Anatomical background and generalized detectability in tomosynthesis and cone-beam CT

Direct estimation of the noise power spectrum from patient data to generate synthesized CT noise for denoising network training

BACKGROUND

Developing a deep-learning network for denoising low-dose CT (LDCT) images necessitates paired computed tomography (CT) images acquired at different dose levels. However, it is challenging to obtain these images from the same patient.

PURPOSE

In this study, we introduce a novel approach to generate CT images at different dose levels.

METHODS

Our method involves the direct estimation of the quantum noise power spectrum (NPS) from patient CT images without the need for prior information. By modeling the anatomical NPS using a power-law function and estimating the quantum NPS from the measured NPS after removing the anatomical NPS, we create synthesized quantum noise by applying the estimated quantum NPS as a filter to random noise. By adding synthesized noise to CT images, synthesized CT images can be generated as if these are obtained at a lower dose. This leads to the generation of paired images at different dose levels for training denoising networks.

RESULTS

The proposed method accurately estimates the reference quantum NPS. The denoising network trained with paired data generated using synthesized quantum noise achieves denoising performance comparable to networks trained using Mayo Clinic data, as justified by the mean-squared-error (MSE), structural similarity index (SSIM)and peak signal-to-noise ratio (PSNR) scores.

CONCLUSIONS

This approach offers a promising solution for LDCT image denoising network development without the need for multiple scans of the same patient at different doses.

Examining the Influence of Digital Phantom Models in Virtual Imaging Trials for Tomographic Breast Imaging

Purpose

Digital phantoms are one of the key components of virtual imaging trials (VITs) that aims to assess and optimize new medical imaging systems and algorithms. However, these phantoms vary in their voxel resolution, appearance and structural details. This study aims to examine whether and how variations between digital phantoms influence system optimization with digital breast tomosynthesis (DBT) as a chosen modality.

Methods

We selected widely used and open access digital breast phantoms generated with different methods. For each phantom type, we created an ensemble of DBT images to test acquisition strategies. Human observer localization ROC (LROC) was used to assess observer performance studies for each case. Noise power spectrum (NPS) was estimated to compare the phantom structural components. Further, we computed several gaze metrics to quantify the gaze pattern when viewing images generated from different phantom types.

Results

Our LROC results show that the arc samplings for peak performance were approximately 2.5° and 6° in Bakic and XCAT breast phantoms respectively for 3-mm lesion detection task and indicate that system optimization outcomes from VITs can vary with phantom types and structural frequency components. Additionally, a significant correlation (p¡0.01) between gaze metrics and diagnostic performance suggests that gaze analysis can be used to understand and evaluate task difficulty in VITs.

Conclusion

Our results point to the critical need to evaluate realism in digital phantoms as well as ensuring sufficient structural variations at spatial frequencies relevant to the signal size for an intended task. In addition, standardizing phantom generation and validation tools might aid in lower discrepancies among independently conducted VITs for system or algorithmic optimizations.

Task-based detectability in anatomical background in digital mammography, digital breast tomosynthesis and synthetic mammography

Objective.Determining the detectability of targets for the different imaging modalities in mammography in the presence of anatomical background noise is challenging. This work proposes a method to compare the image quality and detectability of targets in digital mammography (DM), digital breast tomosynthesis (DBT) and synthetic mammography.Approach. The low-frequency structured noise produced by a water phantom with acrylic spheres was used to simulate anatomical background noise for the different types of images. A method was developed to apply the non-prewhitening observer model with eye filter (NPWE) in these conditions. A homogeneous poly(methyl) methacrylate phantom with a 0.2 mm thick aluminium disc was used to calculate 2D in-plane modulation transfer function (MTF), noise power spectrum (NPS), noise equivalent quanta, and system detective quantum efficiency for 30, 50 and 70 mm thicknesses. The in-depth MTFs of DBT volumes were determined using a thin tungsten wire. The MTF, system NPS and anatomical NPS were used in the NPWE model to calculate the threshold gold thickness of the gold discs contained in the CDMAM phantom, which was taken as reference. Main results.The correspondence between the NPWE model and the CDMAM phantom (linear Pearson correlation 0.980) yielded a threshold detectability index that was used to determine the threshold diameter of spherical microcalcifications and masses. DBT imaging improved the detection of masses, which depended mostly on the reduction of anatomical background noise. Conversely, DM images yielded the best detection of microcalcifications.Significance.The method presented in this study was able to quantify image quality and object detectability for the different imaging modalities and levels of anatomical background noise.

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.

Power-law spectrum-based objective function to train a generative adversarial network with transfer learning for the synthetic breast CT image

Objective.This paper proposes a new objective function to improve the quality of synthesized breast CT images generated by the GAN and compares the GAN performances on transfer learning datasets from different image domains.Approach.The proposed objective function, named beta loss function, is based on the fact that x-ray-based breast images follow the power-law spectrum. Accordingly, the exponent of the power-law spectrum (beta value) for breast CT images is approximately two. The beta loss function is defined in terms of L1 distance between the beta value of synthetic images and validation samples. To compare the GAN performances for transfer learning datasets from different image domains, ImageNet and anatomical noise images are used in the transfer learning dataset. We employ styleGAN2 as the backbone network and add the proposed beta loss function. The patient-derived breast CT dataset is used as the training and validation dataset; 7355 and 212 images are used for network training and validation, respectively. We use the beta value evaluation and Fréchet inception distance (FID) score for quantitative evaluation.Main results.For qualitative assessment, we attempt to replicate the images from the validation dataset using the trained GAN. Our results show that the proposed beta loss function achieves a more similar beta value to real images and a lower FID score. Moreover, we observe that the GAN pretrained with anatomical noise images achieves better equality than ImageNet for beta value evaluation and FID score. Finally, the beta loss function with anatomical noise as the transfer learning dataset achieves the lowest FID score.Significance.Overall, the GAN using the proposed beta loss function with anatomical noise images as the transfer learning dataset provides the lowest FID score among all tested cases. Hence, this work has implications for developing GAN-based breast image synthesis methods for medical imaging applications.

Convolutional neural network-based model observer for signal known statistically task in breast tomosynthesis images

BACKGROUND

Since human observer studies are resource-intensive, mathematical model observers are frequently used to assess task-based image quality. The most common implementation of these model observers assume that the signal information is exactly known. However, these tasks cannot thoroughly represent situations where the signal information is not exactly known in terms of size and shape.

PURPOSE

Considering the limitations of the tasks for which signal information is exactly known, we proposed a convolutional neural network (CNN)-based model observer for signal known statistically (SKS) and background known statistically (BKS) detection tasks in breast tomosynthesis images.

METHODS

A wide parameter search was conducted from six different acquisition angles (i.e., 10°, 20°, 30°, 40°, 50°, and 60°) within the same dose level (i.e., 2.3 mGy) under two separate acquisition schemes: (1) constant total number of projections, and (2) constant angular separation between projections. Two different types of signals: spherical (i.e., SKE tasks) and spiculated (i.e., SKS tasks) were used. The detection performance of the CNN-based model observer was compared with that of the Hotelling observer (HO) instead of the IO. Pixel-wise gradient-weighted class activation mapping (pGrad-CAM) map was extracted from each reconstructed tomosynthesis image to provide an intuitive understanding of the trained CNN-based model observer.

RESULTS

The CNN-based model observer achieved a higher detection performance compared to that of the HO for all tasks. Moreover, the improvement in its detection performance was greater for SKS tasks compared to that for SKE tasks. These results demonstrated that the addition of nonlinearity improved the detection performance owing to the variation of the background and signal. Interestingly, the pGrad-CAM results effectively localized the class-specific discriminative region, further supporting the quantitative evaluation results of the CNN-based model observer. In addition, we verified that the CNN-based model observer required fewer images to achieve the detection performance of the HO.

CONCLUSIONS

In this work, we proposed a CNN-based model observer for SKS and BKS detection tasks in breast tomosynthesis images. Throughout the study, we demonstrated that the detection performance of the proposed CNN-based model observer was superior to that of the HO.

Experimental optimization of single-exposure dual-energy angiography with photon-counting x-ray detectors

BACKGROUND

Photon-counting x-ray detectors may enable single-exposure dual-energy (DE) x-ray angiography.

PURPOSE

The purpose of this paper is to experimentally optimize the energy thresholds and tube voltage for single-exposure DE x-ray angiography.

METHODS

We optimized single-exposure DE x-ray angiography using the iodine signal-difference-to-noise ratio (SDNR) per root patient air kerma (κ) as a figure of merit. We measured the iodine SDNR by imaging an iodine stepwedge immersed in a water tank with a depth of 30 cm in the direction of x-ray propagation. The stepwedge was imaged using tube voltages ranging from 90 to 150 kV and a cadmium telluride (CdTe) x-ray detector with two energy bins and analog charge summing for charge sharing suppression. The energy threshold that separates the two energy bins was varied from approximately 35 keV to approximately 75% of the maximum energy of the x-ray beam. Curve fitting was used to determine the threshold that maximized SDNR / κ $\mathrm{SDNR}/\sqrt {\kappa }$ . The effect of scatter was determined from measurements of the scatter-to-primary ratios (SPRs) of the low-energy and high-energy images and a semi-empirical model of the relationship between SDNR and SPR. Using the optimal parameters, we imaged a phantom with vessel-simulating structures and background clutter.

RESULTS

The optimal energy thresholds increased monotonically from ∼50 to ∼85 keV over the range of tube voltages considered. For tube voltages greater than 90 kV, the optimal energy thresholds consistently allocated approximately two thirds of all detected primary photons to the low energy bin; this ratio was preserved without scatter. Consistent with prior modeling studies, SDNR / κ $\mathrm{SDNR}/\sqrt {\kappa }$ increased monotonically with tube voltage from 90 to 150 kV; SDNR / κ $\mathrm{SDNR}/\sqrt {\kappa }$ at 150 kV was approximately 38% higher than that at 90 kV for an iodine area density of ∼50 mg/cm² . Scatter reduced SDNR by approximately 25% for SPRs of ∼1 and 0.4 in low-energy and high-energy images, respectively.

CONCLUSIONS

Achieving optimal image quality in single-exposure DE angiography with photon-counting x-ray detectors will require high tube voltages (i.e., >130 kV) and, for thick patients, energy thresholds that allocate approximately two thirds of all primary photons to the low-energy image. Future work will compare the image quality of singe-exposure photon-counting and kV-switching approaches to DE x-ray angiography.

Xenon-enhanced dual-energy tomosynthesis for functional imaging of respiratory disease-Concept and phantom study

BACKGROUND

Xenon-enhanced dual-energy (DE) computed tomography (CT) and hyperpolarized noble-gas magnetic resonance imaging (MRI) provide maps of lung ventilation that can be used to detect chronic obstructive pulmonary disease (COPD) early in its development and predict respiratory exacerbations. However, xenon-enhanced DE-CT requires high radiation doses and hyper-polarized noble-gas MRI is expensive and only available at a handful of institutions globally.

PURPOSE

To present xenon-enhanced dual-energy tomosynthesis (XeDET) for low-dose, low-cost functional imaging of respiratory disease in an experimental phantom study.

METHODS

We propose using digital tomosynthesis to produce Xe-enhanced low-energy (LE) and high-energy (HE) coronal images. DE subtraction of the LE and HE images is used to suppress soft tissues. We used an imaging phantom to investigate image quality in terms of the area under the reciever operating characteristic curve (AUC) for the Non-PreWhitening model observer with an Eye filter and internal noise (NPWEi). The phantom simulated anatomic clutter due to lung parenchyma and attenuation due to soft tissue and lung tissue. Aluminum slats were used to simulate rib structures. A stepwedge consisting of an acrylic casing with sealed cylindrical air-filled cavities was used to simulate ventilation defects with step thicknesses of 0.5, 1, and 2 cm and cylindrical radii of 0.5, 0.75, and 1 cm. The phantom was ventilated with Xe and projection data were acquired using a flat-panel detector, a tube-voltage combination of 60/140 kV with 1.2  mm of copper filtration on the HE spectrum and an angular range of ± 15 ∘ $\pm 15^{\circ}$ in 1° increments. The AUC of a NPWEi observer that has access only to a single coronal slice was calculated from measurements of the three-dimensional noise power spectrum and signal template. The AUC was calculated as a function of ventilation defect thickness and radius for total patient entrance air kermas ranging from 1.42 to 2.84 mGy with and without rib-simulating Al slats. For the AUC analysis, the observer internal noise level was obtained from an ad hoc calibration to a high-dose data set.

RESULTS

XeDET was able to suppress parenchyma-simulating clutter in coronal images enabling visualization of the simulated ventilation defects, but the limited angle acquisition resulted in residual clutter due to out-of-plane bone-mimmicking structures. The signal power of the defects increased linearly with defect radius and showed a ten-fold to fifteen-fold increase in signal power when the defect thickness increased from 0.5 to 2 cm. These trends agreed with theoretical predictions. Along the depth dimension, the power of the defects decreased exponentially with distance from the center of the defects with full-width half maxima that varied from 1.85 to 2.85 cm depending on the defect thickness and radius. The AUCs of the 1-cm-radius defect that was 2 cm in thickness ranged from good (0.8-0.9) to excellent (0.9-1.0) over the range of air kermas considered.

CONCLUSIONS

Xenon-enhanced DE tomosynthesis has the potential to enable functional imaging of respiratory disease and should be further investigated as a low-cost alternative to MRI-based approaches and a low-dose alternative to CT-based approaches.

Comparison of lesion detection and conspicuity between narrow-angle and wide-angle digital breast tomosynthesis for dense and non-dense breasts

Digital breast tomosynthesis (DBT) has been shown to improve both sensitivity and specificity for breast cancer detection compared to full-field digital mammography. However, its performance could be limited for patients with dense breasts. Clinical DBT systems vary in their system designs, one of which is the acquisition angular range (AR), which leads to varied performance for different imaging tasks. In this study, we aim to compare DBT systems with different AR. We used a previously validated cascaded linear system model to investigate the dependence of in-plane breast structural noise (BSN) and detectability of masses on AR. We conducted a pilot clinical study to compare the lesion conspicuity between clinical DBT systems with the narrowest and the widest AR. Patients called back for diagnostic imaging on suspicious findings were imaged with both narrow-angle (NA) and wide-angle (WA) DBT. We analyzed the BSN for clinical images using noise power spectrum (NPS) analysis. A 5-point Likert scale was used in the reader study to compare the lesion conspicuity. Our theoretical calculation results show that increasing AR leads to reduced BSN and improved mass detectability. The NPS analysis on clinical images shows the lowest BSN for WA DBT. The WA DBT provides better lesion conspicuity for masses and asymmetries and shows a greater advantage for non-microcalcification lesions in dense breasts. The NA DBT provides better characterizations for microcalcifications. The WA DBT can downgrade false-positive findings seen on NA DBT. In conclusion, WA DBT could improve the detection of masses and asymmetries for patients with dense breasts.

Experimental feasibility of xenon-enhanced dual-energy radiography for imaging of lung function

Chronic obstructive pulmonary disease (COPD) is a leading cause of death worldwide. We experimentally investigated the feasibility of two-dimensional xenon-enhanced dual-energy (XeDE) radiography for imaging of lung function. We optimized image quality under quantum-noise-limited conditions using a chest phantom consisting of a rectangular chamber representing the thoracic volume and PMMA slabs simulating x-ray attenuation by soft tissue. A sealed, air-filled cavity with thin PMMA walls was positioned inside the chamber to simulate a 2 cm thick ventilation defect. The chamber was ventilated with xenon and dual-energy imaging was performed using a diagnostic x-ray tube and a flat-panel detector. The contrast-to-noise ratio of ventilation defects normalized by patient x-ray exposure maximized at a kV-pair of approximately 60/140-kV and when approximately one third of the total exposure was allocated to the HE image. We used the optimized technique to image a second phantom that contained lung-parenchyma-mimicking PMMA clutter, rib-mimicking aluminum slats and an insert that simulated ventilation defects with thicknesses ranging from 0.5 cm to 2 cm and diameters ranging from 1 cm to 2 cm. From the resulting images we computed the area under the receiver operating characteristic curve (AUC) of the non-prewhitening model observer with an eye filter and internal noise. For a xenon concentration of 75%, good AUCs (i.e. 0.8-0.9) to excellent AUCs (i.e. >0.9) were obtained when the defect diameter is greater than 1.3 cm and defect thickness is 1 cm. When the xenon concentration was reduced to 50%, the AUC was ∼0.9 for defects 1.2 cm in diameter and ∼1.5 cm in thickness. Two-dimensional XeDE radiography may therefore enable detection of functional abnormalities associated with early-stage COPD, for which xenon ventilation defects can occupy up to 20% of the lung volume, and should be further developed as a low-cost alternative to MRI-based approaches and a low-dose alternative to CT-based approaches.

Performance evaluation of digital breast tomosynthesis systems: physical methods and experimental data

Digital breast tomosynthesis (DBT) has become a well-established breast imaging technique, whose performance has been investigated in many clinical studies, including a number of prospective clinical trials. Results from these studies generally point to non-inferiority in terms of microcalcification detection and superior mass-lesion detection for DBT imaging compared to digital mammography (DM). This modality has become an essential tool in the clinic for assessment and ad-hoc screening but is not yet implemented in most breast screening programmes at a state or national level. While evidence on the clinical utility of DBT has been accumulating, there has also been progress in the development of methods for technical performance assessment and quality control of these imaging systems. DBT is a relatively complicated ‘pseudo-3D’ modality whose technical assessment poses a number of difficulties. This paper reviews methods for the technical performance assessment of DBT devices, starting at the component level in part one and leading up to discussion of system evaluation with physical test objects in part two. We provide some historical and basic theoretical perspective, often starting from methods developed for DM imaging. Data from a multi-vendor comparison are also included, acquired under the medical physics quality control protocol developed by EUREF and currently being consolidated by a European Federation of Organisations for Medical Physics working group. These data and associated methods can serve as a reference for the development of reference data and provide some context for clinical studies.

Universal orbit design for metal artifact elimination

Objective.Metal artifacts are a persistent problem in CT and cone-beam CT. In this work, we propose to reduce or even eliminate metal artifacts by providing better sampling of data using non-circular orbits.Approach.We treat any measurements intersecting metal as missing data, and aim to design a universal orbit that can generally accommodate arbitrary metal shapes and locations. We adapted a local sampling completeness metric based on Tuy's condition to quantify the extent of sampling in the presence of metal. A maxi-min objective over all possible metal locations was used for orbit design. A simple class of sinusoidal orbits was evaluated as a function of frequencies, maximum tilt angles, and orbital extents. Experimental implementation of these orbits were performed on an imaging bench and evaluated on two phantoms, one containing metal balls and the other containing a pedicle screw assembly for spine fixation. Metal artifact reduction (MAR) performance was compared amongst three approaches: non-circular orbits only, algorithmic correction only, and a combined approach.Main results.Theoretical evaluations of the objective favor sinusoidal orbits with large tilt angles and large orbital extents. Furthermore, orbits that leverage redundant azimuthal angles to sample non-redundant data have better performance, e.g. even or non-integer frequency sinusoids for a 360° acquisition. Experimental data support the trends observed in theoretical evaluations. Reconstructions using even or non-integer frequency orbits present less streaking artifacts and background details with finer resolution, even when multiple metal objects are present and even in the absence of MAR algorithms. The combined approach of non-circular orbits and MAR algorithm yields the best performance. The observed trend in image quality is supported by quantitative measures of sampling and severity of streaking artifact.Significance.This work demonstrates that sinusoidal orbits are generally robust against metal artifacts and can provide an avenue for improved image quality in interventional imaging.

Low dose cone beam CT for paediatric image-guided radiotherapy: Image quality and practical recommendations

PURPOSE

Cone beam CT (CBCT) is used in paediatric image-guided radiotherapy (IGRT) for patient setup and internal anatomy assessment. Adult CBCT protocols lead to excessive doses in children, increasing the risk of radiation-induced malignancies. Reducing imaging dose increases quantum noise, degrading image quality. Patient CBCTs also include 'anatomical noise' (e.g. motion artefacts), further degrading quality. We determine noise contributions in paediatric CBCT, recommending practical imaging protocols and thresholds above which increasing dose yields no improvement in image quality.

METHODS AND MATERIALS

Sixty CBCTs including the thorax or abdomen/pelvis from 7 paediatric patients (aged 6-13 years) were acquired at a range of doses and used to simulate lower dose scans, totalling 192 scans (0.5-12.8 mGy). Noise measured in corresponding regions of each patient and a 10-year-old phantom were compared, modelling total (including anatomical) noise, and quantum noise contributions as a function of dose. Contrast-to-noise ratio (CNR) was measured between fat/muscle. Soft tissue registration was performed on the kidneys, comparing accuracy to the highest dose scans.

RESULTS

Quantum noise contributed <20% to total noise in all cases, suggesting anatomical noise is the largest determinant of image quality in the abdominal/pelvic region. CNR exceeded 3 in over 90% of cases ≥ 1 mGy, and 57% of cases at 0.5 mGy. Soft tissue registration was accurate for doses > 1 mGy.

CONCLUSION

Anatomical noise dominates quantum noise in paediatric CBCT. Appropriate soft tissue contrast and registration accuracy can be achieved for doses as low as 1 mGy. Increasing dose above 1 mGy holds no benefit in improving image quality or registration accuracy due to the presence of anatomical noise.

Impact of iterative reconstruction algorithms on the applicability of Fourier-based detectability index for x-ray CT imaging

PURPOSE

The increasing application of iterative reconstruction algorithms in clinical computed tomography to improve image quality and reduce radiation dose, elicits strong interest, and needs model observers to optimize CT scanning protocols objectively and efficiently. The current paradigm for evaluating imaging system performance relies on Fourier methods, which presuppose a linear, wide-sense stationary system. Long-range correlations introduced by iterative reconstruction algorithms may narrow the applicability of Fourier techniques. Differences in the implementation of reconstruction algorithms between manufacturers add further complexity. The present work set out to quantify the errors entailed by the use of Fourier methods, which can lead to design decisions that do not correlate with detectability.

METHODS

To address this question, we evaluated the noise properties and the detectability index of the ideal linear observer using the spatial approach and the Fourier-based approach. For this purpose, a homogeneous phantom was imaged on two scanners: the Revolution CT (GE Healthcare) and the Somatom Definition AS+ (Siemens Healthcare) at different exposure levels. Images were reconstructed using different strength levels of IR algorithms available on the systems considered: Adaptative Statistical Iterative Reconstruction (ASIR-V) and Sinogram Affirmed Iterative Reconstruction (SAFIRE).

RESULTS

Our findings highlight that the spatial domain estimate of the detectability index is higher than the Fourier domain estimate. This trend is found to be dependent on the specific regularization used by IR algorithms as well as the signal to be detected. The eigenanalysis of the noise covariance matrix and of its circulant approximation yields explanation about the evoked trends. In particular, this analysis suggests that the predictive power of the Fourier-based ideal linear observer depends on the ability of each basis analyzed to be relevant to the signal to be detected.

CONCLUSION

The applicability of Fourier techniques is dependent on the specific regularization used by IR algorithms. These results argue for verifying the assumptions made when using Fourier methods since Fourier-task-based detectability index does not always correlate with signal detectability.

Technical evaluation of image quality in synthetic mammograms obtained from 15° and 40° digital breast tomosynthesis in a commercial system: a quantitative comparison

Digital breast tomosynthesis (DBT) has recently gained interest both for breast cancer screening and diagnosis. Its employment has increased also in conjunction with digital mammography (DM), to improve cancer detection and reduce false positive recall rate. Synthetic mammograms (SMs) reconstructed from DBT data have been introduced to replace DM in the DBT + DM approach, for preserving the benefits of the dual-acquisition modality whilst reducing radiation dose and compression time. Therefore, different DBT models have been commercialized and the effective potential of each system has been investigated. In particular, wide-angle DBT was shown to provide better depth resolution than narrow-angle DBT, while narrow-angle DBT allows better identification of microcalcifications compared to wide-angle DBT. Given the increasing employment of SMs as supplement to DBT, a comparison of image quality between SMs obtained in narrow-angle and wide-angle DBT is of practical interest. Therefore, the aim of this phantom study was to evaluate and compare the image quality of SMs reconstructed from 15° (SM₁₅) and 40° (SM₄₀) DBT in a commercial system. Spatial resolution, noise and contrast properties were evaluated through the modulation transfer function (MTF), noise power spectrum, maps of signal-to-noise ratio (SNR), image contrast, contrast-to-noise ratio (CNR) and contrast-detail (CD) thresholds. SM₄₀ expressed higher MTF than SM₁₅, but also lower SNR and CNR levels. SM₁₅ and SM₄₀ were characterized by slight different texture, and a different behavior in terms of contrast was found. SM₁₅ provided better CD performances than SM₄₀. These results suggest that the employment of wide/narrow-angle DBT + SM images should be optimized based on the specific image task.

Effect of Filtered Back-Projection Filters to Low-Contrast Object Imaging in Ultra-High-Resolution (UHR) Cone-Beam Computed Tomography (CBCT)

In this study, the effect of filter schemes on several low-contrast materials was compared using standard and ultra-high-resolution (UHR) cone-beam computed tomography (CBCT) imaging. The performance of the UHR-CBCT was quantified by measuring the modulation transfer function (MTF) and the noise power spectrum (NPS). The MTF was measured at the radial location around the cylindrical phantom, whereas the NPS was measured in the eight different homogeneous regions of interest. Six different filter schemes were designed and implemented in the CT sinogram from each imaging configuration. The experimental results indicated that the filter with smaller smoothing window preserved the MTF up to the highest spatial frequency, but larger NPS. In addition, the UHR imaging protocol provided 1.77 times better spatial resolution than the standard acquisition by comparing the specific spatial frequency (f₅₀) under the same conditions. The f₅₀s with the flat-top window in UHR mode was 1.86, 0.94, 2.52, 2.05, and 1.86 lp/mm for Polyethylene (Material 1, M1), Polystyrene (M2), Nylon (M3), Acrylic (M4), and Polycarbonate (M5), respectively. The smoothing window in the UHR protocol showed a clearer performance in the MTF according to the low-contrast objects, showing agreement with the relative contrast of materials in order of M3, M4, M1, M5, and M2. In conclusion, although the UHR-CBCT showed the disadvantages of acquisition time and radiation dose, it could provide greater spatial resolution with smaller noise property compared to standard imaging; moreover, the optimal window function should be considered in advance for the best UHR performance.

Relative Contributions of Anatomical and Quantum Noise in Signal Detection and Perception of Tomographic Digital Breast Images

Anatomical and quantum noise inhibits detection of malignancies in clinical images such as in digital mammography (DM), digital breast tomosynthesis (DBT) and breast CT (bCT). In this work, we examine the relative influence and interactions of these two types of noise on the task of low contrast mass detectability in DBT. We show how the changing levels of quantum noise contributes to the estimated power-law slope β by changing DBT acquisition parameters as well as with spatial filtering like an adaptive Weiner filtering. Finally, we examine via human observer LROC studies whether power spectral parameters obtained from DBT images correlate with mass detectability in those images. Our results show that lower values of power-law slope β can result from heightened quantum noise or image artifacts and do not necessarily imply reduced anatomical noise or improved signal detectability for the given imaging system. These results strengthen the argument that when power-law magnitude K is varying, β is less relevant to lesion detectability. Our preliminary results also point to K values having strong correlation to human observer performance, at least for the task shown in this paper. As a byproduct of these main results, we also show that while changes in acquisition geometry can improve mass detectability, the use of efficient filters like an adaptive Weiner filtering can significantly improve the detection of low contrast masses in DBT.

In-plane image quality and NPWE detectability index in digital breast tomosynthesis

A rigorous 2D analysis of signal and noise transfer applied to reconstructed planes in digital breast tomosynthesis (DBT) is necessary for system characterization and optimization. This work proposes a method for assessing technical image quality and system detective quantum efficiency (DQE_sys) for reconstructed planes in DBT. Measurements of 2D in-plane modulation transfer function (MTF) and noise power spectrum (NPS) were made on five DBT systems using different acquisition parameters, reconstruction algorithms and plane spacing. This work develops the noise equivalent quanta (NEQ), DQE_sys and detectability index (d') calculated using a non-prewhitening model observer with eye filter (NPWE) for reconstructed DBT planes. The images required for this implementation were acquired using a homogeneous test object of thickness 40 mm poly(methyl) methacrylate plus 0.5 mm Al; 2D MTF was calculated from an Al disc of thickness 0.2 mm and diameter 50 mm positioned within the phantom. The radiant contrast of the MTF disc and the air kerma at the system input were used as normalization factors. The NPWE detectability index was then compared to the in-plane contrast-detail (c-d) threshold measured using the CDMAM phantom. The MTF and NPS measured on the different systems showed a strong anisotropy, consistent with the cascaded models developed in the literature for DBT. Detectability indices calculated from the measured MTF and NPS successfully predicted changes in c-d detectability for details between 0.1 mm and 2.0 mm, for DBT plane spacings between 0.5 mm and 10 mm, and for air kerma values at the system input between 157 µGy and 1170 μGy. The linear Pearson correlation between the detectability index and threshold gold thickness of the CDMAM phantom was -0.996. The method implements a parametric means of assessing the technical image quality of reconstructed DBT planes, providing valuable information for optimization of DBT systems.

Frequency-dependent signal and noise in spectroscopic x-ray imaging

Purpose

We present a new framework for theoretical analysis of the noise power spectrum (NPS) of photon‐counting x‐ray detectors, including simple photon‐counting detectors (SPCDs) and spectroscopic x‐ray detectors (SXDs), the latter of which use multiple energy thresholds to discriminate photon energies.

Methods

We show that the NPS of SPCDs and SXDs, including spatio‐energetic noise correlations, is determined by the joint probability density function (PDF) of deposited photon energies, which describes the probability of recording two photons of two different energies in two different elements following a single‐photon interaction. We present an analytic expression for this joint PDF and calculate the presampling and digital NPS of CdTe SPCDs and SXDs. We calibrate our charge sharing model using the energy response of a cadmium zinc telluride (CZT) spectroscopic x‐ray detector and compare theoretical results with Monte Carlo simulations.

Results

Our analysis shows that charge sharing increases pixel signal‐to‐noise ratio (SNR), but degrades the zero‐frequency signal‐to‐noise performance of SPCDs and SXDs. In all cases considered, this degradation was greater than 10%. Comparing the presampling NPS with the sampled NPS showed that degradation in zero‐frequency performance is due to zero‐frequency noise aliasing induced by charge sharing.

Conclusions

Noise performance, including spatial and energy correlations between elements and energy bins, are described by the joint PDF of deposited energies which provides a method of determining the photon‐counting NPS, including noise‐aliasing effects and spatio‐energetic effects in spectral imaging. Our approach enables separating noise due to x‐ray interactions from that associated with sampling, consistent with cascaded systems analysis of energy‐integrating systems. Our methods can be incorporated into task‐based assessment of image quality for the design and optimization of spectroscopic x‐ray detectors.

Cascaded systems analysis of anatomic noise in digital mammography and dual-energy digital mammography

In x-ray based imaging of the breast, contrast between fibroglandular (Fg) tissue and adipose (Ad) tissue is a source of anatomic noise. The goal of this work was to validate by simulation and experiment a mathematical framework for modelling the Fg component of anatomic noise in digital mammograpy (DM) and dual-energy (DE) DM. Our mathematical framework unifies and generalizes existing approaches. We compared mathematical predictions directly with empirical measurements of the anatomic noise power spectrum of the CIRS BR3D structured breast phantom using two clinical mammography systems and four beam qualities. Our simulation and experimental results showed agreement with mathematical predictions. As a demonstration of utility, we used our mathematical framework in a theoretical spectral optimization of DM for the task of detecting breast masses. Our theoretical optimization showed that the optimal tube voltage for DM may be higher than that based on predictions that do not account for anatomic noise, in agreement with recent theoretical findings. Additionally, our theoretical optimization predicts that filtering tungsten-anode x-ray spectra with rhodium has little influence on lesion detectability, in contrast with previous findings. The mathematical methods validated in this work can be incorporated easily into cascaded systems analysis of breast imaging systems and will be useful when optimizating novel techniques for x-ray-based imaging of the breast.

A Statistical Model for Rigid Image Registration Performance: The Influence of Soft-Tissue Deformation as a Confounding Noise Source

Soft-tissue deformation presents a confounding factor to rigid image registration by introducing image content inconsistent with the underlying motion model, presenting non-correspondent structure with potentially high power, and creating local minima that challenge iterative optimization. In this paper, we introduce a model for registration performance that includes deformable soft tissue as a power-law noise distribution within a statistical framework describing the Cramer-Rao lower bound (CRLB) and root-mean-squared error (RMSE) in registration performance. The model incorporates both cross-correlation and gradient-based similarity metrics, and the model was tested in application to 3D-2D (CT-to-radiograph) and 3D-3D (CT-to-CT) image registration. Predictions accurately reflect the trends in registration error as a function of dose (quantum noise), and the choice of similarity metrics for both registration scenarios. Incorporating soft-tissue deformation as a noise source yields important insight on the limits of registration performance with respect to algorithm design and the clinical application or anatomical context. For example, the model quantifies the advantage of gradient-based similarity metrics in 3D-2D registration, identifies the low-dose limits of registration performance, and reveals the conditions for which the registration performance is fundamentally limited by soft-tissue deformation.

Predicting image properties in penalized-likelihood reconstructions of flat-panel CBCT

PURPOSE

Model-based iterative reconstruction (MBIR) algorithms such as penalized-likelihood (PL) methods exhibit data-dependent and shift-variant properties. Image quality predictors have been derived to prospectively estimate local noise and spatial resolution, facilitating both system hardware design and tuning of reconstruction methods. However, current MBIR image quality predictors rely on idealized system models, ignoring physical blurring effects and noise correlations found in real systems. In this work, we develop and validate a new set of predictors using a physical system model specific to flat-panel cone-beam CT (FP-CBCT).

METHODS

Physical models appropriate for integration with MBIR analysis are developed and parameterized to represent nonidealities in FP projection data including focal spot blur, scintillator blur, detector aperture effect, and noise correlations. Flat-panel-specific predictors for local spatial resolution and local noise properties in PL reconstructions are developed based on these realistic physical models. Estimation accuracy of conventional (idealized) and FP-specific predictors is investigated and validated against experimental CBCT measurements using specialized phantoms.

RESULTS

Validation studies show that flat-panel-specific predictors can accurately estimate the local spatial resolution and noise properties, while conventional predictors show significant deviations in the magnitude and scale of the spatial resolution and local noise. The proposed predictors show accurate estimations over a range of imaging conditions including varying x-ray technique and regularization strength. The conventional spatial resolution prediction is sharper than ground truth. Using conventional spatial resolution predictor, the full width at half maximum (FWHM) of local point spread function (PSF) is underestimated by 0.2 mm. This mismatch is mostly eliminated in FP-specific prediction. The general shape and amplitude of local noise power spectrum (NPS) FP-specific predictions are consistent with measurement, while the conventional predictions underestimated the noise level by 70%.

CONCLUSION

The proposed image quality predictors permit accurate estimation of local spatial resolution and noise properties for PL reconstruction, accounting for dependencies on the system geometry, x-ray technique, and patient-specific anatomy in real FP-CBCT. Such tools enable prospective analysis of image quality for a range of goals including novel system and acquisition design, adaptive and task-driven imaging, and tuning of MBIR for robust and reliable behavior.

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.

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'.

Detector Blur and Correlated Noise Modeling for Digital Breast Tomosynthesis Reconstruction

This paper describes a new image reconstruction method for digital breast tomosynthesis (DBT). The new method incorporates detector blur into the forward model. The detector blur in DBT causes correlation in the measurement noise. By making a few approximations that are reasonable for breast imaging, we formulated a regularized quadratic optimization problem with a data-fit term that incorporates models for detector blur and correlated noise (DBCN). We derived a computationally efficient separable quadratic surrogate (SQS) algorithm to solve the optimization problem that has a non-diagonal noise covariance matrix. We evaluated the SQS-DBCN method by reconstructing DBT scans of breast phantoms and human subjects. The contrast-to-noise ratio and sharpness of microcalcifications were analyzed and compared with those by the simultaneous algebraic reconstruction technique. The quality of soft tissue lesions and parenchymal patterns was examined. The results demonstrate the potential to improve the image quality of reconstructed DBT images by incorporating the system physics model. This paper is a first step toward model-based iterative reconstruction for DBT.

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.

Effects of Image Quality on the Fundamental Limits of Image Registration Accuracy

For image-guided procedures, the imaging task is often tied to the registration of intraoperative and preoperative images to a common coordinate system. While the accuracy of this registration is a vital factor in system performance, there is a relatively little work that relates registration accuracy to image quality factors, such as dose, noise, and spatial resolution. To create a theoretical model for such a relationship, we present a Fisher information approach to analyze registration performance in explicit dependence on the underlying image quality factors of image noise, spatial resolution, and signal power spectrum. The model yields analysis of the Cramer-Rao lower bound (CRLB), in registration accuracy as a function of factors governing image quality. Experiments were performed in simulation of computed tomography low-contrast soft tissue images and high-contrast bone (head and neck) images to compare the measured accuracy [root mean squared error (RMSE) of the estimated transformations] with the theoretical lower bound. Analysis of the CRLB reveals that registration performance is closely related to the signal-to-noise ratio of the cross-correlation space. While the lower bound is optimistic, it exhibits consistent trends with experimental findings and yields a method for comparing the performance of various registration methods and similarity metrics. Further analysis validated a method for determining optimal post-processing (image filtering) for registration. Two figures of merit (CRLB and RMSE) are presented that unify models of image quality with registration performance, providing an important guide to optimizing intraoperative imaging with respect to the task of registration.

Task-Based Modeling of a 5k Ultra-High-Resolution Medical Imaging System for Digital Breast Tomosynthesis

High-resolution, low-noise X-ray detectors based on CMOS active pixel sensor (APS) technology have demonstrated superior imaging performance for digital breast tomosynthesis (DBT). This paper presents a task-based model for a high-resolution medical imaging system to evaluate its ability to detect simulated microcalcifications and masses as lesions for breast cancer. A 3-D cascaded system analysis for a 50- [Formula: see text] pixel pitch CMOS APS X-ray detector was integrated with an object task function, a medical imaging display model, and the human eye contrast sensitivity function to calculate the detectability index and area under the ROC curve (AUC). It was demonstrated that the display pixel pitch and zoom factor should be optimized to improve the AUC for detecting small microcalcifications. In addition, detector electronic noise of smaller than 300 e^- and a high display maximum luminance (>1000 cd/cm ²) are desirable to distinguish microcalcifications of [Formula: see text] in size. For low contrast mass detection, a medical imaging display with a minimum of 12-bit gray levels is recommended to realize accurate luminance levels. A wide projection angle range of greater than ±30° in combination with the image gray level magnification could improve the mass detectability especially when the anatomical background noise is high. On the other hand, a narrower projection angle range below ±20° can improve the small, high contrast object detection. Due to the low mass contrast and luminance, the ambient luminance should be controlled below 5 cd/ [Formula: see text]. Task-based modeling provides important firsthand imaging performance of the high-resolution CMOS-based medical imaging system that is still at early stage development for DBT. The modeling results could guide the prototype design and clinical studies in the future.

Modified ideal observer model (MIOM) for high-contrast and high-spatial resolution CT imaging tasks

PURPOSE

Although a variety of mathematical observer models have been developed to predict human observer performance for low contrast lesion detection tasks, their predictive power for high contrast and high spatial resolution discrimination imaging tasks, including those in CT bone imaging, could be limited. The purpose of this work was to develop a modified observer model that has improved correlation with human observer performance for these tasks.

METHODS

The proposed observer model, referred to as the modified ideal observer model (MIOM), uses a weight function to penalize components in the task function that have less contribution to the actual human observer performance for high contrast and high spatial resolution discrimination tasks. To validate MIOM, both human observer and observer model studies were performed, each using exactly the same CT imaging task [discrimination of a connected component in a high contrast (1000 HU) high spatial resolution bone fracture model (0.3 mm)] and experimental CT image data. For the human observer studies, three physicist observers rated the connectivity of the fracture model using a five-point Likert scale; for the observer model studies, a total of five observer models, including both conventional models and the proposed MIOM, were used to calculate the discrimination capability of the CT images in resolving the connected component. Images used in the studies encompassed nine different reconstruction kernels. Correlation between human and observer model performance for these kernels were quantified using the Spearman rank correlation coefficient (ρ). After the validation study, an example application of MIOM was presented, in which the observer model was used to select the optimal reconstruction kernel for a High-Resolution (Hi-Res, GE Healthcare) CT scan technique.

RESULTS

The performance of the proposed MIOM correlated well with that of the human observers with a Spearman rank correlation coefficient ρ of 0.88 (P = 0.003). In comparison, the value of ρ was 0.05 (P = 0.904) for the ideal observer, 0.05 (P = 0.904) for the non-prewhitening observer, -0.18 (P = 0.634) for the non-prewhitening observer with eye filter and internal noise, and 0.30 (P = 0.427) for the prewhitening observer with eye filter and internal noise. Using the validated MIOM, the optimal reconstruction kernel for the Hi-Res mode to perform high spatial resolution and high contrast discrimination imaging tasks was determined to be the HD Ultra kernel at the center of the scan field of view (SFOV), or the Lung kernel at the peripheral region of the SFOV. This result was consistent with visual observations of nasal CT images of an in vivo canine subject.

CONCLUSION

Compared with other observer models, the proposed modified ideal observer model provides significantly improved correlation with human observers for high contrast and high spatial resolution CT imaging tasks.

Task-driven optimization of CT tube current modulation and regularization in model-based iterative reconstruction

Tube current modulation (TCM) is routinely adopted on diagnostic CT scanners for dose reduction. Conventional TCM strategies are generally designed for filtered-backprojection (FBP) reconstruction to satisfy simple image quality requirements based on noise. This work investigates TCM designs for model-based iterative reconstruction (MBIR) to achieve optimal imaging performance as determined by a task-based image quality metric. Additionally, regularization is an important aspect of MBIR that is jointly optimized with TCM, and includes both the regularization strength that controls overall smoothness as well as directional weights that permits control of the isotropy/anisotropy of the local noise and resolution properties. Initial investigations focus on a known imaging task at a single location in the image volume. The framework adopts Fourier and analytical approximations for fast estimation of the local noise power spectrum (NPS) and modulation transfer function (MTF)-each carrying dependencies on TCM and regularization. For the single location optimization, the local detectability index (d') of the specific task was directly adopted as the objective function. A covariance matrix adaptation evolution strategy (CMA-ES) algorithm was employed to identify the optimal combination of imaging parameters. Evaluations of both conventional and task-driven approaches were performed in an abdomen phantom for a mid-frequency discrimination task in the kidney. Among the conventional strategies, the TCM pattern optimal for FBP using a minimum variance criterion yielded a worse task-based performance compared to an unmodulated strategy when applied to MBIR. Moreover, task-driven TCM designs for MBIR were found to have the opposite behavior from conventional designs for FBP, with greater fluence assigned to the less attenuating views of the abdomen and less fluence to the more attenuating lateral views. Such TCM patterns exaggerate the intrinsic anisotropy of the MTF and NPS as a result of the data weighting in MBIR. Directional penalty design was found to reinforce the same trend. The task-driven approaches outperform conventional approaches, with the maximum improvement in d' of 13% given by the joint optimization of TCM and regularization. This work demonstrates that the TCM optimal for MBIR is distinct from conventional strategies proposed for FBP reconstruction and strategies optimal for FBP are suboptimal and may even reduce performance when applied to MBIR. The task-driven imaging framework offers a promising approach for optimizing acquisition and reconstruction for MBIR that can improve imaging performance and/or dose utilization beyond conventional imaging strategies.

Generation and analysis of clinically relevant breast imaging x-ray spectra

PURPOSE

The purpose of this work was to develop and make available x-ray spectra for some of the most widely used digital mammography (DM), breast tomosynthesis (BT), and breast CT (bCT) systems in North America.

METHODS

The Monte Carlo code MCNP6 was used to simulate minimally filtered (only beryllium) x-ray spectra at 8 tube potentials from 20 to 49 kV for DM/BT, and 9 tube potentials from 35 to 70 kV for bCT. Vendor-specific anode compositions, effective anode angles, focal spot sizes, source-to-detector distances, and beryllium filtration were simulated. For each 0.5 keV energy bin in all simulated spectra, the fluence was interpolated using cubic splines across the range of simulated tube potentials to produce spectra in 1 kV increments from 20 to 49 kV for DM/BT and from 35 to 70 kV for bCT. The HVL of simulated spectra with conventional filtration (at 35 kV for DM/BT and 49 kV for bCT) was used to assess spectral differences resulting from variations in: (a) focal spot size (0.1 and 0.3 mm IEC), (b) solid angle at the detector (i.e., small and large FOV size), and (c) geometrical specifications for vendors that employ the same anode composition.

RESULTS

Averaged across all DM/BT vendors, variations in focal spot and FOV size resulted in HVL differences of 2.2% and 0.9%, respectively. Comparing anode compositions separately, the HVL differences for Mo (GE, Siemens) and W (Hologic, Philips, and Siemens) spectra were 0.3% and 0.6%, respectively. Both the commercial Koning and prototype "Doheny" (UC Davis) bCT systems utilize W anodes with a 0.3 mm focal spot. Averaged across both bCT systems, variations in FOV size resulted in a 2.2% difference in HVL. In addition, the Koning spectrum was slightly harder than Doheny with a 4.2% difference in HVL. Therefore to reduce redundancy, a generic DM/BT system and a generic bCT system were used to generate the new spectra reported herein. The spectral models for application to DM/BT were dubbed the Molybdenum, Rhodium, and Tungsten Anode Spectral Models using Interpolating Cubic Splines (MASMICS_M-T , RASMICS_M-T , and TASMICS_M-T ; subscript "M-T" indicating mammography and tomosynthesis). When compared against reference models (MASMIP_M , RASMIP_M , and TASMIP_M ; subscript "M" indicating mammography), the new spectral models were in close agreement with mean differences of 1.3%, -1.3%, and -3.3%, respectively, across tube potential comparisons of 20, 30, and 40 kV with conventional filtration. TASMICS_bCT -generated bCT spectra were also in close agreement with the reference TASMIP model with a mean difference of -0.8%, across tube potential comparisons of 35, 49, and 70 kV with 1.5 mm Al filtration.

CONCLUSIONS

The Mo, Rh, and W anode spectra for application in DM and BT (MASMICS_M-T , RASMICS_M-T , and TASMICS_M-T ) and the W anode spectra for bCT (TASMICS_bCT ) as described in this study should be useful for individuals interested in modeling the performance of modern breast x-ray imaging systems including dual-energy mammography which extends to 49 kV. These new spectra are tabulated in spreadsheet form and are made available to any interested party.

Task-driven optimization of CT tube current modulation and regularization in model-based iterative reconstruction

Tube current modulation (TCM) is routinely adopted on diagnostic CT scanners for dose reduction. Conventional TCM strategies are generally designed for filtered-backprojection (FBP) reconstruction to satisfy simple image quality requirements based on noise. This work investigates TCM designs for model-based iterative reconstruction (MBIR) to achieve optimal imaging performance as determined by a task-based image quality metric. Additionally, regularization is an important aspect of MBIR that is jointly optimized with TCM, and includes both the regularization strength that controls overall smoothness as well as directional weights that permits control of the isotropy/anisotropy of the local noise and resolution properties. Initial investigations focus on a known imaging task at a single location in the image volume. The framework adopts Fourier and analytical approximations for fast estimation of the local noise power spectrum (NPS) and modulation transfer function (MTF)-each carrying dependencies on TCM and regularization. For the single location optimization, the local detectability index (d') of the specific task was directly adopted as the objective function. A covariance matrix adaptation evolution strategy (CMA-ES) algorithm was employed to identify the optimal combination of imaging parameters. Evaluations of both conventional and task-driven approaches were performed in an abdomen phantom for a mid-frequency discrimination task in the kidney. Among the conventional strategies, the TCM pattern optimal for FBP using a minimum variance criterion yielded a worse task-based performance compared to an unmodulated strategy when applied to MBIR. Moreover, task-driven TCM designs for MBIR were found to have the opposite behavior from conventional designs for FBP, with greater fluence assigned to the less attenuating views of the abdomen and less fluence to the more attenuating lateral views. Such TCM patterns exaggerate the intrinsic anisotropy of the MTF and NPS as a result of the data weighting in MBIR. Directional penalty design was found to reinforce the same trend. The task-driven approaches outperform conventional approaches, with the maximum improvement in d' of 13% given by the joint optimization of TCM and regularization. This work demonstrates that the TCM optimal for MBIR is distinct from conventional strategies proposed for FBP reconstruction and strategies optimal for FBP are suboptimal and may even reduce performance when applied to MBIR. The task-driven imaging framework offers a promising approach for optimizing acquisition and reconstruction for MBIR that can improve imaging performance and/or dose utilization beyond conventional imaging strategies.

Comparison of low-contrast detectability between two CT reconstruction algorithms using voxel-based 3D printed textured phantoms

PURPOSE

To use novel voxel-based 3D printed textured phantoms in order to compare low-contrast detectability between two reconstruction algorithms, FBP (filtered-backprojection) and SAFIRE (sinogram affirmed iterative reconstruction) and determine what impact background texture (i.e., anatomical noise) has on estimating the dose reduction potential of SAFIRE.

METHODS

Liver volumes were segmented from 23 abdominal CT cases. The volumes were characterized in terms of texture features from gray-level co-occurrence and run-length matrices. Using a 3D clustered lumpy background (CLB) model, a fitting technique based on a genetic optimization algorithm was used to find CLB textures that were reflective of the liver textures, accounting for CT system factors of spatial blurring and noise. With the modeled background texture as a guide, four cylindrical phantoms (Textures A-C and uniform, 165 mm in diameter, and 30 mm height) were designed, each containing 20 low-contrast spherical signals (6 mm diameter at nominal contrast levels of ∼3.2, 5.2, 7.2, 10, and 14 HU with four repeats per signal). The phantoms were voxelized and input into a commercial multimaterial 3D printer (Object Connex 350), with custom software for voxel-based printing (using principles of digital dithering). Images of the textured phantoms and a corresponding uniform phantom were acquired at six radiation dose levels (SOMATOM Flash, Siemens Healthcare) and observer model detection performance (detectability index of a multislice channelized Hotelling observer) was estimated for each condition (5 contrasts × 6 doses × 2 reconstructions × 4 backgrounds = 240 total conditions). A multivariate generalized regression analysis was performed (linear terms, no interactions, random error term, log link function) to assess whether dose, reconstruction algorithm, signal contrast, and background type have statistically significant effects on detectability. Also, fitted curves of detectability (averaged across contrast levels) as a function of dose were constructed for each reconstruction algorithm and background texture. FBP and SAFIRE were compared for each background type to determine the improvement in detectability at a given dose, and the reduced dose at which SAFIRE had equivalent performance compared to FBP at 100% dose.

RESULTS

Detectability increased with increasing radiation dose (P = 2.7 × 10^-59) and contrast level (P = 2.2 × 10^-86) and was higher in the uniform phantom compared to the textured phantoms (P = 6.9 × 10^-51). Overall, SAFIRE had higher d' compared to FBP (P = 0.02). The estimated dose reduction potential of SAFIRE was found to be 8%, 10%, 27%, and 8% for Texture-A, Texture-B, Texture-C and uniform phantoms.

CONCLUSIONS

In all background types, detectability was higher with SAFIRE compared to FBP. However, the relative improvement observed from SAFIRE was highly dependent on the complexity of the background texture. Iterative algorithms such as SAFIRE should be assessed in the most realistic context possible.

Design and application of a structured phantom for detection performance comparison between breast tomosynthesis and digital mammography

This paper introduces and applies a structured phantom with inserted target objects for the comparison of detection performance of digital breast tomosynthesis (DBT) against 2D full field digital mammography (FFDM). The phantom consists of a 48 mm thick breast-shaped polymethyl methacrylate (PMMA) container filled with water and PMMA spheres of different diameters. Three-dimensionally (3D) printed spiculated masses (diameter range: 3.8-9.7 mm) and non-spiculated masses (1.6-6.2 mm) along with microcalcifications (90-250 µm) were inserted as targets. Reproducibility of the phantom application was studied on a single system using 30 acquisitions. Next, the phantom was evaluated on five different combined FFDM & DBT systems and target detection was compared for FFDM and DBT modes. Ten phantom images in both FFDM and DBT modes were acquired on these 5 systems using automatic exposure control. Five readers evaluated target detectability. Images were read with the four-alternative forced-choice (4-AFC) paradigm, with always one segment including a target and 3 normal background segments. The percentage of correct responses (PC) was assessed based on 10 trials of each reader for each object type, size and imaging modality. Additionally, detection threshold diameters at 62.5 PC were assessed via non-linear regression fitting of the psychometric curve. The reproducibility study showed no significant differences in PC values. Evaluation of target detection in FFDM showed that microcalcification detection thresholds ranged between 110 and 118 µm and were similar compared to the detection in DBT (range of 106-158 µm). In DBT, detection of both mass types increased significantly (p  =  0.0001 and p  =  0.0002 for non-spiculated and spiculated masses respectively) compared to FFDM, achieving almost 100% detection for all spiculated mass diameters. In conclusion, a structured phantom with inserted targets was able to show evidence for detectability differences between FFDM and DBT modes for five commercial systems. This phantom has potential for application in task-based assessment at acceptance and commissioning testing of DBT systems.

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.

Effect of anatomical noise on the detectability of cone beam CT images with different slice direction, slice thickness, and volume glandular fraction

We investigate the effect of anatomical noise on the detectability of cone beam CT (CBCT) images with different slice directions, slice thicknesses, and volume glandular fractions (VGFs). Anatomical noise is generated using a power law spectrum of breast anatomy, and spherical objects with diameters from 1mm to 11mm are used as breast masses. CBCT projection images are simulated and reconstructed using the FDK algorithm. A channelized Hotelling observer (CHO) with Laguerre-Gauss (LG) channels is used to evaluate detectability for the signal-known-exactly (SKE) binary detection task. Detectability is calculated for various slice thicknesses in the transverse and longitudinal planes for 15%, 30% and 60% VGFs. The optimal slice thicknesses that maximize the detectability of the objects are determined. The results show that the β value increases as the slice thickness increases, but that thicker slices yield higher detectability in the transverse and longitudinal planes, except for the case of a 1mm diameter spherical object. It is also shown that the longitudinal plane with a 0.1mm slice thickness provides higher detectability than the transverse plane, despite its higher β value. With optimal slice thicknesses, the longitudinal plane exhibits better detectability for all VGFs and spherical objects.

Impact of bowtie filter and object position on the two-dimensional noise power spectrum of a clinical MDCT system

PURPOSE

Noise characteristics of clinical multidetector CT (MDCT) systems can be quantified by the noise power spectrum (NPS). Although the NPS of CT has been extensively studied in the past few decades, the joint impact of the bowtie filter and object position on the NPS has not been systematically investigated. This work studies the interplay of these two factors on the two dimensional (2D) local NPS of a clinical CT system that uses the filtered backprojection algorithm for image reconstruction.

METHODS

A generalized NPS model was developed to account for the impact of the bowtie filter and image object location in the scan field-of-view (SFOV). For a given bowtie filter, image object, and its location in the SFOV, the shape and rotational symmetries of the 2D local NPS were directly computed from the NPS model without going through the image reconstruction process. The obtained NPS was then compared with the measured NPSs from the reconstructed noise-only CT images in both numerical phantom simulation studies and experimental phantom studies using a clinical MDCT scanner. The shape and the associated symmetry of the 2D NPS were classified by borrowing the well-known atomic spectral symbols s, p, and d, which correspond to circular, dumbbell, and cloverleaf symmetries, respectively, of the wave function of electrons in an atom. Finally, simulated bar patterns were embedded into experimentally acquired noise backgrounds to demonstrate the impact of different NPS symmetries on the visual perception of the object.

RESULTS

(1) For a central region in a centered cylindrical object, an s-wave symmetry was always present in the NPS, no matter whether the bowtie filter was present or not. In contrast, for a peripheral region in a centered object, the symmetry of its NPS was highly dependent on the bowtie filter, and both p-wave symmetry and d-wave symmetry were observed in the NPS. (2) For a centered region-ofinterest (ROI) in an off-centered object, the symmetry of its NPS was found to be different from that of a peripheral ROI in the centered object, even when the physical positions of the two ROIs relative to the isocenter were the same. (3) The potential clinical impact of the highly anisotropic NPS, caused by the interplay of the bowtie filter and position of the image object, was highlighted in images of specific bar patterns oriented at different angles. The visual perception of the bar patterns was found to be strongly dependent on their orientation.

CONCLUSIONS

The NPS of CT depends strongly on the bowtie filter and object position. Even if the location of the ROI with respect to the isocenter is fixed, there can be different symmetries in the NPS, which depend on the object position and the size of the bowtie filter. For an isolated off-centered object, the NPS of its CT images cannot be represented by the NPS measured from a centered object.

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.

Investigation on slice direction dependent detectability of volumetric cone beam CT images

We investigate the detection performance of transverse and longitudinal planes for various signal sizes (i.e., 1 mm to 8 mm diameter spheres) in cone beam computed tomography (CBCT) images. CBCT images are generated by computer simulation and images are reconstructed using an FDK algorithm. For each slice direction and signal size, a human observer study is conducted with a signal-known-exactly/background-known-exactly (SKE/BKE) binary detection task. The detection performance of human observers is compared with that of a channelized Hotelling observer (CHO). The detection performance of an ideal linear observer is also calculated using a CHO with Laguerre-Gauss (LG) channels. The detectability of high contrast small signals (i.e., up to 4-mm-diameter spheres) is higher in the longitudinal plane than the transverse plane. It is also shown that CHO performance correlates well with human observer performance in both transverse and longitudinal plane images.

Task-based measures of image quality and their relation to radiation dose and patient risk

The theory of task-based assessment of image quality is reviewed in the context of imaging with ionizing radiation, and objective figures of merit (FOMs) for image quality are summarized. The variation of the FOMs with the task, the observer and especially with the mean number of photons recorded in the image is discussed. Then various standard methods for specifying radiation dose are reviewed and related to the mean number of photons in the image and hence to image quality. Current knowledge of the relation between local radiation dose and the risk of various adverse effects is summarized, and some graphical depictions of the tradeoffs between image quality and risk are introduced. Then various dose-reduction strategies are discussed in terms of their effect on task-based measures of image quality.

Task-based strategy for optimized contrast enhanced breast imaging: analysis of six imaging techniques for mammography and tomosynthesis

PURPOSE

The use of contrast agents in breast imaging has the capability of enhancing nodule detectability and providing physiological information. Accordingly, there has been a growing trend toward using iodine as a contrast medium in digital mammography (DM) and digital breast tomosynthesis (DBT). Widespread use raises concerns about the best way to use iodine in DM and DBT, and thus a comparison is necessary to evaluate typical iodine-enhanced imaging methods. This study used a task-based observer model to determine the optimal imaging approach by analyzing six imaging paradigms in terms of their ability to resolve iodine at a given dose: unsubtracted mammography and tomosynthesis, temporal subtraction mammography and tomosynthesis, and dual energy subtraction mammography and tomosynthesis.

METHODS

Imaging performance was characterized using a detectability index d', derived from the system task transfer function (TTF), an imaging task, iodine signal difference, and the noise power spectrum (NPS). The task modeled a 10 mm diameter lesion containing iodine concentrations between 2.1 mg/cc and 8.6 mg/cc. TTF was obtained using an edge phantom, and the NPS was measured over several exposure levels, energies, and target-filter combinations. Using a structured CIRS phantom, d' was generated as a function of dose and iodine concentration.

RESULTS

For all iodine concentrations and dose, temporal subtraction techniques for mammography and tomosynthesis yielded the highest d', while dual energy techniques for both modalities demonstrated the next best performance. Unsubtracted imaging resulted in the lowest d' values for both modalities, with unsubtracted mammography performing the worst out of all six paradigms.

CONCLUSIONS

At any dose, temporal subtraction imaging provides the greatest detectability, with temporally subtracted DBT performing the highest. The authors attribute the successful performance to excellent cancellation of inplane structures and improved signal difference in the lesion.