hypoxia targeted drugs. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02598687 .

Time-resolved versus time-integrated portal dosimetry: the role of an object's position with respect to the isocenter in volumetric modulated arc therapy.

The aim of this work is to compare time-resolved (TR) and time-integrated (TI) portal dosimetry, focussing on the role of an object's position with respect to the isocenter in volumetric modulated arc therapy (VMAT). Portal dose images (PDIs) are simulated and measured for different cases: a sphere (1), a bovine bone (2) and a patient geometry (3). For the simulated case (1) and the experimental case (2), several transformations are applied at different off-axis positions. In the patient case (3), three simple plans with different isocenters are created and pleural effusion is simulated in the patient. The PDIs before and after the sphere transformations, as well as the PDIs with and without simulated pleural effusion, are compared using a TI and TR gamma analysis. In addition, the performance of the TI and TR gamma analyses for the detection of real geometric changes in patients treated with clinical plans is investigated and a correlation analysis is performed between gamma fail rates and differences in dose volume histogram (DVH) metrics. The TI gamma analysis can show large differences in gamma fail rates for the same transformation at different off-axis positions (or for different plan isocenters). The TR gamma analysis, however, shows consistent gamma fail rates. For the detection of real geometric changes in patients treated with clinical plans, the TR gamma analysis has a higher sensitivity than the TI gamma analysis. However, the specificity for the TR gamma analysis is lower than for the TI gamma analysis. Both the TI and TR gamma fail rates show no correlation with changes in DVH metrics. This work shows that TR portal dosimetry is fundamentally superior to TI portal dosimetry, because it removes the strong dependence of the gamma fail rate on the off-axis position/plan isocenter. However, for 2D TR portal dosimetry, it is still difficult to interpret gamma fail rates in terms of changes in DVH metrics for patients treated with VMAT.

Simulation of pseudo-CT images based on deformable image registration of ultrasound images: A proof of concept for transabdominal ultrasound imaging of the prostate during radiotherapy.

PURPOSE: Imaging of patient anatomy during treatment is a necessity for position verification and for adaptive radiotherapy based on daily dose recalculation. Ultrasound (US) image guided radiotherapy systems are currently available to collect US images at the simulation stage (USsim), coregistered with the simulation computed tomography (CT), and during all treatment fractions. The authors hypothesize that a deformation field derived from US-based deformable image registration can be used to create a daily pseudo-CT (CTps) image that is more representative of the patients' geometry during treatment than the CT acquired at simulation stage (CTsim). METHODS: The three prostate patients, considered to evaluate this hypothesis, had coregistered CT and US scans on various days. In particular, two patients had two US-CT datasets each and the third one had five US-CT datasets. Deformation fields were computed between pairs of US images of the same patient and then applied to the corresponding USsim scan to yield a new deformed CTps scan. The original treatment plans were used to recalculate dose distributions in the simulation, deformed and ground truth CT (CTgt) images to compare dice similarity coefficients, maximum absolute distance, and mean absolute distance on CT delineations and gamma index (γ) evaluations on both the Hounsfield units (HUs) and the dose. RESULTS: In the majority, deformation did improve the results for all three evaluation methods. The change in gamma failure for dose (γDose, 3%, 3 mm) ranged from an improvement of 11.2% in the prostate volume to a deterioration of 1.3% in the prostate and bladder. The change in gamma failure for the CT images (γCT, 50 HU, 3 mm) ranged from an improvement of 20.5% in the anus and rectum to a deterioration of 3.2% in the prostate. CONCLUSIONS: This new technique may generate CTps images that are more representative of the actual patient anatomy than the CTsim scan.

External validation of a prognostic CT-based radiomic signature in oropharyngeal squamous cell carcinoma.

BACKGROUND: Oropharyngeal squamous cell carcinoma (OPSCC) is one of the fastest growing disease sites of head and neck cancers. A recently described radiomic signature, based exclusively on pre-treatment computed tomography (CT) imaging of the primary tumor volume, was found to be prognostic in independent cohorts of lung and head and neck cancer patients treated in the Netherlands. Here, we further validate this signature in a large and independent North American cohort of OPSCC patients, also considering CT artifacts. METHODS: A total of 542 OPSCC patients were included for which we determined the prognostic index (PI) of the radiomic signature. We tested the signature model fit in a Cox regression and assessed model discrimination with Harrell's c-index. Kaplan-Meier survival curves between high and low signature predictions were compared with a log-rank test. Validation was performed in the complete cohort (PMH1) and in the subset of patients without (PMH2) and with (PMH3) visible CT artifacts within the delineated tumor region. RESULTS: We identified 267 (49%) patients without and 275 (51%) with visible CT artifacts. The calibration slope (ß) on the PI in a Cox proportional hazards model was 1.27 (H0: ß = 1, p = 0.152) in the PMH1 (n = 542), 0.855 (H0: ß = 1, p = 0.524) in the PMH2 (n = 267) and 1.99 (H0: ß = 1, p = 0.002) in the PMH3 (n = 275) cohort. Harrell's c-index was 0.628 (p = 2.72e-9), 0.634 (p = 2.7e-6) and 0.647 (p = 5.35e-6) for the PMH1, PMH2 and PMH3 cohort, respectively. Kaplan-Meier survival curves were significantly different (p < 0.05) between high and low radiomic signature model predictions for all cohorts. CONCLUSION: Overall, the signature validated well using all CT images as-is, demonstrating a good model fit and preservation of discrimination. Even though CT artifacts were shown to be of influence, the signature had significant prognostic power regardless if patients with CT artifacts were included.

The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis.

FDG-PET-derived textural features describing intra-tumor heterogeneity are increasingly investigated as imaging biomarkers. As part of the process of quantifying heterogeneity, image intensities (SUVs) are typically resampled into a reduced number of discrete bins. We focused on the implications of the manner in which this discretization is implemented. Two methods were evaluated: (1) R(D), dividing the SUV range into D equally spaced bins, where the intensity resolution (i.e. bin size) varies per image; and (2) R(B), maintaining a constant intensity resolution B. Clinical feasibility was assessed on 35 lung cancer patients, imaged before and in the second week of radiotherapy. Forty-four textural features were determined for different D and B for both imaging time points. Feature values depended on the intensity resolution and out of both assessed methods, R(B) was shown to allow for a meaningful inter- and intra-patient comparison of feature values. Overall, patients ranked differently according to feature values­which was used as a surrogate for textural feature interpretation­between both discretization methods. Our study shows that the manner of SUV discretization has a crucial effect on the resulting textural features and the interpretation thereof, emphasizing the importance of standardized methodology in tumor texture analysis.

Stability of FDG-PET Radiomics features: an integrated analysis of test-retest and inter-observer variability.

PURPOSE: Besides basic measurements as maximum standardized uptake value (SUV)max or SUVmean derived from 18F-FDG positron emission tomography (PET) scans, more advanced quantitative imaging features (i.e. "Radiomics" features) are increasingly investigated for treatment monitoring, outcome prediction, or as potential biomarkers. With these prospected applications of Radiomics features, it is a requisite that they provide robust and reliable measurements. The aim of our study was therefore to perform an integrated stability analysis of a large number of PET-derived features in non-small cell lung carcinoma (NSCLC), based on both a test-retest and an inter-observer setup. METHODS: Eleven NSCLC patients were included in the test-retest cohort. Patients underwent repeated PET imaging within a one day interval, before any treatment was delivered. Lesions were delineated by applying a threshold of 50% of the maximum uptake value within the tumor. Twenty-three NSCLC patients were included in the inter-observer cohort. Patients underwent a diagnostic whole body PET-computed tomography (CT). Lesions were manually delineated based on fused PET-CT, using a standardized clinical delineation protocol. Delineation was performed independently by five observers, blinded to each other. Fifteen first order statistics, 39 descriptors of intensity volume histograms, eight geometric features and 44 textural features were extracted. For every feature, test-retest and inter-observer stability was assessed with the intra-class correlation coefficient (ICC) and the coefficient of variability, normalized to mean and range. Similarity between test-retest and inter-observer stability rankings of features was assessed with Spearman's rank correlation coefficient. RESULTS: Results showed that the majority of assessed features had both a high test-retest (71%) and inter-observer (91%) stability in terms of their ICC. Overall, features more stable in repeated PET imaging were also found to be more robust against inter-observer variability. CONCLUSION: Results suggest that further research of quantitative imaging features is warranted with respect to more advanced applications of PET imaging as being used for treatment monitoring, outcome prediction or imaging biomarkers.

A fast three-dimensional gamma evaluation using a GPU utilizing texture memory for on-the-fly interpolations.

PURPOSE: A widely accepted method to quantify differences in dose distributions is the gamma (gamma) evaluation. Currently, almost all gamma implementations utilize the central processing unit (CPU). Recently, the graphics processing unit (GPU) has become a powerful platform for specific computing tasks. In this study, we describe the implementation of a 3D gamma evaluation using a GPU to improve calculation time. METHODS: The gamma evaluation algorithm was implemented on an NVIDIA Tesla C2050 GPU using the compute unified device architecture (CUDA). First, several cubic virtual phantoms were simulated. These phantoms were tested with varying dose cube sizes and set-ups, introducing artificial dose differences. Second, to show applicability in clinical practice, five patient cases have been evaluated using the 3D dose distribution from a treatment planning system as the reference and the delivered dose determined during treatment as the comparison. A calculation time comparison between the CPU and GPU was made with varying thread-block sizes including the option of using texture or global memory. RESULTS: A GPU over CPU speed-up of 66 +/- 12 was achieved for the virtual phantoms. For the patient cases, a speed-up of 57 +/- 15 using the GPU was obtained. A thread-block size of 16 x 16 performed best in all cases. The use of texture memory improved the total calculation time, especially when interpolation was applied. Differences between the CPU and GPU gammas were negligible. CONCLUSIONS: The GPU and its features, such as texture memory, decreased the calculation time for gamma evaluations considerably without loss of accuracy.

[¹8F]fluorodeoxyglucose uptake patterns in lung before radiotherapy identify areas more susceptible to radiation-induced lung toxicity in non-small-cell lung cancer patients.

PURPOSE: Our hypothesis was that pretreatment inflammation in the lung makes pulmonary tissue more susceptible to radiation damage. The relationship between pretreatment [(18)F]fluorodeoxyglucose ([(18)F]FDG) uptake in the lungs (as a surrogate for inflammation) and the delivered radiation dose and radiation-induced lung toxicity (RILT) was investigated. METHODS AND MATERIALS: We retrospectively studied a prospectively obtained cohort of 101 non-small-cell lung cancer patients treated with (chemo)radiation therapy (RT). [(18)F]FDG-positron emission tomography-computed tomography (PET-CT) scans used for treatment planning were studied. Different parameters were used to describe [(18)F]FDG uptake patterns in the lungs, excluding clinical target volumes, and the interaction with radiation dose. An increase in the dyspnea grade of 1 (Common Terminology Criteria for Adverse Events version 3.0) or more points compared to the pre-RT score was used as an endpoint for analysis of RILT. The effect of [(18)F]FDG and CT-based variables, dose, and other patient or treatment characteristics that effected RILT was studied using logistic regression. RESULTS: Increased lung density and pretreatment [(18)F]FDG uptake were related to RILT after RT with univariable logistic regression. The 95th percentile of the [(18)F]FDG uptake in the lungs remained significant in multivariable logistic regression (p = 0.016; odds ratio [OR] = 4.3), together with age (p = 0.029; OR = 1.06), and a pre-RT dyspnea score of ≥1 (p = 0.005; OR = 0.20). Significant interaction effects were demonstrated among the 80th, 90th, and 95th percentiles and the relative lung volume receiving more than 2 and 5 Gy. CONCLUSIONS: The risk of RILT increased with the 95th percentile of the [(18)F]FDG uptake in the lungs, excluding clinical tumor volume (OR = 4.3). The effect became more pronounced as the fraction of the 5%, 10%, and 20% highest standardized uptake value voxels that received more than 2 Gy to 5 Gy increased. Therefore, the risk of RILT may be decreased by applying sophisticated radiotherapy techniques to avoid areas in the lung with high [(18)F]FDG uptake.

The ESTRO Breur Lecture 2009. From population to voxel-based radiotherapy: exploiting intra-tumour and intra-organ heterogeneity for advanced treatment of non-small cell lung cancer.

Evidence is accumulating that radiotherapy of non-small cell lung cancer patients can be optimized by escalating the tumour dose until the normal tissue tolerances are met. To further improve the therapeutic ratio between tumour control probability and the risk of normal tissue complications, we firstly need to exploit inter patient variation. This variation arises, e.g. from differences in tumour shape and size, lung function and genetic factors. Secondly improvement is achieved by taking into account intra-tumour and intra-organ heterogeneity derived from molecular and functional imaging. Additional radiation dose must be delivered to those parts of the tumour that need it the most, e.g. because of increased radio-resistance or reduced therapeutic drug uptake, and away from regions inside the lung that are most prone to complication. As the delivery of these treatments plans is very sensitive for geometrical uncertainties, probabilistic treatment planning is needed to generate robust treatment plans. The administration of these complicated dose distributions requires a quality assurance procedure that can evaluate the treatment delivery and, if necessary, adapt the treatment plan during radiotherapy.

Dose recalculation in megavoltage cone-beam CT for treatment evaluation: removal of cupping and truncation artefacts in scans of the thorax and abdomen.

PURPOSE: To correct megavoltage cone-beam CT (MVCBCT) images of the thorax and abdomen for cupping and truncation artefacts to reconstruct the 3D-delivered dose distribution for treatment evaluation. MATERIALS AND METHODS: MVCBCT scans of three phantoms, three lung and two rectal cancer patients were acquired. The cone-beam projection images were iteratively corrected for cupping and truncation artefacts and the resulting primary transmission was used for cone-beam reconstruction. The reconstructed scans were merged into the planning CT scan (MVCBCT+). Dose distributions of clinical IMRT, stereotactic and conformal treatment plans were recalculated on the uncorrected and corrected MVCBCT+ scans using the treatment planning system and compared to the planned dose distribution. RESULTS: The dose distributions on the corrected MVCBCT+ of the phantoms were accurate for 99% of the voxels within 2% or 2mm. Using this method the errors in mean GTV dose reduced from about 10% to 1% for the patients. CONCLUSIONS: The method corrects cupping and truncation artefacts in cone-beam scans of the thorax and abdomen in addition to head-and-neck (demonstrated previously). The corrected scans can be used to calculate the influence of anatomical changes on the 3D-delivered dose distribution.

Calibration of megavoltage cone-beam CT for radiotherapy dose calculations: correction of cupping artifacts and conversion of CT numbers to electron density.

Megavoltage cone-beam CT (MV CBCT) is used for three-dimensional imaging of the patient anatomy on the treatment table prior to or just after radiotherapy treatment. To use MV CBCT images for radiotherapy dose calculation purposes, reliable electron density (ED) distributions are needed. Patient scatter, beam hardening and softening effects result in cupping artifacts in MV CBCT images and distort the CT number to ED conversion. A method based on transmission images is presented to correct for these effects without using prior knowledge of the object's geometry. The scatter distribution originating from the patient is calculated with pencil beam scatter kernels that are fitted based on transmission measurements. The radiological thickness is extracted from the scatter subtracted transmission images and is then converted to the primary transmission used in the cone-beam reconstruction. These corrections are performed in an iterative manner, without using prior knowledge regarding the geometry and composition of the object. The method was tested using various homogeneous and inhomogeneous phantoms with varying shapes and compositions, including a phantom with different electron density inserts, phantoms with large density variations, and an anthropomorphic head phantom. For all phantoms, the cupping artifact was substantially removed from the images and a linear relation between the CT number and electron density was found. After correction the deviations in reconstructed ED from the true values were reduced from up to 0.30 ED units to 0.03 for the majority of the phantoms; the residual difference is equal to the amount of noise in the images. The ED distributions were evaluated in terms of absolute dose calculation accuracy for homogeneous cylinders of different size; errors decreased from 7% to below 1% in the center of the objects for the uncorrected and corrected images, respectively, and maximum differences were reduced from 17% to 2%, respectively. The presented method corrects the MV CBCT images for cupping artifacts and extracts reliable ED information of objects with varying geometries and composition, making these corrected MV CBCT images suitable for accurate dose calculation purposes.

Treatment verification in the presence of inhomogeneities using EPID-based three-dimensional dose reconstruction.

Treatment verification is a prerequisite for the verification of complex treatments, checking both the treatment planning process and the actual beam delivery. Pretreatment verification can detect errors introduced by the treatment planning system (TPS) or differences between planned and delivered dose distributions. In a previous paper we described the reconstruction of three-dimensional (3-D) dose distributions in homogeneous phantoms using an in-house developed model based on the beams delivered by the linear accelerator measured with an amorphous silicon electronic portal imaging device (EPID), and a dose calculation engine using the Monte Carlo code XVMC. The aim of the present study is to extend the method to situations in which tissue inhomogeneities are present and to make a comparison with the dose distributions calculated by the TPS. Dose distributions in inhomogeneous phantoms, calculated using the fast-Fourier transform convolution (FFTC) and multigrid superposition (MGS) algorithms present in the TPS, were verified using the EPID-based dose reconstruction method and compared to film and ionization chamber measurements. Differences between dose distributions were evaluated using the gamma-evaluation method (3%/3 mm) and expressed as a mean gamma and the percentage of points with gamma> 1 (P(gamma>1)). For rectangular inhomogeneous phantoms containing a low-density region, the differences between film and reconstructed dose distributions were smaller than 3%. In low-density regions there was an overestimation of the planned dose using the FFTC and MGS algorithms of the TPS up to 20% and 8%, respectively, for a 10 MV photon beam and a 3 x 3 cm2 field. For lower energies and larger fields (6 MV, 5 x 5 cm2), these differences reduced to 6% and 3%, respectively. Dose reconstruction performed in an anthropomorphic thoracic phantom for a 3-D conformal and an IMRT plan, showed good agreement between film data and reconstructed dose values (P(gamma>1) <6%). The algorithms of the TPS underestimated the dose in the low-dose regions outside the treatment field, due to an implementation error of the jaws and multileaf collimator of the linac in the TPS. The FFTC algorithm of the TPS showed differences up to 6% or 6 mm at the interface between lung and breast. Two intensity-modulated radiation therapy head and neck plans, reconstructed in a commercial phantom having a bone-equivalent insert and an air cavity, showed good agreement between film measurement, reconstructed and planned dose distributions using the FFTC and MGS algorithm, except in the bone-equivalent regions where both TPS algorithms underestimated the dose with 4%. Absolute dose verification was performed at the isocenter where both planned and reconstructed dose were within 2% of the measured dose. Reproducibility for the EPID measurements was assessed and found to be of negligible influence on the reconstructed dose distribution. Our 3-D dose verification approach is based on the actual dose measured with an EPID in combination with a Monte Carlo dose engine, and therefore independent of a TPS. Because dose values are reconstructed in 3-D, isodose surfaces and dose-volume histograms can be used to detect dose differences in target volume and normal tissues. Using our method, the combined planning and treatment delivery process is verified, offering an easy to use tool for the verification of complex treatments.

A Monte Carlo based three-dimensional dose reconstruction method derived from portal dose images.

The verification of intensity-modulated radiation therapy (IMRT) is necessary for adequate quality control of the treatment. Pretreatment verification may trace the possible differences between the planned dose and the actual dose delivered to the patient. To estimate the impact of differences between planned and delivered photon beams, a three-dimensional (3-D) dose verification method has been developed that reconstructs the dose inside a phantom. The pretreatment procedure is based on portal dose images measured with an electronic portal imaging device (EPID) of the separate beams, without the phantom in the beam and a 3-D dose calculation engine based on the Monte Carlo calculation. Measured gray scale portal images are converted into portal dose images. From these images the lateral scattered dose in the EPID is subtracted and the image is converted into energy fluence. Subsequently, a phase-space distribution is sampled from the energy fluence and a 3-D dose calculation in a phantom is started based on a Monte Carlo dose engine. The reconstruction model is compared to film and ionization chamber measurements for various field sizes. The reconstruction algorithm is also tested for an IMRT plan using 10 MV photons delivered to a phantom and measured using films at several depths in the phantom. Depth dose curves for both 6 and 10 MV photons are reconstructed with a maximum error generally smaller than 1% at depths larger than the buildup region, and smaller than 2% for the off-axis profiles, excluding the penumbra region. The absolute dose values are reconstructed to within 1.5% for square field sizes ranging from 5 to 20 cm width. For the IMRT plan, the dose was reconstructed and compared to the dose distribution with film using the gamma evaluation, with a 3% and 3 mm criterion. 99% of the pixels inside the irradiated field had a gamma value smaller than one. The absolute dose at the isocenter agreed to within 1% with the dose measured with an ionization chamber. It can be concluded that our new dose reconstruction algorithm is able to reconstruct the 3-D dose distribution in phantoms with a high accuracy. This result is obtained by combining portal dose images measured prior to treatment with an accurate dose calculation engine.

A model of heart rate changes to detect seizures in severe epilepsy.

AIM OF THE STUDY: An explorative study to assess the value of a model for the automatic detection and characterization of heart rate (HR) changes during seizures in severe epilepsy. METHODS: Heart rate changes were monitored in 10 patients with 104 seizures, mostly tonic and myoclonic, to assess the value of various modalities for the detection of seizures based on heart rate. EEG/video monitoring served as the golden standard. Two algorithms were developed. First, a curve-fitting algorithm was used to characterize the heart rate patterns. A second algorithm based on a moving median filter was developed for automatic detection of the heart rate change onset. For varying model parameters the sensitivity (SENS) and positive predictive values (PPV) were determined. RESULTS: Changes in heart rate were found in 8 of the 10 patients and 50 of 104 seizures. Patterns of heart rate changes could be quantitatively characterized and were found to be stereotype for each individual patient. Large differences of the curve-fitting pattern were in some cases due to a tachycardia at seizure onset that was followed by a significant postictal bradycardia. In two out of three patients with more than 10 seizures a PPV of at least 50% yielded a SENS above 90%. CONCLUSIONS: Heart rate patterns can be accurately characterized with a new developed curve-fitting algorithm. Heart rate changes can also be used for automatic detection of seizures in patients with severe epilepsy if the model parameters are chosen according to predefined characteristics of the patient.