Patient-specific Scatter Research Articles

Movement towards patient-specific scatter correction in Lung Shunt Fraction (LSF) quantification

Movement towards patient-specific scatter correction in Lung Shunt Fraction (LSF) quantification

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.

On the dosimetric effect of heterogeneities and finite patient dimensions on 60Co HDR brachytherapy

Introduction Model based dose calculation algorithms (MBDCAs) for 192 Ir HDR brachytherapy have become clinically available to account for effects disregarded by TG-43 dosimetry (tissue heterogeneity and patient specific scatter conditions). Purpose To assess the relative benefit from introducing MBDCAs in Co-60 and Ir-192 HDR brachytherapy. Materials and methods Dual plans were prepared for 15 cases (gynaecological, esophagus, breast) using the Ir2.A85-2 and the Co0.A86 HDR sources with a TG-43 based TPS (SagiPlan®, Eckert&Ziegler BEBIG, Germany). Monte Carlo (MC) simulation dosimetry was performed for all plans using the MCNP6 code with input files prepared using the BrachyGuide software tool to parse information from DICOM RT exported plans. Differences between patient-specific (MC) and TG-43 dosimetry were compared in the form of 3D distributions and clinical plan indices and checked for statistical and clinical significance. Results were compared between Co-60 and Ir-192. Results Patient-specific dose distributions are clinically equivalent for the two isotopes. The effect of tissue heterogeneities and patient specific scatter conditions is less for Co-60 HDR treatments. A lower dose to critical organs close to the target and a small increase in the overdose volume were observed for Co-60. The choice of isotope was not found to have an impact on the prescribed dose. Conclusion Co-60 is equivalent to Ir-192 HDR brachytherapy, with similar target coverage. Besides logistical advantages owing to its longer half-life, Co-60 demonstrates reduced effects of tissue heterogeneities and patient dimensions that obviate the need for using MBDCAs. Disclosure Research supported in part by Eckert & Ziegler BEBIG.

A scatter correction method for contrast-enhanced dual-energy digital breast tomosynthesis

Contrast-enhanced dual energy digital breast tomosynthesis (CE-DE-DBT) is designed to image iodinated masses while suppressing breast anatomical background. Scatter is a problem, especially for high energy acquisition, in that it causes severe cupping artifact and iodine quantitation errors. We propose a patient specific scatter correction (SC) algorithm for CE-DE-DBT. The empirical algorithm works by interpolating scatter data outside the breast shadow into an estimate within the breast shadow. The interpolated estimate is further improved by operations that use an easily obtainable (from phantoms) table of scatter-to-primary-ratios (SPR)—a single SPR value for each breast thickness and acquisition angle. We validated our SC algorithm for two breast emulating phantoms by comparing SPR from our SC algorithm to that measured using a beam-passing pinhole array plate. The error in our SC computed SPR, averaged over acquisition angle and image location, was about 5%, with slightly worse errors for thicker phantoms. The SC projection data, reconstructed using OS-SART, showed a large degree of decupping. We also observed that SC removed the dependence of iodine quantitation on phantom thickness. We applied the SC algorithm to a CE-DE-mammographic patient image with a biopsy confirmed tumor at the breast periphery. In the image without SC, the contrast enhanced tumor was masked by the cupping artifact. With our SC, the tumor was easily visible. An interpolation-based SC was proposed by (Siewerdsen et al Med. Phys. 33 187–97) for cone-beam CT (CBCT), but our algorithm and application differ in several respects. Other relevant SC techniques include Monte-Carlo and convolution-based methods for CBCT, storage of a precomputed library of scatter maps for DBT, and patient acquisition with a beam-passing pinhole array for breast CT. Our SC algorithm can be accomplished in clinically acceptable times, requires no additional imaging hardware or extra patient dose and is easily transportable between sites.

Patient-specific scatter correction for flat-panel detector-based cone-beam CT imaging

A patient-specific scatter correction algorithm is proposed to mitigate scatter artefacts in cone-beam CT (CBCT). The approach belongs to the category of convolution-based methods in which a scatter potential function is convolved with a convolution kernel to estimate the scatter profile. A key step in this method is to determine the free parameters introduced in both scatter potential and convolution kernel using a so-called calibration process, which is to seek for the optimal parameters such that the models for both scatter potential and convolution kernel is able to optimally fit the previously known coarse estimates of scatter profiles of the image object. Both direct measurements and Monte Carlo (MC) simulations have been proposed by other investigators to achieve the aforementioned rough estimates. In the present paper, a novel method has been proposed and validated to generate the needed coarse scatter profile for parameter calibration in the convolution method. The method is based upon an image segmentation of the scatter contaminated CBCT image volume, followed by a reprojection of the segmented image volume using a given x-ray spectrum. The reprojected data is subtracted from the scatter contaminated projection data to generate a coarse estimate of the needed scatter profile used in parameter calibration. The method was qualitatively and quantitatively evaluated using numerical simulations and experimental CBCT data acquired on a clinical CBCT imaging system. Results show that the proposed algorithm can significantly reduce scatter artefacts and recover the correct CT number. Numerical simulation results show the method is patient specific, can accurately estimate the scatter, and is robust with respect to segmentation procedure. For experimental and in vivo human data, the results show the CT number can be successfully recovered and anatomical structure visibility can be significantly improved.

High-Performance Soft-Tissue Imaging in Extremity Cone-Beam CT.

Clinical performance studies of an extremity cone-beam CT (CBCT) system indicate excellent bone visualization, but point to the need for improvement of soft-tissue image quality. To this end, a rapid Monte Carlo (MC) scatter correction is proposed, and Penalized Likelihood (PL) reconstruction is evaluated for noise management. The accelerated MC scatter correction involved fast MC simulation with low number of photons implemented on a GPU (107 photons/sec), followed by Gaussian kernel smoothing in the detector plane and across projection angles. PL reconstructions were investigated for reduction of imaging dose for projections acquired at ~2 mGy. The rapid scatter estimation yielded root-mean-squared-errors of scatter projections of ~15% of peak scatter intensity for 5·106 photons/projection (runtime ~0.5 sec/projection) and 25% improvement in fat-muscle contrast in reconstructions of a cadaveric knee. PL reconstruction largely restored soft-tissue visualization at 2 mGy dose to that of 10 mGy FBP image. The combination of rapid (5-10 minutes/scan) MC-based, patient-specific scatter correction and PL reconstruction offers an important means to overcome the current limitations of extremity CBCT in soft-tissue imaging.

Patient-specific scatter correction in clinical cone beam computed tomography imaging made possible by the combination of Monte Carlo simulations and a ray tracing algorithm

Purpose. Cone beam computed tomography (CBCT) image quality is limited by scattered photons. Monte Carlo (MC) simulations provide the ability of predicting the patient-specific scatter contamination in clinical CBCT imaging. Lengthy simulations prevent MC-based scatter correction from being fully implemented in a clinical setting. This study investigates the combination of using fast MC simulations to predict scatter distributions with a ray tracing algorithm to allow calibration between simulated and clinical CBCT images. Material and methods. An EGSnrc-based user code (egs_cbct), was used to perform MC simulations of an Elekta XVI CBCT imaging system. A 60keV x-ray source was used, and air kerma scored at the detector plane. Several variance reduction techniques (VRTs) were used to increase the scatter calculation efficiency. Three patient phantoms based on CT scans were simulated, namely a brain, a thorax and a pelvis scan. A ray tracing algorithm was used to calculate the detector signal due to primary photons. A total of 288 projections were simulated, one for each thread on the computer cluster used for the investigation. Results. Scatter distributions for the brain, thorax and pelvis scan were simulated within 2% statistical uncertainty in two hours per scan. Within the same time, the ray tracing algorithm provided the primary signal for each of the projections. Thus, all the data needed for MC-based scatter correction in clinical CBCT imaging was obtained within two hours per patient, using a full simulation of the clinical CBCT geometry. Conclusions. This study shows that use of MC-based scatter corrections in CBCT imaging has a great potential to improve CBCT image quality. By use of powerful VRTs to predict scatter distributions and a ray tracing algorithm to calculate the primary signal, it is possible to obtain the necessary data for patient specific MC scatter correction within two hours per patient.

PO-0875: Patient specific scatter distributions in CBCT imaging calculated by Monte Carlo simulations

PO-0875: Patient specific scatter distributions in CBCT imaging calculated by Monte Carlo simulations

Single‐scan patient‐specific scatter correction in computed tomography using peripheral detection of scatter and compressed sensing scatter retrieval

X-ray scatter results in a significant degradation of image quality in computed tomography (CT), representing a major limitation in cone-beam CT (CBCT) and large field-of-view diagnostic scanners. In this work, a novel scatter estimation and correction technique is proposed that utilizes peripheral detection of scatter during the patient scan to simultaneously acquire image and patient-specific scatter information in a single scan, and in conjunction with a proposed compressed sensing scatter recovery technique to reconstruct and correct for the patient-specific scatter in the projection space. The method consists of the detection of patient scatter at the edges of the field of view (FOV) followed by measurement based compressed sensing recovery of the scatter through-out the projection space. In the prototype implementation, the kV x-ray source of the Varian TrueBeam OBI system was blocked at the edges of the projection FOV, and the image detector in the corresponding blocked region was used for scatter detection. The design enables image data acquisition of the projection data on the unblocked central region of and scatter data at the blocked boundary regions. For the initial scatter estimation on the central FOV, a prior consisting of a hybrid scatter model that combines the scatter interpolation method and scatter convolution model is estimated using the acquired scatter distribution on boundary region. With the hybrid scatter estimation model, compressed sensing optimization is performed to generate the scatter map by penalizing the L1 norm of the discrete cosine transform of scatter signal. The estimated scatter is subtracted from the projection data by soft-tuning, and the scatter-corrected CBCT volume is obtained by the conventional Feldkamp-Davis-Kress algorithm. Experimental studies using image quality and anthropomorphic phantoms on a Varian TrueBeam system were carried out to evaluate the performance of the proposed scheme. The scatter shading artifacts were markedly suppressed in the reconstructed images using the proposed method. On the Catphan©504 phantom, the proposed method reduced the error of CT number to 13 Hounsfield units, 10% of that without scatter correction, and increased the image contrast by a factor of 2 in high-contrast regions. On the anthropomorphic phantom, the spatial nonuniformity decreased from 10.8% to 6.8% after correction. A novel scatter correction method, enabling unobstructed acquisition of the high frequency image data and concurrent detection of the patient-specific low frequency scatter data at the edges of the FOV, is proposed and validated in this work. Relative to blocker based techniques, rather than obstructing the central portion of the FOV which degrades and limits the image reconstruction, compressed sensing is used to solve for the scatter from detection of scatter at the periphery of the FOV, enabling for the highest quality reconstruction in the central region and robust patient-specific scatter correction.

Hybrid scatter correction for CT imaging

The purpose of this study was to develop and evaluate the hybrid scatter correction algorithm (HSC) for CT imaging. Therefore, two established ways to perform scatter correction, i.e. physical scatter correction based on Monte Carlo simulations and a convolution-based scatter correction algorithm, were combined in order to perform an object-dependent, fast and accurate scatter correction. Based on a reconstructed CT volume, patient-specific scatter intensity is estimated by a coarse Monte Carlo simulation that uses a reduced amount of simulated photons in order to reduce the simulation time. To further speed up the Monte Carlo scatter estimation, scatter intensities are simulated only for a fraction of all projections. In a second step, the high noise estimate of the scatter intensity is used to calibrate the open parameters in a convolution-based algorithm which is then used to correct measured intensities for scatter. Furthermore, the scatter-corrected intensities are used in order to reconstruct a scatter-corrected CT volume data set. To evaluate the scatter reduction potential of HSC, we conducted simulations in a clinical CT geometry and measurements with a flat detector CT system. In the simulation study, HSC-corrected images were compared to scatter-free reference images. For the measurements, no scatter-free reference image was available. Therefore, we used an image corrected with a low-noise Monte Carlo simulation as a reference. The results show that the HSC can significantly reduce scatter artifacts. Compared to the reference images, the error due to scatter artifacts decreased from 100% for uncorrected images to a value below 20% for HSC-corrected images for both the clinical (simulated data) and the flat detector CT geometry (measurement). Compared to a low-noise Monte Carlo simulation, with the HSC the number of photon histories can be reduced by about a factor of 100 per projection without losing correction accuracy. Furthermore, it was sufficient to calibrate the parameters in the convolution model at an angular increment of about 20°. The reduction of the simulated photon histories together with the reduced amount of simulated Monte Carlo scatter projections decreased the total runtime of the scatter correction by about two orders of magnitude for the cases investigated here when using the HSC instead of a low-noise Monte Carlo simulation for scatter correction.

Scatter correction in cone‐beam CT via a half beam blocker technique allowing simultaneous acquisition of scatter and image information

X-ray scatter incurred to detectors degrades the quality of cone-beam computed tomography (CBCT) and represents a problem in volumetric image guided and adaptive radiation therapy. Several methods using a beam blocker for the estimation and subtraction of scatter have been proposed. However, due to missing information resulting from the obstruction of the blocker, such methods require dual scanning or dynamically moving blocker to obtain a complete volumetric image. Here, we propose a half beam blocker-based approach, in conjunction with a total variation (TV) regularized Feldkamp-Davis-Kress (FDK) algorithm, to correct scatter-induced artifacts by simultaneously acquiring image and scatter information from a single-rotation CBCT scan. A half beam blocker, comprising lead strips, is used to simultaneously acquire image data on one side of the projection data and scatter data on the other half side. One-dimensional cubic B-Spline interpolation/extrapolation is applied to derive patient specific scatter information by using the scatter distributions on strips. The estimated scatter is subtracted from the projection image acquired at the opposite view. With scatter-corrected projections where this subtraction is completed, the FDK algorithm based on a cosine weighting function is performed to reconstruct CBCT volume. To suppress the noise in the reconstructed CBCT images produced by geometric errors between two opposed projections and interpolated scatter information, total variation regularization is applied by a minimization using a steepest gradient descent optimization method. The experimental studies using Catphan504 and anthropomorphic phantoms were carried out to evaluate the performance of the proposed scheme. The scatter-induced shading artifacts were markedly suppressed in CBCT using the proposed scheme. Compared with CBCT without a blocker, the nonuniformity value was reduced from 39.3% to 3.1%. The root mean square error relative to values inside the regions of interest selected from a benchmark scatter free image was reduced from 50 to 11.3. The TV regularization also led to a better contrast-to-noise ratio. An asymmetric half beam blocker-based FDK acquisition and reconstruction technique has been established. The proposed scheme enables simultaneous detection of patient specific scatter and complete volumetric CBCT reconstruction without additional requirements such as prior images, dual scans, or moving strips.

TH-E-110-03: Simultaneous Acquisition of Scatter and Image Data in CBCT Using a Novel Half Beam Blocker Method and a FDK Based Reconstruction Algorithm

Purpose: We propose a half blocker‐based approach, in conjunction with a Feldkamp‐Davis‐Kress (FDK) algorithm, to correct scatter‐induced shading artifacts by simultaneously acquiring image and scatter information from a single‐rotation CBCT scan. Methods: A custom beam blocker, comprising of finely spaced lead strips, was mounted to the kV x‐ray source of the Varian TrueBeam OBI system in a novel manner that enables imagedata acquisition on one side of the projection data and scatter data measurement on the other half side. Using the scatter distributions on strips, interpolation and extrapolation are applied to arrive at patient specific scatter information for the CBCT scan. The estimated scatter is subtracted from the image data of projection image acquired at the opposite view. To suppress the noise level produced by geometric errors between two opposed projections and interpolated scatter information, total variation regularization is applied by the minimization procedure based on the steepest gradient decent optimization. With half regions of all projections where subtraction and regularization steps are completed, a FDK algorithm based on cosine weighting function is performed to reconstructCBCT volume. The experimental studies using Catphan504 phantom were carried out to evaluate the performance of the proposed scheme. Results: The scatter‐ induced shading artifacts were markedly suppressed in CBCT using the proposed scheme. Compared with CBCT without a blocker, the mean relative error and CT number errors in the regions of interest were significantly reduced to 2% and less than 20. Also, the proposed approach showed the contrast‐to‐noise ratio with spatial resolution comparable to imagesreconstructed with scatter‐free projections acquired from narrow x‐ ray collimation. Conclusions: A half blocker‐based FDK reconstruction algorithm has been established. The proposed scheme simultaneously reduces imaging dose at shaded regions and leads to complete volumetric information from current CBCT acquisition protocol without additional requirements such as prior images, dual scans, or moving strips. This work was supported in part by Mid‐career Researcher Program (2009‐ 0085999) through NRF grant funded by the MEST and in part by the Industrial Strategic Technology Development Program (10035495) funded by the Ministry of Knowledge Economy (MKE, Korea).

A regional convolution kernel algorithm for scatter correction in dual‐energy images: Comparison to single‐kernel algorithms

Single kernel scatter correction algorithms are based on the model that the scatter field can be predicted by convolution of the primary intensity (Iprim) with a spatially invariant scatter point-spread function (PSF). Practical limitations (Iprim unknown) suggest the substitution of the total detected intensity (Idet) for Iprim as the source image in the convolution. In regions of high scatter fraction (SF), Idet is a poor approximation of Iprim, thereby causing an overestimation of scatter originating in the region. This contributes to errors in estimating detected scatter in the mediastinum and neighboring regions. A technique using a regionally variable point-spread function that significantly reduces RMS error in estimation of the primary image as compared to the single PSF method is investigated. The regionally variable convolution method employs a larger PSF in the mediastinum and a smaller PSF in the lungs to reduce the error in estimating the scatter throughout the image. The method to allow for patient differences has also been expanded and various implementations of these methods have been compared. Results show that the dual-kernel algorithm is always more effective than an equivalent single-kernel algorithm. The dual-kernel algorithm using a predicted scatter fraction curve gives an overall RMS error in the primary of as low as 20.8% which is equivalent to 8.7% RMS error in the scatter. The dual-kernel method using a predicted scatter fraction curve approaches the accuracy of the single-kernel method using patient specific scatter measurements. Because using individual scatter measurements is a less desirable method for clinical use, we feel that the dual-kernel algorithm which uses two regions specific convolution kernels and a variable scatter fraction curve is the preferable method.