Purpose: Assessing renal perfusion in-vivo is challenging and quantitativeinformation regarding renal hemodynamics is hardly incorporated in medicaldecision-making while abnormal renal hemodynamics... Show morePurpose: Assessing renal perfusion in-vivo is challenging and quantitativeinformation regarding renal hemodynamics is hardly incorporated in medicaldecision-making while abnormal renal hemodynamics might play a crucial rolein the onset and progression of renal disease. Combining physiological stimuliwith rubidium-82 positron emission tomography/computed tomography (82 RbPET/CT) offers opportunities to test the kidney perfusion under various condi-tions. The aim of this study is: (1) to investigate the application of a one-tissuecompartment model for measuring renal hemodynamics with dynamic 82 RbPET/CT imaging, and (2) to evaluate whether dynamic PET/CT is sensitiveto detect differences in renal hemodynamics in stress conditions compared toresting state.Methods: A one-tissue compartment model for the kidney was applied to car-diac 82 Rb PET/CT scans that were obtained for ischemia detection as part ofclinical care. Retrospective data, collected from 17 patients undergoing dynamicmyocardial 82 Rb PET/CT imaging in rest, were used to evaluate various CT-based volumes of interest (VOIs) of the kidney. Subsequently, retrospectivedata, collected from 10 patients (five impaired kidney functions and five controls)undergoing dynamic myocardial 82 Rb PET/CT imaging, were used to evaluateimage-derived input functions (IDIFs), PET-based VOIs of the kidney, extractionfractions, and whether dynamic 82 Rb PET/CT can measure renal hemodynam-ics differences using the renal blood flow (RBF) values in rest and after exposureto adenosine pharmacological stress.Results: The delivery rate (K1 ) values showed no significant (p = 0.14) differ-ence between the mean standard deviation (SD) K1 values using one CT-basedVOI and the use of two, three, and four CT-based VOIs, respectively 2.01(0.32),1.90(0.40), 1.93(0.39), and 1.94(0.40) mL/min/mL. The ratio between RBF inrest and RBF in pharmacological stress for the controls were overall significantlylower compared to the impaired kidney function group for both PET-based delin-eation methods (region growing and iso-contouring), with the smallest medianinterquartile range (IQR) of 0.40(0.28–0.66) and 0.96(0.62–1.15), respectively(p < 0.05). The K1 of the impaired kidney function group were close to1.0 mL/min/mL. Show less
Beljaards, L.; Pezzotti, N.; Rao, C.M.; Doneva, M.; Osch, M.J.P. van; Staring, M. 2024
BackgroundMagnetic Resonance acquisition is a time consuming process, making it susceptible to patient motion during scanning. Even motion in the order of a millimeter can introduce severe blurring... Show moreBackgroundMagnetic Resonance acquisition is a time consuming process, making it susceptible to patient motion during scanning. Even motion in the order of a millimeter can introduce severe blurring and ghosting artifacts, potentially necessitating re-acquisition. Magnetic Resonance Imaging (MRI) can be accelerated by acquiring only a fraction of k-space, combined with advanced reconstruction techniques leveraging coil sensitivity profiles and prior knowledge. Artificial intelligence (AI)-based reconstruction techniques have recently been popularized, but generally assume an ideal setting without intra-scan motion.PurposeTo retrospectively detect and quantify the severity of motion artifacts in undersampled MRI data. This may prove valuable as a safety mechanism for AI-based approaches, provide useful information to the reconstruction method, or prompt for re-acquisition while the patient is still in the scanner.MethodsWe developed a deep learning approach that detects and quantifies motion artifacts in undersampled brain MRI. We demonstrate that synthetically motion-corrupted data can be leveraged to train the convolutional neural network (CNN)-based motion artifact estimator, generalizing well to real-world data. Additionally, we leverage the motion artifact estimator by using it as a selector for a motion-robust reconstruction model in case a considerable amount of motion was detected, and a high data consistency model otherwise.ResultsTraining and validation were performed on 4387 and 1304 synthetically motion-corrupted images and their uncorrupted counterparts, respectively. Testing was performed on undersampled in vivo motion-corrupted data from 28 volunteers, where our model distinguished head motion from motion-free scans with 91% and 96% accuracy when trained on synthetic and on real data, respectively. It predicted a manually defined quality label ('Good', 'Medium' or 'Bad' quality) correctly in 76% and 85% of the time when trained on synthetic and real data, respectively. When used as a selector it selected the appropriate reconstruction network 93% of the time, achieving near optimal SSIM values.ConclusionsThe proposed method quantified motion artifact severity in undersampled MRI data with high accuracy, enabling real-time motion artifact detection that can help improve the safety and quality of AI-based reconstructions. Show less
Background: Accuracy and precision assessment in radiomic features is important for the determination of their potential to characterize cancer lesions. In this regard, simulation of different... Show moreBackground: Accuracy and precision assessment in radiomic features is important for the determination of their potential to characterize cancer lesions. In this regard, simulation of different imaging conditions using specialized phantoms is increasingly being investigated. In this study, the design and evaluation of a modular multimodality imaging phantom to simulate heterogeneous uptake and enhancement patterns for radiomics quantification in hybrid imaging is presented. Methods: A modular multimodality imaging phantom was constructed that could simulate different patterns of heterogeneous uptake and enhancement patterns in positron emission tomography (PET), single-photon emission computed tomography (SPECT), computed tomography (CT), and magnetic resonance (MR) imaging. The phantom was designed to be used as an insert in the standard NEMA-NU2 IEC body phantom casing. The entire phantom insert is composed of three segments, each containing three separately fillable compartments. The fillable compartments between segments had different sizes in order to simulate heterogeneous patterns at different spatial scales. The compartments were separately filled with different ratios of Tc-99m-pertechnetate, F-18-fluorodeoxyglucose ([F-18]FDG), iodine- and gadolinium-based contrast agents for SPECT, PET, CT, and T-1-weighted MR imaging respectively. Image acquisition was performed using standard oncological protocols on all modalities and repeated five times for repeatability assessment. A total of 93 radiomic features were calculated. Variability was assessed by determining the coefficient of quartile variation (CQV) of the features. Comparison of feature repeatability at different modalities and spatial scales was performed using Kruskal-Wallis-, Mann-Whitney U-, one-way ANOVA- and independent t-tests. Results: Heterogeneous uptake and enhancement could be simulated on all four imaging modalities. Radiomic features in SPECT were significantly less stable than in all other modalities. Features in PET were significantly less stable than in MR and CT. A total of 20 features, particularly in the gray-level co-occurrence matrix (GLCM) and gray-level run-length matrix (GLRLM) class, were found to be relatively stable in all four modalities for all three spatial scales of heterogeneous patterns (with CQV < 10%). Conclusion: The phantom was suitable for simulating heterogeneous uptake and enhancement patterns in [F-18]FDG-PET, Tc-99m-SPECT, CT, and T-1-weighted MR images. The results of this work indicate that the phantom might be useful for the further development and optimization of imaging protocols for radiomic quantification in hybrid imaging modalities. Show less
Purpose: Upper airway segmentation on MR images is a prerequisite step for quantitatively studying the anatomical structure and function of the upper airway and surrounding tissues. However, the... Show morePurpose: Upper airway segmentation on MR images is a prerequisite step for quantitatively studying the anatomical structure and function of the upper airway and surrounding tissues. However, the complex variability of intensity and shape of anatomical structures and different modes of image acquisition commonly used in this application makes automatic upper airway segmentation challenging. In this paper, we develop and test a comprehensive deep learning-based segmentation system for use on MR images to address this problem. Materials and Methods: In our study, both static and dynamic MRI data sets are utilized, including 58 axial static 3D MRI studies, 22 mid-retropalatal dynamic 2D MRI studies, 21 mid-retroglossal dynamic 2D MRI studies, 36 mid-sagittal dynamic 2D MRI studies, and 23 isotropic dynamic 3D MRI studies, involving a total of 160 subjects and over 20 000 MRI slices. Samples of static and 2D dynamic MRI data sets were randomly divided into training, validation, and test sets by an approximate ratio of 5:2:3. Considering that the variability of annotation data among 3D dynamic MRIs was greater than for other MRI data sets, we increased the ratio of training data for these data to improve the robustness of the model. We designed a unified framework consisting of the following procedures. For static MRI, a generalized region-of-interest (GROI) strategy is applied to localize the partitions of nasal cavity and other portions of upper airway in axial data sets as two separate subobjects. Subsequently, the two subobjects are segmented by two separate 2D U-Nets. The two segmentation results are combined as the whole upper airway structure. The GROI strategy is also applied to other MRI modes. To minimize false-positive and false-negative rates in the segmentation results, we employed a novel loss function based explicitly on these rates to train the segmentation networks. An inter-reader study is conducted to test the performance of our system in comparison to human variability in ground truth (GT) segmentation of these challenging structures. Results: The proposed approach yielded mean Dice coefficients of 0.84 +/- 0.03, 0.89 +/- 0.13, 0.84 +/- 0.07, and 0.86 +/- 0.05 for static 3D MRI, mid-retropalatal/mid-retroglossal 2D dynamic MRI, mid-sagittal 2D dynamic MRI, and isotropic dynamic 3D MRI, respectively. The quantitative results show excellent agreement with manual delineation results. The inter-reader study results demonstrate that the segmentation performance of our approach is statistically indistinguishable from manual segmentations considering the inter-reader variability in GT. Conclusions: The proposed method can be utilized for routine upper airway segmentation from static and dynamic MR images with high accuracy and efficiency. The proposed approach has the potential to be employed in other dynamic MRI-related applications, such as lung or heart segmentation. Show less
Purpose: Efficient compression of images while preserving image quality has the potential to be a major enabler of effective remote clinical diagnosis and treatment, since poor Internet connection... Show morePurpose: Efficient compression of images while preserving image quality has the potential to be a major enabler of effective remote clinical diagnosis and treatment, since poor Internet connection conditions are often the primary constraint in such services. This paper presents a framework for organ-specific image compression for teleinterventions based on a deep learning approach and anisotropic diffusion filter.Methods: The proposed method, deep learning and anisotropic diffusion (DLAD), uses a convolutional neural network architecture to extract a probability map for the organ of interest; this probability map guides an anisotropic diffusion filter that smooths the image except at the location of the organ of interest. Subsequently, a compression method, such as BZ2 and HEVC-visually lossless, is applied to compress the image. We demonstrate the proposed method on three-dimensional (3D) CT images acquired for radio frequency ablation (RFA) of liver lesions. We quantitatively evaluate the proposed method on 151 CT images using peak-signal-to-noise ratio (PSNR), structural similarity (SSIM), and compression ratio (CR) metrics. Finally, we compare the assessments of two radiologists on the liver lesion detection and the liver lesion center annotation using 33 sets of the original images and the compressed images.Results: The results show that the method can significantly improve CR of most well-known compression methods. DLAD combined with HEVC-visually lossless achieves the highest average CR of 6.45, which is 36% higher than that of the original HEVC and outperforms other state-of-the-art lossless medical image compression methods. The means of PSNR and SSIM are 70 dB and 0.95, respectively. In addition, the compression effects do not statistically significantly affect the assessments of the radiologists on the liver lesion detection and the lesion center annotation.Conclusions: We thus conclude that the method has a high potential to be applied in teleintervention applications. (C) 2021 American Association of Physicists in Medicine Show less
Purpose The surgical navigation system that provides guidance throughout the surgery can facilitate safer and more radical liver resections, but such a system should also be able to handle organ... Show morePurpose The surgical navigation system that provides guidance throughout the surgery can facilitate safer and more radical liver resections, but such a system should also be able to handle organ motion. This work investigates the accuracy of intraoperative surgical guidance during open liver resection, with a semi-rigid organ approximation and electromagnetic tracking of the target area.Methods The suggested navigation technique incorporates a preoperative 3D liver model based on diagnostic 4D MRI scan, intraoperative contrast-enhanced CBCT imaging and electromagnetic (EM) tracking of the liver surface, as well as surgical instruments, by means of six degrees-of-freedom micro-EM sensors.Results The system was evaluated during surgeries with 35 patients and resulted in an accurate and intuitive real-time visualization of liver anatomy and tumor's location, confirmed by intraoperative checks on visible anatomical landmarks. Based on accuracy measurements verified by intraoperative CBCT, the system's average accuracy was 4.0 +/- 3.0 mm, while the total surgical delay due to navigation stayed below 20 min.Conclusions The electromagnetic navigation system for open liver surgery developed in this work allows for accurate localization of liver lesions and critical anatomical structures surrounding the resection area, even when the liver was manipulated. However, further clinically integrating the method requires shortening the guidance-related surgical delay, which can be achieved by shifting to faster intraoperative imaging like ultrasound. Our approach is adaptable to navigation on other mobile and deformable organs, and therefore may benefit various clinical applications. Show less
Sun, X.W.; Garg, P.; Plein, S.; Geest, R.J. van der 2021
Purpose Quantification of left ventricular (LV) volume, ejection fraction and myocardial mass from multi-slice multi-phase cine MRI requires accurate segmentation of the LV in many images. We... Show morePurpose Quantification of left ventricular (LV) volume, ejection fraction and myocardial mass from multi-slice multi-phase cine MRI requires accurate segmentation of the LV in many images. We propose a stack attention-based convolutional neural network (CNN) approach for fully automatic segmentation from short-axis cine MR images.Methods To extract the relevant spatiotemporal image features, we introduce two kinds of stack methods, spatial stack model and temporal stack model, combining the target image with its neighboring images as the input of a CNN. A stack attention mechanism is proposed to weigh neighboring image slices in order to extract the relevant features using the target image as a guide. Based on stack attention and standard U-Net, a novel Stack Attention U-Net (SAUN) is proposed and trained to perform the semantic segmentation task. A loss function combining cross-entropy and Dice is used to train SAUN. The performance of the proposed method was evaluated on an internal and a public dataset using technical metrics including Dice, Hausdorff distance (HD), and mean contour distance (MCD), as well as clinical parameters, including left ventricular ejection fraction (LVEF) and myocardial mass (LVM). In addition, the results of SAUN were compared to previously presented CNN methods, including U-Net and SegNet.Results The spatial stack attention model resulted in better segmentation results than the temporal stack model. On the internal dataset comprising of 167 post-myocardial infarction patients and 57 healthy volunteers, our method achieved a mean Dice of 0.91, HD of 3.37 mm, and MCD of 1.08 mm. Evaluation on the publicly available ACDC dataset demonstrated good generalization performance, yielding a Dice of 0.92, HD of 9.4 mm, and MCD of 0.74 mm on end-diastolic images, and a Dice of 0.89, HD of 7.1 mm and MCD of 1.03 mm on end-systolic images. The Pearson correlation coefficient of LVEF and LVM between automatically and manually derived results were higher than 0.98 in both datasets.Conclusion We developed a CNN with a stack attention mechanism to automatically segment the LV chamber and myocardium from the multi-slice short-axis cine MRI. The experimental results demonstrate that the proposed approach exceeds existing state-of-the-art segmentation methods and verify its potential clinical applicability. Show less
Fleury, E.; Trnkova, P.; Erdal, E.; Hassan, M.; Stoel, B.; Jaarma-Coes, M.; ... ; Hoogeman, M. 2021
Purpose: To develop a high-resolution three-dimensional (3D) magnetic resonance imaging (MRI)-based treatment planning approach for uveal melanomas (UM) in proton therapy.Materials/methods: For... Show morePurpose: To develop a high-resolution three-dimensional (3D) magnetic resonance imaging (MRI)-based treatment planning approach for uveal melanomas (UM) in proton therapy.Materials/methods: For eight patients with UM, a segmentation of the gross tumor volume (GTV) and organs-at-risk (OARs) was performed on T1- and T2-weighted 7 Tesla MRI image data to reconstruct the patient MR-eye. An extended contour was defined with a 2.5-mm isotropic margin derived from the GTV. A broad beam algorithm, which we have called pi Dose, was implemented to calculate relative proton absorbed doses to the ipsilateral OARs. Clinically favorable gazing angles of the treated eye were assessed by calculating a global weighted-sum objective function, which set penalties for OARs and extreme gazing angles. An optimizer, which we have named OPT'im-Eye-Tool, was developed to tune the parameters of the functions for sparing critical-OARs.Results: In total, 441 gazing angles were simulated for every patient. Target coverage including margins was achieved in all the cases (V-95% > 95%). Over the whole gazing angles solutions space, maximum dose (D-max) to the optic nerve and the macula, and mean doses (D-mean) to the lens, the ciliary body and the sclera were calculated. A forward optimization was applied by OPT'im-Eye-Tool in three different prioritizations: iso-weighted, optic nerve prioritized, and macula prioritized. In each, the function values were depicted in a selection tool to select the optimal gazing angle(s). For example, patient 4 had a T2 equatorial tumor. The optimization applied for the straight gazing angle resulted in objective function values of 0.46 (iso-weighted situation), 0.90 (optic nerve prioritization) and 0.08 (macula prioritization) demonstrating the impact of that angle in different clinical approaches.Conclusions: The feasibility and suitability of a 3D MRI-based treatment planning approach have been successfully tested on a cohort of eight patients diagnosed with UM. Moreover, a gaze-angle trade-off dose optimization with respect to OARs sparing has been developed. Further validation of the whole treatment process is the next step in the goal to achieve both a non-invasive and a personalized proton therapy treatment. (c) 2020 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine. Show less
Meer, M.C. van der; Bosman, P.A.N.; Niatsetski, Y.; Alderliesten, T.; Wieringen, N. van; Pieters, B.R.; Bel, A. 2020
Purpose Bi-objective simultaneous optimization of catheter positions and dwell times for high-dose-rate (HDR) prostate brachytherapy, based directly on dose-volume indices, has shown promising... Show morePurpose Bi-objective simultaneous optimization of catheter positions and dwell times for high-dose-rate (HDR) prostate brachytherapy, based directly on dose-volume indices, has shown promising results. However, optimization with the state-of-the-art evolutionary algorithm MO-RV-GOMEA so far required several hours of runtime, and resulting catheter positions were not always clinically feasible. The aim of this study is to extend the optimization model and apply GPU parallelization to achieve clinically acceptable computation times. The resulting optimization procedure is compared with a previously introduced method based solely on geometric criteria, the adapted Centroidal Voronoi Tessellations (CVT) algorithm. Methods Bi-objective simultaneous optimization was performed with a GPU-parallelized version of MO-RV-GOMEA. This optimization of catheter positions and dwell times was retrospectively applied to the data of 26 patients previously treated with HDR prostate brachytherapy for 8-16 catheters (steps of 2). Optimization of catheter positions using CVT was performed in seconds, after which optimization of only the dwell times using MO-RV-GOMEA was performed in 1 min. Results Simultaneous optimization of catheter positions and dwell times using MO-RV-GOMEA was performed in 5 min. For 16 down to 8 catheters (steps of 2), MO-RV-GOMEA found plans satisfying the planning-aims for 20, 20, 18, 14, and 11 out of the 26 patients, respectively. CVT achieved this for 19, 17, 13, 9, and 2 patients, respectively. TheP-value for the difference between MO-RV-GOMEA and CVT was 0.023 for 16 catheters, 0.005 for 14 catheters, and <0.001 for 12, 10, and 8 catheters. Conclusions With bi-objective simultaneous optimization on a GPU, high-quality catheter positions can now be obtained within 5 min, which is clinically acceptable, but slower than CVT. For 16 catheters, the difference between MO-RV-GOMEA and CVT is clinically irrelevant. For 14 catheters and less, MO-RV-GOMEA outperforms CVT in finding plans satisfying all planning-aims. Show less
Purpose Cardiac motion tracking enables quantitative evaluation of myocardial strain, which is clinically interesting in cardiovascular disease research. However, motion tracking is difficult to... Show morePurpose Cardiac motion tracking enables quantitative evaluation of myocardial strain, which is clinically interesting in cardiovascular disease research. However, motion tracking is difficult to perform manually. In this paper, we aim to develop and compare two fully automated motion tracking methods for the steady state free precession (SSFP) cine magnetic resonance imaging (MRI), and explore their use in real clinical scenario with different patient groups. Methods We proposed two automated cardiac motion tracking method: (a) a traditional registration-based method, named full cardiac cycle registration, which simultaneously tracks all cine frames within a full cardiac cycle by joint registration of all frames; and (b) a modern convolutional neural network (CNN)-based method, named Groupwise MotionNet, which enhances the temporal coherence by fusing motion along a continuous time scale. Both methods were evaluated on the healthy volunteer data from the MICCAI 2011 STACOM Challenge, as well as on patient data including hypertrophic cardiomyopathy (HCM) and myocardial infarction (MI). Results The full cardiac cycle registration method achieved an average end-point error (EPE) 2.89 +/- 1.57 mm for cardiac motion tracking, with computation time of around 9 min per short-axis cine MRI (size 128 x 128, 30 cardiac phases). In comparison, the Groupwise MotionNet achieved an average EPE of 0.94 +/- 1.59 mm, taking < 1 s for a full cardiac phases. Further experiments showed that registration method had stable performance, independent of patient cohort and MRI machine, while the CNN-based method relied on the training data to deliver consistently accurate results. Conclusion Both registration-based and CNN-based method can track the cardiac motion from SSFP cine MRI in a fully automated manner, while taking temporal coherence into account. The registration method is generic, robust, but relatively slow; the CNN-based method trained with heterogeneous data was able to achieve high tracking accuracy with real-time performance. Show less
Purpose Currently, coronary plaque changes are manually compared between a baseline and follow-up coronary computed tomography angiography (CCTA) images for long-term coronary plaque development... Show morePurpose Currently, coronary plaque changes are manually compared between a baseline and follow-up coronary computed tomography angiography (CCTA) images for long-term coronary plaque development investigation. We propose an automatic method to measure the plaque thickness change over time. Methods We model the lumen and vessel wall for both the baseline coronary artery tree (CAT-BL) and follow-up coronary artery tree (CAT-FU) as smooth three-dimensional (3D) surfaces using a subdivision fitting scheme with the same coarse meshes by which the correspondence among these surface points is generated. Specifically, a rigid point set registration is used to transform the coarse mesh from the CAT-FU to CAT-BL. The plaque thickness and the thickness difference is calculated as the distance between corresponding surface points. To evaluate the registration accuracy, the average distance between manually defined markers on clinical scans is calculated. Artificial CAT-BL and CAT-FU pairs were created to simulate the plaque decrease and increase over time. Results For 116 pairs of markers from nine clinical scans, the average marker distance after registration was 0.95 +/- 0.98 mm (two times the voxel size). On the 10 artificial pairs of datasets, the proposed method successfully located the plaque changes. The average of the calculated plaque thickness difference is the same as the corresponding created value (standard deviation +/- 0.1 mm). Conclusions The proposed method automatically calculates local coronary plaque thickness differences over time and can be used for 3D visualization of plaque differences. The analysis and reporting of coronary plaque progression and regression will benefit from an automatic plaque thickness comparison. Show less
Purpose Vascular remodeling is a significant pathological feature of various pulmonary diseases, which may be assessed by quantitative computed tomography (CT) imaging. The purpose of this study... Show morePurpose Vascular remodeling is a significant pathological feature of various pulmonary diseases, which may be assessed by quantitative computed tomography (CT) imaging. The purpose of this study was therefore to develop and validate an automatic method for quantifying pulmonary vascular morphology in CT images. Methods The proposed method consists of pulmonary vessel extraction and quantification. For extracting pulmonary vessels, a graph-cuts-based method is proposed which considers appearance (CT intensity) and shape (vesselness from a Hessian-based filter) features, and incorporates distance to the airways into the cost function to prevent false detection of airway walls. For quantifying the extracted pulmonary vessels, a radius histogram is generated by counting the occurrence of vessel radii, calculated from a distance transform-based method. Subsequently, two biomarkers, slope alpha and intercept beta, are calculated by linear regression on the radius histogram. A public data set from the VESSEL12 challenge was used to independently evaluate the vessel extraction. The quantitative analysis method was validated using images of a three-dimensional (3D) printed vessel phantom, scanned by a clinical CT scanner and a micro-CT scanner (to obtain a gold standard). To confirm the association between imaging biomarkers and pulmonary function, 77 scleroderma patients were investigated with the proposed method. Results In the independent evaluation with the public data set, our vessel segmentation method obtained an area under the receiver operating characteristic (ROC) curve of 0.976. The median radius difference between clinical and micro-CT scans of a 3D printed vessel phantom was 0.062 +/- 0.020 mm, with interquartile range of 0.199 +/- 0.050 mm. In the studied patient group, a significant correlation between diffusion capacity for carbon monoxide and the biomarkers, alpha (R = -0.27, P = 0.018) and beta (R = 0.321, P = 0.004), was obtained. Conclusion In conclusion, the proposed method was validated independently using a public data set resulting in an area under the ROC curve of 0.976 and using a 3D printed vessel phantom data set, showing a vessel sizing error of 0.062 mm (0.16 in-plane pixel units). The correlation between imaging biomarkers and diffusion capacity in a clinical data set confirmed an association between lung structure and function. This quantification of pulmonary vascular morphology may be helpful in understanding the pathophysiology of pulmonary vascular diseases. Show less
Torres-Xirau, I.; Olaciregui-Ruiz, I.; Heide, U.A. van der; Mans, A. 2019
Purpose: At our institute, in-vivo patient dose distributions are reconstructed for all treatments delivered using conventional linacs from Electronic Portal Imaging Device (EPID) transit images... Show morePurpose: At our institute, in-vivo patient dose distributions are reconstructed for all treatments delivered using conventional linacs from Electronic Portal Imaging Device (EPID) transit images acquired during treatment using a simple back-projection model. Currently, the clinical implementation of MRI-guided radiotherapy systems, which aims for online and real-time adaptation of the treatment plan, is progressing. In our department the MR-linac (Unity, Elekta AB, Stockholm, Sweden) is now in clinical use. The aim of this work is to demonstrate the feasibility of 2D EPID dosimetric verification for the MR-linac by comparing back-projected EPID doses to ionization chamber (IC) array dose distributions. Materials and methods: Our conventional back projection algorithm was adapted for the MR-linac. The most important changes involve modeling of the attenuation by and scatter from the cryostat. The commissioning process involved the acquisition of square field EPID measurements using various phantom setups (varying SSD, phantom thickness and field size). Commissioning models were created for gantry 0, 90 and 180 degrees and verified by comparing EPID reconstructed 2D dose distributions to measurements made with the OCTAVIUS 1500 IC array (PTW, Freiburg, Germany) for 2 prostate and 1 rectum IMRT plans (25 beams total). The average of the γ parameters (y-mean and y-pass rate) and the dose difference at a reference point were reported. Due to their construction, the attenuation of couch, bridge and cryostat shows a much stronger dependence on gantry angle in the MR-linac compared to conventional linacs. We present a method to correct for these effects. This method is validated by dose reconstruction of the 25 IMRT beams recorded at a certain gantry angle using the model of another gantry angle, combined with the correction method. Results: For dose verification performed at a gantry angle identical to the commissioned model the average y-mean and y-pass rate values (3% global dose, 2 mm, 10% isodose) were 0.37 ± 0.07 and 98.1, 95% CI [98.1 ± 2.4], respectively. The average dose difference at the reference point was -0.5% ± 1.8%. Verification at gantry angles different from the commissioned model (i.e., using the gantry angle dependent correction) reported 0.39 ± 0.08 and 97.6, 95% CI [96.9, 98.3] average y-mean and y-pass rate values. The average dose difference at the reference point was: -0.1% ± 1.8%. of MRI-guided radiotherapy systems, which aims for online and real-time adaptation of the treatment plan, is progressing. In our department the MR-linac (Unity, Elekta AB, Stockholm, Sweden) is now in clinical use. The aim of this work is to demonstrate the feasibility of 2D EPID dosimetric verification for the MR-linac by comparing back-projected EPID doses to ionization chamber (IC) array dose distributions. Show less
PurposeTo study the feasibility of a channelized Hotelling observer (CHO) to predict human observer performance in detecting calcification-like signals in mammography images of an anthropomorphic... Show morePurposeTo study the feasibility of a channelized Hotelling observer (CHO) to predict human observer performance in detecting calcification-like signals in mammography images of an anthropomorphic breast phantom, as part of a quality control (QC) framework.MethodsA prototype anthropomorphic breast phantom with inserted gold disks of 0.25 mm diameter was imaged with two different digital mammography x-ray systems at four different dose levels. Regions of interest (ROIs) were extracted from the acquired processed and unprocessed images, signal-present and signal-absent. The ROIs were evaluated by a CHO using four different formulations of the difference of Gaussian (DoG) channel sets. Three human observers scored the ROIs in a two-alternative forced-choice experiment. We compared the human and the CHO performance on the simple task to detect calcification-like disks in ROIs with and without postprocessing. The proportion of correct responses of the human reader (PCH) and the CHO (PCCHO) was calculated and the correlation between the two was analyzed using a mixed-effect regression model. To address the signal location uncertainty, the impact of shifting the DoG channel sets in all directions up to two pixels was evaluated. Correlation results including the goodness of fit (r(2)) of PCH and PCCHO for all different parameters were evaluated.ResultsSubanalysis by system yielded strong correlations between PCH and PCCHO, with r(2) between PCH and PCCHO was found to be between 0.926 and 0.958 for the unshifted and between 0.759 and 0.938 for the shifted channel sets, respectively. However, the linear fit suggested a slight system dependence. PCCHO with shifted channel sets increased CHO performance but the correlation with humans was decreased. These correlations were not considerably affected by of the DoG channel set used.ConclusionsThere is potential for the CHO to be used in QC for the evaluation of detectability of calcification-like signals. The CHO can predict the PC of humans in images of calcification-like signals of two different systems. However, a global model to be used for all systems requires further investigation. Show less
