Thoracic Phantom Research Articles

OD76 - Impact of reduced acquisition time in PET/CT lesion detectability: study with an anthropomorphic female thoracic phantom

OD76 - Impact of reduced acquisition time in PET/CT lesion detectability: study with an anthropomorphic female thoracic phantom

Feasibility of ultra-high-speed acquisition in xSPECT bone algorithm: a phantom study with advanced bone SPECT-specific phantom.

Although xSPECT Bone (xB) provides quantitative single-photon emission computed tomography (SPECT) high-resolution images, patients' burden remains high due to long acquisition time; therefore, this study aimed to investigate the feasibility of shortening the xB acquisition time using a custom-designed phantom. A custom-designed xSPECT bone-specific (xSB) phantom with simulated cortical and spongious bones was developed based on the thoracic bone phantom. Both standard- and ultra-high-speed (UHS) xB acquisitions were performed in a male patient with lung cancer. In this phantom study, SPECT was acquired for 3, 6, 9, 12, and 30min. The clinical SPECT acquisition time per rotation was 9 and 3min for standard and UHS, respectively. SPECT images were reconstructed using ordered subset expectation maximization with three-dimensional resolution recovery (Flash3D; F3D) and xB algorithms. Quantitative SPECT value (QSV) and coefficient of variation (CV) were measured using the volume of interests (VOIs) placed at the center of the vertebral body and hot sphere. A linear profile was plotted on the spinous process at the center of the xSB phantom; then, the full width at half maximum (FWHM) was measured. The standardized uptake value (SUV) and standard deviation from the first thoracic to the fifth lumbar vertebrae in clinical standard- and UHS-xB images were measured using a 1-cm3 VOI. The QSV of F3D images was underestimated even in large regions, whereas those of xB images were close to actual radioactivity concentration. The CV was similar or lower for xB images than that for F3D images but was not decreased with increasing acquisition time for both reconstruction images. The FWHM of xB images was lower than those of F3D images at all acquisition times. The mean SUV values from the first thoracic to fifth lumbar vertebrae for standard- and UHS-xB images were 6.73 ± 0.64 and 6.19 ± 0.87, respectively, showing a strong positive correlation. Results of this phantom study suggest that xB imaging can be obtained in only one-third of the acquisition time without compromising the image quality. The SUV of UHS-xB images can be similar to that of standard-xB images in terms of clinical interpretation.

Suitability Assessment of an Indigenous Heterogeneous Thoracic Phantom for Patient-Specific Quality Assurance in Radiotherapy.

Objective: Patient-specific quality assurance (PSQA) assumes a vital role in precise and accurate radiation delivery to cancer patients. Since the patient body comprises heterogeneous media, the present study aimed to fabricate a heterogeneous thoracic phantom for PSQA. Materials and Methods: Heterogeneous thoracic (HT) phantom was fabricated using rib cage made up of bone equivalent material, kail-wood to mimic lungs and wax to mimic the various body parts. The physical density of all these materials used in phantom fabrication was measured and compared with that of the corresponding part of the actual human thorax. One beam was planned on the computed tomography (CT) images of the phantom and actual patient thorax region. Dose distribution in both the plans was measured and analyzed. Results: The estimated densities of heart, lung, ribs, scapula, spine, and chest wall tissues were 0.804 ± 0.007, 0.186 ± 0.010, 1.796 ± 0.061, 2.017 ± 0.026, 2.106 ± 0.029 and 0.739 ± 0.028 respectively in case of HT phantom while 1.038 ± 0.010, 0.199 ± 0.031, 1.715 ± 0.040, 2.006 ± 0.019, 1.929 ± 0.065 and 0.816 ± 0.028 g/cc, respectively in case of actual human thorax region.The depths of isodose curves in HT phantom were also comparable to the isodose curve’s depths in real patient. The PSQA results were within ± 3% for flat beam (FB) and flattening filtered free beam (FFFB) of 6 megavolts (MV) energy.Conclusion: The density and the dose distribution pattern in the HT phantom were similar to that in the actual human thorax region. Thus, fabricated HT phantom can be utilized for radiation dosimetry in thoracic cancer patients. The materials used to develop HT phantom are easily available in the market at an affordable price and easy to craft.

Automatic quantification package (Hone Graph) for phantom-based image quality assessment in bone SPECT: computerized automatic classification of detectability.

We previously developed a custom-design thoracic bone scintigraphy-specific phantom ("SIM2 bone phantom") to assess image quality in bone single-photon emission computed tomography (SPECT). We aimed to develop an automatic assessment system for imaging technology in bone SPECT and demonstrate the validity of this system. Four spherical lesions of 13-, 17-, 22-, and 28-mm diameters in the vertebrae of SIM2 bone phantom simulating the thorax were filled with radioactivity (target-to-background ratio: 4). Dynamic SPECT acquisitions were performed for 15min; reconstructions were performed using ordered subset expectation maximization at 3-15-min timepoints. Consequently, 216 lesions (54 SPECT images) were obtained: 120 and 96 lesions were used for software development and validation, respectively. The developed software used statistical parametric mapping to rigidly register and automatically calculate quantitative indexes (contrast-to-noise ratio, % coefficient of variance, % detectability equivalence volume, recovery coefficient, target-to-normal bone ratio, and full width at half maximum). A detectability score (DS) was used to define the four observation types (4, excellent; 3, adequate; 2, average; 1, poor) to score hot spherical lesions. The gold standard for DSs was independently classified by three experienced board-certified nuclear medicine technologists using the four observation types; thereafter, a consensus regarding the gold standard for DSs was reached. Using 120 lesions for development, decision tree analysis was performed to determine DS based on the quantitative indexes. We verified the validation of the quantitative indexes and their threshold values for automatic classification using 96 lesions for validation. The trends in the automatically calculated quantitative indices were consistent. Decision tree analysis produced four terminal groups; two quantitative indexes (% detectability equivalence volume and contrast-to-noise ratio) were used to classify DS. The automatically classified DSs exhibited an almost perfect agreement with the gold standard. The percentage agreement and kappa coefficient were 91.7% and 0.93, respectively, in 96 lesions for validation. The developed software automatically classified the detectability of hot lesions in the SIM2 bone phantom using the automatically calculated quantitative indexes, suggesting that this software could provide a means to automatically perform detectability analysis after data input that is excellent in reproducibility and accuracy.

Complementary use of priors for pulmonary imaging with electrical impedance and ultrasound computed tomography

For medical professionals caring for patients undergoing mechanical ventilation due to respiratory failure, the ability to quickly and safely obtain images of pulmonary function at the patient’s bedside would be highly desirable. Such images could be used to provide early warnings of developing pulmonary pathologies in real time, thereby reducing the incidence of complications and improving patient outcomes. Electrical impedance tomography (EIT) and low-frequency ultrasound computed tomography (USCT) are two imaging techniques with the potential to provide real-time non-ionizing pulmonary monitoring in the ICU setting, and each method has its own unique advantages as well as drawbacks. In this work, we describe a new algorithm for a system in which the strengths of the two modalities are combined in a complementary fashion. Specifically, preliminary reconstructions from each modality are used as priors to stabilize subsequent reconstructions, providing improved spatial resolution, sharper organ boundaries, and enhanced appearance of pathologies and other features. Results are validated using three numerically simulated thoracic phantoms representing pulmonary pathologies.

Identifying Robust Radiomics Features for Lung Cancer by Using In-Vivo and Phantom Lung Lesions.

We propose a novel framework for determining radiomics feature robustness by considering the effects of both biological and noise signals. This framework is preliminarily tested in a study predicting the epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer (NSCLC) patients. Pairs of CT images (baseline, 3-week post therapy) of 46 NSCLC patients with known EGFR mutation status were collected and a FDA-customized anthropomorphic thoracic phantom was scanned on two vendors’ scanners at four different tube currents. Delta radiomics features were extracted from the NSCLC patient CTs and reproducible, non-redundant, and informative features were identified. The feature value differences between EGFR mutant and EGFR wildtype patients were quantitatively measured as the biological signal. Similarly, radiomics features were extracted from the phantom CTs. A pairwise comparison between settings resulted in a feature value difference that was quantitatively measured as the noise signal. Biological signals were compared to noise signals at each setting to determine if the distributions were significantly different by two-sample t-test, and thus robust. Four optimal features were selected to predict EGFR mutation status, Tumor-Mass, Sigmoid-Offset-Mean, Gabor-Energy and DWT-Energy, which quantified tumor mass, tumor-parenchyma density transition at boundary, line-like pattern inside tumor and intratumoral heterogeneity, respectively. The first three variables showed robustness across the majority of studied CT acquisition parameters. The textual feature DWT-Energy was less robust. The proposed framework was able to determine robustness of radiomics features at specific settings by comparing biological signal to noise signal. Identification of robust radiomics features may improve the generalizability of radiomics models in future studies.

Temporal feature prior-aided separated reconstruction method for low-dose dynamic myocardial perfusion computed tomography

Dynamic myocardial perfusion computed tomography (DMP-CT) is an effective medical imaging technique for coronary artery disease diagnosis and therapy guidance. However, the radiation dose received by the patient during repeated CT scans is a widespread concern of radiologists because of the increased risk of cancer. The sparse few-view CT scanning protocol can be a feasible approach to reduce the radiation dose of DMP-CT imaging; however, an advanced reconstruction algorithm is needed. In this paper, a temporal feature prior-aided separated reconstruction method (TFP-SR) for low-dose DMP-CT images reconstruction from sparse few-view sinograms is proposed. To implement the proposed method, the objective perfusion image is divided into the baseline fraction and the enhancement fraction introduced by the arrival of the contrast agent. The core of the proposed TFP-SR method is the utilization of the temporal evolution information that naturally exists in the DMP-CT image sequence to aid the enhancement image reconstruction from limited data. The temporal feature vector of an image pixel is defined by the intensities of this pixel in the pre-reconstructed enhancement sequence, and the connection between two related features is calculated via a zero-mean Gaussian function. A prior matrix is constructed based on the connections between the extracted temporal features and used in the iterative reconstruction of the enhancement images. To evaluate the proposed method, the conventional filtered back-projection algorithm, the total variation regularized PWLS (PWLS-TV) and the prior image constrained compressed sensing are compared in this paper based on studies on a digital extended cardiac-torso (XCAT) thoracic phantom and a preclinical porcine DMP-CT data set that take image misregistration into account. The experimental results demonstrate that the proposed TFP-SR method has superior performance in sparse DMP-CT images reconstruction in terms of image quality and the analyses of the time attenuation curve and hemodynamic parameters.

CT-Scan Denoising Using a Charbonnier Loss Generative Adversarial Network

We propose a Generative Adversarial Network (GAN) optimized for noise reduction in CT-scans. The objective of CT scan denoising is to obtain higher quality imagery using a lower radiation exposure to the patient. Recent work in computer vision has shown that the use of Charbonnier distance as a term in the perceptual loss of a GAN can improve the performance of image reconstruction and video super-resolution. However, the use of a Charbonnier structural loss term has not yet been applied or evaluated for the purpose of CT scan denoising. Our proposed GAN makes use of a Wasserstein adversarial loss, a pretrained VGG19 perceptual loss, as well as a Charbonnier distance structural loss. We evaluate our approach using both applied Poisson noise distribution in order to simulate low-dose CT imagery, as well as using an anthropomorphic thoracic phantom at different exposure levels. Our evaluation criteria are Peek Signal to Noise (PSNR) as well as Structured Similarity (SSIM) of the denoised images, and we compare the results of our method versus recent state of the art deep denoising GANs. In addition, we report global noise through uniform soft tissue mediums. Our findings show that the incorporation of the Charbonnier Loss with the VGG-19 network improves the performance of the denoising as measured with the PSNR and SSIM, and that the method greatly reduces soft tissue noise to levels comparable to the NDCT scan.

Optimization of Number of Iterations as a Reconstruction Parameter in Bone SPECT Imaging Using a Novel Thoracic Spine Phantom.

The aim of this study was to optimize the number of iterations in bone SPECT imaging using a novel thoracic spine phantom (ISMM phantom). Methods: The quality and quantitative accuracy of bone SPECT images were evaluated by changing the number of iterations and the size of the hot spot in the phantom. True SUVs in the vertebra, tumor, and background parts were 9.8, 52.2, and 1.0, respectively. The phantom image was reconstructed using the ordered-subset expectation-maximization algorithm with CT-based attenuation correction, scatter correction, and resolution recovery; the number of ordered-subset expectation-maximization subsets was fixed at 10, with iterations ranging from 1 to 40. Full width at half maximum, percentage coefficient of variation, contrast ratio for the sphere and background (contrast), and recovery coefficient were evaluated as a function of the number of iterations for a given number of subsets (10) using the reconstructed images. In addition, SUVmax, SUVpeak, and SUVmean were calculated with various numbers of iterations for each sphere (13, 17, 22, and 28 mm) simulating a tumor. Results: Full width at half maximum decreased as the number of iterations was increased, and full width at half maximum converged uniformly when the number of iterations exceeded 10. The percentage coefficient of variation increased as the number of iterations was increased. Recovery coefficient decreased with decreasing sphere size. Contrast and all SUVs increased as the number of iterations was increased, and contrast and all SUVs converged uniformly when the number of iterations exceeded 5 and 10, respectively, for all sphere sizes. When the SUV was defined as the converged value for 10 iterations in the 28-mm sphere, the converged values of SUVmax, SUVpeak, and SUVmean were 75.1, 66.5, and 55.6, respectively. The relative error in the converged values for SUVmax, SUVpeak, and SUVmean were 43.8%, 27.3%, and 7.2% of the true value (52.2); all SUVs were overestimated. Conclusion: Using a thoracic spine phantom to evaluate the optimal reconstruction parameters in bone SPECT imaging, we determined the optimal number of iterations for 10 subsets to be 10.

Design and fabrication of a thoracic phantom for radiation dose verification in mega-voltage X-ray beam

To assess the radiological properties of racemosa wooden dust (RWD) and Poly (methyl methacrylate) (PMMA) for design and fabrication of a thoracic phantom for radiation dose verification in mega-voltage X-ray beam. The computed tomography images of RWD and PMMA were used to determine the radiological properties viz., Hounsfield units (HUs), relative electron density (RED) and mass density (MD). The estimated HUs, RED and MD were −828.2 ± 11.9, 0.172 ± 0.012, 0.150 ± 0.012 g/cc and 108.5 ± 7.3, 1.104 ± 0.004, 1.088 ± 0.005 g/cc for RWD and PMMA, respectively. The radiological properties of RWD and PMMA were in congruence with the value detailed in literature for lung tissue and muscle tissue of human body. Therefore, a phantom was design and fabricated using the combination of RWD and PMMA for radiation dose verification prior to radiation delivery to the cancer patients. The average deviation between dose computation engine and Ion-chamber estimations were within the stipulated tolerance (±3%). Thus, it was culminated that RWD and PMMA can be utilized for design and fabrication of phantom, which vindicates precise information about the quality assurance program for thoracic cancer patient treated using mega-voltage X-ray beam.

Optimization of a Bayesian penalized likelihood algorithm (Q.Clear) for 18F-NaF bone PET/CT images acquired over shorter durations using a custom-designed phantom

BackgroundThe Bayesian penalized likelihood (BPL) algorithm Q.Clear (GE Healthcare) allows fully convergent iterative reconstruction that results in better image quality and quantitative accuracy, while limiting image noise. The present study aimed to optimize BPL reconstruction parameters for 18F-NaF PET/CT images and to determine the feasibility of 18F-NaF PET/CT image acquisition over shorter durations in clinical practice.MethodsA custom-designed thoracic spine phantom consisting of several inserts, soft tissue, normal spine, and metastatic bone tumor, was scanned using a Discovery MI PET/CT scanner (GE Healthcare). The phantom allows optional adjustment of activity distribution, tumor size, and attenuation. We reconstructed PET images using OSEM + PSF + TOF (2 iterations, 17 subsets, and a 4-mm Gaussian filter), BPL + TOF (β = 200 to 700), and scan durations of 30–120 s. Signal-to-noise ratios (SNR), contrast, and coefficients of variance (CV) as image quality indicators were calculated, whereas the quantitative measures were recovery coefficients (RC) and RC linearity over a range of activity. We retrospectively analyzed images from five persons without bone metastases (male, n = 1; female, n = 4), then standardized uptake values (SUV), CV, and SNR at the 4th, 5th, and 6th thoracic vertebra were calculated in BPL + TOF (β = 400) images.ResultsThe optimal reconstruction parameter of the BPL was β = 400 when images were acquired at 120 s/bed. At 90 s/bed, the BPL with a β value of 400 yielded 24% and 18% higher SNR and contrast, respectively, than OSEM (2 iterations; 120 s acquisitions). The BPL was superior to OSEM in terms of RC and the RC linearity over a range of activity, regardless of scan duration. The SUVmax were lower in BPL, than in OSEM. The CV and vertebral SNR in BPL were superior to those in OSEM.ConclusionsThe optimal reconstruction parameters of 18F-NaF PET/CT images acquired over different durations were determined. The BPL can reduce PET acquisition to 90 s/bed in 18F-NaF PET/CT imaging. Our results suggest that BPL (β = 400) on SiPM-based TOF PET/CT scanner maintained high image quality and quantitative accuracy even for shorter acquisition durations.

Influence of feature calculating parameters on the reproducibility of CT radiomic features: a thoracic phantom study.

Existing studies have demonstrated that imaging parameters may affect radiomic features. However, the influence of feature calculating parameters has been overlooked. The purpose of this study is to investigate the influence of feature calculating parameters (gray-level range and bin size) on the reproducibility of CT radiomic features. Thirty-six CT scans from an anthropomorphic thoracic phantom were acquired with different imaging parameters including effective dose, pitch, slice thicknesses and reconstruction kernels. The influence of feature calculating parameters was investigated in terms of three gray-level ranges and eleven gray-level bin sizes. Feature reproducibility was assessed by the intraclass correlation coefficient (ICC) with the cutoff value of 0.8 and the coefficient of variation (CV) with the cutoff value of 20%. The agreements of reproducible features in different ranges and bin sizes were analyzed by Kendall's W test and Kappa test. The proportions of reproducible features, in terms of two calculating, four imaging and two segmentation parameters, were evaluated using Cochran's Q test and Dunn's test. For the three gray-level ranges, 50% (44/88) of features were reproducible with a perfect agreement (Kendall's W coefficient 0.844, P<0.001). Of the 72 features that may be influenced by gray-level bin size, 33.3% (24/72) were reproducible for 11 bin sizes with a perfect agreement (Kendall's W coefficient 0.879, P<0.001). For the proportions of reproducible features, there was no statistically significant difference among three ranges (P=0.420), but there was among eleven bin sizes (P=0.013). The proportions of reproducible features in feature calculating parameters were statistically significantly lower than those in imaging parameters (adjusted P<0.05). Feature calculating parameters may have a greater influence than imaging parameters on the reproducibility of CT radiomic features, which should be given special attention in clinical applications.

The effect of megavoltage field size on intrafraction cone-beam CT image quality.

To investigate the effects of scatter from a megavoltage treatment beam on intrafraction cone beam CT (CBCT) image quality. The effects of treatment beam field size and phantom geometry were investigated as well as the clinical success of IFI. Intrafraction imaging (IFI) was performed on four phantoms with four different MV field sizes using a 6 MV FFF source. The image quality of the intrafraction CBCT images was compared to that of a baseline CBCT (i.e. with no treatment beam on) and quantified using noise and low contrast visibility. Increasing the kV tube current was explored as a possible method to reduce noise induced by the MV photon scatter in the intrafraction-CBCTs. The clinical success of all IFI patients over a 2 month period was reviewed. Intrafraction-CBCT image quality and low-contrast visibility deteriorated as MV field size increased. The extent of image degradation was found to depend on the mass of the phantom resulting in a more pronounced effect for a pelvic phantom than a thoracic phantom. While increasing the tube current could reduce the noise in the intrafraction-CBCT images, increasing the current by a factor of 4 failed to reach baseline image quality. Anatomy was found to be the primary indication of clinical IFI failure with all observed failures occurring during abdominal treatments. Image quality was found to decrease with increasing MV field size and decrease with increasing treatment anatomy mass. When considering intrafraction imaging clinically, the primary indicator of IFI failure is treatment anatomy. IFI can be used during chest treatments with high success rates but care must be taken for abdominal treatments and failures should be expected.

Towards a thoracic conductive phantom for EIT

Phantom experiments are a crucial step for testing new hardware or imaging algorithms for electrical impedance tomography (EIT) studies. However, constructing an accurate phantom for EIT research remains critical; some studies have attempted to model the skull and breasts, and even fewer, as yet, have considered the thorax. In this study, a critical comparison between the electrical properties (impedance) of three materials is undertaken: a polyurethane foam, a silicone mixture and a thermoplastic polyurethane filament. The latter was identified as the most promising material and adopted for the development of a flexible neonatal torso. The validation is performed by the EIT image reconstruction of the air filled cavities, which mimic the lung regions. The methodology is reproducible for the creation of any phantom that requires a slight flexibility.

Validating defibrillation simulation in a human-shaped phantom

We previously developed a computational model to aid clinicians in positioning implantable cardioverter-defibrillators (ICDs), especially in the case of abnormal anatomies that commonly arise in pediatric cases. We have validated the model clinically on the body surface; however, validation within the volume of the heart is required to establish complete confidence in the model and improve its use in clinical settings. The goal of this study was to use an animal model and thoracic phantom to record the ICD potential field within the heart and on the torso to validate our defibrillation simulation system. We recorded defibrillator shock potentials from an ICD suspended together with an animal heart in a human-shaped torso tank and compared them with simulated values. We also compared the scaled distribution threshold, an analog to the defibrillation threshold, calculated from the measured and simulated electric fields within the myocardium. ICD potentials recorded on the tank and cardiac surface and within the myocardium agreed well with those predicted by the simulation. A quantitative comparison of the recorded and simulated potentials yielded a mean correlation of 0.94 and a relative error of 19.1%. The simulation can also predict scaled distribution thresholds similar to those calculated from the measured potential fields. We found that our simulation could predict potential fields with high correlation with the measured values within the heart and on the torso surface. These results support the use of this model for the optimization of ICD placements.

Positioning errors of metal localization devices with motion artifacts on kV and MV cone beam CT.

Objective:To investigate motion artifacts on kV CBCT and MV CBCT images with metal localization devices for image-guided radiation therapy.Methods:The 8 μ pelvis CBCT template for the Siemens Artiste MVision and Pelvis template for the Varian IX on-board Exact Arms kV were used to acquire CBCT images in this study. Images from both CBCT modalities were compared in CNRs, metal landmark absolute positions, and image volume distortion on three different planes of view. The images were taken on a breathing-simulated thoracic phantom in which several typical metal localization devices were implanted, including clips and wires for breast patients, gold seeds for prostate patients, and BBs as skin markers. To magnify the artifacts, a 4 cm diameter metal ball was also implanted into the thoracic phantom to mimic the metal artifacts.Results:For MV CBCT, the CNR at a 4 sec breathing cycle with 1 cm breathing amplitude was 5.0, 3.4 and 4.6 for clips, gold seeds and BBs, respectively while it was 1.5, 2.0 and 1.6 for the kV CBCT. On the images, the kV CBCT showed symmetric streaking artifacts both in the transverse and longitudinal directions relative to the motion direction. The kV CBCT images predicted 89 % of the expected volume, while the MV CBCT images predicted 95 % of the expected volume. The simulated soft tissue observed in the MVCT could not be detected in the kV CBCT.Conclusion:The MV CBCT images showed better volume prediction, less streaking effects and better CNRs of a moving metal target, i.e. clips, BBs, gold seeds and metal balls than on the kV CBCT images. The MV CBCT was more advantageous compared to the kV CBCT with less motion artifacts for metal localization devices.Advances in knowledge:This study would benefit clinicians to prescribe MV CBCT as localization modality for radiation treatment with moving target when metal markers are implanted.

A homemade thoracic phantom for lung pathologies simulation

Introduction Point-of-care Ultrasonography (PoCUS) for lung pathologies evaluation is used increasingly by emergency physicians to identify the cause of fever and dyspnea1.2. Training with thoracic phantoms could lead to easier understanding of the sonographic findings of lung pathologies such as pneumonia and empyema. Ultrasound models for lung pathologies simulation had been described before3,4. We presented a new, inexpensive formulation for making the phantoms simulating lung consolidation, abscess and collapsed lung with pleural effusion. Methods We used agar powder for making the phantom. We dissolved the agar power with boiled water, after thoroughly mixed up, left it cooled down to room temperature. We froze the formed jelly in home refrigerator for 10 hours then took it our and left it returned to room temperature. The process of freezing and defrosting will make the texture of jelly soft and fragile, hence, the sonographic imaging would be similar to the pattern of lung empyema. By adjusting the amount of agar powder, different pattern would be demonstrated: with the amount of powder increased, the particles seen wound increased, which made it looks like collapsed lung under sonographic study. For creating the model of consolidation, we added Gelatine into the agar powder to make the jelly harder. Conclusion The phantom for lung pathologies created in our study could be made easily with inexpensive cost and the images of sonographic study are similar to the real ones. This model could be used to teach common lung pathologies seen in clinical scenes.

Low-Dose CT Image Denoising Model Based on Sparse Representation by Stationarily Classified Sub-Dictionaries

Low-dose computed tomography (LDCT) technique is an important imaging modality, but LDCT images are always severely degraded by mottle noise and streak artifacts. The recently proposed nonlocally centralized sparse representation (NCSR) algorithm has good performance in natural image denoising, but it suffers from residual streak artifacts and can't preserve edges structure information well when implemented in LDCT image denoising. In addition, it has high computational complexity. To address this problem, in this paper, we propose an improved model, i.e. SNCSR model, based on the stationary PCA sub-dictionaries, nonlocally centralized sparse representation and relative total variation. In the SNCSR model, in order to learn more accurate sub-dictionaries, the LDCT image is preprocessed by the improved total variation (ITV) model in which the weighted coefficient of the regularization term is constructed depending on a clipped and normalized local activity. In addition, the maximum eigenvalue of the gradient covariance matrix of the image patch is used to distinguish edge structure information from background region so that the restored image can be represented more sparsely. Moreover, unlike the NCSR model that needs to learn sub-dictionaries in each outer loop, the proposed model learns stationary sub-dictionaries only once before iteration starts, which shorten the computation time significantly. At last, the relative total variation (RTV) algorithm is applied to further reduce the residual artifacts in the recovered image more thoroughly. The experiments are performed on the simulated pelvis phantom, the actual thoracic phantom and the clinical abdominal data. Compared with several other competitive denoising algorithms, both subjective visual effect and objective evaluation criteria show that the proposed SNCSR model has lower computational complexity and can improve LDCT images quality more effectively.

A feasibility study of spatiotemporally integrated radiotherapy using the LQ model

This paper investigates the feasibility of spatiotemporally modulated radiotherapy (STMRT)—integrated model with explicit constraints on the tumor dose heterogeneity. In particular, we demonstrate the effect of the tumor dose heterogeneity on the tumor biologically effective dose (BED) achievable and optimal fractionation.We propose an STMRT model that simultaneously optimizes the dose distributions and fractionation schedule for each individual case with the maximum and minimum constraints on the tumor BED to explicitly control the level of tumor dose heterogeneity. Sixteen thoracic phantom cases were planned using (1) STMRT and (2) standard fractionation (60 Gy in 30 fractions fixed) IMRT. Constraints on the organs-at-risk (OAR) BED were identical for both plans. BEDs were calculated using the ratio of 10 Gy for the tumor and 3 Gy for all OARs. The maximum tumor BED for STMRT plans was constrained to be less than 100%–150% of the maximum tumor BED resulted from the standard fractionation plans.The mean tumor BED from STMRT plans was up to 110.7%, 128.3%, 135.0% and 148.0% of that from the standard fractionation plans when the maximum tumor BED was constrained to be less than 100%, 120%, 130% and 150% of the maximum BED achieved using the standard plans. The optimal number of fractions varied widely for different phantom geometries for the same radiobiological parameter values. The increase in the tumor BED and the range of optimal fractionation was larger with a larger tumor dose heterogeneity allowed.The results have shown the feasibility of personalizing fractionation schedule using an STMRT integrated model to deliver a maximum feasible BED to the tumor for a fixed OAR BED. The potential increase in the tumor BED was positively correlated to the tumor dose heterogeneity allowed.

An investigation of clinical treatment field delivery verification using cherenkov imaging: IMRT positioning shifts and field matching.

Cherenkov light emission has been shown to correlate with ionizing radiation dose delivery in solid tissue. An important clinical application of Cherenkov light is the real-time verification of radiation treatment delivery invivo. To test the feasibility of treatment field verification, Cherenkov light images were acquired concurrent with radiation beam delivery to standard and anthropomorphic phantoms. Specifically, we tested two clinical treatment scenarios: (a) Observation of field overlaps or gaps in matched 3D fields and (b) Patient positioning shifts during intensity modulated radiation therapy (IMRT) field delivery. Further development of this technique would allow real-time detection of treatment delivery errors on the order of millimeters so that patient safety and treatment quality can be improved. Cherenkov light emission was captured using a PI-MAX4 intensified charge coupled device (ICCD) system (Princeton Instruments). All radiation delivery was performed using a Varian Trilogy linear accelerator (linac) operated at 6MV or 18MV for photon and 6MeV or 16MeV for electron studies. Field matching studies were conducted with photon and electron beams at gantry angles of 0°, 15°, and 45°. For each modality and gantry angle, a total of three data sets were acquired. Overlap and gap distances of 0, 2, 5, and 10mm were tested and delivered to solid phantom material of 30×30×5cm3 . Phantom materials used were white plastic water and brown solid water. Tests were additionally performed on an anthropomorphic phantom with an irregular surface. Positioning shift studies were performed using IMRT fields delivered to a thoracic anthropomorphic phantom. For thoracic phantom measurements, the camera was placed laterally to observe the entire right side of the phantom. Fields were delivered with known translational patient positioning shifts in four directions. Changes in the Cherenkov fluence were evaluated through the generation of difference maps from unshifted Cherenkov images. All images were evaluated using ImageJ, Python, and MATLAB software packages. For matched fields, Cherenkov images were able to quantitate matched field separations with discrepancies between 2 and 4mm, depending on gantry angle and beam energy or modality. For all photon and electron beams delivered at a gantry angle of 0°, image analysis indicated average discrepancies of less than 2mm for all field gaps and overlaps, with 83% of matched fields exhibiting discrepancies less than 1mm. Beams delivered obliquely to the phantom surface exhibited average discrepancies as high as 4mm for electron beams delivered at large oblique angles. Finally, for IMRT field delivery, vertical and lateral patient positioning shifts of 2mm were detected in some cases, indicating the potential detectability threshold of using this technique alone. Our study indicates that Cherenkov imaging can be used to support and bolster current treatment delivery verification techniques, improving our ability to recognize and rectify millimeter-scale delivery and positioning errors.