Cascaded systems analysis of noise reduction algorithms in dual-energy imaging

Optimal weighting strategies for maximizing contrast-to-noise ratio in photon counting CT images

BACKGROUND

Photon counting detectors (PCDs) with energy discrimination capabilities have the potential to generate grayscale CT images with improved contrast-to-noise ratio (CNR) through optimal weighting of their spectral measurements.

PURPOSE

This study evaluates the CNR performance of grayscale CT projections and images generated from spectral measurements of PCDs using three energy-weighting strategies: pre-log weighting, post-log weighting, and material decomposition (MD) weighting. This study provides the expressions of optimal weights and maximum achievable CNR of these energy-weighting strategies, which only require the knowledge of detected bin counts and do not require information of PCD energy responses or imaging techniques.

METHODS

We defined and solved a generalized eigenvalue problem to obtain the maximum achievable CNR in the projection domain for low-contrast tasks using three energy-weighting strategies: pre-log weighting (weighted sum of energy bin counts), post-log weighting (weighted sum of line integrals), and MD weighting (weighted sum of basis material thicknesses, which is equivalent to virtual monoenergetic images [VMIs]). These expressions only contain energy bin counts from PCD measurements. We used a realistic PCD energy response model to simulate the detected bin counts and conducted Monte Carlo simulations of different contrast tasks and phantoms to evaluate the projection- and image-domain CNR performance of these energy-weighting strategies. Additionally, the total counts method (a special case of pre-log weighting with unity weights) was included for comparison. We also conducted Gammex head and body phantom scans on an edge-on-irradiated silicon PCCT prototype to evaluate the image-domain CNR performance of these energy-weighting strategies.

RESULTS

The results show that pre-log, post-log, and MD weighting strategies generate approximately equal projection-domain maximum achievable CNR, with a difference of less than 2%, and outperform the total counts method. These three energy-weighting strategies also generate approximately equal image-domain maximum CNR when the contrast task is located at the center of a homogeneous phantom. Pre-log weighting generates the highest image-domain CNR for an off-center contrast task location or inhomogeneous phantoms while also outperforming the total counts method.

CONCLUSIONS

We derived the expression of projection-domain maximum achievable CNR using three energy-weighting strategies. Our results suggest that using pre-log weighting strategies enables fast grayscale CT image generation with high CNR from spectral PCD measurements for inhomogeneous phantoms and off-center region of interests (ROIs).

Contrast and quantum noise in single-exposure dual-energy thoracic imaging with photon-counting x-ray detectors

Objective.Photon-counting x-ray detectors (PCDs) can produce dual-energy (DE) x-ray images of lung cancer in a single x-ray exposure. It is important to understand the factors that affect contrast, noise and the contrast-to-noise ratio (CNR). This study quantifies the dependence of CNR on tube voltage, energy threshold and patient thickness in single exposure, DE, bone-suppressed thoracic imaging with PCDs, and elucidates how the fundamental processes inherent in x-ray detection by PCDs contribute to CNR degradation.Approach.We modeled the DE CNR for five theoretical PCDs, ranging from an ideal PCD that detects every primary photon in the correct energy bin while rejecting all scattered radiation to a non-ideal PCD that suffers from charge-sharing and electronic noise, and detects scatter. CNR was computed as a function of tube voltage and high energy threshold for average and larger-than-average patients. Model predictions were compared with experimental data extracted from images acquired using a cadmium telluride (CdTe) PCD with two energy bins and analog charge summing for charge-sharing suppression. The imaging phantom simulated attenuation, scatter and contrast in lung nodule imaging. We quantified CNR improvements achievable with anti-correlated noise reduction (ACNR) and measured the range of exposure rates over which pulse pile-up is negligible.Main Results.The realistic model predicted overall trends observed in the experimental data. CNR improvements with ACNR were approximately five-fold, and modeled CNR-enhancements were on average within 10% of experiment. CNR increased modestly (i.e.<20%) when increasing the tube voltage from 90 kV to 130 kV. Optimal energy thresholds ranged from 50 keV to 70 keV across all tube voltages and patient thicknesses with and without ACNR. Quantum efficiency, electronic noise, charge sharing and scatter degraded CNR by ~50%. Charge sharing and scatter had the largest effect on CNR, degrading it by ~30% and ~15% respectively. Dead-time losses were less than 5% for patient exposure rates within the range of clinical exposure rates.Significance.In this study, we (1) employed analytical and computational models to assess the impact of different factors on CNR in single-exposure DE imaging with PCDs, (2) evaluated the accuracy of these models in predicting experimental trends, (3) quantified improvements in CNR achievable through ACNR and (4) determined the range of patient exposure rates at which pulse pile-up can be considered negligible. To the best of our knowledge, this study represents the first systematic investigation of single-exposure DE imaging of lung nodules with PCDs.

Technical note: Characterization, validation, and spectral optimization of a dedicated breast CT system for contrast-enhanced imaging

BACKGROUND

The development of a new imaging modality, such as 4D dynamic contrast-enhanced dedicated breast CT (4D DCE-bCT), requires optimization of the acquisition technique, particularly within the 2D contrast-enhanced imaging modality. Given the extensive parameter space, cascade-systems analysis is commonly used for such optimization.

PURPOSE

To implement and validate a parallel-cascaded model for bCT, focusing on optimizing and characterizing system performance in the projection domain to enhance the quality of input data for image reconstruction.

METHODS

A parallel-cascaded system model of a state-of-the-art bCT system was developed and model predictions of the presampled modulation transfer function (MTF) and the normalized noise power spectrum (NNPS) were compared with empirical data collected in the projection domain. Validation was performed using the default settings of 49 kV with 1.5 mm aluminum filter and at 65 kV and 0.257 mm copper filter. A 10 mm aluminum plate was added to replicate the breast attenuation. Air kerma at the isocenter was measured at different tube current levels. Discrepancies between the measured projection domain metrics and model-predicted values were quantified using percentage error and coefficient of variation (CoV) for MTF and NNPS, respectively. The optimal filtration was for a 5 mm iodine disk detection task at 49, 55, 60, and 65 kV. The detectability index was calculated for the default aluminum filtration and for copper thicknesses ranging from 0.05 to 0.4 mm.

RESULTS

At 49 kV, MTF errors were +5.1% and -5.1% at 1 and 2 cycles/mm, respectively; NNPS CoV was 5.3% (min = 3.7%; max = 8.5%). At 65 kV, MTF errors were -0.8% and -3.2%; NNPS CoV was 13.1% (min = 11.4%; max = 16.9%). Air kerma output was linear, with 11.67 µGy/mA (R2 = 0.993) and 19.14 µGy/mA (R2 = 0.996) at 49 and 65 kV, respectively. For iodine detection, a 0.25 mm-thick copper filter at 65 kV was found optimal, outperforming the default technique by 90%.

CONCLUSION

The model accurately predicts bCT system performance, specifically in the projection domain, under varied imaging conditions, potentially contributing to the enhancement of 2D contrast-enhanced imaging in 4D DCE-bCT.

Framework for dual-energy-like chest radiography image synthesis from single-energy computed tomography based on cycle-consistent generative adversarial network

BACKGROUND

Dual-energy (DE) chest radiography (CXR) enables the selective imaging of two relevant materials, namely, soft tissue and bone structures, to better characterize various chest pathologies (i.e., lung nodule, bony lesions, etc.) and potentially improve CXR-based diagnosis. Recently, deep-learning-based image synthesis techniques have attracted considerable attention as alternatives to existing DE methods (i.e., dual-exposure-based and sandwich-detector-based methods) because software-based bone-only and bone-suppression images in CXR could be useful.

PURPOSE

The objective of this study was to develop a new framework for DE-like CXR image synthesis from single-energy computed tomography (CT) based on a cycle-consistent generative adversarial network.

METHODS

The core techniques of the proposed framework are divided into three categories: (1) data configuration from the generation of pseudo CXR from single energy CT, (2) learning of the developed network architecture using pseudo CXR and pseudo-DE imaging using a single-energy CT, and (3) inference of the trained network on real single-energy CXR. We performed a visual inspection and comparative evaluation using various metrics and introduced a figure of image quality (FIQ) to consider the effects of our framework on the spatial resolution and noise in terms of a single index through various test cases.

RESULTS

Our results indicate that the proposed framework is effective and exhibits potential synthetic imaging ability for two relevant materials: soft tissue and bone structures. Its effectiveness was validated, and its ability to overcome the limitations associated with DE imaging techniques (e.g., increase in exposure dose owing to the requirement of two acquisitions, and emphasis on noise characteristics) via an artificial intelligence technique was presented.

CONCLUSIONS

The developed framework addresses X-ray dose issues in the field of radiation imaging and enables pseudo-DE imaging with single exposure.

Signal-difference-to-noise comparison of temporal subtraction, kV-switching dual-energy and photon-counting dual-energy x-ray angiography

BACKGROUND

Dual-energy (DE) x-ray angiography with photon-counting detectors (PCDs) may enable single-exposure DE imaging of coronary vasculature.

PURPOSE

To compare the iodine signal-difference-to-noise ratio (SDNR) of single-exposure DE angiography with digital subtraction angiography (DSA) and kV-switching DE angiography for matched patient x-ray exposure.

METHODS

In a phantom study, we determined the technique parameters that maximized the iodine SDNR per root entrance air kerma for DSA, kV-switching DE angiography and single-exposure DE angiography. We measured SDNR from images of a phantom consisting of an iodine step-wedge immersed in a water tank of either 20  or 30 cm in thickness. We also imaged a phantom with simulated vessels embedded in background clutter and measured vessel SDNR. For this second phantom, we also applied anti-correlated noise reduction (ACNR) and calculated the resulting iodine SDNR. All images were acquired using a cadmium telluride PCD with two energy bins and analog charge summing for charge sharing suppression. The energy-discrimination capabilities were only used for the single-exposure DE approach. Optimized techniques were compared in terms of SDNR per root air kerma for two levels of x-ray scatter.

RESULTS

For the same patient x-ray exposure, the SDNR of single-exposure DE imaging without ACNR was 75% to 85% of that of kV-switching DE imaging (also without ACNR) and DSA, the latter two of which had nearly equal SDNR. The single-exposure DE approach required ∼50% of the tube load of the kV-switching approach to achieve the same SDNR. For matched patient air kermas, the single exposure approach required only ∼25% of the tube load of the kV-switching approach. ACNR increased SDNR by 2.4 and 3.0 for kV-switching and single-exposure DE imaging, respectively.

CONCLUSIONS

Photon-counting, single-exposure DE angiography can suppress soft tissues and provide iodine SDNR levels comparable to DSA and kV-switching DE angiography for matched patient radiation exposures. When ACNR is used to reduce DE image noise, the SDNR of single-exposure DE imaging and kV-switching DE imaging exceed that of DSA by more than a factor of two. Compared to kV-switching DE imaging, single-exposure DE imaging requires substantially lower tube loading to achieve the same SDNR.

Investigation of test methods for QC in dual-energy based contrast-enhanced digital mammography systems: II. Artefacts/uniformity, exposure time and phantom-based dosimetry

Part II of this study describes constancy tests for artefacts and image uniformity, exposure time, and phantom-based dosimetry; these are applied to four mammography systems equipped with contrast enhanced mammography (CEM) capability. Artefacts were tested using a breast phantom that simulated breast shape and thickness change at the breast edge. Image uniformity was assessed using rectangular poly(methyl)methacrylate PMMA plates at phantom thicknesses of 20, 40 and 60 mm, for the low energy (LE), high energy (HE) images and the recombined CEM image. Uniformity of signal and of the signal to noise ratio was quantified. To estimate CEM exposure times, breast simulating blocks were imaged in automatic exposure mode. The resulting x-ray technique factors were then set manually and exposure time for LE and HE images and total CEM acquisition time was measured with a multimeter. Mean glandular dose (MGD) was assessed as a function of simulated breast thickness using three different phantom compositions: (i) glandular and adipose breast tissue simulating blocks combined to give glandularity values that were typical of those in a screening population, as thickness was changed (ii) PMMA sheets combined with polyethylene blocks (iii) PMMA sheets with spacers. Image uniformity was superior for LE compared to HE images. Two systems did not generate recombined images for the uniformity test when the detector was fully covered. Acquisition time for a CEM image pair for a 60 mm thick breast equivalent phantom ranged from 3.4 to 10.3 s. Phantom composition did not have a strong influence on MGD, with differences generally smaller than 10%. MGD for the HE images was lower than for the LE images, by a factor of between 1.3 and 4.0, depending on system and simulated breast thickness. When combined with the iodine signal assessment in part I, these tests provide a comprehensive assessment of CEM system imaging performance.

Effect of different noise reduction techniques and template matching parameters on markerless tumor tracking using dual-energy imaging

PURPOSE

To evaluate the impact of various noise reduction algorithms and template matching parameters on the accuracy of markerless tumor tracking (MTT) using dual-energy (DE) imaging.

METHODS

A Varian TrueBeam linear accelerator was used to acquire a series of alternating 60 and 120 kVp images (over a 180° arc) using fast kV switching, on five early-stage lung cancer patients. Subsequently, DE logarithmic weighted subtraction was performed offline on sequential images to remove bone. Various noise reduction techniques-simple smoothing, anticorrelated noise reduction (ACNR), noise clipping (NC), and NC-ACNR-were applied to the resultant DE images. Separately, tumor templates were generated from the individual planning CT scans, and band-pass parameter settings for template matching were varied. Template tracking was performed for each combination of noise reduction techniques and templates (based on band-pass filter settings). The tracking success rate (TSR), root mean square error (RMSE), and missing frames (percent unable to track) were evaluated against the estimated ground truth, which was obtained using Bayesian inference.

RESULTS

DE-ACNR, combined with template band-pass filter settings of σ_low  = 0.4 mm and σ_high  = 1.6 mm resulted in the highest TSR (87.5%), RMSE (1.40 mm), and a reasonable amount of missing frames (3.1%). In comparison to unprocessed DE images, with optimized band-pass filter settings of σ_low  = 0.6 mm and σ_high  = 1.2 mm, the TSR, RMSE, and missing frames were 85.3%, 1.62 mm, and 2.7%, respectively. Optimized band-pass filter settings resulted in improved TSR values and a lower missing frame rate for both unprocessed DE and DE-ACNR as compared to the use previously published band-pass parameters based on single energy kV images.

CONCLUSION

Noise reduction strategies combined with the optimal selection of band-pass filter parameters can improve the accuracy and TSR of MTT for lung tumors when using DE imaging.

Adaptive dual-energy algorithm based on pre-calibrated weighting factors for chest radiography

Objective. To develop a dual-energy (DE) algorithm with spatially varying weighting factors for material selection and noise suppression. Approach. Calibration step-phantoms, with overlapping slabs of solid water and bone with different thicknesses, were used to obtain the pre-calibrated material selection and noise reduction weighting factors. The Material selection weighting factors were calculated by finding a zero of contrast-to-noise-ratio (CNR) between regions with two overlapping materials and regions of only target material, while noise suppression weighting factors were determined by maximizing signal-to-noise ratio for overlapping regions. The pre-calibrated weighting factors were fitted with low and high energy radiograph of two Rando phantoms to create maps of material selection and noise suppression weighting factors, which used with DE algorithm and anti-correlated noise reduction (ACNR) algorithm to generate DE images. Three different implementations, including two different sizes of Rando phantoms and two different orientations (oblique and anterior-posterior), were investigated. Soft-tissue and bone only images of Rando phantoms were obtained with five combinations of DE algorithms and CNR, contrast, and noise values of selected regions of interest were compared to evaluate the performance of the novel method: simple log subtraction (SLS), SLS with uniform ACNR, adaptive DE (aDE), aDE with uniform ACNR, and aDE and adaptive ACNR (aACNR). Main results. Compared to SLS, the aDE algorithm demonstrated improved image quality in all three orientations. CNR increased with better contrast for both soft-tissue and bone images. Implementation of aACNR algorithm resulted in further reduction of image noise and improvements in CNR at the cost of contrast. However, aACNR algorithm showed better contrast compared to ACNR method. Significance. A novel DE algorithm was proposed, which showed improved material selection and noise suppression as compared to the conventional DE techniques and can be easily implemented in a clinical environment for real-time DE image generation.

Detection of targets in filtered noise: whitening in space and spatial frequency

Most studies of detection in complex backgrounds have measured and modeled human performance for statistically uniform (stationary) backgrounds. However, natural and medical images have statistical properties that vary over space. We measured detection of various target shapes presented in Gaussian 1/f noise backgrounds that were statistically uniform over space, and in ones that modulated in contrast over space. We find that the pattern of human thresholds is not consistent with the ideal observer but is consistent with a suboptimal observer that performs partial whitening in spatial frequency and whitening (reliability-weighting) in space, and has a small level of intrinsic position uncertainty.

Photon-counting x-ray detectors for CT

The introduction of photon-counting detectors is expected to be the next major breakthrough in clinical x-ray computed tomography (CT). During the last decade, there has been considerable research activity in the field of photon-counting CT, in terms of both hardware development and theoretical understanding of the factors affecting image quality. In this article, we review the recent progress in this field with the intent of highlighting the relationship between detector design considerations and the resulting image quality. We discuss detector design choices such as converter material, pixel size, and readout electronics design, and then elucidate their impact on detector performance in terms of dose efficiency, spatial resolution, and energy resolution. Furthermore, we give an overview of data processing, reconstruction methods and metrics of imaging performance; outline clinical applications; and discuss potential future developments.

Adaptive noise reduction for dual-energy x-ray imaging based on spatial variations in beam attenuation

PURPOSE

The main goal of this work is to improve the previously proposed patient-specific pixel-based dual-energy (PP-DE) algorithm by developing an adaptive anti-correlated noise reduction (ACNR) method, resulting in reduced image noise.

METHODS

Theoretical models of contrast-to-noise (CNR) and signal-to-noise (SNR) ratio were developed as functions of weighting factors for DE bone ω _Bn or soft tissue ω _ST cancellation. These analytical expressions describe CNR and SNR properties of dual-energy (DE) images, obtained with both simple log subtraction (SLS) and ACNR algorithms, and allow for a direct comparison between experimental and theoretical results. The theoretical models demonstrate the importance of ACNR weighting factor (ω _A ) optimization leading to the maximization of the SNR of the final image. A step phantom was constructed, which consisted of overlapping slabs of solid water (0-30 cm) and bone-mimicking material (0-6 cm), resulting in a total of 7 × 7 regions. High-energy (HE) and low-energy (LE) images were acquired at 140 kVp and 60 kVp with a clinical ExacTrac imaging system. The CNR and SNR were obtained for the DE images as functions of ω _Bn,ST and noise reduction weighting factor ω _A for different combinations of thicknesses. Weighting factors for bone cancellation were optimized for each region of interest (ROI) by finding zeros of CNR function for DE images between soft tissue only and soft tissue plus bone regions (and vice versa for soft tissue cancellation). The weighting factor for the ACNR algorithm ω _A was then optimized by maximizing the SNR function for each ROI. HE and LE images for an anthropomorphic Rando phantom were obtained with the same acquisition parameters as for the step phantom. DE images for bone only and soft tissue only were obtained with three algorithms: SLS and PP-DE with conventional ACNR (uniform ω _A ), and PP-DE with adaptive ACNR (region-varying ω _A ). Weighting factor maps for PP-DE and adaptive ACNR methods were obtained for Rando phantom geometry (which was determined from its CT scans) by interpolation (or extrapolation) of weighting factors for the step phantom. CNR values were calculated for different regions.

RESULTS

The CNR and SNR characteristics as functions of material cancellation and noise reduction weighting factors were obtained from theoretical models and experimental data from the step phantom. This showed a good qualitative validation of the models. For the ANCR algorithm, both the theory and experiment demonstrated that the material cancellation weighting factors (ω _Bn,ST ) can be optimized independently of the noise cancellation weighting factors (ω _A ), which can be optimized by maximizing SNR. For each ROI (with different overlapping bone and soft tissue thicknesses) the weighting factors ω _Bn,ST were determined as well as corresponding optimal weighting factors ω _A for noise reduction. For the Rando phantom, CNR values for regions representing different anatomical structures (ribs, spine, and tumor) were evaluated. It was shown that the proposed adaptive ACNR further improves image quality, compared to the conventional ACNR algorithm. The improvement is maximized for regions with bones (ribs or spine), where the largest attenuation is observed.

CONCLUSION

The ACNR weighting factors are dependent on the material thicknesses due to varying beam attenuation leading to different levels of quantum noise. This was shown with the derived theoretical expressions of the CNR and SNR functions and was validated by experimental data. The adaptive ANCR DE algorithm was developed, which allows for an increase in image quality by spatially varying weighting factors for noise reduction. This algorithm complements the previously developed PP-DE algorithm to obtain better quality DE images.

Detective quantum efficiency of photon-counting CdTe and Si detectors for computed tomography: a simulation study

Purpose: Developing photon-counting CT detectors requires understanding the impact of parameters, such as converter material, thickness, and pixel size. We apply a linear-systems framework, incorporating spatial and energy resolution, to study realistic silicon (Si) and cadmium telluride (CdTe) detectors at a low count rate. Approach: We compared CdTe detector designs with 0.5 × 0.5    mm 2 and 0.225 × 0.225    mm 2 pixels and Si detector designs with 0.5 × 0.5    mm 2 pixels of 30 and 60 mm active thickness, with and without tungsten scatter blockers. Monte-Carlo simulations of photon transport were used together with Gaussian charge sharing models fitted to published data. Results: For detection in a 300-mm-thick object at 120 kVp, the 0.5- and 0.225-mm pixel CdTe systems have 28% to 41% and 5% to 29% higher detective quantum efficiency (DQE), respectively, than the 60-mm Si system with tungsten, whereas the corresponding numbers for two-material decomposition are 2% lower to 11% higher DQE and 31% to 54% lower DQE compared to Si. We also show that combining these detectors with dual-spectrum acquisition is beneficial. Conclusions: In the low-count-rate regime, CdTe detector systems outperform the Si systems for detection tasks, whereas silicon outperforms one or both of the CdTe systems for material decomposition.

Comparison of deep learning and human observer performance for detection and characterization of simulated lesions

Detection and characterization of abnormalities in clinical imaging are of utmost importance for patient diagnosis and treatment. We present a comparison of convolutional neural network (CNN) and human observer performance on a simulated lesion detection and characterization task. We apply both conventional performance metrics, including accuracy and nonconventional metrics such as lift charts to perform qualitative and quantitative comparisons of each type of observer. It is determined that the CNN generally outperforms the human observers, particularly at high noise levels. However, high noise correlation reduces the relative performance of the CNN, and human observer performance is comparable to CNN under these conditions. These findings extend into the field of diagnostic radiology, where the adoption of deep learning is starting to become widespread. Consideration of the applications for which deep learning is most effective is of critical importance to this development.

Development of a deep neural network for generating synthetic dual-energy chest x-ray images with single x-ray exposure

Dual-energy chest radiography (DECR) is a medical imaging technology that can improve diagnostic accuracy. This technique can decompose single-energy chest radiography (SECR) images into separate bone- and soft tissue-only images. This can, however, double the radiation exposure to the patient. To address this limitation, we developed an algorithm for the synthesis of DECR from a SECR through deep learning. To predict high resolution images, we developed a novel deep learning architecture by modifying a conventional U-net to take advantage of the high frequency-dominant information that propagates from the encoding part to the decoding part. In addition, we used the anticorrelated relationship (ACR) of DECR for improving the quality of the predicted images. For training data, 300 pairs of SECR and their corresponding DECR images were used. To test the trained model, 50 DECR images from Yonsei University Severance Hospital and 662 publicly accessible SECRs were used. To evaluate the performance of the proposed method, we compared DECR and predicted images using a structural similarity approach (SSIM). In addition, we quantitatively evaluated image quality calculating the modulation transfer function and coefficient of variation. The proposed model selectively predicted the bone- and soft tissue-only CR images from an SECR image. The strategy for improving the spatial resolution by ACR was effective. Quantitative evaluation showed that the proposed method with ACR showed relatively high SSIM (over 0.85). In addition, predicted images with the proposed ACR model achieved better image quality measures than those of U-net. In conclusion, the proposed method can obtain high-quality bone- and soft tissue-only CR images without the need for additional hardware for double x-ray exposures in clinical practice.

Task-driven source-detector trajectories in cone-beam computed tomography: I. Theory and methods

We develop a mathematical framework for the design of orbital trajectories that are optimal to a particular imaging task (or tasks) in advanced cone-beam computed tomography systems that have the capability of general source-detector positioning. The framework allows various parameterizations of the orbit as well as constraints based on imaging system capabilities. To accommodate nonstandard system geometries, a model-based iterative reconstruction method is applied. Such algorithms generally complicate the assessment and prediction of reconstructed image properties; however, we leverage efficient implementations of analytical predictors of local noise and spatial resolution that incorporate dependencies of the reconstruction algorithm on patient anatomy, x-ray technique, and geometry. These image property predictors serve as inputs to a task-based performance metric defined by detectability index, which is optimized with respect to the orbital parameters of data acquisition. We investigate the framework of the task-driven trajectory design in several examples to examine the dependence of optimal source-detector trajectories on the imaging task (or tasks), including location and spatial-frequency dependence. A variety of multitask objectives are also investigated, and the advantages to imaging performance are quantified in simulation studies.

Prototype system for interventional dual-energy subtraction angiography

Dual-energy subtraction angiography (DESA) using fast kV switching has received attention for its potential to reduce misregistration artifacts in thoracic and abdominal imaging where patient motion is difficult to control; however, commercial interventional solutions are not currently available. The purpose of this work was to adapt an x-ray angiography system for 2D and 3D DESA. The platform for the dual-energy prototype was a commercially available x-ray angiography system with a flat panel detector and an 80 kW x-ray tube. Fast kV switching was implemented using custom x-ray tube control software that follows a user-defined switching program during a rotational acquisition. Measurements made with a high temporal resolution kV meter were used to calibrate the relationship between the requested and achieved kV and pulse width. To enable practical 2D and 3D imaging experiments, an automatic exposure control algorithm was developed to estimate patient thickness and select a dual-energy switching technique (kV and ms switching) that delivers a user-specified task CNR at the minimum air kerma to the interventional reference point. An XCAT-based simulation study conducted to evaluate low and high energy image registration for the scenario of 30-60 frame/s pulmonary angiography with respiratory motion found normalized RMSE values ranging from 0.16% to 1.06% in tissue-subtracted DESA images, depending on respiratory phase and frame rate. Initial imaging in a porcine model with a 60 kV, 10 ms, 325 mA / 120 kV, 3.2 ms, 325 mA switching technique demonstrated an ability to form tissue-subtracted images from a single contrast-enhanced acquisition.

Comparison of contrast-enhanced digital mammography and contrast-enhanced digital breast tomosynthesis for lesion assessment

Contrast-enhanced digital mammography (CEDM) reveals neovasculature of breast lesions in a two-dimensional contrast enhancement map. Contrast-enhanced digital breast tomosynthesis (CEDBT) provides contrast enhancement in three dimensions, which may improve lesion characterization and localization. We aim to compare CEDM and CEDBT for lesion assessment. Women with breast imaging-reporting and data system 4 or 5 suspicious breast lesion(s) were recruited in our study and were imaged with CEDM and CEDBT in succession under one breast compression. Two radiologists assessed CEDM and CEDBT with both images displayed side-by-side and compared (1) contrast enhancement of lesions and (2) lesion margin using a five-point scale ranging from - 2 (CEDM much better) to + 2 (CEDBT much better). Biopsy identified 19 malignant lesions with contrast enhancement. Our results show that CEDBT provides better lesion margins than CEDM with limited reduction in contrast enhancement. CEDBT delivers less radiation dose compared to CEDM + DBT. Synthetic CEDM can be generated from CEDBT data and provides lesion contrast enhancement comparable to CEDM. CEDBT has potential for clinical applications, such as treatment response monitoring and guidance for biopsy.

A novel phantom for characterization of dual energy imaging using an on-board imaging system

Dual-energy (DE) imaging using planar imaging with an on-board imager (OBI) is being considered in radiotherapy. We describe here a custom phantom designed to optimize DE imaging parameters using the OBI of a commercial linear accelerator. The phantom was constructed of lung-, tissue- and bone-equivalent material slabs. Five simulated tumors located at two different depths were encased in the lung-equivalent materials. Two slabs with bone-equivalent material inserts were constructed to simulate ribs, which overlap the simulated tumors. DE bone suppression was performed using a weighted logarithmic subtraction based on an iterative method that minimized the contrast between simulated bone- and lung-equivalent materials. The phantom was subsequently used to evaluate different combinations of high-low kV x-ray pairs of images based on the signal-difference-to-noise ratio (SDNR) metric. The results show a strong correlation between tumor visibility and selected energy pairs, where higher energy separation leads to larger SDNR values. To evaluate the effect of image post-processing methods on tumor visibility, an anti-correlated noise reduction (ACNR) technique and adaptive kernel scatter correction method were applied to subsequent DE images. Application of the ACNR technique approximately doubled the SDNR values, hence increasing tumor visibility, while scatter correction had little effect on SDNR values. This phantom allows for quick image acquisition and optimization of imaging parameters and weighting factors. Optimized DE imaging increases soft tissue visibility and may allow for markerless motion tracking of lung tumors.

Patient-specific pixel-based weighting factor dual-energy x-ray imaging system using a priori CT data

PURPOSE

The purpose of this study was to develop a novel patient-specific pixel-based weighting factor dual-energy (PP-DE) algorithm to effectively suppress bone throughout the image and overcome the limitation of the conventional DE algorithm with constant weighting factor which is restricted to regions with uniform patient thickness. Additionally, to derive theoretical expressions to describe the dependence of the weighting factors on several imaging parameters and validate them with measurement.

METHODS

A step phantom was constructed consisting of slabs of solid water and bone materials. Thicknesses of bone ranged [0-6] cm in one direction and solid water [5-30] cm in the other direction. Projection images at 60 and 140 kVp were acquired using a clinical imaging system. Optimal weighting factors were found by iteratively varying it in the range [0-1.4], where bone and soft-tissue contrast-to-noise ratio (CNR) reached zero. Bone and soft-tissue digitally reconstructed thicknesses were created using computed tomography (CT) images of a Rando phantom and ray tracing techniques. A weighting factor image (ω) was calculated using digitally reconstructed thicknesses (DRTs) and precalculated weighting factors from the step phantom. This ω image was then used to generate a PP-DE image. The PP-DE image was compared to the conventional DE image which uses a constant weighting factor throughout the image. The effect of the misaligned ω image on PP-DE images was investigated by acquiring LE and HE images at various shifts of Rando phantom. A rigid registration was used based on mutual information algorithm in Matlab. The signal-to-noise ratios (SNR) were calculated in the step phantom for the PP-DE image and compared to that of conventional DE technique. Analytical expressions for theoretical weighting factors were derived which included various effects such as beam hardening, scatter, and detector response. The analytical expressions were simulated in Spektr3.0 for different bone and solid water thicknesses as per the step phantom. A tray of steel pins was constructed and used with the step phantom to remove the scattered radiation. The simulated theoretical weighting factors were validated by comparing to those from the step phantom measurement.

RESULTS

Optimal weighting factor values for the step phantom varied from 0.633 to 1.372 depending on region thickness. Thicker regions required larger weighting factors for bone cancellation. The PP-DE image of the Rando phantom favorably cancelled both ribs and spine, whereas in the conventional DE image, only one could be cancelled at a time. The misaligned ω image was less effective in removing all bones indicating the importance of alignment as part of the PP-DE algorithm implementation. The SNRs for the PP-DE image was larger than those of the conventional DE images for regions which required smaller weighting factors for bone suppression. Comparisons of measured and simulated weighting factors demonstrated a 3% agreement for all bone overlapped regions except for the thickest region with 30 cm of solid water overlapped with 6 cm bone where the signal was lost due to excess attenuation.

CONCLUSIONS

A novel PP-DE algorithm was developed which can create higher quality DE images with enhanced bone cancellation and improved noise characteristics compared to conventional DE technique. In addition, theoretical weighting factor expressions were derived and validated against measurement.

The evolution of image reconstruction for CT-from filtered back projection to artificial intelligence

Abstract

The first CT scanners in the early 1970s already used iterative reconstruction algorithms; however, lack of computational power prevented their clinical use. In fact, it took until 2009 for the first iterative reconstruction algorithms to come commercially available and replace conventional filtered back projection. Since then, this technique has caused a true hype in the field of radiology. Within a few years, all major CT vendors introduced iterative reconstruction algorithms for clinical routine, which evolved rapidly into increasingly advanced reconstruction algorithms. The complexity of algorithms ranges from hybrid-, model-based to fully iterative algorithms. As a result, the number of scientific publications on this topic has skyrocketed over the last decade. But what exactly has this technology brought us so far? And what can we expect from future hardware as well as software developments, such as photon-counting CT and artificial intelligence? This paper will try answer those questions by taking a concise look at the overall evolution of CT image reconstruction and its clinical implementations. Subsequently, we will give a prospect towards future developments in this domain.

Key Points

• Advanced CT reconstruction methods are indispensable in the current clinical setting.

• IR is essential for photon-counting CT, phase-contrast CT, and dark-field CT.

• Artificial intelligence will potentially further increase the performance of reconstruction methods.

A framework for performance characterization of energy-resolving photon-counting detectors

PURPOSE

Photon-counting, energy-resolving detectors are subject to intense research interest, and there is a need for a general framework for performance assessment of these detectors. The commonly used linear-systems theory framework, which measures detector performance in terms of noise-equivalent quanta (NEQ) and detective quantum efficiency (DQE) is widely used for characterizing conventional x-ray detectors but does not take energy-resolving capabilities into account. The purpose of this work is to extend this framework to encompass energy-resolving photon-counting detectors and elucidate how the imperfect energy response and other imperfections in real-world detectors affect imaging performance, both for feature detection and for material quantification tasks.

METHOD

We generalize NEQ and DQE to matrix-valued quantities as functions of spatial frequency, and show how these matrices can be calculated from simple Monte Carlo simulations. To demonstrate how the new metrics can be interpreted, we compute them for simplified models of fluorescence and Compton scatter in a photon-counting detector and for a Monte Carlo model of a CdTe detector with 0.5 × 0.5 mm 2 pixels.

RESULTS

Our results show that the ideal-linear-observer performance for any detection or material quantification task can be calculated from the proposed generalized NEQ and DQE metrics. We also demonstrate that the proposed NEQ metric is closely related to a generalized version of the Cramér-Rao lower bound commonly used for assessing material quantification performance. Off-diagonal elements in the NEQ and DQE matrices are shown to be related to loss of energy information due to imperfect energy resolution. The Monte Carlo model of the CdTe detector predicts a zero-frequency dose efficiency relative to an ideal detector of 0.86 and 0.65 for detecting water and bone, respectively. When the task instead is to quantify these materials, the corresponding values are 0.34 for water and 0.26 for bone.

CONCLUSIONS

We have developed a framework for assessing the performance of photon-counting energy-resolving detectors and shown that the matrix-valued NEQ and DQE metrics contain sufficient information for calculating the dose efficiency for both detection and quantification tasks, the task having any spatial and energy dependence. This framework will be beneficial for the development and optimization of photon-counting x-ray detectors.

Physics considerations in MV-CBCT multi-layer imager design

Megavoltage (MV) cone-beam computed tomography (CBCT) using an electronic portal imaging (EPID) offers advantageous features, including 3D mapping, treatment beam registration, high-z artifact suppression, and direct radiation dose calculation. Adoption has been slowed by image quality limitations and concerns about imaging dose. Developments in imager design, including pixelated scintillators, structured phosphors, inexpensive scintillation materials, and multi-layer imager (MLI) architecture have been explored to improve EPID image quality and reduce imaging dose. The present study employs a hybrid Monte Carlo and linear systems model to determine the effect of detector design elements, such as multi-layer architecture and scintillation materials. We follow metrics of image quality including modulation transfer function (MTF) and noise power spectrum (NPS) from projection images to 3D reconstructions to in-plane slices and apply a task based figure-of-merit, the ideal observer signal-to-noise ratio (d') to determine the effect of detector design on object detectability. Generally, detectability was limited by detector noise performance. Deploying an MLI imager with a single scintillation material for all layers yields improvement in noise performance and d' linear with the number of layers. In general, improving x-ray absorption using thicker scintillators results in improved DQE(0). However, if light yield is low, performance will be affected by electronic noise at relatively high doses, resulting in rapid image quality degradation. Maximizing image quality in a heterogenous MLI detector (i.e. multiple different scintillation materials) is most affected by limiting total noise. However, while a second-order effect, maximizing total spatial resolution of the MLI detector is a balance between the intensity contribution of each layer against its individual MTF. So, while a thinner scintillator may yield a maximal individual-layer MTF, its quantum efficiency will be relatively low in comparison to a thicker scintillator and thus, intensity contribution may be insufficient to noticeably improve the total detector MTF.

Impact of anti-charge sharing on the zero-frequency detective quantum efficiency of CdTe-based photon counting detector system: cascaded systems analysis and experimental validation

Inter-pixel communication and anti-charge sharing (ACS) technologies have been introduced to photon counting detector (PCD) systems to address the undesirable charge sharing problem. In addition to improving the energy resolution of PCD, ACS may also influence other aspects of PCD performance such as detector multiplicity (i.e. the number of pixels triggered by each interacted photon) and detective quantum efficiency (DQE). In this work, a theoretical model was developed to address how ACS impacts the multiplicity and zero-frequency DQE [DQE(0)] of PCD systems. The work focused on cadmium telluride (CdTe)-based PCD that often involves the generation and transport of K-fluorescence photons. Under the parallel cascaded systems analysis framework, the theory takes both photoelectric and scattering effects into account, and it also considers both the reabsorption and escape of photons. In a new theoretical treatment of ACS, it was considered as a modified version of the conventional single pixel (i.e. non-ACS) mode, but with reduced charge spreading distance and K-fluorescence travel distance. The proposed theoretical model does not require prior knowledge of the detailed ACS implementation method for each specific PCD, and its parameters can be experimentally determined using a radioisotope without invoking any Monte-Carlo simulation. After determining the model parameters, independent validation experiments were performed using a diagnostic x-ray tube and four different polychromatic beams (from 50 to 120 kVp). Both the theoretical and experimental results demonstrate that ACS increased the first and second moments of multiplicity for a majority of the x-ray energy and threshold levels tested, except when the threshold level was much lower than the x-ray energy level. However, ACS always improved DQE(0) at all energy and threshold levels tested.

Leveraging multi-layer imager detector design to improve low-dose performance for megavoltage cone-beam computed tomography

While megavoltage cone-beam computed tomography (CBCT) using an electronic portal imaging device (EPID) provides many advantages over kilovoltage (kV) CBCT, clinical adoption is limited by its high doses. Multi-layer imager (MLI) EPIDs increase DQE(0) while maintaining high resolution. However, even well-designed, high-performance MLIs suffer from increased electronic noise from each readout, degrading low-dose image quality. To improve low-dose performance, shift-and-bin addition (ShiBA) imaging is proposed, leveraging the unique architecture of the MLI. ShiBA combines hardware readout-binning and super-resolution concepts, reducing electronic noise while maintaining native image sampling. The imaging performance of full-resolution (FR); standard, aligned binned (BIN); and ShiBA images in terms of noise power spectrum (NPS), electronic NPS, modulation transfer function (MTF), and the ideal observer signal-to-noise ratio (SNR)-the detectability index (d')-are compared. The FR 4-layer readout of the prototype MLI exhibits an electronic NPS magnitude 6-times higher than a state-of-the-art single layer (SLI) EPID. Although the MLI is built on the same readout platform as the SLI, with each layer exhibiting equivalent electronic noise, the multi-stage readout of the MLI results in electronic noise 50% higher than simple summation. Electronic noise is mitigated in both BIN and ShiBA imaging, reducing its total by ~12 times. ShiBA further reduces the NPS, effectively upsampling the image, resulting in a multiplication by a sinc² function. Normalized NPS show that neither ShiBA nor BIN otherwise affects image noise. The LSF shows that ShiBA removes the pixilation artifact of BIN images and mitigates the effect of detector shift, but does not quantifiably improve the MTF. ShiBA provides a pre-sampled representation of the images, mitigating phase dependence. Hardware binning strategies lower the quantum noise floor, with 2  ×  2 implementation reducing the dose at which DQE(0) degrades by 10% from 0.01 MU to 0.004 MU, representing 20% improvement in d'.

A novel method for quantification of beam's-eye-view tumor tracking performance

PURPOSE

In-treatment imaging using an electronic portal imaging device (EPID) can be used to confirm patient and tumor positioning. Real-time tumor tracking performance using current digital megavolt (MV) imagers is hindered by poor image quality. Novel EPID designs may help to improve quantum noise response, while also preserving the high spatial resolution of the current clinical detector. Recently investigated EPID design improvements include but are not limited to multi-layer imager (MLI) architecture, thick crystalline and amorphous scintillators, and phosphor pixilation and focusing. The goal of the present study was to provide a method of quantitating improvement in tracking performance as well as to reveal the physical underpinnings of detector design that impact tracking quality. The study employs a generalizable ideal observer methodology for the quantification of tumor tracking performance. The analysis is applied to study both the effect of increasing scintillator thickness on a standard, single-layer imager (SLI) design as well as the effect of MLI architecture on tracking performance.

METHODS

The present study uses the ideal observer signal-to-noise ratio (d') as a surrogate for tracking performance. We employ functions which model clinically relevant tasks and generalized frequency-domain imaging metrics to connect image quality with tumor tracking. A detection task for relevant Cartesian shapes (i.e., spheres and cylinders) was used to quantitate trackability of cases employing fiducial markers. Automated lung tumor tracking algorithms often leverage the differences in benign and malignant lung tissue textures. These types of algorithms (e.g., soft-tissue localization - STiL) were simulated by designing a discrimination task, which quantifies the differentiation of tissue textures, measured experimentally and fit as a power-law in trend (with exponent β) using a cohort of MV images of patient lungs. The modeled MTF and NPS were used to investigate the effect of scintillator thickness and MLI architecture on tumor tracking performance.

RESULTS

Quantification of MV images of lung tissue as an inverse power-law with respect to frequency yields exponent values of β = 3.11 and 3.29 for benign and malignant tissues, respectively. Tracking performance with and without fiducials was found to be generally limited by quantum noise, a factor dominated by quantum detective efficiency (QDE). For generic SLI construction, increasing the scintillator thickness (gadolinium oxysulfide - GOS) from a standard 290 μm to 1720 μm reduces noise to about 10%. However, 81% of this reduction is appreciated between 290 and 1000 μm. In comparing MLI and SLI detectors of equivalent individual GOS layer thickness, the improvement in noise is equal to the number of layers in the detector (i.e., 4) with almost no difference in MTF. Further, improvement in tracking performance was slightly less than the square-root of the reduction in noise, approximately 84-90%. In comparing an MLI detector with an SLI with a GOS scintillator of equivalent total thickness, improvement in object detectability is approximately 34-39%.

CONCLUSIONS

We have presented a novel method for quantification of tumor tracking quality and have applied this model to evaluate the performance of SLI and MLI EPID designs. We showed that improved tracking quality is primarily limited by improvements in NPS. When compared to very thick scintillator SLI, employing MLI architecture exhibits the same gains in QDE, but by mitigating the effect of optical Swank noise, results in more dramatic improvements in tracking performance.

Optimization of contrast-enhanced breast imaging: Analysis using a cascaded linear system model

PURPOSE

Contrast-enhanced (CE) breast imaging involves the injection contrast agents (i.e., iodine) to increase conspicuity of malignant lesions. CE imaging may be used in conjunction with digital mammography (DM) or digital breast tomosynthesis (DBT) and has shown promise in improving diagnostic specificity. Both CE-DM and CE-DBT techniques require optimization as clinical diagnostic tools. Physical factors including x-ray spectra, subtraction technique, and the signal from iodine contrast, must be considered to provide the greatest object detectability and image quality. We developed a cascaded linear system model (CLSM) for the optimization of CE-DM and CE-DBT employing dual energy (DE) subtraction or temporal (TE) subtraction.

METHODS

We have previously developed a CLSM for DBT implemented with an a-Se flat panel imager (FPI) and filtered backprojection (FBP) reconstruction algorithm. The model is used to track image quality metrics - modulation transfer function (MTF) and noise power spectrum (NPS) - at each stage of the imaging chain. In this study, the CLSM is extended for CE breast imaging. The effect of x-ray spectrum (varied by changing tube potential and the filter) and DE and TE subtraction techniques on breast structural noise was measured was studied and included as a deterministic source of noise in the CLSM. From the two-dimensional (2D) and three-dimensional (3D) MTF and NPS, the ideal observer signal-to-noise ratio (SNR), also known as the detectability index (d'), may be calculated. Using d' as a FOM, we discuss the optimization of CE imaging for the task of iodinated contrast object detection within structured backgrounds.

RESULTS

Increasing x-ray energy was determined to decrease the magnitude of structural noise and not its correlation. By performing DE subtraction, the magnitude of the structural noise was further reduced at the expense of increased stochastic (quantum and electronic) noise. TE subtraction exhibited essentially no residual structural noise at the expense of increased quantum noise, even over that of the DE case. For DE subtraction, optimization of dose weighting to the HE view (f_h ) results in the minimization of quantum noise. Both subtraction weighting factor (w_Sub ) and the iodine contrast signal were dependent on the LE and HE x-ray spectra. To best detect a 5 mm Gaussian lesion with 5 mg/ml of iodine within a 4 cm thick breast, it was found that the high energy (HE) view should be acquired with a tube potential of 47 kVp (W/Ti spectrum) and the low energy (LE) view with a potential of 23 kVp (W/Rh spectrum). Due to the complete removal of structural noise, TE subtraction produced much higher d' than DE subtraction both as a function of mean glandular dose and iodine concentration.

CONCLUSIONS

We have shown the effect of increasing x-ray energy as well as projection domain subtraction on breast structural noise. Further, we have exhibited the utility of the CLSM for DE and TE subtraction CE imaging in the optimization of imaging parameters such as x-ray energy, f_h , and w_Sub as well as guiding the understanding of their effects on image contrast and noise.

Technical Note: spektr 3.0-A computational tool for x-ray spectrum modeling and analysis

PURPOSE

A computational toolkit (spektr 3.0) has been developed to calculate x-ray spectra based on the tungsten anode spectral model using interpolating cubic splines (TASMICS) algorithm, updating previous work based on the tungsten anode spectral model using interpolating polynomials (TASMIP) spectral model. The toolkit includes a matlab (The Mathworks, Natick, MA) function library and improved user interface (UI) along with an optimization algorithm to match calculated beam quality with measurements.

METHODS

The spektr code generates x-ray spectra (photons/mm(2)/mAs at 100 cm from the source) using TASMICS as default (with TASMIP as an option) in 1 keV energy bins over beam energies 20-150 kV, extensible to 640 kV using the TASMICS spectra. An optimization tool was implemented to compute the added filtration (Al and W) that provides a best match between calculated and measured x-ray tube output (mGy/mAs or mR/mAs) for individual x-ray tubes that may differ from that assumed in TASMICS or TASMIP and to account for factors such as anode angle.

RESULTS

The median percent difference in photon counts for a TASMICS and TASMIP spectrum was 4.15% for tube potentials in the range 30-140 kV with the largest percentage difference arising in the low and high energy bins due to measurement errors in the empirically based TASMIP model and inaccurate polynomial fitting. The optimization tool reported a close agreement between measured and calculated spectra with a Pearson coefficient of 0.98.

CONCLUSIONS

The computational toolkit, spektr, has been updated to version 3.0, validated against measurements and existing models, and made available as open source code. Video tutorials for the spektr function library, UI, and optimization tool are available.

Theoretical and Monte Carlo optimization of a stacked three-layer flat-panel x-ray imager for applications in multi-spectral medical imaging

We propose a new design of a stacked three-layer flat-panel x-ray detector for dual-energy (DE) imaging. Each layer consists of its own scintillator of individual thickness and an underlying thin-film-transistor-based flat-panel. Three images are obtained simultaneously in the detector during the same x-ray exposure, thereby eliminating any motion artifacts. The detector operation is two-fold: a conventional radiography image can be obtained by combining all three layers' images, while a DE subtraction image can be obtained from the front and back layers' images, where the middle layer acts as a mid-filter that helps achieve spectral separation. We proceed to optimize the detector parameters for two sample imaging tasks that could particularly benefit from this new detector by obtaining the best possible signal to noise ratio per root entrance exposure using well-established theoretical models adapted to fit our new design. These results are compared to a conventional DE temporal subtraction detector and a single-shot DE subtraction detector with a copper mid-filter, both of which underwent the same theoretical optimization. The findings are then validated using advanced Monte Carlo simulations for all optimized detector setups. Given the performance expected from initial results and the recent decrease in price for digital x-ray detectors, the simplicity of the three-layer stacked imager approach appears promising to usher in a new generation of multi-spectral digital x-ray diagnostics.

The effect of amorphous selenium detector thickness on dual-energy digital breast imaging

PURPOSE

Contrast enhanced (CE) imaging techniques for both planar digital mammography (DM) and three-dimensional (3D) digital breast tomosynthesis (DBT) applications requires x-ray photon energies higher than the k-edge of iodine (33.2 keV). As a result, x-ray tube potentials much higher (>40 kVp) than those typical for screening mammography must be utilized. Amorphous selenium (a-Se) based direct conversion flat-panel imagers (FPI) have been widely used in DM and DBT imaging systems. The a-Se layer is typically 200 μm thick with quantum detective efficiency (QDE) >87% for x-ray energies below 26 keV. However, QDE decreases substantially above this energy. To improve the object detectability of either CE-DM or CE-DBT, it may be advantageous to increase the thickness (dSe) of the a-Se layer. Increasing the dSe will improve the detective quantum efficiency (DQE) at the higher energies used in CE imaging. However, because most DBT systems are designed with partially isocentric geometries, where the gantry moves about a stationary detector, the oblique entry of x-rays will introduce additional blur to the system. The present investigation quantifies the effect of a-Se thickness on imaging performance for both CE-DM and CE-DBT, discussing the effects of improving photon absorption and blurring from oblique entry of x-rays.

METHODS

In this paper, a cascaded linear system model (CLSM) was used to investigate the effect of dSe on the imaging performance (i.e., MTF, NPS, and DQE) of FPI in CE-DM and CE-DBT. The results from the model are used to calculate the ideal observer signal-to-noise ratio, d', which is used as a figure-of-merit to determine the total effect of increasing dSe for CE-DM and CE-DBT.

RESULTS

The results of the CLSM show that increasing dSe causes a substantial increase in QDE at the high energies used in CE-DM. However, at the oblique projection angles used in DBT, the increased length of penetration through a-Se introduces additional image blur. The reduced MTF and DQE at high spatial frequencies lead to reduced two-dimensional d'. These losses in projection image resolution may subsequently result in a decrease in the 3D d', but the degree of which is largely dependent on the DBT reconstruction algorithm. For a filtered backprojection (FBP) algorithm with spectral apodization and slice-thickness filters, which dominate the blur for reconstructed images at oblique angles, the effect of oblique entry of x-rays on 3D d' is minimal. Thus, increasing dSe results in an improvement in d' for both CE-DM and CE-DBT with typical FBP reconstruction parameters.

CONCLUSIONS

Increased dSe improves CE breast imaging performance by increasing QDE of detectors at higher energies, e.g., 49 kVp. Although there is additional blur in the oblique angled projections of a DBT scan, the overall 3D d' for DBT is not degraded because the dominant source blur at these angles results from the reconstruction filters of the employed FBP algorithm.

Automatic CT simulation optimization for radiation therapy: A general strategy

PURPOSE

In radiation therapy, x-ray computed tomography (CT) simulation protocol specifications should be driven by the treatment planning requirements in lieu of duplicating diagnostic CT screening protocols. The purpose of this study was to develop a general strategy that allows for automatically, prospectively, and objectively determining the optimal patient-specific CT simulation protocols based on radiation-therapy goals, namely, maintenance of contouring quality and integrity while minimizing patient CT simulation dose.

METHODS

The authors proposed a general prediction strategy that provides automatic optimal CT simulation protocol selection as a function of patient size and treatment planning task. The optimal protocol is the one that delivers the minimum dose required to provide a CT simulation scan that yields accurate contours. Accurate treatment plans depend on accurate contours in order to conform the dose to actual tumor and normal organ positions. An image quality index, defined to characterize how simulation scan quality affects contour delineation, was developed and used to benchmark the contouring accuracy and treatment plan quality within the predication strategy. A clinical workflow was developed to select the optimal CT simulation protocols incorporating patient size, target delineation, and radiation dose efficiency. An experimental study using an anthropomorphic pelvis phantom with added-bolus layers was used to demonstrate how the proposed prediction strategy could be implemented and how the optimal CT simulation protocols could be selected for prostate cancer patients based on patient size and treatment planning task. Clinical IMRT prostate treatment plans for seven CT scans with varied image quality indices were separately optimized and compared to verify the trace of target and organ dosimetry coverage.

RESULTS

Based on the phantom study, the optimal image quality index for accurate manual prostate contouring was 4.4. The optimal tube potentials for patient sizes of 38, 43, 48, 53, and 58 cm were 120, 140, 140, 140, and 140 kVp, respectively, and the corresponding minimum CTDIvol for achieving the optimal image quality index 4.4 were 9.8, 32.2, 100.9, 241.4, and 274.1 mGy, respectively. For patients with lateral sizes of 43-58 cm, 120-kVp scan protocols yielded up to 165% greater radiation dose relative to 140-kVp protocols, and 140-kVp protocols always yielded a greater image quality index compared to the same dose-level 120-kVp protocols. The trace of target and organ dosimetry coverage and the γ passing rates of seven IMRT dose distribution pairs indicated the feasibility of the proposed image quality index for the predication strategy.

CONCLUSIONS

A general strategy to predict the optimal CT simulation protocols in a flexible and quantitative way was developed that takes into account patient size, treatment planning task, and radiation dose. The experimental study indicated that the optimal CT simulation protocol and the corresponding radiation dose varied significantly for different patient sizes, contouring accuracy, and radiation treatment planning tasks.

Cascaded systems analysis of photon counting detectors

PURPOSE

Photon counting detectors (PCDs) are an emerging technology with applications in spectral and low-dose radiographic and tomographic imaging. This paper develops an analytical model of PCD imaging performance, including the system gain, modulation transfer function (MTF), noise-power spectrum (NPS), and detective quantum efficiency (DQE).

METHODS

A cascaded systems analysis model describing the propagation of quanta through the imaging chain was developed. The model was validated in comparison to the physical performance of a silicon-strip PCD implemented on an experimental imaging bench. The signal response, MTF, and NPS were measured and compared to theory as a function of exposure conditions (70 kVp, 1-7 mA), detector threshold, and readout mode (i.e., the option for coincidence detection). The model sheds new light on the dependence of spatial resolution, charge sharing, and additive noise effects on threshold selection and was used to investigate the factors governing PCD performance, including the fundamental advantages and limitations of PCDs in comparison to energy-integrating detectors (EIDs) in the linear regime for which pulse pileup can be ignored.

RESULTS

The detector exhibited highly linear mean signal response across the system operating range and agreed well with theoretical prediction, as did the system MTF and NPS. The DQE analyzed as a function of kilovolt (peak), exposure, detector threshold, and readout mode revealed important considerations for system optimization. The model also demonstrated the important implications of false counts from both additive electronic noise and charge sharing and highlighted the system design and operational parameters that most affect detector performance in the presence of such factors: for example, increasing the detector threshold from 0 to 100 (arbitrary units of pulse height threshold roughly equivalent to 0.5 and 6 keV energy threshold, respectively), increased the f50 (spatial-frequency at which the MTF falls to a value of 0.50) by ∼30% with corresponding improvement in DQE. The range in exposure and additive noise for which PCDs yield intrinsically higher DQE was quantified, showing performance advantages under conditions of very low-dose, high additive noise, and high fidelity rejection of coincident photons.

CONCLUSIONS

The model for PCD signal and noise performance agreed with measurements of detector signal, MTF, and NPS and provided a useful basis for understanding complex dependencies in PCD imaging performance and the potential advantages (and disadvantages) in comparison to EIDs as well as an important guide to task-based optimization in developing new PCD imaging systems.

Dual-energy cone-beam CT with a flat-panel detector: effect of reconstruction algorithm on material classification

PURPOSE

Cone-beam CT (CBCT) with a flat-panel detector (FPD) is finding application in areas such as breast and musculoskeletal imaging, where dual-energy (DE) capabilities offer potential benefit. The authors investigate the accuracy of material classification in DE CBCT using filtered backprojection (FBP) and penalized likelihood (PL) reconstruction and optimize contrast-enhanced DE CBCT of the joints as a function of dose, material concentration, and detail size.

METHODS

Phantoms consisting of a 15 cm diameter water cylinder with solid calcium inserts (50-200 mg/ml, 3-28.4 mm diameter) and solid iodine inserts (2-10 mg/ml, 3-28.4 mm diameter), as well as a cadaveric knee with intra-articular injection of iodine were imaged on a CBCT bench with a Varian 4343 FPD. The low energy (LE) beam was 70 kVp (+0.2 mm Cu), and the high energy (HE) beam was 120 kVp (+0.2 mm Cu, +0.5 mm Ag). Total dose (LE+HE) was varied from 3.1 to 15.6 mGy with equal dose allocation. Image-based DE classification involved a nearest distance classifier in the space of LE versus HE attenuation values. Recognizing the differences in noise between LE and HE beams, the LE and HE data were differentially filtered (in FBP) or regularized (in PL). Both a quadratic (PLQ) and a total-variation penalty (PLTV) were investigated for PL. The performance of DE CBCT material discrimination was quantified in terms of voxelwise specificity, sensitivity, and accuracy.

RESULTS

Noise in the HE image was primarily responsible for classification errors within the contrast inserts, whereas noise in the LE image mainly influenced classification in the surrounding water. For inserts of diameter 28.4 mm, DE CBCT reconstructions were optimized to maximize the total combined accuracy across the range of calcium and iodine concentrations, yielding values of ∼ 88% for FBP and PLQ, and ∼ 95% for PLTV at 3.1 mGy total dose, increasing to ∼ 95% for FBP and PLQ, and ∼ 98% for PLTV at 15.6 mGy total dose. For a fixed iodine concentration of 5 mg/ml and reconstructions maximizing overall accuracy across the range of insert diameters, the minimum diameter classified with accuracy >80% was ∼ 15 mm for FBP and PLQ and ∼ 10 mm for PLTV, improving to ∼ 7 mm for FBP and PLQ and ∼ 3 mm for PLTV at 15.6 mGy. The results indicate similar performance for FBP and PLQ and showed improved classification accuracy with edge-preserving PLTV. A slight preference for increased smoothing of the HE data was found. DE CBCT discrimination of iodine and bone in the knee was demonstrated with FBP and PLTV at 6.2 mGy total dose.

CONCLUSIONS

For iodine concentrations >5 mg/ml and detail size ∼ 20 mm, material classification accuracy of >90% was achieved in DE CBCT with both FBP and PL at total doses <10 mGy. Optimal performance was attained by selection of reconstruction parameters based on the differences in noise between HE and LE data, typically favoring stronger smoothing of the HE data, and by using penalties matched to the imaging task (e.g., edge-preserving PLTV in areas of uniform enhancement).

Simultaneous reduction in noise and cross-contamination artifacts for dual-energy X-ray CT

Purpose. Dual-energy CT imaging tends to suffer from much lower signal-to-noise ratio than single-energy CT. In this paper, we propose an improved anticorrelated noise reduction (ACNR) method without causing cross-contamination artifacts. Methods. The proposed algorithm diffuses both basis material density images (e.g., water and iodine) at the same time using a novel correlated diffusion algorithm. The algorithm has been compared to the original ACNR algorithm in a contrast-enhanced, IRB-approved patient study. Material density accuracy and noise reduction are quantitatively evaluated by the percent density error and the percent noise reduction. Results. Both algorithms have significantly reduced the noises of basis material density images in all cases. The average percent noise reduction is 69.3% and 66.5% with the ACNR algorithm and the proposed algorithm, respectively. However, the ACNR algorithm alters the original material density by an average of 13% (or 2.18 mg/cc) with a maximum of 58.7% (or 8.97 mg/cc) in this study. This is evident in the water density images as massive cross-contaminations are seen in all five clinical cases. On the contrary, the proposed algorithm only changes the mean density by 2.4% (or 0.69 mg/cc) with a maximum of 7.6% (or 1.31 mg/cc). The cross-contamination artifacts are significantly minimized or absent with the proposed algorithm. Conclusion. The proposed algorithm can significantly reduce image noise present in basis material density images from dual-energy CT imaging, with minimized cross-contaminations compared to the ACNR algorithm.

Fundamental relationship between the noise properties of grating-based differential phase contrast CT and absorption CT: theoretical framework using a cascaded system model and experimental validation

PURPOSE

Using a grating interferometer, a conventional x-ray cone beam computed tomography (CT) data acquisition system can be used to simultaneously generate both conventional absorption CT (ACT) and differential phase contrast CT (DPC-CT) images from a single data acquisition. Since the two CT images were extracted from the same set of x-ray projections, it is expected that intrinsic relationships exist between the noise properties of the two contrast mechanisms. The purpose of this paper is to investigate these relationships.

METHODS

First, a theoretical framework was developed using a cascaded system model analysis to investigate the relationship between the noise power spectra (NPS) of DPC-CT and ACT. Based on the derived analytical expressions of the NPS, the relationship between the spatial-frequency-dependent noise equivalent quanta (NEQ) of DPC-CT and ACT was derived. From these fundamental relationships, the NPS and NEQ of the DPC-CT system can be derived from the corresponding ACT system or vice versa. To validate these theoretical relationships, a benchtop cone beam DPC-CT/ACT system was used to experimentally measure the modulation transfer function (MTF) and NPS of both DPC-CT and ACT. The measured three-dimensional (3D) MTF and NPS were then combined to generate the corresponding 3D NEQ.

RESULTS

Two fundamental relationships have been theoretically derived and experimentally validated for the NPS and NEQ of DPC-CT and ACT: (1) the 3D NPS of DPC-CT is quantitatively related to the corresponding 3D NPS of ACT by an inplane-only spatial-frequency-dependent factor 1∕f (2), the ratio of window functions applied to DPC-CT and ACT, and a numerical factor C(g) determined by the geometry and efficiency of the grating interferometer. Note that the frequency-dependent factor is independent of the frequency component f(z) perpendicular to the axial plane. (2) The 3D NEQ of DPC-CT is related to the corresponding 3D NEQ of ACT by an f (2) scaling factor and numerical factors that depend on both the attenuation and refraction properties of the image object, as well as C(g) and the MTF of the grating interferometer.

CONCLUSIONS

The performance of a DPC-CT system is intrinsically related to the corresponding ACT system. As long as the NPS and NEQ of an ACT system is known, the corresponding NPS and NEQ of the DPC-CT system can be readily estimated using additional characteristics of the grating interferometer.

Dual energy imaging using a clinical on-board imaging system

Dual energy (DE) imaging consists of obtaining kilovoltage (kV) x-ray images at two different diagnostic energies and performing a weighted subtraction of these images. A third image is then produced that highlights soft tissue. DE imaging has been used by radiologists to aid in the detection of lung malignancies. However, it has not been used clinically in radiotherapy. The goal of this study is to assess the feasibility of performing DE imaging using a commercial on-board imaging system. Both a simple and an anthropomorphic phantom were constructed for this analysis. Planar kV images of the phantoms were obtained using varied imaging energies and mAs. Software was written to perform DE subtraction using empirically determined weighting factors. Tumor detectability was assessed quantitatively using the signal-difference-to-noise ratio (SDNR). Overall DE subtraction suppressed high density objects in both phantoms. The optimal imaging technique, providing the largest SDNR with a dose less than our reference technique was 140 kVp, 1.0 mAs and 60 kVp, 3.2 mAs. Based on this analysis, DE subtraction imaging is feasible using a commercial on-board imaging system and may improve the visualization of tumors in lung cancer patients undergoing image-guided radiotherapy.

Evaluating noise reduction techniques while considering anatomical noise in dual-energy contrast-enhanced mammography

PURPOSE

The authors describe modifications to previously developed cascaded systems analysis to include the anatomical noise in evaluation of dual-energy noise reduction techniques. Previous models have ignored the anatomical noise in theoretical analysis of noise reduction techniques. The inclusion of anatomical noise leads to more accurate estimation of potential noise reduction improvements and optimization.

METHODS

The model is applied to dual-energy contrast-enhanced mammography. The effect of linear noise reduction filters on the anatomical noise is taken into account using cascaded systems analysis. The noise model is included in the ideal observer detectability for performance evaluation of the noise reduction techniques.

RESULTS

Dual-energy image noise with and without including the effect of anatomical noise in noise reduction technique analysis is reported. The theoretical model is compared with clinical images from a previous dual-energy contrast enhanced mammography clinical study and good agreement is observed. The results suggest that the inclusion of anatomical noise in the evaluation and comparison of noise reduction techniques is highly warranted for more accurate analysis.

CONCLUSIONS

This work establishes a useful extension to dual-energy cascaded systems analysis for maximizing image quality using noise reduction techniques. The extension includes the effect of linear image filtering, such as that used for noise reduction, on anatomical noise. The results suggest that the inclusion of anatomical noise in the evaluation of noise reduction techniques can lead to more accurate optimization, noise, and performance estimations.

Cascaded systems analysis of noise and detectability in dual-energy cone-beam CT

PURPOSE

Dual-energy computed tomography and dual-energy cone-beam computed tomography (DE-CBCT) are promising modalities for applications ranging from vascular to breast, renal, hepatic, and musculoskeletal imaging. Accordingly, the optimization of imaging techniques for such applications would benefit significantly from a general theoretical description of image quality that properly incorporates factors of acquisition, reconstruction, and tissue decomposition in DE tomography. This work reports a cascaded systems analysis model that includes the Poisson statistics of x rays (quantum noise), detector model (flat-panel detectors), anatomical background, image reconstruction (filtered backprojection), DE decomposition (weighted subtraction), and simple observer models to yield a task-based framework for DE technique optimization.

METHODS

The theoretical framework extends previous modeling of DE projection radiography and CBCT. Signal and noise transfer characteristics are propagated through physical and mathematical stages of image formation and reconstruction. Dual-energy decomposition was modeled according to weighted subtraction of low- and high-energy images to yield the 3D DE noise-power spectrum (NPS) and noise-equivalent quanta (NEQ), which, in combination with observer models and the imaging task, yields the dual-energy detectability index (d(')). Model calculations were validated with NPS and NEQ measurements from an experimental imaging bench simulating the geometry of a dedicated musculoskeletal extremities scanner. Imaging techniques, including kVp pair and dose allocation, were optimized using d(') as an objective function for three example imaging tasks: (1) kidney stone discrimination; (2) iodine vs bone in a uniform, soft-tissue background; and (3) soft tissue tumor detection on power-law anatomical background.

RESULTS

Theoretical calculations of DE NPS and NEQ demonstrated good agreement with experimental measurements over a broad range of imaging conditions. Optimization results suggest a lower fraction of total dose imparted by the low-energy acquisition, a finding consistent with previous literature. The selection of optimal kVp pair reveals the combined effect of both quantum noise and contrast in the kidney stone discrimination and soft-tissue tumor detection tasks, whereas the K-edge effect of iodine was the dominant factor in determining kVp pairs in the iodine vs bone task. The soft-tissue tumor task illustrated the benefit of dual-energy imaging in eliminating anatomical background noise and improving detectability beyond that achievable by single-energy scans.

CONCLUSIONS

This work established a task-based theoretical framework that is predictive of DE image quality. The model can be utilized in optimizing a broad range of parameters in image acquisition, reconstruction, and decomposition, providing a useful tool for maximizing DE-CBCT image quality and reducing dose.

Towards task-based assessment of CT performance: system and object MTF across different reconstruction algorithms

PURPOSE

To investigate a measurement method for evaluating the resolution properties of CT imaging systems across reconstruction algorithms, dose, and contrast.

METHODS

An algorithm was developed to extract the task-based modulation transfer function (MTF) from disk images generated from the rod inserts in the ACR phantom (model 464 Gammex, WI). These inserts are conventionally employed for HU accuracy assessment. The edge of the disk objects was analyzed to determine the edge-spread function, which was differentiated to yield the line-spread function and Fourier-transformed to generate the object-specific MTF for task-based assessment, denoted MTF(Task). The proposed MTF measurement method was validated against the conventional wire technique and further applied to measure the MTF of CT images reconstructed with an adaptive statistical iterative algorithm (ASIR) and a model-based iterative (MBIR) algorithm. Results were further compared to the standard filtered back projection (FBP) algorithm. Measurements were performed and compared across different doses and contrast levels to ascertain the MTF(Task) dependencies on those factors.

RESULTS

For the FBP reconstructed images, the MTF(Task) measured with the inserts were the same as the MTF measured from the wire-based method. For the ASIR and MBIR data, the MTF(Task) using the high contrast insert was similar to the wire-based MTF and equal or superior to that of FBP. However, results for the MTF(Task) measured using the low-contrast inserts, the MTF(Task) for ASIR and MBIR data was lower than for the FBP, which was constant throughout all measurements. Similarly, as a function of mA, the MTF(Task) for ASIR and MBIR varied as a function of noise--with MTF(Task) being proportional to mA. Overall greater variability of MTF(Task) across dose and contrast was observed for MBIR than for ASIR.

CONCLUSIONS

This approach provides a method for assessing the task-based MTF of a CT system using conventional and iterative reconstructions. Results demonstrated that the object-specific MTF can vary as a function of dose and contrast. The analysis highlighted the paradigm shift for iterative reconstructions when compared to FBP, where iterative reconstructions generally offer superior noise performance but with varying resolution as a function of dose and contrast. The MTF(Task) generated by this method is expected to provide a more comprehensive assessment of image resolution across different reconstruction algorithms and imaging tasks.

The effect of angular dose distribution on the detection of microcalcifications in digital breast tomosynthesis

PURPOSE

Substantial effort has been devoted to the clinical development of digital breast tomosynthesis (DBT). DBT is a three-dimensional (3D) x-ray imaging modality that reconstructs a number of thin image slices parallel to a stationary detector plane. Preliminary clinical studies have shown that the removal of overlapping breast tissue reduces image clutter and increases detectability of large, low contrast lesions. However, some studies, as well as anecdotal evidence, suggested decreased conspicuity of small, high contrast objects such as microcalcifications. Several investigators have proposed alternative imaging methods for improving microcalcification detection by delivering half of the total dose to the central view in addition to a separate DBT scan. Preliminary observer studies found possible improvement by either viewing the central projection alone or combining all views with a reconstruction algorithm.

METHODS

In this paper, we developed a generalized imaging theory based on a cascaded linear-system model for DBT to calculate the effect of variable angular dose distribution on the 3D modulation transfer function (MTF) and noise power spectrum (NPS). Using the ideal observer signal-to-noise ratio (SNR), d', as a figure-of-merit (FOM) for a signal embedded in a uniform background, we compared the detectability of objects with different sizes under different imaging conditions (e.g., angular dose distribution and reconstruction filters). Experimental investigation was conducted for three different angular dose schemes (ADS) using a Siemens Novation(TOMO) prototype unit.

RESULTS

Our results show excellent agreement between modeled and experimental measurements of 3D NPS with different angular dose distribution. The ideal observer detectability index for the detection of Gaussian objects with different angular dose distributions depends strongly on the applied reconstruction filter as well as the imaging task. For detection tasks of small calcifications with reconstruction filters used typically in a clinical setting, variable angular dose distribution with more dose delivered to the central views may lead to higher d' than a uniform angular dose distribution.

CONCLUSIONS

The conspicuity of the detection of small calcifications may be improved, under certain imaging conditions, by delivering higher dose toward the central views of a tomosynthesis scan, while also reducing the dose at peripheral angles to keep total administered radiation dose equivalent. The degree of improvement depends on the choice of reconstruction filters as well as the imaging task. The improvement is more substantial for high-frequency imaging tasks and when an aggressive slice-thickness (ST) filter is applied to reduced the high-frequency noise at peripheral angles.

Quantitative imaging in breast tomosynthesis and CT: comparison of detection and estimation task performance

PURPOSE

This work investigates a framework for modeling volumetric breast imaging to compare detection and estimation task performance and optimize quantitative breast imaging.

METHODS

Volumetric reconstructions of a breast phantom, which incorporated electronic, quantum, and anatomical noise with embedded spherical lesions, were simulated over a range of acquisition angles varying from 4 degrees to 204 degrees with a constant total acquisition dose of 1.5 mGy. A maximum likelihood estimator was derived in terms of the noise power spectrum, which yielded figures of merit for quantitative imaging performance in terms of accuracy and precision. These metrics were computed for estimation of lesion area, volume, and location. Estimation task performance was optimized as a function of acquisition angle and compared to the performance of a more conventional lesion detection task.

RESULTS

Results revealed tradeoffs between electronic, quantum, and anatomical noise. The detection of a 4 mm sphere was optimal at an acquisition angle of 84 degrees, where reconstructed images using a smaller acquisition angle exhibited increased anatomical noise and reconstructed images using a larger acquisition angle exhibited increased quantum and electronic noise. For all estimation tasks, accuracy was found to be fairly constant as a function acquisition angle indicating adequate system calibration, whereas a more significant dependence on acquisition angle was observed for precision performance. Precision for the 2D area estimation task was optimal at approximately 104 degrees, while precision of the 3D volume estimation task was optimal at larger angles (approximately 124 degrees). Precision for the localization task showed orientation dependence where localization was significantly inferior in the depth direction. Overall, precision for localization was optimal at larger angles (i.e., > 125 degrees) compared to the size estimation tasks. Results suggested that for quantitative imaging tasks, the acquisition angle should be larger than currently used in conventional breast tomosynthesis for lesion detection.

CONCLUSIONS

Analysis of quantitative imaging performance using Fourier-based metrics highlights the difference between estimation and detection task in volumetric breast imaging and provides a meaningful framework for optimizing the performance of breast imaging systems for quantitative imaging applications.