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.

Transport-of-Intensity Model for Single-Mask X-ray Differential Phase Contrast Imaging.

X-ray phase contrast imaging holds great promise for improving the visibility of light-element materials such as soft tissues and tumors. Single-mask differential phase contrast imaging method stands out as a simple and effective approach to yield differential phase contrast. In this work, we introduce a novel model for a single-mask phase imaging system based on the transport-of-intensity equation. Our model provides an accessible understanding of signal and contrast formation in single-mask X-ray phase imaging, offering a clear perspective on the image formation process, for example, the origin of alternate bright and dark fringes in phase contrast intensity images. Aided by our model, we present an efficient retrieval method that yields differential phase contrast imagery in a single acquisition step. Our model gives insight into the contrast generation and its dependence on the system geometry and imaging parameters in both the initial intensity image as well as in retrieved images. The model validity as well as the proposed retrieval method is demonstrated via both experimental results on a system developed in-house as well as with Monte Carlo simulations. In conclusion, our work not only provides a model for an intuitive visualization of image formation but also offers a method to optimize differential phase imaging setups, holding tremendous promise for advancing medical diagnostics and other applications.

Image quality and perception: introduction.

This feature issue focuses on image quality and perception, including image and video quality, subjective and objective quality, and enhancement. The feature issue contains papers on several important topics, such as contrast discrimination, analysis of color imaging in cameras, image quality assessment, and more. The papers represent different important aspects in image quality and perception, contributing to the advancement of the field.

Quantitative phase retrieval with low photon counts using an energy resolving quantum detector.

X-ray phase contrast imaging (PCI) combined with phase retrieval has the potential to improve soft-material visibility and discrimination. This work examined the accuracy, image quality gains, and robustness of a spectral phase retrieval method proposed by our group. Spectroscopic PCI measurements of a physical phantom were obtained using state-of-the-art photon-counting detectors in combination with a polychromatic x-ray source. The phantom consisted of four poorly attenuating materials. Excellent accuracy was demonstrated in simultaneously retrieving the complete refractive properties (photoelectric absorption, attenuation, and phase) of these materials. Approximately 10 times higher SNR was achieved in retrieved images compared to the original PCI intensity image. These gains are also shown to be robust against increasing quantum noise, even for acquisition times as low as 1 s with a low-flux microfocus x-ray tube (average counts of 250 photons/pixels). We expect that this spectral phase retrieval method, adaptable to several PCI geometries, will allow significant dose reduction and improved material discrimination in clinical and industrial x-ray imaging applications.

On the correlation between second order texture features and human observer detection performance in digital images.

Image texture, the relative spatial arrangement of intensity values in an image, encodes valuable information about the scene. As it stands, much of this potential information remains untapped. Understanding how to decipher textural details would afford another method of extracting knowledge of the physical world from images. In this work, we attempt to bridge the gap in research between quantitative texture analysis and the visual perception of textures. The impact of changes in image texture on human observer's ability to perform signal detection and localization tasks in complex digital images is not understood. We examine this critical question by studying task-based human observer performance in detecting and localizing signals in tomographic breast images. We have also investigated how these changes impact the formation of second-order image texture. We used digital breast tomosynthesis (DBT) an FDA approved tomographic X-ray breast imaging method as the modality of choice to show our preliminary results. Our human observer studies involve localization ROC (LROC) studies for low contrast mass detection in DBT. Simulated images are used as they offer the benefit of known ground truth. Our results prove that changes in system geometry or processing leads to changes in image texture magnitudes. We show that the variations in several well-known texture features estimated in digital images correlate with human observer detection-localization performance for signals embedded in them. This insight can allow efficient and practical techniques to identify the best imaging system design and algorithms or filtering tools by examining the changes in these texture features. This concept linking texture feature estimates and task based image quality assessment can be extended to several other imaging modalities and applications as well. It can also offer feedback in system and algorithm designs with a goal to improve perceptual benefits. Broader impact can be in wide array of areas including imaging system design, image processing, data science, machine learning, computer vision, perceptual and vision science. Our results also point to the caution that must be exercised in using these texture features as image-based radiomic features or as predictive markers for risk assessment as they are sensitive to system or image processing changes.

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 ß 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 ß 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, ß 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.

Spectral Signatures of X-ray Scatter Using Energy-Resolving Photon-Counting Detectors.

Energy-resolving photon-counting detectors (PCDs) separate photons from a polychromatic X-ray source into a number of separate energy bins. This spectral information from PCDs would allow advancements in X-ray imaging, such as improving image contrast, quantitative imaging, and material identification and characterization. However, aspects like detector spectral distortions and scattered photons from the object can impede these advantages if left unaccounted for. Scattered X-ray photons act as noise in an image and reduce image contrast, thereby significantly hindering PCD utility. In this paper, we explore and outline several important characteristics of spectral X-ray scatter with examples of soft-material imaging (such as cancer imaging in mammography or explosives detection in airport security). Our results showed critical spectral signatures of scattered photons that depend on a few adjustable experimental factors. Additionally, energy bins over a large portion of the spectrum exhibit lower scatter-to-primary ratio in comparison to what would be expected when using a conventional energy-integrating detector. These important findings allow flexible choice of scatter-correction methods and energy-bin utilization when using PCDs. Our findings also propel the development of efficient spectral X-ray scatter correction methods for a wide range of PCD-based applications.

Multi-step material decomposition for spectral computed tomography.

Spectral images from photon counting detectors are being explored for material decomposition applications such as for obtaining quantitative maps of tissue types and contrast agents. While these detectors allow acquisition of multi-energy data in a single exposure, separating the total photon counts into multiple energy bins can lead to issues of count starvation and increased quantum noise in resultant maps. Furthermore, the complex decomposition problem is often solved in a single inversion step making it difficult to separate materials with close properties. We propose a multi-step decomposition method which allows solving the problem in multiple steps using the same spectral data collected in a single exposure. During each step, quantitative accuracy of a single material is under focus and one can flexibly optimize the bins chosen in that step. The result thus obtained becomes part of the input data for the next step in the multi-step process. This makes the problem less ill-conditioned and allows better quantitation of more challenging materials within the object. In comparison to a conventional single-step method, we show excellent quantitative accuracy for decomposing up to six materials involving a mix of soft tissue types and contrast agents in micro-CT sized digital phantoms.

Robust Energy Calibration Technique for Photon Counting Spectral Detectors.

This paper describes the implementation of a novel and robust threshold energy calibration method for photon counting detectors using polychromatic X-ray tubes. Methods often used for such energy calibration may require re-orientation of the detector or introduce calibration errors that are flux and acquisition time-dependent. Our newly proposed "differential intensity ratios" (DIR) method offers a practical and robust alternative to existing methods. We demonstrate this robustness against photon flux used in calibration, spectral errors such as pulse pile-up as well as the detector's inherent spectral resolution limits. The demonstrated significant insensitivity of the proposed DIR signature to detector spectral distortions and energy resolution is a key finding. The proposed DIR calibration method is demonstrated using Medipix3RX detectors with a CdTe sensor under varying flux conditions. A per pixel calibration using the DIR method has also been implemented to demonstrate an improvement over the global energy resolution of the PCD.

Analyzing visual-search observers using eye-tracking data for digital breast tomosynthesis images.

Visual-search (VS) model observers have the potential to provide reliable predictions of human-observer performance in detection-localization tasks. The purpose of this work was to examine some characteristics of human gaze on breast images with the goal of informing the design of our VS observers. Using a helmet-mounted eye-tracking system, we recorded the movement of gaze from human observers as they searched for masses in sets of 2D digital breast tomosynthesis (DBT) images. The masses in this study were of a single profile. The DBT images were extracted from image volumes reconstructed with the filtered backprojection method. Fixation times associated with observer points of interest were computed from the observer data. We used the k-mean clustering algorithm to get dwell times of gaze data. The dwell times were then compared to sets of morphological feature values extracted from the images. These features, extracted as cross correlations involving the mass profile and the test image, included the matched filter (MF), gradient MF, Laplacian MF, and adaptive MF. The adaptive MF combining four feature maps was computed using a hotelling discriminant generated from training data. For this investigation, we computed correlation coefficients between the fixation times and the feature values. We also conducted a significance test by computing p-values of correlation coefficients for five features. Of all these features, the adaptive MF provided the highest correlation coefficients for DBT images with different densities.

Visual-search observers for assessing tomographic x-ray image quality.

PURPOSE: Mathematical model observers commonly used for diagnostic image-quality assessments in x-ray imaging research are generally constrained to relatively simple detection tasks due to their need for statistical prior information. Visual-search (VS) model observers that employ morphological features in sequential search and analysis stages have less need for such information and fewer task constraints. The authors compared four VS observers against human observers and an existing scanning model observer in a pilot study that quantified how mass detection and localization in simulated digital breast tomosynthesis (DBT) can be affected by the number P of acquired projections. METHODS: Digital breast phantoms with embedded spherical masses provided single-target cases for a localization receiver operating characteristic (LROC) study. DBT projection sets based on an acquisition arc of 60° were generated for values of P between 3 and 51. DBT volumes were reconstructed using filtered backprojection with a constant 3D Butterworth postfilter; extracted 2D slices were used as test images. Three imaging physicists participated as observers. A scanning channelized nonprewhitening (CNPW) observer had knowledge of the mean lesion-absent images. The VS observers computed an initial single-feature search statistic that identified candidate locations as local maxima of either a template matched-filter (MF) image or a gradient-template MF (GMF) image. Search inefficiencies that modified the statistic were also considered. Subsequent VS candidate analyses were carried out with (i) the CNPW statistical discriminant and (ii) the discriminant computed from GMF training images. These location-invariant discriminants did not utilize covariance information. All observers read 36 training images and 108 study images per P value. Performance was scored in terms of area under the LROC curve. RESULTS: Average human-observer performance was stable for P between 7 and 35. In the absence of search inefficiencies, the VS models based on the GMF analysis provided the best correlation (Pearson ρ ≥ 0.62) with the human results. The CNPW-based VS observers deviated from the humans primarily at lower values of P. In this limited study, search inefficiencies allowed for good quantitative agreement with the humans for most of the VS observers. CONCLUSIONS: The computationally efficient training requirements for the VS observer are suitable for high-resolution imaging, indicating that the observer framework has the potential to overcome important task limitations of current model observers for x-ray applications.

Spectral x-ray phase contrast imaging for single-shot retrieval of absorption, phase, and differential-phase imagery.

In this Letter, we propose the first single-shot, noninterferometric x-ray imaging method for simultaneous retrieval of absorption, phase, and differential-phase imagery with quantitative accuracy. Our method utilizes a photon-counting spectral x-ray detector in conjunction with a simplified transport-of-intensity equation for coded-aperture phase-contrast imaging to efficiently solve the retrieval problem. This method can utilize an incoherent and polychromatic (clinical or laboratory) x-ray tube and can enable retrieval for a wide range and composition of material properties. The proposed method has been validated via computer simulations and is expected to significantly benefit applications that are sensitive to complexity of measurement, radiation dose and imaging time.

Approximated transport-of-intensity equation for coded-aperture x-ray phase-contrast imaging.

Transport-of-intensity equations (TIEs) allow better understanding of image formation and assist in simplifying the "phase problem" associated with phase-sensitive x-ray measurements. In this Letter, we present for the first time to our knowledge a simplified form of TIE that models x-ray differential phase-contrast (DPC) imaging with coded-aperture (CA) geometry. The validity of our approximation is demonstrated through comparison with an exact TIE in numerical simulations. The relative contributions of absorption, phase, and differential phase to the acquired phase-sensitive intensity images are made readily apparent with the approximate TIE, which may prove useful for solving the inverse phase-retrieval problem associated with these CA geometry based DPC.

Towards Visual-Search Model Observers for Mass Detection in Breast Tomosynthesis.

We are investigating human-observer models that perform clinically realistic detection and localization tasks as a means of making reliable assessments of digital breast tomosynthesis images. The channelized non-prewhitening (CNPW) observer uses the background known exactly task for localization and detection. Visual-search observer models attempt to replicate the search patterns of trained radiologists. The visual-search observer described in this paper utilizes a two-phase approach, with an initial holistic search followed by directed analysis and decision making. Gradient template matching is used for the holistic search, and the CNPW observer is used for analysis and decision making. Spherical masses were embedded into anthropomorphic breast phantoms, and simulated projections were made using ray-tracing and a serial cascade model. A localization ROC study was performed on these images using the visual-search model observer and the CNPW observer. Observer performance from the two computer observers was compared to human observer performance. The visual-search observer was able to produce area under the LROC curve values similar to those from human observers; however, more research is needed to increase the robustness of the algorithm.

Single-step absorption and phase retrieval with polychromatic x rays using a spectral detector.

In this Letter, we present a single-step method to simultaneously retrieve x-ray absorption and phase images valid for a broad range of imaging energies and material properties. Our method relies on the availability of spectrally resolved intensity measurements, which is now possible using semiconductor x-ray photon counting detectors. The retrieval method is derived and presented, with results showing good agreement.

Generation of voxelized breast phantoms from surgical mastectomy specimens.

PURPOSE: In the research and development of dedicated tomographic breast imaging systems, digital breast object models, also known as digital phantoms, are useful tools. While various digital breast phantoms do exist, the purpose of this study was to develop a realistic high-resolution model suitable for simulating three-dimensional (3D) breast imaging modalities. The primary goal was to design a model capable of producing simulations with realistic breast tissue structure. METHODS: The methodology for generating an ensemble of digital breast phantoms was based on imaging surgical mastectomy specimens using a benchtop, cone-beam computed tomography system. This approach allowed low-noise, high-resolution projection views of the mastectomy specimens at each angular position. Reconstructions of these projection sets were processed using correction techniques and diffusion filtering prior to segmentation into breast tissue types in order to generate phantoms. RESULTS: Eight compressed digital phantoms and 20 uncompressed phantoms from which an additional 96 pseudocompressed digital phantoms with voxel dimensions of 0.2 mm(3) were generated. Two distinct tissue classification models were used in forming breast phantoms. The binary model classified each tissue voxel as either adipose or fibroglandular. A multivalue scaled model classified each tissue voxel as percentage of adipose tissue (range 1%-99%). Power spectral analysis was performed to compare simulated reconstructions using the breast phantoms to the original breast specimen reconstruction, and fits were observed to be similar. CONCLUSIONS: The digital breast phantoms developed herein provide a high-resolution anthropomorphic model of the 3D uncompressed and compressed breast that are suitable for use in evaluating and optimizing tomographic breast imaging modalities. The authors believe that other research groups might find the phantoms useful, and therefore they offer to make them available for wider use.

Penalized maximum likelihood reconstruction for improved microcalcification detection in breast tomosynthesis.

We examined the application of an iterative penalized maximum likelihood (PML) reconstruction method for improved detectability of microcalcifications (MCs) in digital breast tomosynthesis (DBT). Localized receiver operating characteristic (LROC) psychophysical studies with human observers and 2-D image slices were conducted to evaluate the performance of this reconstruction method and to compare its performance against the commonly used Feldkamp FBP algorithm. DBT projections were generated using rigorous computer simulations that included accurate modeling of the noise and detector blur. Acquisition dose levels of 0.7, 1.0, and 1.5 mGy in a 5-cm-thick compressed breast were tested. The defined task was to localize and detect MC clusters consisting of seven MCs. The individual MC diameter was 150 µm. Compressed-breast phantoms derived from CT images of actual mastectomy specimens provided realistic background structures for the detection task. Four observers each read 98 test images for each combination of reconstruction method and acquisition dose. All observers performed better with the PML images than with the FBP images. With the acquisition dose of 0.7 mGy, the average areas under the LROC curve (A(L)) for the PML and FBP algorithms were 0.69 and 0.43, respectively. For the 1.0-mGy dose, the values of A(L) were 0.93 (PML) and 0.7 (FBP), while the 1.5-mGy dose resulted in areas of 1.0 and 0.9, respectively, for the PML and FBP algorithms. A 2-D analysis of variance applied to the individual observer areas showed statistically significant differences (at a significance level of 0.05) between the reconstruction strategies at all three dose levels. There were no significant differences in observer performance for any of the dose levels.

Evaluation of a variable dose acquisition technique for microcalcification and mass detection in digital breast tomosynthesis.

In this article the authors evaluate a recently proposed variable dose (VD)-digital breast tomosynthesis (DBT) acquisition technique in terms of the detection accuracy for breast masses and microcalcification (MC) clusters. With this technique, approximately half of the total dose is used for one center projection and the remaining dose is split among the other tomosynthesis projection views. This acquisition method would yield both a projection view and a reconstruction view. One of the aims of this study was to evaluate whether the center projection alone of the VD acquisition can provide equal or superior MC detection in comparison to the 3D images from uniform dose (UD)-DBT. Another aim was to compare the mass-detection capabilities of 3D reconstructions from VD-DBT and UD-DBT. In a localization receiver operating characteristic (LROC) observer study of MC detection, the authors compared the center projection of a VD acquisitioh scheme (at 2 mGy dose) with detector pixel size of 100 microm with the UD-DBT reconstruction (at 4 mGy dose) obtained with a voxel size of 100 microm. MCs with sizes of 150 and 180 microm were used in the study, with each cluster consisting of seven MCs distributed randomly within a small volume. Reconstructed images in UD-DBT were obtained from a projection set that had a total of 4 mGy dose. The current study shows that for MC detection, using the center projection alone of VD acquisition scheme performs worse with area under the LROC curve (AL) of 0.76 than when using the 3D reconstructed image using the UD acquisition scheme (AL=0.84). A 2D ANOVA found a statistically significant difference (p=0.038) at a significance level of 0.05. In the current study, although a reconstructed image was also available using the VD acquisition scheme, it was not used to assist the MC detection task which was done using the center projection alone. In the case of evaluation of detection accuracy of masses, the reconstruction with VD-DBT (AL=0.71) was compared to that obtained from the UD-DBT (AL=0.78). The authors found no statistically significant difference between the two (p-value=0.22), although all the observers performed better for UD-DBT.

Image reconstruction method for a two-layer tissue structure accounts for chest-wall effects in breast imaging.

We develop a new tomographic imaging reconstruction algorithm for a two-layer tissue structure. Simulations and phantom experiments show more accurate reconstruction of target optical properties compared with those results obtained from a semi-infinite tissue model for layered structures. This improvement is mainly attributed to the more accurate estimation of background optical properties and more accurate estimation of weight matrix for imaging reconstruction by considering the light propagation effect in the second layer. Clinical results of breast lesions are also presented to demonstrate the utility of this new imaging algorithm.

Analytical solution for light propagation in a two-layer tissue structure with a tilted interface for breast imaging.

Reflectance measurement of breast tissue is influenced by the underlying chest wall, which is often tilted as seen by the detection probe. We develop an analytical solution of light propagation in a two-layer tissue structure with tilted interface and refractive index difference between the layers. We validate the analytical solution with Monte Carlo simulations and phantom experiments, and a good agreement is seen. The influence of varying the tilting angle of the interface on the reflectance is discussed for two types of layered structures. Further, we apply the developed analytical solution to obtain the optical properties of breast tissue and chest wall from clinical data. Inverse calculation using the developed solution applied to the data obtained from Monte Carlo simulations shows that the optical properties of both layers are obtained with higher accuracy as compared to using a simple two-layer model ignoring the interface tilt. This is expected to improve the accuracy in estimating the optical properties of breast tissue, thus enhancing the accuracy of optical tomography of breast tumors.