A patient-specific hybrid phantom for calculating radiation dose and equivalent dose to the whole body

2021 ◽  
Author(s):  
Erika Kollitz ◽  
Haegin Han ◽  
Chan Hyeong Kim ◽  
Marco Pinto ◽  
Marco Schwarz ◽  
...  
Keyword(s):  
Proton Therapy ◽  
Late Effects ◽  
Ground Truth ◽  
Organ Dose ◽  
Equivalent Dose ◽  
Whole Body ◽  
Ct Scans ◽  
Patient Specific ◽  
Monte Carlo Code ◽  
Hybrid Phantoms

Abstract Objective: As cancer survivorship increases, there is growing interest in minimizing the late effects of radiation therapy such as radiogenic second cancer, which may occur anywhere in the body. Assessing the risk of late effects requires knowledge of the dose distribution throughout the whole body, including regions far from the treatment field, beyond the typical anatomical extent of clinical CT scans. Approach: A hybrid phantom was developed which consists of in-field patient CT images extracted from ground truth whole-body CT (WBCT) scans, out-of-field mesh phantoms scaled to basic patient measurements, and a blended transition region. Four of these hybrid phantoms were created, representing male and female patients receiving proton therapy treatment in pelvic and cranial sites. To assess the performance of the hybrid approach, we simulated treatments using the hybrid phantoms, the scaled and unscaled mesh phantoms, and the ground truth whole-body CTs. We calculated absorbed dose and equivalent dose in and outside of the treatment field, with a focus on neutrons induced in the patient by proton therapy. Proton and neutron dose was calculated using a general purpose Monte Carlo code. Main Results: The hybrid phantom provided equal or superior accuracy in calculated organ dose and equivalent dose values relative to those obtained using the mesh phantoms in 78% in all selected organs and calculated dose quantities. Comparatively the default mesh and scaled mesh were equal or superior to the other phantoms in 21% and 28% of cases respectively. Significance: The proposed methodology for hybrid synthesis provides a tool for whole-body organ dose estimation for individual patients without requiring CT scans of their entire body. Such a capability would be useful for personalized assessment of late effects and risk-optimization of treatment plans.

scholarly journals Patient-Specific Simulation of Pneumoperitoneum for Laparoscopic Surgical Planning

2019 ◽  
Vol 43 (10) ◽  
Author(s):  
Shivali Dawda ◽  
Mafalda Camara ◽  
Philip Pratt ◽  
Justin Vale ◽  
Ara Darzi ◽  
...  
Keyword(s):  
Model Simulation ◽  
Ground Truth ◽  
Ct Scans ◽  
Patient Specific ◽  
Average Error ◽  
Interactive Simulation ◽  
Realistic Representation ◽  
Preoperative Ct ◽  
Simulation Parameters

Abstract Gas insufflation in laparoscopy deforms the abdomen and stretches the overlying skin. This limits the use of surgical image-guidance technologies and challenges the appropriate placement of trocars, which influences the operative ease and potential quality of laparoscopic surgery. This work describes the development of a platform that simulates pneumoperitoneum in a patient-specific manner, using preoperative CT scans as input data. This aims to provide a more realistic representation of the intraoperative scenario and guide trocar positioning to optimize the ergonomics of laparoscopic instrumentation. The simulation was developed by generating 3D reconstructions of insufflated and deflated porcine CT scans and simulating an artificial pneumoperitoneum on the deflated model. Simulation parameters were optimized by minimizing the discrepancy between the simulated pneumoperitoneum and the ground truth model extracted from insufflated porcine scans. Insufflation modeling in humans was investigated by correlating the simulation’s output to real post-insufflation measurements obtained from patients in theatre. The simulation returned an average error of 7.26 mm and 10.5 mm in the most and least accurate datasets respectively. In context of the initial discrepancy without simulation (23.8 mm and 19.6 mm), the methods proposed here provide a significantly improved picture of the intraoperative scenario. The framework was also demonstrated capable of simulating pneumoperitoneum in humans. This study proposes a method for realistically simulating pneumoperitoneum to achieve optimal ergonomics during laparoscopy. Although further studies to validate the simulation in humans are needed, there is the opportunity to provide a more realistic, interactive simulation platform for future image-guided minimally invasive surgery.

Generating and using patient-specific whole-body models for organ dose estimates in CT with increased accuracy: Feasibility and validation

2014 ◽  
Vol 30 (8) ◽  
pp. 925-933 ◽  
Author(s):  
Willi A. Kalender ◽  
Natalia Saltybaeva ◽  
Daniel Kolditz ◽  
Martin Hupfer ◽  
Marcel Beister ◽  
...  
Keyword(s):  
Organ Dose ◽  
Whole Body ◽  
Patient Specific

PACS-based Calculation of Organ Dose to Patients undergoing Chest Computed Tomography with Automatic Tube Current Modulation (Preprint)

2020 ◽  
Author(s):  
Ying Huang ◽  
Yang Yang ◽  
Xin Chen ◽  
Yiming Gao ◽  
Weihai Zhuo ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Organ Dose ◽  
Chest Ct ◽  
Ct Scans ◽  
Patient Specific ◽  
Organ Doses ◽  
Chest Computed Tomography ◽  
Tube Current ◽  
Specific Organ ◽  
Patient Size

BACKGROUND CT imaging is one of the most important contributors to medical radiation exposure(1). The frequency of CT scans and radiation doses accepted by patients attracted serious concerns for health physics researchers. The utilization of advanced technology ATCM has the potentials to reduce CT radiation doses while diagnostic image quality is maintained (2-7). As ATCM adjusted tube currents slice by slice it brought challenges to organ dose estimation using conversion factors derived from fixed tube current. Cross-system communication with hospital Picture Archive and Communication System (PACS),made it possible to read massive data automatically like the scanning parameters of each slice in each case. Monte Carlo simulations are probably the most reliable techniques which could be used for accurate dose assessment. [8-11]. However, specific patient model development and specific patient dose simulations are computationally demanding and may require dedicated hardware resources, this limitation constrained its application in large scale investigation. As an alternative method, patient specific organ doses could be calculated using the patient specific scan parameters and the Monte Carlo simulated organ doses with reference human phantom, and then correct the results with patient size factors. Dw is referred as the preferred patient size metric that determined the patient group and affected organ dose. The distance of the pathway traversed by the X-ray beam could provide the best approximation of tissue length traversed during the examination (12, 13),as CT image is a cross-sectional map normalized to the linear attenuation of water (14). The purpose of current study was to establish a method to access patient-specific organ dose associated with ATCM in chest computed tomography (CT) scans by combining Monte Carlo simulation with parameters contracted from clinical CT images of each patient underwent chest CT scan with ATCM. OBJECTIVE To explore a method to access patient-specific organ dose associated with automatic tube current modulation (ATCM) in chest computed tomography (CT) scans based on the information extracted from PACS automatically. METHODS 176cases of chest CT scans were read through cross-system communication with hospital PACS. A total of 8468 images were collected and analyzed automatically using in-house software. The scanning parameters (kVp, tube current, collimation width, etc.) of each CT examination were collected in real time, and a middle CT image of each case was collected for patient size(water equivalent diameter, Dw) calculation. Based on the reference human phantom, organ doses were simulated slice by slice using Monte Carlo method. The patient specific organ doses were calculated by combining tube currents of each patient slice with the simulated results, and doses were revised by correction factors that related to patient size. RESULTS A sum of 8468 slice of tube currents were extracted and analyzed in this study, the average mAs for large size patient group was about 1.6 times to the small size patient group. For organs that covered in the scan range like lung, breast, heart, the dose values were 18.30±2.91mGy, 15.13 ±2.75mGy and 17.87±2.96mGy in small size patients(Dw smaller than 22cm).The dose values of lung, breast, heart, in medium-sized patients (Dw from 22cm to 25cm) were 21.89±4.60mGy, 18.16 ±4.13mGy and 21.46±4.60mGy, while the values were 24.98±4.40mGy, 20.81±3.66mGy and 24.77±4.46mGy respectively in large size patients(Dw larger than 25cm). The organ doses increase with the patient size due to the increase of mAs. CONCLUSIONS The PACS-based method of large batch organ dose calculation to patients undergoing chest CT with ATCM was established. The methods and results may provide guidance to the design and optimization of chest CT protocols with ATCM.

Incorporating Patient-Specific Variability in the Simulation of Realistic Whole-Body $^{18}{\hbox{F-FDG}}$ Distributions for Oncology Applications

2009 ◽  
Vol 97 (12) ◽  
pp. 2026-2038 ◽  
Author(s):  
Amandine Le Maitre ◽  
William Paul Segars ◽  
Simon Marache ◽  
Anthonin Reilhac ◽  
Mathieu Hatt ◽  
...  
Keyword(s):  
Whole Body ◽  
Patient Specific

scholarly journals Fully Data-Driven Pseudohealthy Synthesis for Planning Valve-Sparing Aortic Root Reconstruction using Conditional Variational Autoencoders

2020 ◽  
Vol 6 (3) ◽  
pp. 284-287
Author(s):  
Jannis Hagenah ◽  
Mohamad Mehdi ◽  
Floris Ernst
Keyword(s):  
Aortic Root ◽  
Similarity Index ◽  
Ground Truth ◽  
Representation Learning ◽  
Patient Specific ◽  
Ultrasound Images ◽  
Specific Geometry ◽  
The Individual ◽  
Native Root ◽  
Original Information

AbstractAortic root aneurysm is treated by replacing the dilated root by a grafted prosthesis which mimics the native root morphology of the individual patient. The challenge in predicting the optimal prosthesis size rises from the highly patient-specific geometry as well as the absence of the original information on the healthy root. Therefore, the estimation is only possible based on the available pathological data. In this paper, we show that representation learning with Conditional Variational Autoencoders is capable of turning the distorted geometry of the aortic root into smoother shapes while the information on the individual anatomy is preserved. We evaluated this method using ultrasound images of the porcine aortic root alongside their labels. The observed results show highly realistic resemblance in shape and size to the ground truth images. Furthermore, the similarity index has noticeably improved compared to the pathological images. This provides a promising technique in planning individual aortic root replacement.

scholarly journals Improved PET/MRI attenuation correction in the pelvic region using a statistical decomposition method on T2-weighted images

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Elin Wallstén ◽  
Jan Axelsson ◽  
Joakim Jonsson ◽  
Camilla Thellenberg Karlsson ◽  
Tufve Nyholm ◽  
...  
Keyword(s):  
Attenuation Correction ◽  
Research Study ◽  
Standardized Uptake Value ◽  
Ground Truth ◽  
Whole Body ◽  
Mr Images ◽  
Pelvic Region ◽  
Mri Scans ◽  
The Mean ◽  
Prostate Cancer Research

Abstract Background Attenuation correction of PET/MRI is a remaining problem for whole-body PET/MRI. The statistical decomposition algorithm (SDA) is a probabilistic atlas-based method that calculates synthetic CTs from T2-weighted MRI scans. In this study, we evaluated the application of SDA for attenuation correction of PET images in the pelvic region. Materials and method Twelve patients were retrospectively selected from an ongoing prostate cancer research study. The patients had same-day scans of [11C]acetate PET/MRI and CT. The CT images were non-rigidly registered to the PET/MRI geometry, and PET images were reconstructed with attenuation correction employing CT, SDA-generated CT, and the built-in Dixon sequence-based method of the scanner. The PET images reconstructed using CT-based attenuation correction were used as ground truth. Results The mean whole-image PET uptake error was reduced from − 5.4% for Dixon-PET to − 0.9% for SDA-PET. The prostate standardized uptake value (SUV) quantification error was significantly reduced from − 5.6% for Dixon-PET to − 2.3% for SDA-PET. Conclusion Attenuation correction with SDA improves quantification of PET/MR images in the pelvic region compared to the Dixon-based method.

scholarly journals Automated analysis of 3D-echocardiography using spatially registered patient-specific CMR meshes

2021 ◽  
Vol 22 (Supplement_1) ◽  
Author(s):  
D Zhao ◽  
E Ferdian ◽  
GD Maso Talou ◽  
GM Quill ◽  
K Gilbert ◽  
...  
Keyword(s):  
New Zealand ◽  
Interobserver Variability ◽  
Ground Truth ◽  
Automated Analysis ◽  
3D Echocardiography ◽  
Training Data ◽  
Patient Specific ◽  
Manual Analysis ◽  
Lv Mass ◽  
3D Echo

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Heart Foundation (NHF) of New Zealand Health Research Council (HRC) of New Zealand Artificial intelligence shows considerable promise for automated analysis and interpretation of medical images, particularly in the domain of cardiovascular imaging. While application to cardiac magnetic resonance (CMR) has demonstrated excellent results, automated analysis of 3D echocardiography (3D-echo) remains challenging, due to the lower signal-to-noise ratio (SNR), signal dropout, and greater interobserver variability in manual annotations. As 3D-echo is becoming increasingly widespread, robust analysis methods will substantially benefit patient evaluation.  We sought to leverage the high SNR of CMR to provide training data for a convolutional neural network (CNN) capable of analysing 3D-echo. We imaged 73 participants (53 healthy volunteers, 20 patients with non-ischaemic cardiac disease) under both CMR and 3D-echo (<1 hour between scans). 3D models of the left ventricle (LV) were independently constructed from CMR and 3D-echo, and used to spatially align the image volumes using least squares fitting to a cardiac template. The resultant transformation was used to map the CMR mesh to the 3D-echo image. Alignment of mesh and image was verified through volume slicing and visual inspection (Fig. 1) for 120 paired datasets (including 47 rescans) each at end-diastole and end-systole. 100 datasets (80 for training, 20 for validation) were used to train a shallow CNN for mesh extraction from 3D-echo, optimised with a composite loss function consisting of normalised Euclidian distance (for 290 mesh points) and volume. Data augmentation was applied in the form of rotations and tilts (<15 degrees) about the long axis. The network was tested on the remaining 20 datasets (different participants) of varying image quality (Tab. I). For comparison, corresponding LV measurements from conventional manual analysis of 3D-echo and associated interobserver variability (for two observers) were also estimated. Initial results indicate that the use of embedded CMR meshes as training data for 3D-echo analysis is a promising alternative to manual analysis, with improved accuracy and precision compared with conventional methods. Further optimisations and a larger dataset are expected to improve network performance. (n = 20) LV EDV (ml) LV ESV (ml) LV EF (%) LV mass (g) Ground truth CMR 150.5 ± 29.5 57.9 ± 12.7 61.5 ± 3.4 128.1 ± 29.8 Algorithm error -13.3 ± 15.7 -1.4 ± 7.6 -2.8 ± 5.5 0.1 ± 20.9 Manual error -30.1 ± 21.0 -15.1 ± 12.4 3.0 ± 5.0 Not available Interobserver error 19.1 ± 14.3 14.4 ± 7.6 -6.4 ± 4.8 Not available Tab. 1. LV mass and volume differences (means ± standard deviations) for 20 test cases. Algorithm: CNN – CMR (as ground truth). Abstract Figure. Fig 1. CMR mesh registered to 3D-echo.

scholarly journals A method for evaluation of patient-specific lean body mass from limited-coverage CT images and its application in PERCIST: comparison with predictive equation

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Jingjie Shang ◽  
Zhiqiang Tan ◽  
Yong Cheng ◽  
Yongjin Tang ◽  
Bin Guo ◽  
...  
Keyword(s):  
Lean Body Mass ◽  
Body Mass ◽  
Whole Body ◽  
Patient Specific ◽  
Predictive Equation ◽  
Reference Standard ◽  
Pet Ct ◽  
Before And After ◽  
Percist 1.0 ◽  
Limited Coverage

Abstract Background Standardized uptake value (SUV) normalized by lean body mass ([LBM] SUL) is recommended as metric by PERCIST 1.0. The James predictive equation (PE) is a frequently used formula for LBM estimation, but may cause substantial error for an individual. The purpose of this study was to introduce a novel and reliable method for estimating LBM by limited-coverage (LC) CT images from PET/CT examinations and test its validity, then to analyse whether SUV normalised by LC-based LBM could change the PERCIST 1.0 response classifications, based on LBM estimated by the James PE. Methods First, 199 patients who received whole-body PET/CT examinations were retrospectively retrieved. A patient-specific LBM equation was developed based on the relationship between LC fat volumes (FVLC) and whole-body fat mass (FMWB). This equation was cross-validated with an independent sample of 97 patients who also received whole-body PET/CT examinations. Its results were compared with the measurement of LBM from whole-body CT (reference standard) and the results of the James PE. Then, 241 patients with solid tumours who underwent PET/CT examinations before and after treatment were retrospectively retrieved. The treatment responses were evaluated according to the PE-based and LC-based PERCIST 1.0. Concordance between them was assessed using Cohen’s κ coefficient and Wilcoxon’s signed-ranks test. The impact of differing LBM algorithms on PERCIST 1.0 classification was evaluated. Results The FVLC were significantly correlated with the FMWB (r=0.977). Furthermore, the results of LBM measurement evaluated with LC images were much closer to the reference standard than those obtained by the James PE. The PE-based and LC-based PERCIST 1.0 classifications were discordant in 27 patients (11.2%; κ = 0.823, P=0.837). These discordant patients’ percentage changes of peak SUL (SULpeak) were all in the interval above or below 10% from the threshold (±30%), accounting for 43.5% (27/62) of total patients in this region. The degree of variability is related to changes in LBM before and after treatment. Conclusions LBM algorithm-dependent variability in PERCIST 1.0 classification is a notable issue. SUV normalised by LC-based LBM could change PERCIST 1.0 response classifications based on LBM estimated by the James PE, especially for patients with a percentage variation of SULpeak close to the threshold.

Cyclic Breathing Simulations in Large Scale Models of the Lung Airway From the Oronasal Opening to the Terminal Bronchioles

2012 ◽  
Author(s):  
D. Keith Walters ◽  
Greg W. Burgreen ◽  
Robert L. Hester ◽  
David S. Thompson ◽  
David M. Lavallee ◽  
...  
Keyword(s):  
Steady State ◽  
Boundary Conditions ◽  
Large Scale ◽  
Whole Body ◽  
Patient Specific ◽  
Cfd Simulations ◽  
Reduced Order ◽  
Scale Models ◽  
Steady State Simulation ◽  
Conducting Zone

Computational fluid dynamics (CFD) simulations were performed for unsteady periodic breathing conditions, using large-scale models of the human lung airway. The computational domain included fully coupled representations of the orotracheal region and large conducting zone up to generation four (G4) obtained from patient-specific CT data, and the small conducting zone (to G16) obtained from a stochastically generated airway tree with statistically realistic geometrical characteristics. A reduced-order geometry was used, in which several airway branches in each generation were truncated, and only select flow paths were retained to G16. The inlet and outlet flow boundaries corresponded to the oronasal opening (superior), the inlet/outlet planes in terminal bronchioles (distal), and the unresolved airway boundaries arising from the truncation procedure (intermediate). The cyclic flow was specified according to the predicted ventilation patterns for a healthy adult male at three different activity levels, supplied by the whole-body modeling software HumMod. The CFD simulations were performed using Ansys FLUENT. The mass flow distribution at the distal boundaries was prescribed using a previously documented methodology, in which the percentage of the total flow for each boundary was first determined from a steady-state simulation with an applied flow rate equal to the average during the inhalation phase of the breathing cycle. The distal pressure boundary conditions for the steady-state simulation were set using a stochastic coupling procedure to ensure physiologically realistic flow conditions. The results show that: 1) physiologically realistic flow is obtained in the model, in terms of cyclic mass conservation and approximately uniform pressure distribution in the distal airways; 2) the predicted alveolar pressure is in good agreement with previously documented values; and 3) the use of reduced-order geometry modeling allows accurate and efficient simulation of large-scale breathing lung flow, provided care is taken to use a physiologically realistic geometry and to properly address the unsteady boundary conditions.

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