Sinogram Data Research Articles

CASToR: a generic data organization and processing code framework for multi-modal and multi-dimensional tomographic reconstruction

In tomographic medical imaging (PET, SPECT, CT), differences in data acquisition and organization are a major hurdle for the development of tomographic reconstruction software. The implementation of a given reconstruction algorithm is usually limited to a specific set of conditions, depending on the modality, the purpose of the study, the input data, or on the characteristics of the reconstruction algorithm itself. It causes restricted or limited use of algorithms, differences in implementation, code duplication, impractical code development, and difficulties for comparing different methods. This work attempts to address these issues by proposing a unified and generic code framework for formatting, processing and reconstructing acquired multi-modal and multi-dimensional data.The proposed iterative framework processes in the same way elements from list-mode (i.e. events) and histogrammed (i.e. sinogram or other bins) data sets. Each element is processed separately, which opens the way for highly parallel execution. A unique iterative algorithm engine makes use of generic core components corresponding to the main parts of the reconstruction process. Features that are specific to different modalities and algorithms are embedded into specific components inheriting from the generic abstract components. Temporal dimensions are taken into account in the core architecture.The framework is implemented in an open-source C++ parallel platform, called CASToR (customizable and advanced software for tomographic reconstruction). Performance assessments show that the time loss due to genericity remains acceptable, being one order of magnitude slower compared to a manufacturer’s software optimized for computational efficiency for a given system geometry. Specific optimizations were made possible by the underlying data set organization and processing and allowed for an average speed-up factor ranging from 1.54 to 3.07 when compared to more conventional implementations. Using parallel programming, an almost linear speed-up increase (factor of 0.85 times number of cores) was obtained in a realistic clinical PET setting. In conclusion, the proposed framework offers a substantial flexibility for the integration of new reconstruction algorithms while maintaining computation efficiency.

Correction of the projection center of rotation based on the sinogram using translation matching method

The accurate position of the center of rotation (COR) is a key factor to ensure the quality of computed tomography (CT) reconstructed images. The classic cross-correlation matching algorithm can not satisfy the requirements of high-quality CT imaging when the projection angle is 0 and 180°, and thus needs to be improved and innovated. In this study, considering the symmetric characteristic of the 0° and flipped 180° projection data in sinogram, a novel COR correction algorithm based on the translation and match of the 0° and 180° projection data was proposed. The OTSU method was applied to reduce noise on the background, and the minimum offset of COR was quantified using the L1-norm, and then a precise COR was obtained for the image correction and reconstruction. The Sheep-Logan simulation model with random gradients and Gaussian noise and the real male SD rats samples which contained the heterogenous tooth image and the homogenous liver image, were adopted to verify the performance of the new algorithm and the cross-correlation matching algorithm. The results show that the proposed algorithm has better robustness and higher accuracy of the correction (when the sampled data is from 10% to 50% of the full projection data, the COR value can still be measured accurately using the proposed algorithm) with less computational burden compared with the cross-correlation matching algorithm, and it is able to significantly improve the quality of the reconstructed images.

Enhancing the Image Quality via Transferred Deep Residual Learning of Coarse PET Sinograms.

Increasing the image quality of positron emission tomography (PET) is an essential topic in the PET community. For instance, thin-pixelated crystals have been used to provide high spatial resolution images but at the cost of sensitivity and manufacture expense. In this paper, we proposed an approach to enhance the PET image resolution and noise property for PET scanners with large pixelated crystals. To address the problem of coarse blurred sinograms with large parallax errors associated with large crystals, we developed a data-driven, single-image super-resolution (SISR) method for sinograms, based on the novel deep residual convolutional neural network (CNN). Unlike the CNN-based SISR on natural images, periodically padded sinogram data and dedicated network architecture were used to make it more efficient for PET imaging. Moreover, we included the transfer learning scheme in the approach to process cases with poor labeling and small training data set. The approach was validated via analytically simulated data (with and without noise), Monte Carlo simulated data, and pre-clinical data. Using the proposed method, we could achieve comparable image resolution and better noise property with large crystals of bin sizes of thin crystals with a bin size from to . Our approach uses external PET data as the prior knowledge for training and does not require additional information during inference. Meanwhile, the method can be added into the normal PET imaging framework seamlessly, thus potentially finds its application in designing low-cost high-performance PET systems.

Spatio-energetic cross-talk in photon counting detectors: Numerical detector model (PcTK) and workflow for CT image quality assessment.

The interpixel cross-talk of energy-sensitive photon counting x-ray detectors (PCDs) has been studied and an analytical model (version 2.1) has been developed for double-counting between neighboring pixels due to charge sharing and K-shell fluorescence x-ray emission followed by its reabsorption (Taguchi K, etal., Medical Physics 2016;43(12):6386-6404). While the model version 2.1 simulated the spectral degradation well, it had the following problems that has been found to be significant recently: (1) The spectrum is inaccurate with smaller pixel sizes; (2) the charge cloud sizemust be smaller than the pixel size; (3) the model underestimates the spectrum/counts for 10-40keV; and (4) the model version 2.1 cannot handlen-tuple-counting withn>2 (i.e., triple-counting or higher). These problems are inherent to the design of the model version 2.1; therefore, we developed a new model and addressed these problems in this study. We propose a new PCD cross-talk model (version 3.2; Pc TK for "photon counting toolkit") that is based on a completely different design concept from the previous version. It uses a numerical approach and starts with a 2-D model of charge sharing (as opposed to an analytical approach and a 1-D model with version 2.1) and addresses all of the four problems. The model takes the following factors into account: (1) shift-variant electron density of the charge cloud (Gaussian-distributed), (2) detection efficiency, (3) interactions between photons and PCDs via photoelectric effect, and (4) electronic noise. Correlated noisy PCD data can be generated using either a multivariate normal random number generator or a Poisson random number generator. The effect of the two parameters, the effective charge cloud diameter (d0 ) and pixel size (dpix ), was studied and results were compared with Monte Carlo simulations and the previous model version 2.1. Finally, a script for the workflow for CT image quality assessment has been developed, which started with a few material density images, generated material-specific sinogram (line integrals) data, noisy PCD data with spectral distortion using the model version 3.2, and reconstructed PCD- CT images for four energy windows. The model version 3.2 addressed all of the four problems listed above. The spectra withdpix =56-113μm agreed with that of Medipix3 detector withdpix =55-110μm without charge summing mode qualitatively. The counts for 10-40keV were larger than the previous model (version 2.1) and agreed with MC simulations very well (root-mean-square difference values with model version 3.2 were decreased to 16%-67% of the values with version 2.1). There were many non-zero off-diagonal elements withn-tuple-counting withn>2 in the normalized covariance matrix of 3×3 neighboring pixels. Reconstructed images showed biases and artifacts attributed to the spectral distortion due to the charge sharing and fluorescence x rays. We have developed a new PCD model for spatio-energetic cross-talk and correlation between PCD pixels. The workflow demonstrated the utility of the model for general or task-specific image quality assessments for the PCD- CT.Note: The program (Pc TK) and the workflow scripts have been made available to academic researchers. Interested readers should visit the website (pctk.jhu.edu) or contact the corresponding author.

Iterative reconstruction for low dose dual energy CT using information-divergence constrained spectral redundancy information.

Dual energy computed tomography (DECT) can improve the capability of differentiating different materials compared with conventional CT. However, due to non-negligible radiation exposure to patients, dose reduction has recently become a critical concern in CT imaging field. In this work, to reduce noise at the same time maintain DECT images quality, we present an iterative reconstruction algorithm for low-dose DECT images where in the objective function of the algorithm consists of a data-fidelity term and a regularization term. The former term is based on alpha-divergence to describe the statistical distribution of the DE sinogram data. And the latter term is based on the redundant information to reflect the prior information of the desired DECT images. For simplicity, the presented algorithm is termed as "AlphaD-aviNLM". To minimize the associative objective function, a modified proximal forward-backward splitting algorithm is proposed. Digital phantom, physical phantom, and patient data were utilized to validate and evaluate the presented AlphaD-aviNLM algorithm. The experimental results characterize the performance of the presented AlphaD-aviNLM algorithm. Speficically, in the digital phantom study, the presented AlphaD-aviNLM algorithm performs better than the PWLS-TV, PWLS-aviNLM, and AlphaD-TV with more than 49%, 34%, and 40% gains for the RMSE metric, 1.3%, 0.4%, and 0.7% gains for the FSIM metric and 13%, 8%, and 11% gains for the PSNR metric. In the physical phantom study, the presented AlphaD-aviNLM algorithm performs better than the PWLS-TV, PWLS-aviNLM, and AlphaD-TV with more than 0.55%, 0.07%, and 0.16% gains for the FSIM metric.

Reduction of Metal Artifact in Dental Computed Tomography by Homomorphic Wavelet Filtering in Sinogram Domain

In X-ray computed tomography (CT), existence of metallic implants in the subject may corrupt images and produce dark and bright streaking artifacts. In this paper a new method for reducing metal artifact from dental X-ray CT images is introduced. In the proposed method, the Radon transform is used in order to project the CT data into the sinogram domain. The sinogram of data can be decomposed into its illumination and reflectance components by using the homomorphic wavelet filtering. The investigation of the CT images shows that the degradations caused by metallic artifacts appear mainly in the illumination component. Therefore, in our approach the corrupted illumination component is restored by using the apriori information driven from the previous artifact-free sections. The results show that the metal artifacts are considerably reduced without eliminating the important details of the CT images. The proposed method is also compared with other existing methods on a set of dental CT images. Comparisons show the superiority of the proposed method over other existing methods.

Comparison of TOF-PET and Bremsstrahlung SPECT Images of Yttrium-90: A Monte Carlo Simulation Study.

Objective(s):Yttrium-90 (90Y) is a beta particle nuclide used in targeted radionuclide therapy which is available to both single-photon emission computed tomography (SPECT) and time-of-flight (TOF) positron emission tomography (PET) imaging. The purpose of this study was to assess the image quality of PET and Bremsstrahlung SPECT by simulating PET and SPECT images of 90Y using Monte Carlo simulation codes under the same conditions and to compare them.Methods:In-house Monte Carlo codes, MCEP-PET and MCEP-SPECT, were employed to simulate images. The phantom was a torso-shaped phantom containing six hot spheres of various sizes. The background concentrations of 90Y were set to 50, 100, 150, and 200 kBq/mL, and the concentrations of the hot spheres were 10, 20, and 40 times of those of the background concentrations. The acquisition time was set to 30 min, and the simulated sinogram data were reconstructed using the ordered subset expectation maximization method. The contrast recovery coefficient (CRC) and contrast-to-noise ratio (CNR) were employed to evaluate the image qualities.Results:The CRC values of SPECT images were less than 40%, while those of PET images were more than 40% when the hot sphere was larger than 20 mm in diameter. The CNR values of PET images of hot spheres of diameter smaller than 20 mm were larger than those of SPECT images. The CNR values mostly exceeded 4, which is a criterion to evaluate the discernibility of hot areas. In the case of SPECT, hot spheres of diameter smaller than 20 mm were not discernable. On the contrary, the CNR values of PET images decreased to the level of SPECT, in the case of low concentration.Conclusion:In almost all the cases examined in this investigation, the quantitative indexes of TOF-PET 90Y images were better than those of Bremsstrahlung SPECT images. However, the superiority of PET image became critical in the case of low activity concentrations.

Parametric PET Image Reconstruction via Regional Spatial Bases and Pharmacokinetic Time Activity Model

It is known that the process of reconstruction of a Positron Emission Tomography (PET) image from sinogram data is very sensitive to measurement noises; it is still an important research topic to reconstruct PET images with high signal-to-noise ratios. In this paper, we propose a new reconstruction method for a temporal series of PET images from a temporal series of sinogram data. In the proposed method, PET images are reconstructed by minimizing the Kullback–Leibler divergence between the observed sinogram data and sinogram data derived from a parametric model of PET images. The contributions of the proposition include the following: (1) regions of targets in images are explicitly expressed using a set of spatial bases in order to ignore the noises in the background; (2) a parametric time activity model of PET images is explicitly introduced as a constraint; and (3) an algorithm for solving the optimization problem is clearly described. To demonstrate the advantages of the proposed method, quantitative evaluations are performed using both synthetic and clinical data of human brains.

Spiral scanning X-ray fluorescence computed tomography.

Scanning X-ray fluorescence tomography was once considered impractical due to prohibitive measurement time requirements but is now common for investigating metal distributions within small systems. A recent look-ahead to the possibilities of 4th-generation synchrotron light sources [J. Synchrotron. Radiat. 21, 1031 (2014)] raised the possibility of a spiral-scanning measurement scheme where motion overheads are almost completely eliminated. Here we demonstrate the spiral scanning measurement and use Fourier ring correlation analysis to interrogate sources of resolution degradation. We develop an extension to the Fourier ring correlation formalism that enables direct determination of resolution from the measured sinogram data, greatly enhancing its power as a diagnostic tool for computed tomography.

Compression of CT sinogram data by decimation in the view direction.

In clinical computed tomography (CT), the image data is acquired during continuous rotation. If the time during which the signal is integrated (the frame time) is too long, the data is blurred in the view direction (i.e., azimuthal blur). This can be overcome by having a high angular sampling rate, but for systems with limited bandwidth, the increased amount of data can be a problem. In this paper, we evaluate the benefit of maintaining a high angular sampling rate on the CT gantry and performing a decimation (digital low-pass filtration followed by a downsampling) in the view direction before the bottleneck of the data transfer chain. A theoretical evaluation of the effects of the decimation is presented and the implementation of the digital filter is discussed. The compression scheme is evaluated on image data of a CATPHAN® 504 phantom and a human skull phantom. It is shown that digital decimation can be used to compress data before read-out with more remaining data fidelity compared to having longer frame times. Specifically, the method is shown to preserve the detail in the reconstruction of the CATPHAN resolution patterns and the human skull phantom. It is also demonstrated that the method can be used to prevent aliasing artifacts. Decimation in the view direction is presented as an alternative to increasing the frame time for CT systems with limited bandwidth of the data read-out. The method can be used to eitherremove aliasing artifacts or preserve spatial resolution. The proposed compression scheme can be implemented on the CT gantry and thus reduce the bandwidth requirements on the data transfer.

Subsampling and inpainting approaches for electron tomography

With the aim of addressing the issue of sample damage during electron tomography data acquisition, we propose a number of new reconstruction strategies based on subsampling (which uses only a subset of a full image) and inpainting (recovery of a full image from subsampled one). We point out that the total-variation (TV) inpainting model commonly used to inpaint subsampled images may be inappropriate for 2D projection images of typical TEM specimens. Thus, we propose higher-order TV (HOTV) inpainting, which accommodates the fact that projection images may be inherently smooth, as a more suitable image inpainting scheme. We also describe how the HOTV method can be extended to 3D, a scheme which makes use of both image data and sinogram data. Additionally, we propose gradient subsampling as a more efficient scheme than random subsampling. We make a rigorous comparison of our proposed new reconstruction schemes with existing ones. The new schemes are demonstrated to perform better than or as well as existing schemes, and we show that they outperform existing schemes at low subsampling rates.

Iterative reconstruction for sparse-view x-ray CT using alpha-divergence constrained total generalized variation minimization.

Accurate statistical model of the measured projection data is essential for computed tomography (CT) image reconstruction. The transmission data can be described by a compound Poisson distribution upon an electronic noise background. However, such a statistical distribution is numerically intractable for image reconstruction. Although the sinogram data is easily manipulated, it lacks a statistical description for image reconstruction. To address this problem, we present an alpha-divergence constrained total generalized variation (AD-TGV) method for sparse-view x-ray CT image reconstruction. The AD-TGV method is formulated as an optimization problem, which balances the alpha-divergence (AD) fidelity and total generalized variation (TGV) regularization in one framework. The alpha-divergence is used to measure the discrepancy between the measured and estimated projection data. The TGV regularization can effectively eliminate the staircase and patchy artifacts which is often observed in total variation (TV) regularization. A modified proximal forward-backward splitting algorithm was proposed to minimize the associated objective function. Qualitative and quantitative evaluations were carried out on both phantom and patient data. Compared with the original TV-based method, the evaluations clearly demonstrate that the AD-TGV method achieves higher accuracy and lower noise, while preserving structural details. The experimental results show that the presented AD-TGV method can achieve more gains over the AD-TV method in preserving structural details and suppressing image noise and undesired patchy artifacts. The authors can draw the conclusion that the presented AD-TGV method is potential for radiation dose reduction by lowering the milliampere-seconds (mAs) and/or reducing the number of projection views.

Measured PET Data Characterization with the Negative Binomial Distribution Model

Accurate statistical model of PET measurements is a prerequisite for a correct image reconstruction when using statistical image reconstruction algorithms, or when pre-filtering operations must be performed. Although radioactive decay follows a Poisson distribution, deviation from Poisson statistics occurs on projection data prior to reconstruction due to physical effects, measurement errors, correction of scatter and random coincidences. Modelling projection data can aid in understanding the statistical nature of the data in order to develop efficient processing methods and to reduce noise. This paper outlines the statistical behaviour of measured emission data evaluating the goodness of fit of the negative binomial (NB) distribution model to PET data for a wide range of emission activity values. An NB distribution model is characterized by the mean of the data and the dispersion parameter α that describes the deviation from Poisson statistics. Monte Carlo simulations were performed to evaluate: (a) the performances of the dispersion parameter α estimator, (b) the goodness of fit of the NB model for a wide range of activity values. We focused on the effect produced by correction for random and scatter events in the projection (sinogram) domain, due to their importance in quantitative analysis of PET data. The analysis developed herein allowed us to assess the accuracy of the NB distribution model to fit corrected sinogram data, and to evaluate the sensitivity of the dispersion parameter α to quantify deviation from Poisson statistics. By the sinogram ROI-based analysis, it was demonstrated that deviation on the measured data from Poisson statistics can be quantitatively characterized by the dispersion parameter α, in any noise conditions and corrections.

Refinement of clinical X-ray computed tomography (CT) scans containing metal implants

X-ray computed tomography (CT) data contains artefacts from many sources, with sufficient prominence to affect diagnostic utility when metal is present in the scans. These artefacts can be reduced, usually by the removal and in-filling of any sinogram data which has been affected by metal, and several such techniques have been proposed. Most of them are prone to introducing new artefacts into the CT data or may take a long time to correct the data. It is the purpose of this paper to introduce a new technique which is fast, yet can effectively remove most artefacts without introducing significant new ones. The new metal artefact reduction technique (RMAR) consists of an iterative refinement of the CT data by alternately forward- and back-projecting the part of the reconstruction near to metal. The forward-projection is corrected by making use of a prior derived from the reconstructed data which is independently estimated for each projection angle, and smoothed using a newly developed Bitonic filter. The new technique is compared with previously published (LI, NMAR, MDT) and commercial (O-MAR, IMAR) alternatives, quantitatively on phantom data, and qualitatively on a selection of clinical scans, mostly of the hip. The phantom data is from two recently published studies, enabling direct comparison with previous results. The results show an increased reduction of artefacts on the four phantom data sets tested. On two of the phantom data sets, RMAR is significantly better (p<0.001) than all other techniques; on one it is as good as any other technique, and on the last it is only beaten by the Metal Deletion Technique (p<0.001), which is significantly slower. On the clinical data sets, RMAR shows visually similar performance to MDT, with better preservation of bony features close to metal implants, but perhaps slightly reduced homogeneity in the far field. For typical CT data, RMAR can correct each image in 3–8s, which is more than one hundred times faster than MDT. The new technique is demonstrated to have performance at least as good as MDT, with both out-performing other approaches. However, it is much faster then the latter technique, and shows better preservation of data very close to metal.

Direct Reconstruction of CT-based Attenuation Correction Images for PET with Cluster-Based Penalties.

Extremely low-dose CT acquisitions used for PET attenuation correction have high levels of noise and potential bias artifacts due to photon starvation. This work explores the use of a priori knowledge for iterative image reconstruction of the CT-based attenuation map. We investigate a maximum a posteriori framework with cluster-based multinomial penalty for direct iterative coordinate decent (dICD) reconstruction of the PET attenuation map. The objective function for direct iterative attenuation map reconstruction used a Poisson log-likelihood data fit term and evaluated two image penalty terms of spatial and mixture distributions. The spatial regularization is based on a quadratic penalty. For the mixture penalty, we assumed that the attenuation map may consist of four material clusters: air+background, lung, soft tissue, and bone. Using simulated noisy sinogram data, dICD reconstruction was performed with different strengths of the spatial and mixture penalties. The combined spatial and mixture penalties reduced the RMSE by roughly 2 times compared to a weighted least square and filtered backprojection reconstruction of CT images. The combined spatial and mixture penalties resulted in only slightly lower RMSE compared to a spatial quadratic penalty alone. For direct PET attenuation map reconstruction from ultra-low dose CT acquisitions, the combination of spatial and mixture penalties offers regularization of both variance and bias and is a potential method to reconstruct attenuation maps with negligible patient dose. The presented results, using a best-case histogram suggest that the mixture penalty does not offer a substantive benefit over conventional quadratic regularization and diminishes enthusiasm for exploring future application of the mixture penalty.

Characterization of Metal Artifacts in X‐Ray Computed Tomography

Metal streak artifacts in X‐ray computerized tomography (CT) are characterized here using the notion of the wavefront set from microlocal analysis. The metal artifacts are caused mainly from the mismatch of the forward model of the filtered back‐projection; the presence of metallic subjects in an imaging subject violates the model's assumption of the CT sinogram data being the Radon transform of an image. The increasing use of metallic implants has increased demand for the reduction of metal artifacts in the field of dental and medical radiography. However, it is a challenging issue due to the serious difficulties in analyzing the X‐ray data, which depends nonlinearly on the distribution of the metallic subject. In this paper, we, for the first time, provide a mathematical analysis to characterize the structure of metal streaking artifacts. The metal streaking artifacts are produced along the line tangent to boundaries of the metal region touching at least two different boundaries. We also found a sufficient condition for the nonexistence of the metal streaking artifacts. © 2017 Wiley Periodicals, Inc.

Variability in CT lung-nodule quantification: Effects of dose reduction and reconstruction methods on density and texture based features.

Purpose:To investigate the effects of dose level and reconstruction method on density and texture based features computed from CT lung nodules.Methods:This study had two major components. In the first component, a uniform water phantom was scanned at three dose levels and images were reconstructed using four conventional filtered backprojection (FBP) and four iterative reconstruction (IR) methods for a total of 24 different combinations of acquisition and reconstruction conditions. In the second component, raw projection (sinogram) data were obtained for 33 lung nodules from patients scanned as a part of their clinical practice, where low dose acquisitions were simulated by adding noise to sinograms acquired at clinical dose levels (a total of four dose levels) and reconstructed using one FBP kernel and two IR kernels for a total of 12 conditions. For the water phantom, spherical regions of interest (ROIs) were created at multiple locations within the water phantom on one reference image obtained at a reference condition. For the lung nodule cases, the ROI of each nodule was contoured semiautomatically (with manual editing) from images obtained at a reference condition. All ROIs were applied to their corresponding images reconstructed at different conditions. For 17 of the nodule cases, repeat contours were performed to assess repeatability. Histogram (eight features) and gray level co-occurrence matrix (GLCM) based texture features (34 features) were computed for all ROIs. For the lung nodule cases, the reference condition was selected to be 100% of clinical dose with FBP reconstruction using the B45f kernel; feature values calculated from other conditions were compared to this reference condition. A measure was introduced, which the authors refer to as Q, to assess the stability of features across different conditions, which is defined as the ratio of reproducibility (across conditions) to repeatability (across repeat contours) of each feature.Results:The water phantom results demonstrated substantial variability among feature values calculated across conditions, with the exception of histogram mean. Features calculated from lung nodules demonstrated similar results with histogram mean as the most robust feature (Q ≤ 1), having a mean and standard deviation Q of 0.37 and 0.22, respectively. Surprisingly, histogram standard deviation and variance features were also quite robust. Some GLCM features were also quite robust across conditions, namely, diff. variance, sum variance, sum average, variance, and mean. Except for histogram mean, all features have a Q of larger than one in at least one of the 3% dose level conditions.Conclusions:As expected, the histogram mean is the most robust feature in their study. The effects of acquisition and reconstruction conditions on GLCM features vary widely, though trending toward features involving summation of product between intensities and probabilities being more robust, barring a few exceptions. Overall, care should be taken into account for variation in density and texture features if a variety of dose and reconstruction conditions are used for the quantification of lung nodules in CT, otherwise changes in quantification results may be more reflective of changes due to acquisition and reconstruction conditions than in the nodule itself.

SU‐F‐R‐39: Effects of Radiation Dose Reduction On Renal Cell Carcinoma Discrimination Using Multi‐Phasic CT Imaging

Purpose:A method to discriminate different types of renal cell carcinoma (RCC) was developed using attenuation values observed in multiphasic contrast‐enhanced CT. This work evaluates the sensitivity of this RCC discrimination task at different CT radiation dose levels.Methods:We selected 5 cases of kidney lesion patients who had undergone four‐phase CT scans covering the abdomen to the lilac crest. Through an IRB‐approved study, the scans were conducted on 64‐slice CT scanners (Definition AS/Definition Flash, Siemens Healthcare) using automatic tube‐current modulation (TCM). The protocol included an initial baseline unenhanced scan, followed by three post‐contrast injection phases. CTDIvol (32 cm phantom) measured between 9 to 35 mGy for any given phase. As a preliminary study, we limited the scope to the cortico‐medullary phase—shown previously to be the most discriminative phase. A previously validated method was used to simulate a reduced dose acquisition via adding noise to raw CT sinogram data, emulating corresponding images at simulated doses of 50%, 25%, and 10%. To discriminate the lesion subtype, ROIs were placed in the most enhancing region of the lesion. The mean HU value of an ROI was extracted and used to discriminate to the worst‐case RCC subtype, ranked in the order of clear cell, papillary, chromophobe and the benign oncocytoma.Results:Two patients exhibited a change of worst case RCC subtype between original and simulated scans, at 25% and 10% doses. In one case, the worst‐case RCC subtype changed from oncocytoma to chromophobe at 10% and 25% doses, while the other case changed from oncocytoma to clear cell at 10% dose.Conclusion:Based on preliminary results from an initial cohort of 5 patients, worst‐case RCC subtypes remained constant at all simulated dose levels except for 2 patients. Further study conducted on more patients will be needed to confirm our findings.Institutional research agreement, Siemens Healthcare; Past recipient, research grant support, Siemens Healthcare; Consultant, Toshiba America Medical Systems; Consultant, Samsung Electronics; NIH Grant Support from: U01 CA181156

SU-G-IeP2-15: Virtual Insertion of Digital Kidney Stones Into Dual-Source, Dual- Energy CT Projection Data

Purpose: In order to investigate novel methods to more accurately estimate the mineral composition of kidney stones using dual energy CT, it is desirable to be able to combine digital stones of known composition with actual phantom and patient scan data. In this work, we developed and validated a method to insert digital kidney stones into projection data acquired on a dual-source, dual-energy CT system. Methods: Attenuation properties of stones of different mineral composition were computed using tabulated mass attenuation coefficients, the chemical formula for each stone type, and the effective beam energy at each evaluated tube potential. A previously developed method to insert lesions into x-ray CT projection data was extended to include simultaneous dual-energy CT projections acquired on a dual-source gantry (Siemens Somatom Flash). Digital stones were forward projected onto both detectors and the resulting projections added to the physically acquired sinogram data. To validate the accuracy of the technique, digital stones were inserted into different locations in the ACR CT accreditation phantom; low and high contrast resolution, CT number accuracy and noise properties were compared before and after stone insertion. The procedure was repeated for two dual-energy tube potential pairs in clinical use on the scanner, 80/Sn140 kV and 100/Sn140 kV, respectively. Results: The images reconstructed after the insertion of digital kidney stones were consistent with the images reconstructed from the scanner. The largest average CT number difference for the 4 insert in the CT number accuracy module of the phantom was 3 HU. Conclusion: A framework was developed and validated for the creation of digital kidney stones of known mineral composition, and their projection-domain insertion into commercial dual-source, dual-energy CT projection data. This will allow a systematic investigation of the impact of scan and reconstruction parameters on stone attenuation and dual-energy behavior under rigorously controlled conditions. Dr. McCollough receives research support from Siemens Healthcare

MO-DE-207A-03: Characterization of Potential Gains in a Novel Exponential 4DCT Reconstruction Algorithm

Purpose: To sample the breathing cycle and obtain accurate target volumes, 4DCT requires the entire field of view (FOV) to be illuminated during one breathing period. However, interplay effects between internal motion and couch pitch may adversely impact the temporal resolution, thereby blurring object edges. This work performs a characterization of exponential 4DCT reconstruction to highlight potential gains over standard cos2 weighted reconstruction algorithm. Methods: A respiratory motion platform translated several objects in the superior-inferior direction at varied breathing rates (8–20 breaths/minute) and couch pitches (0.06–0.1 A.U.) to evaluate the interplay between parameters. Ten-phase 4DCTs were acquired (0.5s rotation) and data were reconstructed with cos2 and exponential weighting factors. To isolate a metric to quantify temporal resolution (i.e. remove couch interplay effects), a small object was translated in the anterior-posterior direction. Full-width half maximum (FWHM) intensity distributions were quantified between reconstruction algorithms and a static case. 4DCT sinogram data for fifteen lung and abdomen patients were retrospectively reconstructed using cos2 and exponential weighting factors. Image subtractions were generated to elucidate intensity and boundary differences. Results: After taking the static object size into account, the FWHM of exponential weighting was 1.5±1.2 mm (range: 0.1–4.1) as compared to the FWHM of cos2 3.4±2.4 mm (range: 0.3–8.0). This translated to estimated improvements in temporal resolution of 33.5±48.2 ms (range: 0.03–170.9). Slower breathing periods, faster couch pitches, and intermediate 4DCT phases where velocity is highest showed a tendency to have the largest improvements in temporal resolution with exponential reconstruction. For patient cases, coronal views showed less blurring at object boundaries and local intensity differences near moving features. Conclusion: Exponential weighted 4DCT offers potential for improving temporal resolution in 4DCT, thus improving the image sharpness near the boundaries. Understanding the potential implications on delineation ability is an important next step of this work. The submitting institution holds research agreements with Philips Healthcare.