The effect of averaging adjacent planes for artifact reduction in matrix inversion tomosynthesis

Abstract

Purpose: Matrix inversion tomosynthesis (MITS) uses linear systems theory and knowledge of the imaging geometry to remove tomographic blur that is present in conventional backprojection tomosynthesis reconstructions, leaving in-plane detail rendered clearly. The use of partial-pixel interpolation during the backprojection process introduces imprecision in the MITS modeling of tomographic blur, and creates low-contrast artifacts in some MITS planes. This paper examines the use of MITS slabs, created by averaging several adjacent MITS planes, as a method for suppressing partialpixel artifacts. Methods: Human chest tomosynthesis projection data, acquired as part of an IRB-approved pilot study, were used to generate MITS planes, three-plane MITS slabs (MITSa3), five-plane MITS slabs (MITSa5), and seven-plane MITS slabs (MITSa7). These were qualitatively examined for partialpixel artifacts and the visibility of normal and abnormal anatomy. Additionally, small (5 mm) subtle pulmonary nodules were simulated and digitally superimposed upon human chest tomosynthesis projection images, and their visibility was qualitatively assessed in the different reconstruction techniques. Simulated images of a thin wire were used to generate modulation transfer function (MTF) and slice-sensitivity profile curves for the different MITS and MITS slab techniques, and these were examined for indications of partial-pixel artifacts and frequency response uniformity. Finally, meansubtracted, exposure-normalized noise power spectra (ENNPS) estimates were computed and compared for MITS and MITS slab reconstructions, generated from 10 sets of tomosynthesis projection data of an acrylic slab. The simulated in-plane MTF response of each technique was also combined with the square root of the ENNPS estimate to yield stochastic signal-to-noise ratio (SNR) information about the different reconstruction techniques. Results: For scan angles of 20• and 5 mm plane separation, seven MITS planes must be averaged to sufficiently remove partial-pixel artifacts. MITSa7 does appear to subtly reduce the contrast of highfrequency "edge" information, but the removal of partial-pixel artifacts makes the appearance of low-contrast, fine-detail anatomy even more conspicuous in MITSa7 slices. MITSa7 also appears to render simulated subtle 5 mm pulmonary nodules with greater visibility than MITS alone, in both the open lung and regions overlying the mediastinum. Finally, the MITSa7 technique reduces stochastic image variance, though the in-plane stochastic SNR (for very thin objects which do not span multiple MITS planes) is only improved at spatial frequencies between 0.05 and 0.20 cycles/mm. Conclusions: The MITSa7 method is an improvement over traditional single-plane MITS for thoracic imaging and the pulmonary nodule detection task, and thus the authors plan to use the MITSa7 approach for all future MITS research at the authors' institution.

Studies of a prototype linear stationary x-ray source for tomosynthesis imaging

“…Without a full geometric calibration of the imaging system ( e.g . Yang, et al ., 2006) filtered backprojection (Bushberg, 2012) or matrix inversion (Godfrey et al ., 2013) techniques were not possible. Hence, TS reconstruction was performed using the shift and add method (Niklason et al 1997), which is more robust when geometric factors are known with reduced precision.…”

A prototype Multi-X-ray-source array (MXA) for digital breast tomosynthesis

“…As our studies proceed, we plan with FBP, in addition to the standard ramp filter, to use a cosine-shaped anodization filter to suppress high-frequency noise. In addition, low frequency enhancement (Godfrey et al 2003, Mertelmeier 2014) will be used to restore some of the contrast suppressed by the ramp filter, so the images have less of a 'flat' With MITS, the sliding average approach that sums adjacent planes (Godfrey et al 2013) will be optimized for the number of slices to slab according to the response of microcalcifications with the given tube angle used. This sliding average technique reduces image noise while also suppressing most tomosynthesis artifacts.…”

A Novel Recurrent Neural Network-Based Ultra-Fast, Robust, and Scalable Solver for Inverting a “Time-Varying Matrix”

“…Solving a real-time/online matrix inversion is part of mathematics and control theory. It finds important applications in various areas such as traffic simulation and/or online control in the frame of intelligent transportation systems, robotics (e.g., for kinematics and inverse kinematics), communications [3], machine learning [4], smart/complex antennas (MIMO) [5,6,7], Field Programmable Gate Array (FPGA) [8,9], signal processing [10], image processing [11,12], and robotics [13,14,15], etc.…”