Accurate Scatter Estimation Research Articles

Physics-Guided Deep Scatter Estimation by Weak Supervision for Quantitative SPECT.

Accurate scatter estimation is important in quantitative SPECT for improving image contrast and accuracy. With a large number of photon histories, Monte-Carlo (MC) simulation can yield accurate scatter estimation, but is computationally expensive. Recent deep learning-based approaches can yield accurate scatter estimates quickly, yet full MC simulation is still required to generate scatter estimates as ground truth labels for all training data. Here we propose a physics-guided weakly supervised training framework for fast and accurate scatter estimation in quantitative SPECT by using a 100× shorter MC simulation as weak labels and enhancing them with deep neural networks. Our weakly supervised approach also allows quick fine-tuning of the trained network to any new test data for further improved performance with an additional short MC simulation (weak label) for patient-specific scatter modelling. Our method was trained with 18 XCAT phantoms with diverse anatomies / activities and then was evaluated on 6 XCAT phantoms, 4 realistic virtual patient phantoms, 1 torso phantom and 3 clinical scans from 2 patients for 177Lu SPECT with single / dual photopeaks (113, 208 keV). Our proposed weakly supervised method yielded comparable performance to the supervised counterpart in phantom experiments, but with significantly reduced computation in labeling. Our proposed method with patient-specific fine-tuning achieved more accurate scatter estimates than the supervised method in clinical scans. Our method with physics-guided weak supervision enables accurate deep scatter estimation in quantitative SPECT, while requiring much lower computation in labeling, enabling patient-specific fine-tuning capability in testing.

Deep-learning convolutional neural network-based scatter correction for contrast enhanced digital breast tomosynthesis in both cranio-caudal and mediolateral-oblique views.

Scatter radiation in contrast-enhanced digital breast tomosynthesis (CEDBT) reduces the image quality and iodinated lesion contrast. Monte Carlo simulation can provide accurate scatter estimation at the cost of computational burden. A model-based convolutional method trades off accuracy for processing speed. The purpose of this study is to develop a fast and robust deep-learning (DL) convolutional neural network (CNN)-based scatter correction method for CEDBT. Projection images and scatter maps of digital anthropomorphic breast phantoms were generated using Monte Carlo simulations. Experiments were conducted to validate the simulated scatter-to-primary ratio (SPR) at different locations of a breast phantom. Simulated projection images were used for CNN training and testing. Two separate U-Nets [low-energy (LE)-CNN and high-energy (HE)-CNN] were trained for LE and HE spectrum, respectively. CNN-based scatter correction was applied to a clinical case with a malignant iodinated mass to evaluate the influence on the lesion detection. The average and standard deviation of mean absolute percentage error of LE-CNN and HE-CNN estimated scatter map are and , respectively. For clinical cases, the lesion signal difference to noise ratio average improvement was 190% after CNN-based scatter correction. To conduct scatter correction on clinical CEDBT images, the whole process of loading CNNs parameters and scatter correction for LE and HE images took , with 9GB GPU computational cost. The SPR variation across the breast agrees between experimental measurements and Monte Carlo simulations. We developed a CNN-based scatter correction method for CEDBT in both CC view and mediolateral-oblique view with high accuracy and fast speed.

Energy-sensitive scatter estimation and correction for spectral x-ray imaging with photon-counting detectors

As photon counting detectors are being explored for medical and industrial imaging applications, there is a critical need to understand spectral characteristics of scattered x-ray photons. Scattered radiation is detrimental to x-ray imaging by reducing image quality and quantitative accuracy. While various scatter correction techniques have been proposed for x-ray imaging with conventional energy-integrating detectors, additional efforts are required to develop approaches for spectral x-ray imaging with energy-resolving PCDs. We show the benefits of accurate scatter estimation and correction for each energy bin when using a photon counting detector. We propose a scatter estimation model that accounts for the energy-dependent scatter characteristics in projection imaging. This can then be used to restore quantitative accuracy for spectral x-ray imaging with PCDs. Results are shown in the context of contrast-enhanced spectral mammography using dual-energy subtraction to digitally isolate iodine targets (2.5–40 mg/ml). In the presence of scatter, the projected iodine densities are increasingly underestimated as the object thickness increases. The energy-sensitive scatter correction improves the iodine density estimation up to 46%. These results suggest that our scatter estimation model can accurately account for the energy-dependent scatter distribution, which can be an effective tool for scatter compensation in spectral x-ray imaging. Implementing this scatter estimation model does not require any modifications to the acquisition parameters and is transferable to other x-ray imaging applications such as tomosynthesis and CT.

A correlated sampling-based Monte Carlo simulation for fast CBCT iterative scatter correction.

In recent years, cone-beam computed tomography (CBCT) has played an important role in medical imaging. However, the applications of CBCT are limited due to the severe scatter contamination. Conventional Monte Carlo (MC) simulation can provide accurate scatter estimation for scatter correction, but the expensive computational cost has always been the bottleneck of MC method in clinical application. In this work, an MC simulation method combined with a variance reduction technique called correlated sampling is proposed for fast iterative scatter correction. Correlated sampling exploits correlation between similar simulation systems to reduce the variance of interest quantities. Specifically, conventional MC simulation is first performed on the scatter-contaminated CBCT to generate the initial scatter signal. In the subsequent correction iterations, scatter estimation is then updated by applying correlated MC sampling to the latest corrected CBCT images by reusing the random number sequences of the task-related photons in conventional MC. Afterward, the corrected projections obtained by subtracting the scatter estimation from raw projections are utilized for FDK reconstruction. These steps are repeated until an adequate scatter correction is obtained. The performance of the proposed framework is evaluated by the accuracy of the scatter estimation, the quality of corrected CBCT images and efficiency. Overall, the difference in mean absolute percentage error between scatter estimation with and without correlated sampling is 0.25% for full-fan case and 0.34% for half-fan case, respectively. In simulation studies, scatter artifacts are substantially eliminated, where the mean absolute error value is reduced from 15 to 2HU in full-fan case and from 53 to 13HU in half-fan case. Scatter-to-primary ratio is reduced to 0.02 for full-fan and 0.04 for half-fan, respectively. In phantom study, the contrast-to-noise ratio (CNR) is increased by a factor of 1.63, and the contrast is increased by a factor of 1.77. As for clinical studies, the CNR is improved by 11% and 14% for half-fan and full-fan, respectively. The contrast after correction is increased by 19% for half-fan and 44% for full-fan. Furthermore, root mean square error is also effectively reduced, especially from 78 to 4HU for full-fan. Experimental results demonstrate that the figure of merit is improved between 23 and 43 folds when using correlated sampling. The proposed method takes less than 25s for the whole iterative scatter correction process. The proposed correlated sampling-based MC simulation method can achieve fast and accurate scatter correction for CBCT, making it suitable for real-time clinical use.

Feasibility of Dual-Energy Cone-Beam CT of Bone Marrow Edema Using Dual-Layer Flat Panel Detectors.

We investigated the feasibility of detection and quantification of bone marrow edema (BME) using dual-energy (DE) Cone-Beam CT (CBCT) with a dual-layer flat panel detector (FPD) and three-material decomposition. A realistic CBCT system simulator was applied to study the impact of detector quantization, scatter, and spectral calibration errors on the accuracy of fat-water-bone decompositions of dual-layer projections. The CBCT system featured 975 mm source-axis distance, 1,362 mm source-detector distance and a 430 × 430 mm2 dual-layer FPD (top layer: 0.20 mm CsI:Tl, bottom layer: 0.55 mm CsI:Tl; a 1 mm Cu filter between the layers to improve spectral separation). Tube settings were 120 kV (+2 mm Al, +0.2 mm Cu) and 10 mAs per exposure. The digital phantom consisted of a 160 mm water cylinder with inserts containing mixtures of water (volume fraction ranging 0.18 to 0.46) - fat (0.5 to 0.7) - Ca (0.04 to 0.12); decreasing fractions of fat indicated increasing degrees of BME. A two-stage three-material DE decomposition was applied to DE CBCT projections: first, projection-domain decomposition (PDD) into fat-aluminum basis, followed by CBCT reconstruction of intermediate base images, followed by image-domain change of basis into fat, water and bone. Sensitivity to scatter was evaluated by i) adjusting source collimation (12 to 400 mm width) and ii) subtracting various fractions of the true scatter from the projections at 400 mm collimation. The impact of spectral calibration was studied by shifting the effective beam energy (± 2 keV) when creating the PDD lookup table. We further simulated a realistic BME imaging framework, where the scatter was estimated using a fast Monte Carlo (MC) simulation from a preliminary decomposition of the object; the object was a realistic wrist phantom with an 0.85 mL BME stimulus in the radius. The decomposition is sensitive to scatter: approx. <20 mm collimation width or <10% error of scatter correction in a full field-of-view setting is needed to resolve BME. A mismatch in PDD decomposition calibration of ± 1 keV results in ~25% error in fat fraction estimates. In the wrist phantom study with MC scatter corrections, we were able to achieve ~0.79 mL true positive and ~0.06 mL false positive BME detection (compared to 0.85 mL true BME volume). Detection of BME using DE CBCT with dual-layer FPD is feasible, but requires scatter mitigation, accurate scatter estimation, and robust spectral calibration.

A deep neural network for fast and accurate scatter estimation in quantitative SPECT/CT under challenging scatter conditions.

A major challenge for accurate quantitative SPECT imaging of some radionuclides is the inadequacy of simple energy window-based scatter estimation methods, widely available on clinic systems. A deep learning approach for SPECT/CT scatter estimation is investigated as an alternative to computationally expensive Monte Carlo (MC) methods for challenging SPECT radionuclides, such as 90Y. A deep convolutional neural network (DCNN) was trained to separately estimate each scatter projection from the measured 90Y bremsstrahlung SPECT emission projection and CT attenuation projection that form the network inputs. The 13-layer deep architecture consisted of separate paths for the emission and attenuation projection that are concatenated before the final convolution steps. The training label consisted of MC-generated "true" scatter projections in phantoms (MC is needed only for training) with the mean square difference relative to the model output serving as the loss function. The test data set included a simulated sphere phantom with a lung insert, measurements of a liver phantom, and patients after 90Y radioembolization. OS-EM SPECT reconstruction without scatter correction (NO-SC), with the true scatter (TRUE-SC) (available for simulated data only), with the DCNN estimated scatter (DCNN-SC), and with a previously developed MC scatter model (MC-SC) were compared, including with 90Y PET when available. The contrast recovery (CR) vs. noise and lung insert residual error vs. noise curves for images reconstructed with DCNN-SC and MC-SC estimates were similar. At the same noise level of 10% (across multiple realizations), the average sphere CR was 24%, 52%, 55%, and 67% for NO-SC, MC-SC, DCNN-SC, and TRUE-SC, respectively. For the liver phantom, the average CR for liver inserts were 32%, 73%, and 65% for NO-SC, MC-SC, and DCNN-SC, respectively while the corresponding values for average contrast-to-noise ratio (visibility index) in low-concentration extra-hepatic inserts were 2, 19, and 61, respectively. In patients, there was high concordance between lesion-to-liver uptake ratios for SPECT reconstruction with DCNN-SC (median 4.8, range 0.02-13.8) compared with MC-SC (median 4.0, range 0.13-12.1; CCC = 0.98) and with 90Y PET (median 4.9, range 0.02-11.2; CCC = 0.96) while the concordance with NO-SC was poor (median 2.8, range 0.3-7.2; CCC = 0.59). The trained DCNN took ~ 40s (using a single i5 processor on a desktop computer) to generate the scatter estimates for all 128 views in a patient scan, compared to ~ 80min for the MC scatter model using 12 processors. For diverse 90Y test data that included patient studies, we demonstrated comparable performance between images reconstructed with deep learning and MC-based scatter estimates using metrics relevant for dosimetry and for safety. This approach that can be generalized to other radionuclides by changing the training data is well suited for real-time clinical use because of the high speed, orders of magnitude faster than MC, while maintaining high accuracy.

Real-time scatter estimation for medical CT using the deep scatter estimation: Method and robustness analysis with respect to different anatomies, dose levels, tube voltages, and data truncation.

X-ray scattering leads to CT images with a reduced contrast, inaccurate CT values as well as streak and cupping artifacts. Therefore, scatter correction is crucial to maintain the diagnostic value of CT and CBCT examinations. However, existing approaches are not able to combine both high accuracy and high computational performance. Therefore, we propose the deep scatter estimation (DSE): a deep convolutional neural network to derive highly accurate scatter estimates in real time. Gold standard scatter estimation approaches rely on dedicated Monte Carlo (MC) photon transport codes. However, being computationally expensive, MC methods cannot be used routinely. To enable real-time scatter correction with similar accuracy, DSE uses a deep convolutional neural network that is trained to predict MC scatter estimates based on the acquired projection data. Here, the potential of DSE is demonstrated using simulations of CBCT head, thorax, and abdomen scans as well as measurements at an experimental table-top CBCT. Two conventional computationally efficient scatter estimation approaches were implemented as reference: a kernel-based scatter estimation (KSE) and the hybrid scatter estimation (HSE). The simulation study demonstrates that DSE generalizes well to varying tube voltages, varying noise levels as well as varying anatomical regions as long as they are appropriately represented within the training data. In any case the deviation of the scatter estimates from the ground truth MC scatter distribution is less than 1.8% while it is between 6.2% and 293.3% for HSE and between 11.2% and 20.5% for KSE. To evaluate the performance for real data, measurements of an anthropomorphic head phantom were performed. Errors were quantified by a comparison to a slit scan reconstruction. Here, the deviation is 278HU (no correction), 123HU (KSE), 65HU (HSE), and 6HU (DSE), respectively. The DSE clearly outperforms conventional scatter estimation approaches in terms of accuracy. DSE is nearly as accurate as Monte Carlo simulations but is superior in terms of speed (≈10ms/projection) by orders of magnitude.

Deep Scatter Estimation (DSE): Accurate Real-Time Scatter Estimation for X-Ray CT Using a Deep Convolutional Neural Network

X-ray scatter is a major cause of image quality degradation in dimensional CT. Especially, in case of highly attenuating components scatter-to-primary ratios may easily be higher than 1. The corresponding artifacts which appear as cupping or dark streaks in the CT reconstruction may impair a metrological assessment. Therefore, an appropriate scatter correction is crucial. Thereby, the gold standard is to predict the scatter distribution using a Monte Carlo (MC) code and subtract the corresponding scatter estimate from the measured raw data. MC, however, is too slow to be used routinely. To correct for scatter in real-time, we developed the deep scatter estimation (DSE). It uses a deep convolutional neural network which is trained to reproduce the output of MC simulations using only the acquired projection data as input. Once trained, DSE can be applied in real-time. The present study demonstrates the potential of the proposed approach using simulations and measurements. In both cases the DSE yields highly accurate scatter estimates that differ by< 3% from our MC scatter predictions. Further, DSE clearly outperforms kernel-based scatter estimation techniques and hybrid approaches, as they are in use today.

Investigation on Beam-Blocker-Based Scatter Correction Method for Improving CT Number Accuracy

Cone-beam computed tomography (CBCT) is gaining widespread use in various medical and industrial applications but suffers from substantially larger amount of scatter than that in the conventional diagnostic CT resulting in relatively poor image quality. Various methods that can reduce and/or correct for the scatter in the CBCT have therefore been developed. Scatter correction method that uses a beam-blocker has been considered a direct measurement-based approach providing accurate scatter estimation from the data in the shadows of the beam-blocker. To the best of our knowledge, there has been no record reporting the significance of the scatter from the beam-blocker itself in such correction methods. In this paper, we identified the scatter from the beam-blocker that is detected in the object-free projection data investigated its influence on the image accuracy of CBCT reconstructed images, and developed a scatter correction scheme that takes care of this scatter as well as the scatter from the scanned object.

Quantifying intraocular scatter with near diffraction-limited double-pass point spread function.

Measurement of the double-pass (DP) point-spread function (PSF) can provide an objective and non-invasive method for estimating intraocular scatter in the human eye. The objective scatter index (OSI), which is calculated from the DP PSF images, is commonly used to quantify intraocular scatter. In this article, we simulated the effect of higher-order ocular aberrations on OSI, and the results showed that higher-order ocular aberrations had a significant influence on OSI. Then we developed an adaptive optics DP PSF measurement system (AO-DPPMS) which was capable of correcting ocular aberrations up to eighth-order radial Zernike modes over a 6.0-mm pupil. Employing this system, we obtained DP PSF images of four subjects at the fovea. OSI values with aberrations corrected up to 2nd, 5th and 8th Zernike order were calculated respectively, from the DP PSF images of the four subjects. The experimental results were consistent with the simulation, suggesting that it is necessary to compensate for the higher-order ocular aberrations for accurate intraocular scatter estimation.

SU‐F‐J‐211: Scatter Correction for Clinical Cone‐Beam CT System Using An Optimized Stationary Beam Blocker with a Single Scan

Purpose:X‐ray scatter photons result in significant image quality degradation of cone‐beam CT (CBCT). Measurement based algorithms using beam blocker directly acquire the scatter samples and achieve significant improvement on the quality of CBCT image. Within existing algorithms, single‐scan and stationary beam blocker proposed previously is promising due to its simplicity and practicability. Although demonstrated effectively on tabletop system, the blocker fails to estimate the scatter distribution on clinical CBCT system mainly due to the gantry wobble. In addition, the uniform distributed blocker strips in our previous design results in primary data loss in the CBCT system and leads to the image artifacts due to data insufficiency.Methods:We investigate the motion behavior of the beam blocker in each projection and design an optimized non‐uniform blocker strip distribution which accounts for the data insufficiency issue. An accurate scatter estimation is then achieved from the wobble modeling. Blocker wobble curve is estimated using threshold‐based segmentation algorithms in each projection. In the blocker design optimization, the quality of final image is quantified using the number of the primary data loss voxels and the mesh adaptive direct search algorithm is applied to minimize the objective function. Scatter‐corrected CT images are obtained using the optimized blocker.Results:The proposed method is evaluated using Catphan@504 phantom and a head patient. On the Catphan©504, our approach reduces the average CT number error from 115 Hounsfield unit (HU) to 11 HU in the selected regions of interest, and improves the image contrast by a factor of 1.45 in the high‐contrast regions. On the head patient, the CT number error is reduced from 97 HU to 6 HU in the soft tissue region and image spatial non‐uniformity is decreased from 27% to 5% after correction.Conclusion:The proposed optimized blocker design is practical and attractive for CBCT guided radiation therapy.This work is supported by grants from Guangdong Innovative Research Team Program of China (Grant No. 2011S013), National 863 Programs of China (Grant Nos. 2012AA02A604 and 2015AA043203), the National High‐tech R&amp;D Program for Young Scientists by the Ministry of Science and Technology of China (Grant No. 2015AA020917)

Scatter and crosstalk corrections for 99mTc/123I dual‐radionuclide imaging using a CZT SPECT system with pinhole collimators

The energy spectrum for a cadmium zinc telluride (CZT) detector has a low energy tail due to incomplete charge collection and intercrystal scattering. Due to these solid-state detector effects, scatter would be overestimated if the conventional triple-energy window (TEW) method is used for scatter and crosstalk corrections in CZT-based imaging systems. The objective of this work is to develop a scatter and crosstalk correction method for (99m)Tc/(123)I dual-radionuclide imaging for a CZT-based dedicated cardiac SPECT system with pinhole collimators (GE Discovery NM 530c/570c). A tailing model was developed to account for the low energy tail effects of the CZT detector. The parameters of the model were obtained using (99m)Tc and (123)I point source measurements. A scatter model was defined to characterize the relationship between down-scatter and self-scatter projections. The parameters for this model were obtained from Monte Carlo simulation using SIMIND. The tailing and scatter models were further incorporated into a projection count model, and the primary and self-scatter projections of each radionuclide were determined with a maximum likelihood expectation maximization (MLEM) iterative estimation approach. The extracted scatter and crosstalk projections were then incorporated into MLEM image reconstruction as an additive term in forward projection to obtain scatter- and crosstalk-corrected images. The proposed method was validated using Monte Carlo simulation, line source experiment, anthropomorphic torso phantom studies, and patient studies. The performance of the proposed method was also compared to that obtained with the conventional TEW method. Monte Carlo simulations and line source experiment demonstrated that the TEW method overestimated scatter while their proposed method provided more accurate scatter estimation by considering the low energy tail effect. In the phantom study, improved defect contrasts were observed with both correction methods compared to no correction, especially for the images of (99m)Tc in dual-radionuclide imaging where there is heavy contamination from (123)I. In this case, the nontransmural defect contrast was improved from 0.39 to 0.47 with the TEW method and to 0.51 with their proposed method and the transmural defect contrast was improved from 0.62 to 0.74 with the TEW method and to 0.73 with their proposed method. In the patient study, the proposed method provided higher myocardium-to-blood pool contrast than that of the TEW method. Similar to the phantom experiment, the improvement was the most substantial for the images of (99m)Tc in dual-radionuclide imaging. In this case, the myocardium-to-blood pool ratio was improved from 7.0 to 38.3 with the TEW method and to 63.6 with their proposed method. Compared to the TEW method, the proposed method also provided higher count levels in the reconstructed images in both phantom and patient studies, indicating reduced overestimation of scatter. Using the proposed method, consistent reconstruction results were obtained for both single-radionuclide data with scatter correction and dual-radionuclide data with scatter and crosstalk corrections, in both phantom and human studies. The authors demonstrate that the TEW method leads to overestimation in scatter and crosstalk for the CZT-based imaging system while the proposed scatter and crosstalk correction method can provide more accurate self-scatter and down-scatter estimations for quantitative single-radionuclide and dual-radionuclide imaging.

TU-CD-207-09: Analysis of the 3-D Shape of Patients’ Breast for Breast Imaging and Surgery Planning

Purpose: Develop a method to accurately capture the 3-D shape of patients’ external breast surface before and during breast compression for mammography/tomosynthesis. Methods: During this IRB-approved, HIPAA-compliant study, 50 women were recruited to undergo 3-D breast surface imaging during breast compression and imaging for the cranio-caudal (CC) view on a digital mammography/breast tomosynthesis system. Digital projectors and cameras mounted on tripods were used to acquire 3-D surface images of the breast, in three conditions: (a) positioned on the support paddle before compression, (b) during compression by the compression paddle and (c) the anterior-posterior view with the breast in its natural, unsupported position. The breast was compressed to standard full compression with the compression paddle and a tomosynthesis image was acquired simultaneously with the 3-D surface. The 3-D surface curvature and deformation with respect to the uncompressed surface was analyzed using contours. The 3-D surfaces were voxelized to capture breast shape in a format that can be manipulated for further analysis. Results: A protocol was developed to accurately capture the 3-D shape of patients’ breast before and during compression for mammography. Using a pair of 3-D scanners, the 50 patient breasts were scanned in three conditions, resulting in accurate representations of the breast surfaces. The surfaces were post processed, analyzed using contours and voxelized, with 1 mm3 voxels, converting the breast shape into a format that can be easily modified as required. Conclusion: Accurate characterization of the breast curvature and shape for the generation of 3-D models is possible. These models can be used for various applications such as improving breast dosimetry, accurate scatter estimation, conducting virtual clinical trials and validating compression algorithms. Ioannis Sechopoulos is consultant for Fuji Medical Systems USA.

SU‐D‐12A‐07: Optimization of a Moving Blocker System for Cone‐Beam Computed Tomography Scatter Correction

Purpose:A moving blocker based strategy has shown promising results for scatter correction in cone‐beam computed tomography (CBCT). Different parameters of the system design affect its performance in scatter estimation and image reconstruction accuracy. The goal of this work is to optimize the geometric design of the moving block system.Methods:In the moving blocker system, a blocker consisting of lead strips is inserted between the x‐ray source and imaging object and moving back and forth along rotation axis during CBCT acquisition. CT image of an anthropomorphic pelvic phantom was used in the simulation study. Scatter signal was simulated by Monte Carlo calculation with various combinations of the lead strip width and the gap between neighboring lead strips, ranging from 4 mm to 80 mm (projected at the detector plane). Scatter signal in the unblocked region was estimated by cubic B‐spline interpolation from the blocked region. Scatter estimation accuracy was quantified as relative root mean squared error by comparing the interpolated scatter to the Monte Carlo simulated scatter. CBCT was reconstructed by total variation minimization from the unblocked region, under various combinations of the lead strip width and gap. Reconstruction accuracy in each condition is quantified by CT number error as comparing to a CBCT reconstructed from unblocked full projection data.Results:Scatter estimation error varied from 0.5% to 2.6% as the lead strip width and the gap varied from 4mm to 80mm. CT number error in the reconstructed CBCT images varied from 12 to 44. Highest reconstruction accuracy is achieved when the blocker lead strip width is 8 mm and the gap is 48 mm.Conclusions:Accurate scatter estimation can be achieved in large range of combinations of lead strip width and gap. However, image reconstruction accuracy is greatly affected by the geometry design of the blocker.

SU-E-I-04: An Analytical Model for Fast Computation of Scatter Estimation in KV Cone-Beam CT Images

Purpose: To develop an analytical model for fast and accurate scatter estimation in kV cone‐beam CT images. Methods: With a point source of 100 keV x‐rays we used the Klein‐Nishina (KN) formula for Compton scattering differential cross‐section and integrated the point scatter along an interaction line of tissue‐equivalent medium to obtain a beam‐scatter‐kernel (BSK). We limited our derivation to the case of small imaging field and small scattering angles. The Taylor series of the integrand was approximated by terms up to the 4th order. The integral was then simplified to an analytical form. To calculate attenuation in the medium for scattered photons, we assumed all the scattered photons from a BSK originate from the beam center and have the same energy as the primary photons. We then performed robust calculations using the point scatter theorem for a cubic water phantom (10×10×10 cm3) and treated the results as theoretical expectations for inter‐comparison. Results: Compared to the point‐kernel method, BSK calculations show a +10% systematic difference for either a perpendicular or a tilted beam for the absolute value of scatter fluence. However, the largest difference on the relative scatter distribution is as low as 0.4% and 2.5% for perpendicular and tilted beams, respectively. The largest relative difference for the whole phantom is about ‐ 1.8%. In computation time, our integrated scatter model is faster by over 2 orders than the point kernel method. Conclusion: Our kV scatter model shows a very high computational efficiency in scatter estimation. The expected behavior of scattering distribution is observed and well estimated. We are validating more thoroughly for small imaging fields (∼10×10cm2) and the model is expected to have great potential for imaging small objects and with interior reconstruction algorithms. Future work will extend the frame for multiple photon energies and tissue non‐homogeneity.

TU-C-214-09: Single-Scan Scatter Correction for Volumetric CT Using a Stationary Beam Blocker: Phantom Studies

Purpose: Although volumetric CT (VCT) is widely used nowadays, its imaging performance is greatly hindered by the inferior image quality mainly owing to large x-ray scatter signals, which lead to CT number inaccuracy and image contrast loss. Among existing algorithms, measurement-based scatter correction methods efficiently obtain accurate scatter estimation without prior knowledge on the object. However, with a beam blocker in the field to attenuate primary, it is generally believed that the acquired data in scatter measurement projections are not complete for an accurate reconstruction. Thus an extra scan or moving the blocker during the scan is typically required. In this work, we propose a new measurement- based scatter correction algorithm without primary compensation. An accurate reconstruction is achieved with one single scan and a stationary x- ray beam blocker, two seemingly incompatible features. Methods: The blocked areas are distributed over the projection where primary signals are redundant using a new beam blocker with a “crossing finger” shape. Scatter is then accurately estimated by interpolation and scatter-corrected CT images are obtained using an FDK-based reconstruction. Three blockers with different strip gaps are manufactured for optimal strip gap selection. Results: The proposed method is evaluated using two phantom studies on our tabletop CBCT system. On the Catphan©600 phantom, our approach reduces the reconstruction error from 207 to 9 Hounsfield unit (HU) in the selected ROI, and improves the image contrast by a factor of 2 in the high- contrast regions. On an anthropomorphic head phantom, the reconstruction error is reduced from 154 to 3 HU in the soft-tissue region. A blocker with a strip gap of 20mm achieves optimal image quality. Conclusions: Our method inherits the main advantages of measurement-based methods while avoiding their shortcomings. It has the potential to become a practical scatter correction solution widely implementable on different VCT systems.

Improvements on the linear discrimination technique with application to face recognition

In this paper, new improvements for the linear discrimination technique are proposed. These improvements include effective solutions for the small sample size problem, the selection of appropriate principal component and more accurate within-class scatter estimation for the Fisher criterion. The effectiveness of our approach is proved by experimental results on the Yale face database.

Measurement of scatter fractions in clinical bedside radiography.

The authors present measurements of scatter fraction (SF), the ratio of scattered to total imaged photons, from clinical bedside radiographs of 102 patients. These measurements were obtained by using a new posterior beam-stop technique that does not alter the diagnostic image but that simultaneously provides SF measurements at 224 locations in the image. The SF values in the lung were found to be consistent with previous measurements, while the SF values in the mediastinal and retrocardiac areas were larger than previously reported. SFs in diseased lung were significantly larger than SFs in normal lung. The range of SF values was large for all anatomic locations. For applications in which accurate scatter estimation is required, this wide range of values suggests that SFs should be measured in each individual image.