Attenuation correction (AC) is important for an accurate interpretation and quantitative analysis of SPECT myocardial perfusion imaging. Dedicated cardiac SPECT systems have invaluable efficacy in the evaluation and risk stratification of patients with known or suspected cardiovascular disease. However, most dedicated cardiac SPECT systems are standalone, not combined with a transmission imaging capability such as computed tomography (CT) for generating attenuation maps for AC. To address this problem, we propose to apply a conditional generative adversarial network (cGAN) for generating attenuation-corrected SPECT images (SPECTGAN ) directly from non-corrected SPECT images (SPECTNC) in image domain as a one-step process without requiring additional intermediate step. The proposed network was trained and tested for 100 cardiac SPECT/CT data from a GE Discovery NM 570c SPECT/CT, collected retrospectively at Yale New Haven Hospital.The generated images were evaluated quantitatively through the normalized root mean square error (NRMSE), peak signal to noise ratio (PSNR), and structural similarity index (SSIM) and statistically through joint histogram and error maps. In comparison to the reference CT-based correction (SPECTCTAC ), NRMSEs were 0.2258±0.0777 and 0.1410±0.0768 (37.5% reduction of errors); PSNRs 31.7712±2.9965 and 36.3823±3.7424 (14.5% improvement in signal to noise ratio); SSIMs 0.9877±0.0075 and 0.9949±0.0043 (0.7% improvement in structural similarity) for SPECTNC and SPECTGAN , respectively. This work demonstrates that the conditional adversarial training can achieve accurate CT-less attenuation correction for SPECT MPI, that is quantitatively comparable to CTAC. Standalone dedicated cardiac SPECT scanners can benefit from the proposed GAN to reduce attenuation artifacts efficiently.
The goal of this study is to investigate whether reduced breast compression in digital breast tomosynthesis (DBT) exams causes larger internal breast motion that would adversely affect DBT image quality. We designed an experiment to collect real-time breast motion data from patients using ultrasound under three levels of DBT compression (full, medium and half). The ultrasound RF data had a pixel size of 21.5 μm and 150 μm in the axial and lateral directions of the probe, allowing the tracking of very fine movement of internal structure. We have successfully collected data from six human subjects and continue to recruit patients. The data were analyzed using speckle-tracking techniques to extract internal tissue movement trajectories pixel by pixel at multiple locations. Initial data analysis showed that internal breast tissue movement is highly correlated with breathing. Based on the first four patient datasets we have processed, the internal motion magnitudes on average were smaller than 1 mm under the full and reduced compression levels. The statistical distributions of the motion magnitudes among the three compression levels were similar, indicating that the internal breast motion may not necessarily increase even when compression is reduced by half. However, more data will be collected and analyzed to strengthen this study for more solid conclusions.
KEYWORDS: Image segmentation, Ultrasonography, Breast, Breast cancer, Probability theory, Image processing algorithms and systems, Mammography, CAD systems
Breast cancer is one of the most commonly diagnosed neoplasms among American women and the second leading cause of death among women all over the world. In order to reduce the mortality rate and cost of treatment, early diagnosis and treatment are essential. Accurate and reliable diagnosis is required in order to ensure the most effective treatment and a second opinion is often advisable. In this paper, we address the problem of breast lesion detection from ultrasound imagery by means of active contours, whose evolution is driven by maximizing the Bhattacharyya distance1 between the probability density functions (PDFs). The proposed method was applied to ultrasound breast imagery, and the lesion boundary was obtained by maximizing the distance-based energy functional such that the maximum (optimal contour) is attained at the boundary of the potential lesion. We compared the results of the proposed method quantitatively using the Dice coefficient (similarity index)2 to well-known GrowCut segmentation method3 and demonstrated that Bhattacharyya approach outperforms GrowCut in most of the cases.
KEYWORDS: Image quality, 3D metrology, X-ray computed tomography, Signal detection, Error analysis, Interference (communication), Image processing, 3D scanning, 3D image processing, Imaging systems, Computing systems
For CT whose noise is nonstationary, a local NPS is often needed to characterize the system’s noise property. A good
estimation of the local NPS for CT usually requires many repeated scans. To overcome this data demand, we have
previously developed a radial NPS method to estimate the 2D local NPS for FBP-reconstructed fan-beam CT from a few
repeats utilizing the polar separability of CT NPS in polar coordinates [1]. In this work we extend the 2D approach to
estimate the 3D local NPS for FDK-reconstructed cone-beam CT (CBCT) scans, since the CBCT NPS has similar
separability in cylindrical coordinates. We evaluate the accuracy of the 3D local radial NPS method by comparing it to
the traditional local NPS estimates using simulated CBCT data. The results show that the 3D radial local NPS method
with only 2 to 6 scans yields mean squared error less than 5%) relative to the reference local NPS and can predict signal
detectability accurately for evaluating system performance.
Sulci are groove-like regions lying in the depth of the cerebral cortex between gyri, which together, form a
folded appearance in human and mammalian brains. Sulci play an important role in the structural analysis of
the brain, morphometry (i.e., the measurement of brain structures), anatomical labeling and landmark-based
registration.1 Moreover, sulcal morphological changes are related to cortical thickness, whose measurement
may provide useful information for studying variety of psychiatric disorders. Manually extracting sulci requires
complying with complex protocols, which make the procedure both tedious and error prone.2 In this paper, we
describe an automatic procedure, employing geometric active contours, which extract the sulci. Sulcal boundaries
are obtained by minimizing a certain energy functional whose minimum is attained at the boundary of the given
sulci.
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