Gated myocardial perfusion SPECT (MPS) is widely used to assess the left ventricular (LV) function. Its performance relies on the accuracy of segmentation on LV cavity. We propose a novel machine-learningbased method to automatically segment LV cavity and measure its volume in gated MPS imaging. To perform end-to-end segmentation, a multi-label V-Net is used to build the network architecture. The network segments a probability map for each heart contour (epicardium, endocardium and myocardium). To evaluate the accuracy of segmentation, we retrospectively investigated gated MPS images from 32 patients. The LV cavity was automatically segmented by the proposed method, and compared to manually outlined contours, which were taken as the ground truth. The derived LV cavity volumes were extracted from both ground truth and results of proposed method for comparison and evaluation. The mean DSC, sensitivity and specificity of the contours delineated by our method are all above 0.9 among all 32 patients and 8 phases. The correlation coefficient of the LV cavity volume between ground truth and results produced by the proposed method is 0.910±0.061, and the mean relative error of LV cavity volume among all patients and all phases is - 1.09±3.66 %. These results indicate that the proposed method accurately quantifies the changes in LV cavity volume during the cardiac cycle. It also demonstrates the potential of our learning-based segmentation methods in gated MPS imaging for clinical use.
KEYWORDS: Veins, Lead, Image fusion, CRTs, Single photon emission computed tomography, 3D modeling, Image registration, 3D image processing, 3D acquisition
Multi-modality image fusion of 3D coronary venous anatomy from fluoroscopic venograms with left ventricular (LV) epicardial surface from single-photon emission computed tomography (SPECT) myocardial perfusion image (MPI) can provide both LV physiological information and venous anatomy for guiding CRT LV lead placement. However, it is difficult to match the time points between MPI and venograms because of heart beating and thus image acquisition of the different cardiac frames, which affects the accuracy of 3D fusion. To address this issue, this study introduces a scale ratio iterative closest point (S-ICP) algorithm to non-rigidly fuse images from two different modalities. Three steps, including the image reconstruction, image registration, and image overlay were implemented to complete the images fusion. First, the 3D fluoroscopic venous anatomy and SPECT LV epicardial surface were reconstructed. Second, a landmark-based registration method was performed as an initial registration of S-ICP. With the initialization, the S-ICP algorithm with a preset scale range completed a fine registration for SPECT-vein fusion. Third, the registered venous anatomy was overlaid onto the SPECT LV epicardial surface. Moreover, in order to validate the accuracy of the fusion, 3D CT venous anatomy was manually fused with the same SPECT LV epicardial surface, and then the distance-based mismatch errors between fluoroscopic veins and CT veins were evaluated. Five patients were enrolled. As a result, the overall mismatch error was 5.6±4.1mm, which is smaller than the pixel size of SPECT images (6.4mm).
KEYWORDS: Reconstruction algorithms, Signal attenuation, X-rays, Digital breast tomosynthesis, Breast, Mammography, Expectation maximization algorithms, 3D modeling, X-ray detectors, Breast cancer
As a breast-imaging technique, digital breast tomosynthesis has great potential to improve the diagnosis of early breast cancer over mammography. Ray-tracing-based reconstruction algorithms, such as ray-tracing back projection, maximum-likelihood expectation maximization (MLEM), ordered-subset MLEM (OS-MLEM), and simultaneous algebraic reconstruction technique (SART), have been developed as reconstruction methods for different breast tomosynthesis systems. This paper provides a comparative study to investigate these algorithms by computer simulation and phantom study. Experimental results suggested that, among the four investigated reconstruction algorithms, OS-MLEM and SART performed better in interplane artifact removal with a fast speed convergence.
The recent commercialization of digital breast tomosynthesis systems realizes the clinical applications of
this novel three-dimensional imaging technology. The total dosage of breast tomosynthesis for single
patient is comparable to that of the traditional mammography. This paper presents our continuous work
on image quality analysis for the optimization of a new multi-beam breast tomosynthesis system based on
carbon nanotube X-ray emission technology. Several tomosynthesis reconstruction algorithms were
implemented to reconstruct the phantom data. Noise power spectrum and modulation transfer function
were investigated to evaluate the image quality.
Early detection, diagnosis, and suitable treatment are known to significantly improve the chance of survival for breast
cancer (BC) patients. To date, the most cost effective method for screening and early detection is mammography, which
is also the tool that has demonstrated its ability to reduce BC mortality. Tomosynthesis is an emerging technology that
offers an alternative to conventional two-dimensional mammography. Tomosynthesis produces three-dimensional
(volumetric) images of the breast that may be superior to planar imaging due to improved visualization. In this paper we
examined the effect of varying the number of projections (N) and total view angle (VA) on the shift-and-add (SAA),
back projection (BP) and filtered back projection (FBP) image reconstruction response characterized by impulse
response (IR) simulations. IR data were generated by simulating the projection images of a very thin wire, using various
combinations of VA and N. Results suggested that BP and FBP performed better for in-plane performance than that of SAA. With bigger number of projection images, the investigated reconstruction algorithms performed the best by obtaining sharper in-focus IR with simulated parallel imaging configurations.
Digital breast tomosynthesis is a new technique to improve the early detection of breast cancer by providing threedimensional
reconstruction volume of the object with limited-angle projection images. This paper investigated the image reconstruction with a standard biopsy training breast phantom using a novel multi-beam X-ray sources breast tomosynthesis system. Carbon nanotube technology based X-ray tubes were lined up along a parallel-imaging geometry
to decrease the motion blur. Five representative reconstruction algorithms, including back projection (BP), filtered back
projection (FBP), matrix inversion tomosynthesis (MITS), maximum likelihood expectation maximization (MLEM) and
simultaneous algebraic reconstruction technique (SART), were investigated to evaluate the image reconstruction of the
tomosynthesis system. Reconstructed images of the masses and
micro-calcification clusters embedded in the phantom
were studied. The evaluated multi-beam X-ray breast tomosynthesis system is able to generate three-dimensional
information of the breast phantom with clearly-identified regions of the masses and calcifications. Future study will be
done soon to further improve the imaging parameters' measurement and reconstruction.
As a new three-dimensional breast imaging technique, breast tomosynthesis allows the reconstruction of an arbitrary set
of planes in the breast from a limited-angle series of x-ray projection images. The breast tomosynthesis technique has
been demonstrated as promising to improve early breast cancer detection. This paper represents a preliminary phantom
study and computer simulation results of different breast tomosynthesis reconstruction algorithms with a novel carbon
nanotube based multi-beam x-ray source. Five representative tomosynthesis reconstruction algorithms, including back
projection (BP), filtered back projection (FBP), matrix inversion tomosynthesis (MITS), maximum likelihood
expectation maximization (MLEM), and simultaneous algebraic reconstruction technique (SART) were investigated.
Tomosynthesis projection images of a phantom were acquired with the stationary multi-beam x-ray tomosynthesis
system. Reconstruction results from different algorithms were studied. A computer simulation study was further done to
investigate the sharpness of reconstructed in-plane structures and to see how effective each algorithm is at removing
out-of-plane blur with parallel-imaging geometries. Datasets with 9 and 25 projection images of a defined 3D spherical
object were simulated with a total view angle of 50 degrees. Results showed that the multi-beam x-ray system is capable
to generate 3D tomosynthesis images with faster speed compared with current commercial prototype systems. With
simulated parallel-imaging geometry, MITS and FBP showed edge enhancement in-plane performance. BP, FBP and
MLEM performed better at out-of-plane structure removal with larger number of projection images.
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