Gezheng Wen

Objective image characterization of a spectral CT scanner with dual-layer detector

This work evaluated the performance of a detector-based spectral CT system by obtaining objective reference data, evaluating attenuation response of iodine and accuracy of iodine quantification, and comparing conventional CT and virtual monoenergetic images in three common phantoms. Scanning was performed using the hospitals clinical adult body protocol. Modulation transfer function (MTF) was calculated for a tungsten wire and visual line pair targets were evaluated. Image noise power spectrum (NPS) and pixel standard deviation were calculated. MTF for monoenergetic images agreed with conventional images within 0.05 lp cm-1. NPS curves indicated that noise texture of 70 keV monoenergetic images is similar to conventional images. Standard deviation measurements showed monoenergetic images have lower noise except at 40 keV. Mean CT number and CNR agreed with conventional images at 75 keV. Measured iodine concentration agreed with true concentration within 6% for inserts at the center of the phantom. Performance of monoenergetic images at detector based spectral CT is the same as, or better than, that of conventional images. Spectral acquisition and reconstruction with a detector based platform represents the physical behaviour of iodine as expected and accurately quantifies the material concentration.

Model observer design for multi-signal detection in the presence of anatomical noise

As psychophysical studies are resource-intensive to conduct, model observers are commonly used to assess and optimize medical imaging quality. Model observers are typically designed to detect at most one signal. However, in clinical practice, there may be multiple abnormalities in a single image set (e.g. multifocal multicentric (MFMC) breast cancer), which can impact treatment planning. Prevalence of signals can be different across anatomical regions, and human observers do not know the number or location of signals a priori. As new imaging techniques have the potential to improve multiple-signal detection (e.g. digital breast tomosynthesis may be more effective for diagnosis of MFMC than mammography), image quality assessment approaches addressing such tasks are needed. In this study, we present a model observer to detect multiple signals in an image dataset. A novel implementation of partial least squares (PLS) was developed to estimate different sets of efficient channels directly from the images. The PLS channels are adaptive to the characteristics of signals and the background, and they capture the interactions among signal locations. Corresponding linear decision templates are employed to generate both image-level and location-specific scores on the presence of signals. Our results show that: (1) the model observer can achieve high performance with a reasonably small number of channels; (2) the model observer with PLS channels outperforms that with benchmark modified Laguerre-Gauss channels, especially when realistic signal shapes and complex background statistics are involved; (3) the tasks of clinical interest, and other constraints such as sample size would alter the optimal design of the model observer.

WE-FG-207B-11: Objective Image Characterization of Spectral CT with a Dual-Layer Detector

PURPOSE To obtain objective reference data for the spectral performance on a dual-layer detector CT platform (IQon, Philips) and compare virtual monoenergetic to conventional CT images. METHODS Scanning was performed using the hospitals clinical adult body protocol: helical acquisition at 120kVp, with CTDIvol=15mGy. Multiple modules (591, 515, 528) of a CATPHAN 600 phantom and a 20 cm diameter cylindrical water phantom were scanned. No modifications to the standard protocol were necessary to enable spectral imaging. Both conventional and virtual monoenergetic images were generated from acquired data. Noise characteristics were assessed through Noise Power Spectra (NPS) and pixel standard deviation from water phantom images. Spatial resolution was evaluated using Modulation Transfer Functions (MTF) of a tungsten wire as well as resolution bars. Low-contrast detectability was studied using contrast-to-noise ratio (CNR) of a low contrast object. RESULTS MTF curves of monoenergetic and conventional images were almost identical. MTF 50%, 10%, and 5% levels for monoenergetic images agreed with conventional images within 0.05lp/cm. These observations were verified by the resolution bars, which were clearly resolved at 7lp/cm but started blurring at 8lp/cm for this protocol in both conventional and 70 keV images. NPS curves indicated that, compared to conventional images, the noise power distribution of 70 keV monoenergetic images is similar (i.e. noise texture is similar) but exhibit a low frequency peak at keVs higher and lower than 70 keV. Standard deviation measurements show monoenergetic images have lower noise except at 40 keV where it is slightly higher. CNR of monoenergetic images is mostly flat across keV values and is superior to that of conventional images. CONCLUSION Values for standard image quality metrics are the same or better for monoenergetic images compared to conventional images. Results indicate virtual monoenergetic images can be used without any loss in image quality or noise penalties relative to conventional images. This study was performed as part of a research agreement among Philips Healthcare, University Hospitals of Cleveland, and Case Western Reserve University.

Computational assessment of visual search strategies in volumetric medical images

Abstract. When searching through volumetric images [e.g., computed tomography (CT)], radiologists appear to use two different search strategies: “drilling” (restrict eye movements to a small region of the image while quickly scrolling through slices), or “scanning” (search over large areas at a given depth before moving on to the next slice). To computationally identify the type of image information that is used in these two strategies, 23 naïve observers were instructed with either “drilling” or “scanning” when searching for target T’s in 20 volumes of faux lung CTs. We computed saliency maps using both classical two-dimensional (2-D) saliency, and a three-dimensional (3-D) dynamic saliency that captures the characteristics of scrolling through slices. Comparing observers’ gaze distributions with the saliency maps showed that search strategy alters the type of saliency that attracts fixations. Drillers’ fixations aligned better with dynamic saliency and scanners with 2-D saliency. The computed saliency was greater for detected targets than for missed targets. Similar results were observed in data from 19 radiologists who searched five stacks of clinical chest CTs for lung nodules. Dynamic saliency may be superior to the 2-D saliency for detecting targets embedded in volumetric images, and thus “drilling” may be more efficient than “scanning.”

A stereo matching model observer for stereoscopic viewing of 3D medical images

Stereoscopic viewing of 3D medical imaging data has the potential to increase the detection of abnormalities. We present a new stereo model observer inspired by the characteristics of stereopsis in human vision. Given a stereo pair of images of an object (i.e., left and right images separated by a small displacement), the model observer rst nds the corresponding points between the two views, and then fuses them together to create a 2D cyclopean view. Assuming that the cyclopean view has extracted most of the 3D information presented in the stereo pair, a channelized Hotelling observer (CHO) can be utilized to make decisions. We conduct a simulation study that attempts to mimic the detection of breast lesions on stereoscopic viewing of breast tomosynthesis projection images. We render voxel datasets that contain random 3D power-law noise to model normal breast tissues with various breast densities. 3D Gaussian signal is added to some of the datasets to model the presence of a breast lesion. By changing the separation angle between the two views, multiple stereo pairs of projection images are generated for each voxel dataset. The performance of the model is evaluated in terms of the accuracy of binary decisions on the presence of the simulated lesions.

Lesion detectability in stereoscopically viewed digital breast tomosynthesis projection images: a model observer study with anthropomorphic computational breast phantoms

Stereoscopic views of 3D breast imaging data may better reveal the 3D structures of breasts, and potentially improve the detection of breast lesions. The imaging geometry of digital breast tomosynthesis (DBT) lends itself naturally to stereo viewing because a stereo pair can be easily formed by two projection images with a reasonable separation angle for perceiving depth. This simulation study attempts to mimic breast lesion detection on stereo viewing of a sequence of stereo pairs of DBT projection images. 3D anthropomorphic computational breast phantoms were scanned by a simulated DBT system, and spherical signals were inserted into different breast regions to imitate the presence of breast lesions. The regions of interest (ROI) had different local anatomical structures and consequently different background statistics. The projection images were combined into a sequence of stereo pairs, and then presented to a stereo matching model observer for determining lesion presence. The signal-to-noise ratio (SNR) was used as the figure of merit in evaluation, and the SNR from the stack of reconstructed slices was considered as the benchmark. We have shown that: 1) incorporating local anatomical backgrounds may improve lesion detectability relative to ignoring location-dependent image characteristics. The SNR was lower for the ROIs with the higher local power-law-noise coefficient β. 2) Lesion detectability may be inferior on stereo viewing of projection images relative to conventional viewing of reconstructed slices, but further studies are needed to confirm this observation.

Digital breast tomosynthesis for detecting multifocal and multicentric breast cancer: influence of acquisition geometry on model observer performance in breast phantom images

Multifocal and multicentric breast cancer (MFMC), i.e., the presence of two or more tumor foci within the same breast, has an immense clinical impact on treatment planning and survival outcomes. Detecting multiple breast tumors is challenging as MFMC breast cancer is relatively uncommon, and human observers do not know the number or locations of tumors a priori. Digital breast tomosynthesis (DBT), in which an x-ray beam sweeps over a limited angular range across the breast, has the potential to improve the detection of multiple tumors.1, 2 However, prior efforts to optimize DBT image quality only considered unifocal breast cancers (e.g.,3-9), so the recommended geometries may not necessarily yield images that are informative for the task of detecting MFMC. Hence, the goal of this study is to employ a 3D multi-lesion (ml) channelized-Hotelling observer (CHO) to identify optimal DBT acquisition geometries for MFMC. Digital breast phantoms and simulated DBT scanners of different geometries (e.g., wide or narrow arc scans, different number of projections in each scan) were used to generate image data for the simulation study. Multiple 3D synthetic lesions were inserted into different breast regions to simulate MF cases and MC cases. 3D partial least squares (PLS) channels, and 3D Laguerre-Gauss (LG) channels were estimated to capture discriminant information and correlations among signals in locally varying anatomical backgrounds, enabling the model observer to make both image-level and location-specific detection decisions. The 3D ml-CHO with PLS channels outperformed that with LG channels in this study. The simulated MC cases and MC cases were not equally difficult for the ml-CHO to detect across the different simulated DBT geometries considered in this analysis. Also, the results suggest that the optimal design of DBT may vary as the task of clinical interest changes, e.g., a geometry that is better for finding at least one lesion may be worse for counting the number of lesions.

Model observer design for detecting multiple abnormalities in anatomical background images

As psychophysical studies are resource-intensive to conduct, model observers are commonly used to assess and optimize medical imaging quality. Existing model observers were typically designed to detect at most one signal. However, in clinical practice, there may be multiple abnormalities in a single image set (e.g., multifocal and multicentric breast cancers (MMBC)), which can impact treatment planning. Prevalence of signals can be different across anatomical regions, and human observers do not know the number or location of signals a priori. As new imaging techniques have the potential to improve multiple-signal detection (e.g., digital breast tomosynthesis may be more effective for diagnosis of MMBC than planar mammography), image quality assessment approaches addressing such tasks are needed. In this study, we present a model-observer mechanism to detect multiple signals in the same image dataset. To handle the high dimensionality of images, a novel implementation of partial least squares (PLS) was developed to estimate different sets of efficient channels directly from the images. Without any prior knowledge of the background or the signals, the PLS channels capture interactions between signals and the background which provide discriminant image information. Corresponding linear decision templates are employed to generate both image-level and location-specific scores on the presence of signals. Our preliminary results show that the model observer using PLS channels, compared to our first attempts with Laguerre-Gauss channels, can achieve high performance with a reasonably small number of channels, and the optimal design of the model observer may vary as the tasks of clinical interest change.

WE-DE-207B-03: Influence of Local Anatomical Variations On Detection of Multifocal and Multicentric Breast Cancer

PURPOSE We employed a novel model observer to assess the impact of local anatomic variations on the detection of multiple breast tumors depicted on mammograms. We expect the study to be valuable for future task-based assessments and optimizations of x-ray based breast imaging techniques (e.g., digital breast tomosynthesis for diagnosis of multifocal multicentric breast cancer (MMBC)). METHODS Regions of interest (ROIs) from four different sets of clustered lumpy background simulations were extracted as the image background. A random number of circular Gaussians were inserted to simulate cases with multicentric lesions. The task of the model observer was to perform a multiple-lesion detection task, making both image-level and location-specific detection decisions based on the overall information from individual ROIs and their interactions. The detectability at each of the possible signal location was measured using channelized Hotelling observers with a novel implementation of partial least squares channels. The power law exponent β of local anatomical noise power spectrum, and spatial covariances K within the ROIs were computed to represent local background variations. The relationships between β, K and the detectability were evaluated. RESULTS β and/or K were different across the ROIs. Location-specific signal-to-noise ratio (SNR) results showed statistically significant correlations between β, K and the detectability. Specifically, a ROI with a higher value of β, and/or K with larger and more variable off-diagonal elements was associated with a lower SNR at that ROI. These results also demonstrate the potential of the model observer to adapt to the variations in anatomical backgrounds. CONCLUSION We conducted a model observer study to explore the influence of local anatomical backgrounds on detecting MMBC. The study suggests that the optimal imaging settings may need to be adjusted when the clinical task of interest changes (e.g., find only the largest tumor versus find every tumor). This work is supported by National Science Foundation (NSF) Award CBET-1445713.

Computational assessment of stereoscopic viewing a sequence of stereo pairs of breast tomosynthesis projection images

Digital breast tomosynthesis (DBT) is a 3D imaging technology in which an x-ray fan beam rotates around the breast, producing a series of projection images. The imaging geometry of DBT lends itself naturally to stereo viewing because a stereo pair can be easily formed by two projection images with a reasonable separation angle. Stereo viewing may reveal the 3D structures of breasts thus has the potential to increase the sensitivity and specificity of breast imaging. In this study, we conduct a simulation study that mimics the detection of breast lesions on stereoscopic viewing of DBT projections. The presentation approach we investigate here is one in which the reader is presented with a sequence of stereo pairs from a rotating point of view. We render voxel datasets that contain random 3D power-law noise to model normal breast tissues with different breast densities. A 3D Gaussian signal is inserted to some of the datasets to model the presence of a breast lesion. Sequences of stereo pairs of projection images are generated for each voxel dataset by varying the projection angles of the two views. The diagnostic performance, in terms of the accuracy of binary decisions on the presence of the simulated lesions, is evaluated with a numerical model observer.