Jered R. Wells

How does c-view image quality compare with conventional 2D FFDM?

PURPOSE The FDA approved the use of digital breast tomosynthesis (DBT) in 2011 as an adjunct to 2D full field digital mammography (FFDM) with the constraint that all DBT acquisitions must be paired with a 2D image to assure adequate interpretative information is provided. Recently manufacturers have developed methods to provide a synthesized 2D image generated from the DBT data with the hope of sparing patients the radiation exposure from the FFDM acquisition. While this much needed alternative effectively reduces the total radiation burden, differences in image quality must also be considered. The goal of this study was to compare the intrinsic image quality of synthesized 2D c-view and 2D FFDM images in terms of resolution, contrast, and noise. METHODS Two phantoms were utilized in this study: the American College of Radiology mammography accreditation phantom (ACR phantom) and a novel 3D printed anthropomorphic breast phantom. Both phantoms were imaged using a Hologic Selenia Dimensions 3D system. Analysis of the ACR phantom includes both visual inspection and objective automated analysis using in-house software. Analysis of the 3D anthropomorphic phantom includes visual assessment of resolution and Fourier analysis of the noise. RESULTS Using ACR-defined scoring criteria for the ACR phantom, the FFDM images scored statistically higher than c-view according to both the average observer and automated scores. In addition, between 50% and 70% of c-view images failed to meet the nominal minimum ACR accreditation requirements-primarily due to fiber breaks. Software analysis demonstrated that c-view provided enhanced visualization of medium and large microcalcification objects; however, the benefits diminished for smaller high contrast objects and all low contrast objects. Visual analysis of the anthropomorphic phantom showed a measureable loss of resolution in the c-view image (11 lp/mm FFDM, 5 lp/mm c-view) and loss in detection of small microcalcification objects. Spectral analysis of the anthropomorphic phantom showed higher total noise magnitude in the FFDM image compared with c-view. Whereas the FFDM image contained approximately white noise texture, the c-view image exhibited marked noise reduction at midfrequency and high frequency with far less noise suppression at low frequencies resulting in a mottled noise appearance. CONCLUSIONS Their analysis demonstrates many instances where the c-view image quality differs from FFDM. Compared to FFDM, c-view offers a better depiction of objects of certain size and contrast, but provides poorer overall resolution and noise properties. Based on these findings, the utilization of c-view images in the clinical setting requires careful consideration, especially if considering the discontinuation of FFDM imaging. Not explicitly explored in this study is how the combination of DBT + c-view performs relative to DBT + FFDM or FFDM alone.

Population of 224 realistic human subject-based computational breast phantoms

PURPOSE To create a database of highly realistic and anatomically variable 3D virtual breast phantoms based on dedicated breast computed tomography (bCT) data. METHODS A tissue classification and segmentation algorithm was used to create realistic and detailed 3D computational breast phantoms based on 230 + dedicated bCT datasets from normal human subjects. The breast volume was identified using a coarse three-class fuzzy C-means segmentation algorithm which accounted for and removed motion blur at the breast periphery. Noise in the bCT data was reduced through application of a postreconstruction 3D bilateral filter. A 3D adipose nonuniformity (bias field) correction was then applied followed by glandular segmentation using a 3D bias-corrected fuzzy C-means algorithm. Multiple tissue classes were defined including skin, adipose, and several fractional glandular densities. Following segmentation, a skin mask was produced which preserved the interdigitated skin, adipose, and glandular boundaries of the skin interior. Finally, surface modeling was used to produce digital phantoms with methods complementary to the XCAT suite of digital human phantoms. RESULTS After rejecting some datasets due to artifacts, 224 virtual breast phantoms were created which emulate the complex breast parenchyma of actual human subjects. The volume breast density (with skin) ranged from 5.5% to 66.3% with a mean value of 25.3% ± 13.2%. Breast volumes ranged from 25.0 to 2099.6 ml with a mean value of 716.3 ± 386.5 ml. Three breast phantoms were selected for imaging with digital compression (using finite element modeling) and simple ray-tracing, and the results show promise in their potential to produce realistic simulated mammograms. CONCLUSIONS This work provides a new population of 224 breast phantoms based on in vivo bCT data for imaging research. Compared to previous studies based on only a few prototype cases, this dataset provides a rich source of new cases spanning a wide range of breast types, volumes, densities, and parenchymal patterns.

Population of 100 Realistic, Patient-Based Computerized Breast Phantoms for Multi-modality Imaging Research

Breast imaging is an important area of research with many new techniques being investigated to further reduce the morbidity and mortality of breast cancer through early detection. Computerized phantoms can provide an essential tool to quantitatively compare new imaging systems and techniques. Current phantoms, however, lack sufficient realism in depicting the complex 3D anatomy of the breast. In this work, we created one-hundred realistic and detailed 3D computational breast phantoms based on high-resolution CT datasets from normal patients. We also developed a finiteelement application to simulate different compression states of the breast, making the phantoms applicable to multimodality imaging research. The breast phantoms and tools developed in this work were packaged into user-friendly software applications to distribute for breast imaging research.

Frequency response and distortion properties of nonlinear image processing algorithms and the importance of imaging context.

PURPOSE The most common metrics for resolution analysis in medical imaging are valid only for (approximately) linear systems. While analogues to these metrics have been used in attempts to describe resolution performance in nonlinear systems, the analysis is incomplete since distortion effects are often ignored. The authors have developed a methodology to analyze the amplitude modulation and waveform distortion properties of nonlinear systems with specific application to medical image processing algorithms. METHODS Using sinusoidal basis functions, two metrics were derived which distinguish amplitude modulation from nonlinear waveform distortion: principle frequency response and distortion power spectrum, respectively. Additionally, two figures of merit were developed to describe the relative impact of nonlinear distortion as a result of image processing: distortion index (DI) and ΣDI. Three nonlinear denoising algorithms, the median, bilateral, and wavelet denoising filters, were selected as example functions to demonstrate the utility of the metrics derived in this study. RESULTS Each filter showed very different resolution and waveform distortion properties. In particular, the amplitude and extent of nonlinear distortion depended strongly on image context and the type of nonlinear mechanism employed. Nonlinear waveform distortion constituted up to 30% of the median filter output signal power in high contrast-to-noise ratio (CNR) scenarios. Conversely, nonlinear distortion never exceeded 1% of the bilateral filter output signal power. The wavelet denoising response contained between 1% and 9% distortion which varied weakly as a function of CNR. CONCLUSIONS The analytical metrics described in the paper demonstrate the importance of considering both resolution- and distortion-related effects in characterizing the performance of nonlinear imaging systems with specific application to image processing algorithms. Findings with three common nonlinear algorithms demonstrate a range of CNR values over which it is important to consider the impact of the nonlinear nature of each algorithm. Background context is also shown to influence the degree to which the nonlinear nature of the algorithm influences resolution and distortion. While no single metric can yet anticipate observer performance in nonlinear systems, the approach described can demonstrate the range of imaging contexts over which such nonlinear effects are important to consider.

Estimation of the two-dimensional presampled modulation transfer function of digital radiography devices using one-dimensional test objects

PURPOSE The modulation transfer function (MTF) of medical imaging devices is commonly reported in the form of orthogonal one-dimensional (1D) measurements made near the vertical and horizontal axes with a slit or edge test device. A more complete description is found by measuring the two-dimensional (2D) MTF. Some 2D test devices have been proposed, but there are some issues associated with their use: (1) they are not generally available; (2) they may require many images; (3) the results may have diminished accuracy; and (4) their implementation may be particularly cumbersome. This current work proposes the application of commonly available 1D test devices for practical and accurate estimation of the 2D presampled MTF of digital imaging systems. METHODS Theory was developed and applied to ensure adequate fine sampling of the system line spread function for 1D test devices at orientations other than approximately vertical and horizontal. Methods were also derived and tested for slit nonuniformity correction at arbitrary angle. Techniques were validated with experimental measurements at ten angles using an edge test object and three angles using a slit test device on an indirect-detection flat-panel system [GE Revolution XQ∕i (GE Healthcare, Waukesha, WI)]. The 2D MTF was estimated through a simple surface fit with interpolation based on Delaunay triangulation of the 1D edge-based MTF measurements. Validation by synthesis was also performed with simulated images from a hypothetical direct-detection flat-panel device. RESULTS The 2D MTF derived from physical measurements yielded an average relative precision error of 0.26% for frequencies below the cutoff (2.5 mm(-1)) and approximate circular symmetry at frequencies below 4 mm(-1). While slit analysis generally agreed with the results of edge analysis, the two showed subtle differences at frequencies above 4 mm(-1). Slit measurement near 45° revealed radial asymmetry in the MTF resulting from the square pixel aperture (0.2 mm × 0.2 mm), a characteristic which was not necessarily appreciated with the orthogonal 1D MTF measurements. In simulation experiments, both slit- and edge-based measurements resolved the radial asymmetries in the 2D MTF. The average absolute relative accuracy error in the 2D MTF between the DC and cutoff (2.5 mm(-1)) frequencies was 0.13% with average relative precision error of 0.11%. Other simulation results were similar to those derived from physical data. CONCLUSIONS Overall, the general availability, acceptance, accuracy, and ease of implementation of 1D test devices for MTF assessment make this a valuable technique for 2D MTF estimation.

Preliminary investigation of the frequency response and distortionproperties of nonlinear image processing algorithms

Assessment of the resolution properties of nonlinear imaging systems is a useful but challenging task. While the modulation transfer function (MTF) fully describes contrast resolution as a function of spatial frequency for linear systems, an equivalent metric does not exist for systems with significant nonlinearity. Therefore, this preliminary investigation attempts to classify and quantify the amount of scaling and distortion imposed on a given image signal as the result of a nonlinear process (nonlinear image processing algorithm). As a proof-of-concept, a median filter is assessed in terms of its principle frequency response (PFR) and distortion response (DR) functions. These metrics are derived in frequency space using a sinusoidal basis function, and it is shown that, for a narrow-band sinusoidal input signal, the scaling and distortion properties of the nonlinear filter are described exactly by PFR and DR, respectively. The use of matched sinusoidal basis and input functions accurately reveals the frequency response to long linear structures of different scale. However, when more complex (multi-band) input signals are considered, PFR and DR fail to adequately characterize the frequency response due to nonlinear interaction effects between different frequency components during processing. Overall, the results reveal the context-dependent nature of nonlinear image processing algorithm performance, and they emphasize the importance of the basis function choice in algorithm assessment. In the future, more complex forms of nonlinear systems analysis may be necessary to fully characterize the frequency response properties of nonlinear algorithms in a context-dependent manner.

TH‐A‐103‐10: Improved Segmentation of Low‐Contrast Fibroglandular Structures in High‐Noise Breast CT Volumes for XCAT Modeling

PURPOSE This work improves the accuracy and realism of automated breast computed tomography (bCT) tissue segmentation by refining the detection of low-contrast fibroglandular structures to produce high-resolution realistic computer-generated (XCAT) breast phantoms from empirical human subject data. METHODS Previous work by Hsu et al. [Med. Phys. 38, 5756-5770 (2011)] produced high-resolution realistic computer-generated breast phantoms from empirical human subject data but challenges were encountered with the accurate segmentation of fine, low-contrast glandular structures. The current work addresses those challenges. A 3-D anisotropic diffusion algorithm was used to denoise fourteen bCT datasets. After breast masking, two adipose non-uniformity correction techniques were applied. The first has been described by Altunbas, et al. [Med. Phys. 34, 3109-3118 (2007)]. The second approach employed an original technique using higher-order polynomials to correct for residual adipose non-uniformity. Histogram thresholding then produced initial gland and skin segmentations. This was followed by a novel glandular linking and extension protocol based on skeletonization of the skin and glandular segmentations and a pixel gray-level-weighted distance transform. Skin mask definition and glandular density differentiation completed the segmentation. RESULTS Volumetric denoising reduced the standard deviation of the adipose background by an average of 60.4%. The Altunbas method corrected for radially symmetric, quadratic non-uniformities in breasts with circular coronal cross sections, but performed poorly on high-density breasts and breasts with asymmetric adipose non-uniformity. Follow-up correction using the novel method improved adipose uniformity by an average of 24.6%. The new fibroglandular linking and extension protocol improved the detection of low-contrast fibroglandular structures, including Coopers ligaments. The total number of fibroglandular tissue islands was also reduced. CONCLUSION The semi-automated bCT segmentation protocol improved low-contrast glandular fiber detection in high-noise reconstructions. Linking of disparate fibroglandular tissue islands and capture of Coopers ligaments will contribute to the overall accuracy and realism of empirically-derived XCAT breast phantoms. This work was supported by NIH Grant 5R01-CA-134658.

Initial investigation into lower-cost CT for resource limited regions of the world

This paper describes an initial investigation into means for producing lower-cost CT scanners for resource limited regions of the world. In regions such as sub-Saharan Africa, intermediate level medical facilities serving millions have no CT machines, and lack the imaging resources necessary to determine whether certain patients would benefit from being transferred to a hospital in a larger city for further diagnostic workup or treatment. Low-cost CT scanners would potentially be of immense help to the healthcare system in such regions. Such scanners would not produce state-of-theart image quality, but rather would be intended primarily for triaging purposes to determine the patients who would benefit from transfer to larger hospitals. The lower-cost scanner investigated here consists of a fixed digital radiography system and a rotating patient stage. This paper describes initial experiments to determine if such a configuration is feasible. Experiments were conducted using (1) x-ray image acquisition, a physical anthropomorphic chest phantom, and a flat-panel detector system, and (2) a computer-simulated XCAT chest phantom. Both the physical phantom and simulated phantom produced excellent image quality reconstructions when the phantom was perfectly aligned during acquisition, but artifacts were noted when the phantom was displaced to simulate patient motion. An algorithm was developed to correct for motion of the phantom and demonstrated success in correcting for 5-mm motion during 360-degree acquisition of images. These experiments demonstrated feasibility for this approach, but additional work is required to determine the exact limitations produced by patient motion.

TU-FG-209-07: Medical Physics 1.0 Versus Medical Physics 2.0: A Case Study

PURPOSE To illustrate how performance analytics can identify performance decrement in digital radiography systems. METHODS Subsequent to a radiologists image quality complaint, four different advanced methods contributed to root cause analysis. Our system was a GE Revolution XQi digital radiography unit. Initially, we reviewed weekly GE Quality Assurance Procedures (QAP) results in a database dating from 2001. Next, we evaluated objective image quality metrics of individual PA Chest radiographs acquired. These images were anonymized, securely transferred, and analyzed by the Duke University Clinical Imaging Physics Group with software previously described1 and validated2 . Third, we compared the exposure-dependent SNR2 (NEQ) of the unit with previously established confidence limits3 . Finally, we explored our service database to reveal events that might affect detector performance. RESULTS QAP reported a decrease in CNR reflected in a significant increase in lung noise(Ln), mediastinum noise(Mn), and subdiaphragm-lung contrast(Slc) with a significant decrease in lung grey level(Lgl) after detector replacement. Most change occurred during week 1, before the QAP indicated one-half the ultimate decrease in CNR. After detector recalibration, QAP CNR improved, but was not restored to previous levels. Lgl and Slc were no longer significantly different from before, however Ln and Mn remained significantly different. Exposure-dependent SNR2 show the detector to be operating within limits in October 2006 but subsequently became miscalibrated sometime before acquisition of the 2011-2014 data. Service records revealed catastrophic failure of the Image Detection Controller that contained the 2007 calibration. Traditional metrics did not indicate that the system was performing outside of normal limits. CONCLUSION Performance analytics are powerful tools whose proper application could allow early intervention in degraded system performance. The image-quality metrics appear to be highly sensitive to system performance and are reported with every acquisition rather than at arbitrary intervals. Confidence intervals may require customization for individual systems or detectors.

TH‐AB‐201‐12: A Consumer Report for Mobile Digital Radiography: A Holistic Comparative Evaluation Across Four Systems

Purpose: To provide a template for the comprehensive clinical evaluation of new imaging technologies with initial application to mobile digital radiography (DR). Methods: Four mobile DR devices (GE Optima XR220amx+Flashpad; Carestream DRX Revolution+DRX-1C; Philips Mobile Diagnost wDR; Philips Mobile Diagnost wDR+Skyplate) were evaluated under four categories: 1)Technical specifications: Vendor data were collected and complied into a unified nomenclature. 2)Physical performance: Each unit underwent imaging physics evaluation including MTF, NPS, DQE, and grid artifact analysis. 3)Clinical performance: Fifteen bedside chest radiographs were acquired using each unit. Metadata were stripped and images cropped to 14:17 aspect ratio to ensure vendor anonymity. Six cardiothoracic radiologists scored the randomized images on PACS workstations using five criteria: overall quality, mediastinum noise, rib-lung contrast, lung density, and lung detail. Results were processed using multiple linear regression analysis. 4)Operability performance: A survey was designed and administered to technologists asking questions which captured use, preference, and experiential data for each unit. To avoid impropriety, units were randomly assigned numbers 1–4. Results: Vendor specifications were compiled into a single-page table enabling ready comparative review. All systems had MTF within 20% and NNPS within 27% of the average response at frequencies below 2.5 mm−1. Units [1,2,4] had DQE within 19% of average while unit 3 was 49% below average at high-frequencies. According to grid artifact analysis, the best and worst results were from units [2,3,4,1], respectively. The radiologist study revealed high inter-radiologist variability which limited the number of significant overall results. Survey results uncovered clear technologist biases. In general, technologists value practical over optional features. Conclusion: The compilation of vendor, physicist, radiologist, and technologist data provides an easy means for healthcare professionals to compare different medical equipment options using a data-driven approach. This type of comprehensive assessment now serves as a model for new technology review at our institution. This work was supported by Duke University Health Systems.