Anatomical complexity in breast parenchyma and its implications for optimal breast imaging strategies

Greater benefits of deep learning-based computer-aided detection systems for finding small signals in 3D volumetric medical images

Purpose

Radiologists are tasked with visually scrutinizing large amounts of data produced by 3D volumetric imaging modalities. Small signals can go unnoticed during the 3D search because they are hard to detect in the visual periphery. Recent advances in machine learning and computer vision have led to effective computer-aided detection (CADe) support systems with the potential to mitigate perceptual errors.

Approach

Sixteen nonexpert observers searched through digital breast tomosynthesis (DBT) phantoms and single cross-sectional slices of the DBT phantoms. The 3D/2D searches occurred with and without a convolutional neural network (CNN)-based CADe support system. The model provided observers with bounding boxes superimposed on the image stimuli while they looked for a small microcalcification signal and a large mass signal. Eye gaze positions were recorded and correlated with changes in the area under the ROC curve (AUC).

Results

The CNN-CADe improved the 3D search for the small microcalcification signal ( Δ   AUC = 0.098 , p = 0.0002 ) and the 2D search for the large mass signal ( Δ   AUC = 0.076 , p = 0.002 ). The CNN-CADe benefit in 3D for the small signal was markedly greater than in 2D ( Δ Δ   AUC = 0.066 , p = 0.035 ). Analysis of individual differences suggests that those who explored the least with eye movements benefited the most from the CNN-CADe ( r = - 0.528 , p = 0.036 ). However, for the large signal, the 2D benefit was not significantly greater than the 3D benefit ( Δ Δ   AUC = 0.033 , p = 0.133 ).

Conclusion

The CNN-CADe brings unique performance benefits to the 3D (versus 2D) search of small signals by reducing errors caused by the underexploration of the volumetric data.

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.

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.

Local texture statistics augment the power spectrum in modeling radiographic judgments of breast density

Purpose

Anatomical "noise" is an important limitation of full-field digital mammography. Understanding its impact on clinical judgments is made difficult by the complexity of breast parenchyma, which results in image texture not fully captured by the power spectrum. While the number of possible parameters for characterizing anatomical noise is quite large, a specific set of local texture statistics has been shown to be visually salient, and human sensitivity to these statistics corresponds to their informativeness in natural scenes.

Approach

We evaluate these local texture statistics in addition to standard power-spectral measures to determine whether they have additional explanatory value for radiologists' breast density judgments. We analyzed an image database consisting of 111 disease-free mammographic screening exams (4 views each) acquired at the University of Pittsburgh Medical Center. Each exam had a breast density score assigned by the examining radiologist. Power-spectral descriptors and local image statistics were extracted from images of breast parenchyma. Model-selection criteria and accuracy were used to assess the explanatory and predictive value of local image statistics for breast density judgments.

Results

The model selection criteria show that adding local texture statistics to descriptors of the power spectra produce better explanatory and predictive models of radiologists' judgments of breast density. Thus, local texture statistics capture, in some form, non-Gaussian aspects of texture that radiologists are using.

Conclusions

Since these local texture statistics are expected to be impacted by imaging factors like modality, dose, and image processing, they suggest avenues for understanding and optimizing observer performance.

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.

Power Spectrum Analysis of Breast Parenchyma with Digital Breast Tomosynthesis Images in a Longitudinal Screening Cohort from Two Vendors

RATIONALE AND OBJECTIVES

To quantitatively compare breast parenchymal texture between two Digital Breast Tomosynthesis (DBT) vendors using images from the same patients.

MATERIALS AND METHODS

This retrospective study included consecutive patients who had normal screening DBT exams performed in January 2018 from GE and normal screening DBT exams in adjacent years from Hologic. Power spectrum analysis was performed within the breast tissue region. The slope of a linear function between log-frequency and log-power, β, was derived as a quantitative measure of breast texture and compared within and across vendors along with secondary parameters (laterality, view, year, image format, and breast density) with correlation tests and t-tests.

RESULTS

A total of 24,339 DBT slices or synthetic 2D images from 85 exams in 25 women were analyzed. Strong power-law behavior was verified from all images. Values of β d did not differ significantly for laterality, view, or year. Significant differences of β were observed across vendors for DBT images (Hologic: 3.4±0.2 vs GE: 3.1±0.2, 95% CI on difference: 0.27 to 0.30) and synthetic 2D images (Hologic: 2.7±0.3 vs GE: 3.0±0.2, 95% CI on difference: -0.36 to -0.27), and density groups with each vendor: scattered (GE: 3.0±0.3, Hologic: 3.3±0.3) vs. heterogeneous (GE: 3.2±0.2, Hologic: 3.4±0.1), 95% CI (-0.27, -0.08) and (-0.21, -0.05), respectively.

CONCLUSION

There are quantitative differences in the presentation of breast imaging texture between DBT vendors and across breast density categories. Our findings have relevance and importance for development and optimization of AI algorithms related to breast density assessment and cancer detection.

Effect of optical blurring of X-ray source on breast tomosynthesis image quality: Modulation transfer function, anatomical noise power spectrum, and signal detectability perspectives

We investigated the effect of the optical blurring of X-ray source on digital breast tomosynthesis (DBT) image quality using well-designed DBT simulator and table-top experimental systems. To measure the in-plane modulation transfer function (MTF), we used simulated sphere phantom and Teflon sphere phantom and generated their projection data using two acquisition modes (i.e., step-and-shoot mode and continuous mode). After reconstruction, we measured the in-plane MTF using reconstructed sphere phantom images. In addition, we measured the anatomical noise power spectrum (aNPS) and signal detectability. We constructed simulated breast phantoms with a 50% volume glandular fraction (VGF) of breast anatomy using the power law spectrum and inserted spherical objects with 1 mm, 2 mm, and 5 mm diameters as breast masses. Projection data were acquired using two acquisition modes, and in-plane breast images were reconstructed using the Feldkamp-Davis-Kress (FDK) algorithm. For the experimental study, we used BR3D breast phantom with 50% VGF and obtained projection data using a table-top experimental system. To compare the detection performance of the two acquisition modes, we calculated the signal detectability using the channelized Hotelling observer (CHO) with Laguerre-Gauss (LG) channels. Our results show that spatial resolution of in-plane image in continuous mode was degraded due to the optical blurring of X-ray source. This blurring effect was reflected in aNPS, resulting in large β values. From a signal detectability perspective, the signal detectability in step-and-shoot mode is higher than that in continuous mode for small spherical signals but not large spherical signals. Although the step-and-shoot mode has disadvantage in terms of scan time compared to the continuous mode, scanning in step-and-shoot mode is better for detecting small signals, indicating that there is a tradeoff between scan time and image quality.

Fabrication of 3D printed patient-derived anthropomorphic breast phantoms for mammography and digital breast tomosynthesis: Imaging assessment with clinical X-ray spectra

PURPOSE

To design, fabricate and characterize 3D printed, anatomically realistic, compressed breast phantoms for digital mammography (DM) and digital breast tomosynthesis (DBT) x-ray imaging.

MATERIALS

We realized 3D printed phantoms simulating healthy breasts, via fused deposition modeling (FDM), with a layer resolution of 0.1 mm and 100% infill density, using a dual extruder printer. The digital models were derived from a public dataset of segmented clinical breast computed tomography scans. Three physical phantoms were printed in polyethylene terephthalate (PET), acrylonitrile-butadiene-styrene (ABS), or in polylactic-acid (PLA) materials, using ABS as a substitute for adipose tissue, and PLA or PET filaments for replicating glandular and skin tissues. 3D printed phantoms were imaged at three clinical centers with DM and DBT scanners, using typical spectra. Anatomical noise of the manufactured phantoms was evaluated via the estimates of the β parameter both in DM images and in images acquired via a clinical computed tomography (CT) scanner.

RESULTS

DM and DBT phantom images showed an inner texture qualitatively similar to the images of a clinical DM or DBT exam, suitably reproducing the glandular structure of their computational phantoms. β parameters evaluated in DM images of the manufactured phantoms ranged between 2.84 and 3.79; a lower β was calculated from the CT scan.

CONCLUSIONS

FDM 3D printed compressed breast phantoms have been fabricated using ABS, PLA and PET filaments. DM and DBT images with clinical x-ray spectra showed realistic textures. These phantoms appear promising for clinical applications in quality assurance, image quality and dosimetry assessments.

Dedicated breast CT: state of the art-Part II. Clinical application and future outlook

Dedicated breast CT is being increasingly used for breast imaging. This technique provides images with no compression, removal of tissue overlap, rapid acquisition, and available simultaneous assessment of microcalcifications and contrast enhancement. In this second installment in a 2-part review, the current status of clinical applications and ongoing efforts to develop new imaging systems are discussed, with particular emphasis on how to achieve optimized practice including lesion detection and characterization, response to therapy monitoring, density assessment, intervention, and implant evaluation. The potential for future screening with breast CT is also addressed. KEY POINTS: • Dedicated breast CT is an emerging modality with enormous potential in the future of breast imaging by addressing numerous clinical needs from diagnosis to treatment. • Breast CT shows either noninferiority or superiority with mammography and numerical comparability to MRI after contrast administration in diagnostic statistics, demonstrates excellent performance in lesion characterization, density assessment, and intervention, and exhibits promise in implant evaluation, while potential application to breast cancer screening is still controversial. • New imaging modalities such as phase-contrast breast CT, spectral breast CT, and hybrid imaging are in the progress of R & D.

Improved Classification of Benign and Malignant Breast Lesions Using Deep Feature Maximum Intensity Projection MRI in Breast Cancer Diagnosis Using Dynamic Contrast-enhanced MRI

PURPOSE

To develop a deep transfer learning method that incorporates four-dimensional (4D) information in dynamic contrast-enhanced (DCE) MRI to classify benign and malignant breast lesions.

MATERIALS AND METHODS

The retrospective dataset is composed of 1990 distinct lesions (1494 malignant and 496 benign) from 1979 women (mean age, 47 years ± 10). Lesions were split into a training and validation set of 1455 lesions (acquired in 2015-2016) and an independent test set of 535 lesions (acquired in 2017). Features were extracted from a convolutional neural network (CNN), and lesions were classified as benign or malignant using support vector machines. Volumetric information was collapsed into two dimensions by taking the maximum intensity projection (MIP) at the image level or feature level within the CNN architecture. Performances were evaluated using the area under the receiver operating characteristic curve (AUC) as the figure of merit and were compared using the DeLong test.

RESULTS

The image MIP and feature MIP methods yielded AUCs of 0.91 (95% CI: 0.87, 0.94) and 0.93 (95% CI: 0.91, 0.96), respectively, for the independent test set. The feature MIP method achieved higher performance than the image MIP method (∆AUC 95% CI: 0.003, 0.051; P = .03).

CONCLUSION

Incorporating 4D information in DCE MRI by MIP of features in deep transfer learning demonstrated superior classification performance compared with using MIP images as input in the task of distinguishing between benign and malignant breast lesions.Keywords: Breast, Computer Aided Diagnosis (CAD), Convolutional Neural Network (CNN), MR-Dynamic Contrast Enhanced, Supervised learning, Support vector machines (SVM), Transfer learning, Volume Analysis © RSNA, 2021.

Comparative observer effects in 2D and 3D localization tasks

Purpose: Three-dimensional "volumetric" imaging methods are now a common component of medical imaging across many imaging modalities. Relatively little is known about how human observers localize targets masked by noise and clutter as they scroll through a 3D image and how it compares to a similar task confined to a single 2D slice. Approach: Gaussian random textures were used to represent noisy volumetric medical images. Subjects were able to freely inspect the images, including scrolling through 3D images as part of their search process. A total of eight experimental conditions were evaluated (2D versus 3D images, large versus small targets, power-law versus white noise). We analyze performance in these experiments using task efficiency and the classification image technique. Results: In 3D tasks, median response times were roughly nine times longer than 2D, with larger relative differences for incorrect trials. The efficiency data show a dissociation in which subjects perform with higher statistical efficiency in 2D tasks for large targets and higher efficiency in 3D tasks with small targets. The classification images suggest that a critical mechanism behind this dissociation is an inability to integrate across multiple slices to form a 3D localization response. The central slices of 3D classification images are remarkably similar to the corresponding 2D classification images. Conclusions: 2D and 3D tasks show similar weighting patterns between 2D images and the central slice of 3D images. There is relatively little weighting across slices in the 3D tasks, leading to lower task efficiency with respect to the ideal observer.

DIR-DBTnet: Deep iterative reconstruction network for three-dimensional digital breast tomosynthesis imaging

PURPOSE

The goal of this study is to develop a three-dimensional (3D) iterative reconstruction framework based on the deep learning (DL) technique to improve the digital breast tomosynthesis (DBT) imaging performance.

METHODS

In this work, the DIR-DBTnet is developed for DBT image reconstruction by mapping the conventional iterative reconstruction (IR) algorithm to the deep neural network. By design, the DIR-DBTnet learns and optimizes the regularizer and the iteration parameters automatically during the network training with a large amount of simulated DBT data. Numerical, experimental, and clinical data are used to evaluate its performance. Quantitative metrics such as the artifact spread function (ASF), breast density, and the signal difference to noise ratio (SDNR) are measured to assess the image quality.

RESULTS

Results show that the proposed DIR-DBTnet is able to reduce the in-plane shadow artifacts and the out-of-plane signal leaking artifacts compared to the filtered backprojection (FBP) and the total variation (TV)-based IR methods. Quantitatively, the full width half maximum (FWHM) of the measured ASF from the clinical data is 27.1% and 23.0% smaller than those obtained with the FBP and TV methods, while the SDNR is increased by 194.5% and 21.8%, respectively. In addition, the breast density obtained from the DIR-DBTnet network is more accurate and consistent with the ground truth.

CONCLUSIONS

In conclusion, a deep iterative reconstruction network, DIR-DBTnet, has been proposed for 3D DBT image reconstruction. Both qualitative and quantitative analyses of the numerical, experimental, and clinical results demonstrate that the DIR-DBTnet has superior DBT imaging performance than the conventional algorithms.

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.

Human observer performance on in-plane digital breast tomosynthesis images: Effects of reconstruction filters and data acquisition angles on signal detection

For digital breast tomosynthesis (DBT) systems, we investigate the effects of the reconstruction filters for different data acquisition angles on signal detection. We simulated a breast phantom with a 30% volume glandular fraction (VGF) of breast anatomy using the power law spectrum and modeled the breast mass as a spherical object with a 1 mm diameter. Projection data were acquired using two different data acquisition angles and numbers of projection view pairs, and in-plane breast images were reconstructed using the Feldkamp-Davis-Kress (FDK) algorithm with three different reconstruction filter schemes. To measure the ability to detect a signal, we conducted the human observer study with a binary detection task and compared the signal detectability of human to that of channelized Hotelling observer (CHO) with Laguerre-Gauss (LG) channels and dense difference-of-Gaussian (D-DOG) channels. We also measured the contrast-to-noise ratio (CNR), signal power spectrum (SPS), and β values of the anatomical noise power spectrum (NPS) to show the association between human observer performance and these traditional metrics. Our results show that using a slice thickness (ST) filter degraded the signal detection performance of human observers at the same data acquisition angle. This could be predicted by D-DOG CHO with internal noise, but the correlation between the traditional metrics and signal detectability was not observed in this work.

Digital Mammography versus Breast Tomosynthesis: Impact of Breast Density on Diagnostic Performance in Population-based Screening

BackgroundPrevious studies comparing digital breast tomosynthesis (DBT) to digital mammography (DM) have shown conflicting results regarding breast density and diagnostic performance.PurposeTo compare true-positive and false-positive interpretations in DM versus DBT according to volumetric density, age, and mammographic findings.Materials and MethodsFrom November 2010 to December 2012, 24 301 women aged 50-69 years (mean age, 59.1 years ± 5.7) were prospectively included in the Oslo Tomosynthesis Screening Trial. Participants received same-compression DM and DBT with independent double reading for both DM and DM plus DBT reading modes. Eight experienced radiologists rated the images by using a five-point scale for probability of malignancy. Participants were followed up for 2 years to assess for interval cancers. Breast density was assessed by using automatic volumetric software (scale, 1-4). Differences in true-positive rates, false-positive rates, and mammographic findings were assessed by using confidence intervals (Newcombe paired method) and P values (McNemar and χ² tests).ResultsThe true-positive rate of DBT was higher than that of DM for density groups (range, 12%-24%; P < .001 for density scores of 2 and 3, and P > .05 for density scores of 1 and 4) and age groups (range, 15%-35%; P < .05 for all age groups), mainly due to the higher number of spiculated masses and architectural distortions found at DBT (P < .001 for density scores of 2 and 3; P < .05 for women aged 55-69 years). The false-positive rate was lower for DBT than for DM in all age groups (range, -0.6% to -1.2%; P < .01) and density groups (range, -0.7 to -1.0%; P < .005) owing to fewer asymmetric densities (P ≤ .001), except for extremely dense breasts (0.1%, P = .82).ConclusionDigital breast tomosynthesis enabled the detection of more cancers in all density and age groups compared with digital mammography, especially cancers classified as spiculated masses and architectural distortions. The improvement in cancer detection rate showed a positive correlation with age. With use of digital breast tomosynthesis, false-positive findings were lower due to fewer asymmetric densities, except in extremely dense breasts.© RSNA, 2019Online supplemental material is available for this article.See also the editorial by Fuchsjäger and Adelsmayr in this issue.

Evaluation of non-Gaussian statistical properties in virtual breast phantoms

Images derived from a "virtual phantom" can be useful in characterizing the performance of imaging systems. This has driven the development of virtual breast phantoms implemented in simulation environments. In breast imaging, several such phantoms have been proposed. We analyze the non-Gaussian statistical properties from three classes of virtual breast phantoms and compare them to similar statistics from a database of breast images. These include clustered-blob lumpy backgrounds (CBLBs), truncated binary textures, and the UPenn virtual breast phantoms. We use Laplacian fractional entropy (LFE) as a measure of the non-Gaussian statistical properties of each simulation procedure. Our results show that, despite similar power spectra, the simulation approaches differ considerably in LFE with very low scores for the CBLB to high values for the UPenn phantom at certain frequencies. These results suggest that LFE may have value in developing and tuning virtual phantom simulation procedures.

Investigating simulation-based metrics for characterizing linear iterative reconstruction in digital breast tomosynthesis

PURPOSE

Simulation-based image quality metrics are adapted and investigated for characterizing the parameter dependences of linear iterative image reconstruction for DBT.

METHODS

Three metrics based on a 2D DBT simulation are investigated: (1) a root-mean-square-error (RMSE) between the test phantom and reconstructed image, (2) a gradient RMSE where the comparison is made after taking a spatial gradient of both image and phantom, and (3) a region-of-interest (ROI) Hotelling observer (HO) for signal-known-exactly/background-known-exactly (SKE/BKE) and signal-known-exactly/background-known-statistically (SKE/BKS) detection tasks. Two simulation studies are performed using the aforementioned metrics, varying voxel aspect ratio, and regularization strength for two types of Tikhonov-regularized least-squares optimization. The RMSE metrics are applied to a 2D test phantom with resolution bar patterns at varying angles, and the ROI-HO metric is applied to two tasks relevant to DBT: lesion detection, modeled by use of a large, low-contrast signal, and microcalcification detection, modeled by use of a small, high-contrast signal. The RMSE metric trends are compared with visual assessment of the reconstructed bar-pattern phantom. The ROI-HO metric trends are compared with 3D reconstructed images from ACR phantom data acquired with a Hologic Selenia Dimensions DBT system.

RESULTS

Sensitivity of the image RMSE to mean pixel value is found to limit its applicability to the assessment of DBT image reconstruction. The image gradient RMSE is insensitive to mean pixel value and appears to track better with subjective visualization of the reconstructed bar-pattern phantom. The ROI-HO metric shows an increasing trend with regularization strength for both forms of Tikhonov-regularized least-squares; however, this metric saturates at intermediate regularization strength indicating a point of diminishing returns for signal detection. Visualization with the reconstructed ACR phantom images appear to show a similar dependence with regularization strength.

CONCLUSIONS

From the limited studies presented it appears that image gradient RMSE trends correspond with visual assessment better than image RMSE for DBT image reconstruction. The ROI-HO metric for both detection tasks also appears to reflect visual trends in the ACR phantom reconstructions as a function of regularization strength. We point out, however, that the true utility of these metrics can only be assessed after amassing more data.

Reduced anatomical clutter in digital breast tomosynthesis with statistical iterative reconstruction

PURPOSE

Digital breast tomosynthesis (DBT) has been shown to somewhat alleviate the breast tissue overlapping issues of two-dimensional (2D) mammography. However, the improvement in current DBT systems over mammography is still limited. Statistical image reconstruction (SIR) methods have the potential to reduce through-plane artifacts in DBT, and thus may be used to further reduce anatomical clutter. The purpose of this work was to study the impact of SIR on anatomical clutter in the reconstructed DBT image volumes.

METHODS

An SIR with a slice-wise total variation (TV) regularizer was implemented to reconstruct DBT images which were compared with the clinical reconstruction method (filtered backprojection). The artifact spread function (ASF) was measured to quantify the reduction of the through-plane artifacts level in phantom studies and microcalcifications in clinical cases. The anatomical clutter was quantified by the anatomical noise power spectrum with a power law fitting model: NPS_a ( f) = α f^-β . The β values were measured from the reconstructed image slices when the two reconstruction methods were applied to a cohort of clinical breast exams (N = 101) acquired using Hologic Selenia Dimensions DBT systems.

RESULTS

The full width half maximum (FWHM) of the measured ASF was reduced from 8.7 ± 0.1 mm for clinical reconstruction to 6.5 ± 0.1 mm for SIR which yields a 25% reduction in FWHM in phantom studies and the same amount of ASF reduction was also found in clinical measurements from microcalcifications. The measured β values for the two reconstruction methods were 3.17 ± 0.36 and 2.14 ± 0.39 for the clinical reconstruction method and the SIR method, respectively. This difference was statistically significant (P << 0.001). The dependence of β on slice location using either method was negligible.

CONCLUSIONS

Statistical image reconstruction enabled a significant reduction of both the through-plane artifacts level and anatomical clutter in the DBT reconstructions. The β value was found to be β≈2.14 with the SIR method. This value stays in the middle between the β≈1.8 for cone beam CT and β≈3.2 for mammography. In contrast, the measured β value in the clinical reconstructions (β≈3.17) remains close to that of mammography.

Human and model observer performance for lesion detection in breast cone beam CT images with the FDK reconstruction

We investigate the detectability of breast cone beam computed tomography images using human and model observers and the variations of exponent, β, of the inverse power-law spectrum for various reconstruction filters and interpolation methods in the Feldkamp-Davis-Kress (FDK) reconstruction. Using computer simulation, a breast volume with a 50% volume glandular fraction and a 2mm diameter lesion are generated and projection data are acquired. In the FDK reconstruction, projection data are apodized using one of three reconstruction filters; Hanning, Shepp-Logan, or Ram-Lak, and back-projection is performed with and without Fourier interpolation. We conduct signal-known-exactly and background-known-statistically detection tasks. Detectability is evaluated by human observers and their performance is compared with anthropomorphic model observers (a non-prewhitening observer with eye filter (NPWE) and a channelized Hotelling observer with either Gabor channels or dense difference-of-Gaussian channels). Our results show that the NPWE observer with a peak frequency of 7cyc/degree attains the best correlation with human observers for the various reconstruction filters and interpolation methods. We also discover that breast images with smaller β do not yield higher detectability in the presence of quantum noise.

Visual adaptation and the amplitude spectra of radiological images

We examined how visual sensitivity and perception are affected by adaptation to the characteristic amplitude spectra of X-ray mammography images. Because of the transmissive nature of X-ray photons, these images have relatively more low-frequency variability than natural images, a difference that is captured by a steeper slope of the amplitude spectrum (~ − 1.5) compared to the ~ 1/f (slope of − 1) spectra common to natural scenes. Radiologists inspecting these images are therefore exposed to a different balance of spectral components, and we measured how this exposure might alter spatial vision. Observers (who were not radiologists) were adapted to images of normal mammograms or the same images sharpened by filtering the amplitude spectra to shallower slopes. Prior adaptation to the original mammograms significantly biased judgments of image focus relative to the sharpened images, demonstrating that the images are sufficient to induce substantial after-effects. The adaptation also induced strong losses in threshold contrast sensitivity that were selective for lower spatial frequencies, though these losses were very similar to the threshold changes induced by the sharpened images. Visual search for targets (Gaussian blobs) added to the images was also not differentially affected by adaptation to the original or sharper images. These results complement our previous studies examining how observers adapt to the textural properties or phase spectra of mammograms. Like the phase spectrum, adaptation to the amplitude spectrum of mammograms alters spatial sensitivity and visual judgments about the images. However, unlike the phase spectrum, adaptation to the amplitude spectra did not confer a selective performance advantage relative to more natural spectra.

Evaluation of the BreastSimulator software platform for breast tomography

The aim of this work was the evaluation of the software BreastSimulator, a breast x-ray imaging simulation software, as a tool for the creation of 3D uncompressed breast digital models and for the simulation and the optimization of computed tomography (CT) scanners dedicated to the breast. Eight 3D digital breast phantoms were created with glandular fractions in the range 10%-35%. The models are characterised by different sizes and modelled realistic anatomical features. X-ray CT projections were simulated for a dedicated cone-beam CT scanner and reconstructed with the FDK algorithm. X-ray projection images were simulated for 5 mono-energetic (27, 32, 35, 43 and 51 keV) and 3 poly-energetic x-ray spectra typically employed in current CT scanners dedicated to the breast (49, 60, or 80 kVp). Clinical CT images acquired from two different clinical breast CT scanners were used for comparison purposes. The quantitative evaluation included calculation of the power-law exponent, β, from simulated and real breast tomograms, based on the power spectrum fitted with a function of the spatial frequency, f, of the form S(f)  =  α/f   ^β . The breast models were validated by comparison against clinical breast CT and published data. We found that the calculated β coefficients were close to that of clinical CT data from a dedicated breast CT scanner and reported data in the literature. In evaluating the software package BreastSimulator to generate breast models suitable for use with breast CT imaging, we found that the breast phantoms produced with the software tool can reproduce the anatomical structure of real breasts, as evaluated by calculating the β exponent from the power spectral analysis of simulated images. As such, this research tool might contribute considerably to the further development, testing and optimisation of breast CT imaging techniques.

Two-dimensional breast dosimetry improved using three-dimensional breast image data

Conventional mammographic dosimetry has been developed over the past 40 years. Prior to the availability of high-resolution three-dimensional breast images, certain assumptions about breast anatomy were required. These assumptions were based on the information evident on two-dimensional mammograms; they included assumptions of thick skin, a uniform mixture of glandular and adipose tissue, and a median breast density of 50%. Recently, the availability of high-resolution breast CT studies has provided more accurate data about breast anatomy, and this, in turn, has provided the opportunity to update mammographic dosimetry. Based on hundreds of data sets on breast CT volume, a number of studies were performed and reported which have shed light on the basic breast anatomy specific to dosimetry in mammography. It was shown that the average skin thickness of the breast was approximately 1.5 mm, instead of the 4 or 5 mm in the past. In another study, 3-D breast CT data sets were used for validation of the 2-D algorithm developed at the University of Toronto, leading to data suggesting that the overall average breast density is of the order of 16-20%, rather than the previously assumed 50%. Both of these assumptions led to normalized glandular dose (DgN) coefficients which are higher than those of the past. However, a comprehensive study on hundreds of breast CT data sets confirmed the findings of other investigators that there is a more centralized average location of glandular tissue within the breast. Combined with Monte Carlo studies for dosimetry, when accurate models of the distribution of glandular tissue were used, a 30% reduction in the radiation dose (as determined by the DgN coefficient) was found as an average across typical molybdenum and tungsten spectra used clinically. The 30% average reduction was found even when the thinner skin and the lower average breast density were considered. The article reviews three specific anatomic observations made possible based on high-resolution breast CT data by several different research groups. It is noted that, periodically, previous assumptions pertaining to dosimetry can be updated when new information becomes available, so that more accurate dosimetry is achieved. Dogmatic practices typically change slowly, but it is hoped that the medical physics community will continue to evaluate changes in DgN coefficients such that they become more accurate.

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.

Evolution of spatial resolution in breast CT at UC Davis

PURPOSE

Dedicated breast computed tomography (bCT) technology for the purpose of breast cancer screening has been a focus of research at UC Davis since the late 1990s. Previous studies have shown that improvement in spatial resolution characteristics of this modality correlates with greater microcalcification detection, a factor considered a potential limitation of bCT. The aim of this study is to improve spatial resolution as characterized by the modulation transfer function (MTF) via changes in the scanner hardware components and operational schema.

METHODS

Four prototypes of pendant-geometry, cone-beam breast CT scanners were designed and developed spanning three generations of design evolution. To improve the system MTF in each bCT generation, modifications were made to the imaging components (x-ray tube and flat-panel detector), system geometry (source-to-isocenter and detector distance), and image acquisition parameters (technique factors, number of projections, system synchronization scheme, and gantry rotational speed).

RESULTS

Characterization of different generations of bCT systems shows these modifications resulted in a 188% improvement of the limiting MTF properties from the first to second generation and an additional 110% from the second to third. The intrinsic resolution degradation in the azimuthal direction observed in the first generation was corrected by changing the acquisition from continuous to pulsed x-ray acquisition. Utilizing a high resolution detector in the third generation, along with modifications made in system geometry and scan protocol, resulted in a 125% improvement in limiting resolution. An additional 39% improvement was obtained by changing the detector binning mode from 2 × 2 to 1 × 1.

CONCLUSIONS

These results underscore the advancement in spatial resolution characteristics of breast CT technology. The combined use of a pulsed x-ray system, higher resolution flat-panel detector and changing the scanner geometry and image acquisition logic resulted in a significant fourfold improvement in MTF.

Characteristic performance evaluation of a photon counting Si strip detector for low dose spectral breast CT imaging

PURPOSE

The possible clinical applications which can be performed using a newly developed detector depend on the detector's characteristic performance in a number of metrics including the dynamic range, resolution, uniformity, and stability. The authors have evaluated a prototype energy resolved fast photon counting x-ray detector based on a silicon (Si) strip sensor used in an edge-on geometry with an application specific integrated circuit to record the number of x-rays and their energies at high flux and fast frame rates. The investigated detector was integrated with a dedicated breast spectral computed tomography (CT) system to make use of the detector's high spatial and energy resolution and low noise performance under conditions suitable for clinical breast imaging. The aim of this article is to investigate the intrinsic characteristics of the detector, in terms of maximum output count rate, spatial and energy resolution, and noise performance of the imaging system.

METHODS

The maximum output count rate was obtained with a 50 W x-ray tube with a maximum continuous output of 50 kVp at 1.0 mA. A109Cd source, with a characteristic x-ray peak at 22 keV from Ag, was used to measure the energy resolution of the detector. The axial plane modulation transfer function (MTF) was measured using a 67 μm diameter tungsten wire. The two-dimensional (2D) noise power spectrum (NPS) was measured using flat field images and noise equivalent quanta (NEQ) were calculated using the MTF and NPS results. The image quality parameters were studied as a function of various radiation doses and reconstruction filters. The one-dimensional (1D) NPS was used to investigate the effect of electronic noise elimination by varying the minimum energy threshold.

RESULTS

A maximum output count rate of 100 million counts per second per square millimeter (cps/mm2) has been obtained (1 million cps per 100×100 μm pixel). The electrical noise floor was less than 4 keV. The energy resolution measured with the 22 keV photons from a 109Cd source was less than 9%. A reduction of image noise was shown in all the spatial frequencies in 1D NPS as a result of the elimination of the electronic noise. The spatial resolution was measured just above 5 line pairs per mm (lp/mm) where 10% of MTF corresponded to 5.4 mm(-1). The 2D NPS and NEQ shows a low noise floor and a linear dependence on dose. The reconstruction filter choice affected both of the MTF and NPS results, but had a weak effect on the NEQ.

CONCLUSIONS

The prototype energy resolved photon counting Si strip detector can offer superior imaging performance for dedicated breast CT as compared to a conventional energy-integrating detector due to its high output count rate, high spatial and energy resolution, and low noise characteristics, which are essential characteristics for spectral breast CT imaging.

Simulated lesion, human observer performance comparison between thin-section dedicated breast CT images versus computed thick-section simulated projection images of the breast

The objective of this study was to compare the lesion detection performance of human observers between thin-section computed tomography images of the breast, with thick-section (>40 mm) simulated projection images of the breast. Three radiologists and six physicists each executed a two alterative force choice (2AFC) study involving simulated spherical lesions placed mathematically into breast images produced on a prototype dedicated breast CT scanner. The breast image data sets from 88 patients were used to create 352 pairs of image data. Spherical lesions with diameters of 1, 2, 3, 5, and 11 mm were simulated and adaptively positioned into 3D breast CT image data sets; the native thin section (0.33 mm) images were averaged to produce images with different slice thicknesses; average section thicknesses of 0.33, 0.71, 1.5 and 2.9 mm were representative of breast CT; the average 43 mm slice thickness served to simulate simulated projection images of the breast.The percent correct of the human observer's responses were evaluated in the 2AFC experiments. Radiologists lesion detection performance was significantly (p < 0.05) better in the case of thin-section images, compared to thick section images similar to mammography, for all but the 1 mm lesion diameter lesions. For example, the average of three radiologist's performance for 3 mm diameter lesions was 92% correct for thin section breast CT images while it was 67% for the simulated projection images. A gradual reduction in observer performance was observed as the section thickness increased beyond about 1 mm. While a performance difference based on breast density was seen in both breast CT and the projection image results, the average radiologist performance using breast CT images in dense breasts outperformed the performance using simulated projection images in fatty breasts for all lesion diameters except 11 mm. The average radiologist performance outperformed that of the average physicist observer, however trends in performance were similar. Human observers demonstrate significantly better mass-lesion detection performance on thin-section CT images of the breast, compared to thick-section simulated projection images of the breast.

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.

The potential role of dedicated 3D breast CT as a diagnostic tool: review and early clinical examples

Mammography is the gold standard in routine screening for the detection of breast cancer in the general population. However, limitations in sensitivity, particularly in dense breasts, has motivated the development of alternative imaging techniques such as digital breast tomosynthesis, whole breast ultrasound, breast-specific gamma imaging, and more recently dedicated breast computed tomography or "breast CT". Virtually all diagnostic work-ups of asymptomatic nonpalpable findings arise from screening mammography. In most cases, diagnostic mammography and ultrasound are sufficient for diagnosis, with magnetic resonance imaging (MRI) playing an occasional role. Digital breast tomosynthesis, a limited-angle tomographic technique, is increasingly being used for screening. Dedicated breast CT has full three-dimensional (3D) capability with near-isotropic resolution, which could potentially improve diagnostic accuracy. In current dedicated breast CT clinical prototypes, 300-500 low-dose projections are acquired in a circular trajectory around the breast using a flat panel detector, followed by image reconstruction to provide the 3D breast volume. The average glandular dose to the breast from breast CT can range from as little as a two-view screening mammogram to approximately that of a diagnostic mammography examination. Breast CT displays 3D images of the internal structures of the breast; therefore, evaluation of suspicious features like microcalcifications, masses, and asymmetries can be made in multiple anatomical planes from a single scan. The potential role of breast CT for diagnostic imaging is illustrated here through clinical examples such as imaging soft tissue abnormalities and microcalcifications. The potential for breast CT to serve as an imaging tool for extent of disease evaluation and for monitoring neo-adjuvant chemotherapy response is also illustrated.

Emerging modalities in breast cancer imaging

Breast cancer continues to be the most frequently diagnosed malignancy and the second leading cause of death caused by cancer in women in the United States. Although each of the emerging imaging techniques discussed in this article has advantages compared with standard mammography, they are not perfect, and each has inherent limitations. To date, none have been studied by large randomized clinical trials to match the proven benefits of screening mammography; namely the reduction of mortality caused by breast cancer by nearly 30%.

Observer efficiency in free-localization tasks with correlated noise

The efficiency of visual tasks involving localization has traditionally been evaluated using forced choice experiments that capitalize on independence across locations to simplify the performance of the ideal observer. However, developments in ideal observer analysis have shown how an ideal observer can be defined for free-localization tasks, where a target can appear anywhere in a defined search region and subjects respond by localizing the target. Since these tasks are representative of many real-world search tasks, it is of interest to evaluate the efficiency of observer performance in them. The central question of this work is whether humans are able to effectively use the information in a free-localization task relative to a similar task where target location is fixed. We use a yes-no detection task at a cued location as the reference for this comparison. Each of the tasks is evaluated using a Gaussian target profile embedded in four different Gaussian noise backgrounds having power-law noise power spectra with exponents ranging from 0 to 3. The free localization task had a square 6.7° search region. We report on two follow-up studies investigating efficiency in a detect-and-localize task, and the effect of processing the white-noise backgrounds. In the fixed-location detection task, we find average observer efficiency ranges from 35 to 59% for the different noise backgrounds. Observer efficiency improves dramatically in the tasks involving localization, ranging from 63 to 82% in the forced localization tasks and from 78 to 92% in the detect-and- localize tasks. Performance in white noise, the lowest efficiency condition, was improved by filtering to give them a power-law exponent of 2. Classification images, used to examine spatial frequency weights for the tasks, show better tuning to ideal weights in the free-localization tasks. The high absolute levels of efficiency suggest that observers are well-adapted to free-localization tasks.

Power spectrum analysis of the x-ray scatter signal in mammography and breast tomosynthesis projections

PURPOSE

To analyze the frequency domain characteristics of the signal in mammography images and breast tomosynthesis projections with patient tissue texture due to detected scattered x-rays.

METHODS

Acquisitions of x-ray projection images of 19 different patient breasts were simulated using previously acquired volumetric patient images. Acquisition of these images was performed with a dedicated breast CT prototype system, and the images were classified into voxels representing skin, adipose, and glandular tissue with a previously validated automated algorithm. The classified three dimensional images then underwent simulated mechanical compression representing that which is performed during acquisition of mammography and breast tomosynthesis images. The acquisition of projection images of each patient breast was simulated using Monte Carlo methods with each simulation resulting in two images: one of the primary (non-scattered) signal and one of the scatter signal. To analyze the scatter signal for both mammography and breast tomosynthesis, two projections images of each patient breast were simulated, one with the x-ray source positioned at 0° (mammography and central tomosynthesis projection) and at 30° (wide tomosynthesis projection). The noise power spectra (NPS) for both the scatter signal alone and the total signal (primary + scatter) for all images were obtained and the combined results of all patients analyzed. The total NPS was fit to the expected power-law relationship NPS(f) = k/f β and the results were compared with those previously published on the power spectrum characteristics of mammographic texture. The scatter signal alone was analyzed qualitatively and a power-law fit was also performed.

RESULTS

The mammography and tomosynthesis projections of three patient breasts were too small to analyze, so a total of 16 patient breasts were analyzed. The values of β for the total signal of the 0° projections agreed well with previously published results. As expected, the scatter power spectrum reflected a fast drop-off with increasing spatial frequency, with a reduction of four orders of magnitude by 0.1 lp/mm. The β values for the scatter signal were 6.14 and 6.39 for the 0° and 30° projections, respectively.

CONCLUSIONS

Although the low-frequency characteristics of scatter in mammography and breast tomosynthesis were known, a quantitative analysis of the frequency domain characteristics of this signal was needed in order to optimize previously proposed software-based x-ray scatter reduction algorithms for these imaging modalities.

Optimal photon energy comparison between digital breast tomosynthesis and mammography: a case study

A comparison, in terms of the optimal energy that maximizes the image quality between digital breast tomosynthesis (DBT) and digital mammography (DM) was performed in a MAMMOMAT Inspiration system (Siemens) based on amorphous selenium flat panel detector. In this paper we measured the image quality by the signal difference-to-noise ratio (SDNR), and the patient risk by the mean glandular dose (MGD). Using these quantities we compared the optimal voltage that maximizes the image quality both in breast tomosynthesis and standard mammography acquisition mode. The comparison for the two acquisition modes was performed for a W/Rh anode filter combinations by using a 4.5 cm tissue equivalent mammography phantom. Moreover, in order to check if the used equipment was quantum noise limited, the relation of the relative noise with respect to the detector dose was evaluated. Results showed that in the tomosynthesis acquisition mode the optimal voltage is 28 kV, whereas in standard mammography the optimal voltage is 30 kV. The automatic exposure control (AEC) of the system selects 28 kV as optimal voltage both for DBT and DM. Monte Carlo simulations showed a qualitative agreement with the AEC selection system, since an optimal monochromatic energy of 20 keV was found both for DBT and DM. Moreover, the check about the noise showed that the system is not completely quantum noise limited, and this issue could explain the experimental slight difference in terms of optimal voltage between DBT and DM. According to these results, the use of higher voltage settings is not justified for the improvement of the image quality during a DBT examination.

Measurement of breast tissue composition with dual energy cone-beam computed tomography: a postmortem study

PURPOSE

To investigate the feasibility of a three-material compositional measurement of water, lipid, and protein content of breast tissue with dual kVp cone-beam computed tomography (CT) for diagnostic purposes.

METHODS

Simulations were performed on a flat panel-based computed tomography system with a dual kVp technique in order to guide the selection of experimental acquisition parameters. The expected errors induced by using the proposed calibration materials were also estimated by simulation. Twenty pairs of postmortem breast samples were imaged with a flat-panel based dual kVp cone-beam CT system, followed by image-based material decomposition using calibration data obtained from a three-material phantom consisting of water, vegetable oil, and polyoxymethylene plastic. The tissue samples were then chemically decomposed into their respective water, lipid, and protein contents after imaging to allow direct comparison with data from dual energy decomposition.

RESULTS

Guided by results from simulation, the beam energies for the dual kVp cone-beam CT system were selected to be 50 and 120 kVp with the mean glandular dose divided equally between each exposure. The simulation also suggested that the use of polyoxymethylene as the calibration material for the measurement of pure protein may introduce an error of -11.0%. However, the tissue decomposition experiments, which employed a calibration phantom made out of water, oil, and polyoxymethylene, exhibited strong correlation with data from the chemical analysis. The average root-mean-square percentage error for water, lipid, and protein contents was 3.58% as compared with chemical analysis.

CONCLUSIONS

The results of this study suggest that the water, lipid, and protein contents can be accurately measured using dual kVp cone-beam CT. The tissue compositional information may improve the sensitivity and specificity for breast cancer diagnosis.

Personalized estimates of radiation dose from dedicated breast CT in a diagnostic population and comparison with diagnostic mammography

This study retrospectively analyzed the mean glandular dose (MGD) to 133 breasts from 132 subjects, all women, who participated in a clinical trial evaluating dedicated breast CT in a diagnostic population. The clinical trial was conducted in adherence to a protocol approved by institutional review boards and the study participants provided written informed consent. Individual estimates of MGD to each breast from dedicated breast CT was obtained by combining x-ray beam characteristics with estimates of breast dimensions and fibroglandular fraction from volumetric breast CT images, and using normalized glandular dose coefficients. For each study participant and for the breast corresponding to that imaged with breast CT, an estimate of the MGD from diagnostic mammography (including supplemental views) was obtained from the DICOM image headers for comparison. This estimate uses normalized glandular dose coefficients corresponding to a breast with 50% fibroglandular weight fraction. The median fibroglandular weight fraction for the study cohort determined from volumetric breast CT images was 15%. Hence, the MGD from diagnostic mammography was corrected to be representative of the study cohort. Individualized estimates of MGD from breast CT ranged from 5.7 to 27.8 mGy. Corresponding to the breasts imaged with breast CT, the MGD from diagnostic mammography ranged from 2.6 to 31.6 mGy. The mean (± inter-breast SD) and the median MGD (mGy) from dedicated breast CT exam were 13.9 ± 4.6 and 12.6, respectively. For the corresponding breasts, the mean (± inter-breast SD) and the median MGD (mGy) from diagnostic mammography were 12.4 ± 6.3 and 11.1, respectively. Statistical analysis indicated that at the 0.05 level, the distributions of MGD from dedicated breast CT and diagnostic mammography were significantly different (Wilcoxon signed ranks test, p = 0.007). While the interquartile range and the range (maximum-minimum) of MGD from dedicated breast CT was lower than diagnostic mammography, the median MGD from dedicated breast CT was approximately 13.5% higher than that from diagnostic mammography. The MGD for breast CT is based on a 1.45 mm skin layer and that for diagnostic mammography is based on a 4 mm skin layer; thus, favoring a lower estimate for MGD from diagnostic mammography. The median MGD from dedicated breast CT corresponds to the median MGD from four to five diagnostic mammography views. In comparison, for the same 133 breasts, the mean and the median number of views per breast during diagnostic mammography were 4.53 and 4, respectively. Paired analysis showed that there was approximately equal likelihood of receiving lower MGD from either breast CT or diagnostic mammography. Future work will investigate methods to reduce and optimize radiation dose from dedicated breast CT.

Adaptation aftereffects in the perception of radiological images

Radiologists must classify and interpret medical images on the basis of visual inspection. We examined how the perception of radiological scans might be affected by common processes of adaptation in the visual system. Adaptation selectively adjusts sensitivity to the properties of the stimulus in current view, inducing an aftereffect in the appearance of stimuli viewed subsequently. These perceptual changes have been found to affect many visual attributes, but whether they are relevant to medical image perception is not well understood. To examine this we tested whether aftereffects could be generated by the characteristic spatial structure of radiological scans, and whether this could bias their appearance along dimensions that are routinely used to classify them. Measurements were focused on the effects of adaptation to images of normal mammograms, and were tested in observers who were not radiologists. Tissue density in mammograms is evaluated visually and ranges from "dense" to "fatty." Arrays of images varying in intermediate levels between these categories were created by blending dense and fatty images with different weights. Observers first adapted by viewing image samples of dense or fatty tissue, and then judged the appearance of the intermediate images by using a texture matching task. This revealed pronounced perceptual aftereffects - prior exposure to dense images caused an intermediate image to appear more fatty and vice versa. Moreover, the appearance of the adapting images themselves changed with prolonged viewing, so that they became less distinctive as textures. These aftereffects could not be accounted for by the contrast differences or power spectra of the images, and instead tended to follow from the phase spectrum. Our results suggest that observers can selectively adapt to the properties of radiological images, and that this selectivity could strongly impact the perceived textural characteristics of the images.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Scaling-law for the energy dependence of anatomic power spectrum in dedicated breast CT

PURPOSE

To determine the x-ray photon energy dependence of the anatomic power spectrum of the breast when imaged with dedicated breast computed tomography (CT).

METHODS

A theoretical framework for scaling the empirically determined anatomic power spectrum at one x-ray photon energy to that at any given x-ray photon energy when imaged with dedicated breast CT was developed. Theory predicted that when the anatomic power spectrum is fitted with a power curve of the form k f(-β), where k and β are fit coefficients and f is spatial frequency, the exponent β would be independent of x-ray photon energy (E), and the amplitude k scales with the square of the difference in energy-dependent linear attenuation coefficients of fibroglandular and adipose tissues. Twenty mastectomy specimens based numerical phantoms that were previously imaged with a benchtop flat-panel cone-beam CT system were converted to 3D distribution of glandular weight fraction (f(g)) and were used to verify the theoretical findings. The 3D power spectrum was computed in terms of f(g) and after converting to linear attenuation coefficients at monoenergetic x-ray photon energies of 20-80 keV in 5 keV intervals. The 1D power spectra along the axes were extracted and fitted with a power curve of the form k f(-β). The energy dependence of k and β were analyzed.

RESULTS

For the 20 mastectomy specimen based numerical phantoms used in the study, the exponent β was found to be in the range of 2.34-2.42, depending on the axis of measurement. Numerical simulations agreed with the theoretical predictions that for a power-law anatomic spectrum of the form k f(-β), β was independent of E and k(E) = k(1)[μ(g)(E) - μ(a)(E)](2), where k(1) is a constant, and μ(g)(E) and μ(a)(E) represent the energy-dependent linear attenuation coefficients of fibroglandular and adipose tissues, respectively.

CONCLUSIONS

Numerical simulations confirmed the theoretical predictions that in dedicated breast CT, the spatial frequency dependence of the anatomic power spectrum will be independent of x-ray photon energy, and the amplitude of the anatomic power spectrum scales by the square of difference in linear attenuation coefficients of fibroglandular and adipose tissues.

Validation of a power-law noise model for simulating small-scale breast tissue

We have validated a small-scale breast tissue model based on power-law noise. A set of 110 patient images served as truth. The statistical model parameters were determined by matching the radially averaged power-spectrum of the projected simulated tissue with that of the central tomosynthesis patient breast projections. Observer performance in a signal-known exactly detection task in simulated and actual breast backgrounds was compared. Observers included human readers, a pre-whitening observer model and a channelized Hotelling observer model. For all observers, good agreement between performance in the simulated and actual backgrounds was found, both in the tomosynthesis central projections and the reconstructed images. This tissue model can be used for breast x-ray imaging system optimization. The complete statistical description of the model is provided.

Comprehensive assessment of the slice sensitivity profiles in breast tomosynthesis and breast CT

PURPOSE

This study experimentally evaluated the slice sensitivity profile (SSP) and its relationship between acquisition angle, object size, and cone angle. The sensitivity profile metric was used to characterize a breast tomosynthesis system's resolution in the z-axis. The SSP was also measured on a prototype breast computed tomography (bCT) system.

METHODS

The SSP was measured using brass disks placed within adipose tissue-equivalent breast phantoms. The digital tomosynthesis system (Selenia Dimensions, Hologic Corporation, Bedford, MA) acquires projection images over a 15° angular range and the bCT scanner acquires projection images over a 360° angular range. Angular ranges between 15° and 360° were studied by using a subset of the projection images acquired on the bCT scanner. The SSP was determined by measuring a background-corrected mean gray scale value as a function of the z-position (axis normal to the plane of the detector).

RESULTS

The results show that SSP improves when the angular acquisition range is increased and the SSP approaches a delta function for angles greater than 180°. Smaller objects have a narrower SSP and the SSP is not significantly dependent on the cone angle. For a 2.5, 5, 10 mm disk, the full width at half maximum of the SSP was 35, 61, 115 mm, respectively, on the tomosynthesis system (at 15°) and was 0.5 mm for all disk diameters on the bCT scanner (at 360°).

CONCLUSIONS

The SSP is dependent on object size and angular acquisition range. These dependencies are overcome once the angular acquisition range is increased beyond 180°.