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Chest tomosynthesis - optimization and quantitation
Chest tomosynthesis has shown the potential to greatly improve detection of pulmonary nodules. A recent publication from our group showed that chest tomosynthesis using flat-panel detectors can triple the detection sensitivity of subtle pulmonary nodules compared with conventional chest radiography. (see also link to page on clinical evaluations of chest tomosynthesis).
Our laboratory has been involved in chest tomosynthesis research for over 20 years (including 2 NIH grants [R01 CA80490 and continuation] and 2 corporate research agreements), and we will describe here some of the experimental work done to optimize the technique for pulmonary nodule detection.
Part of our work in chest tomosynthesis has focused on developing improved algorithms for reconstruction. Our reconstruction algorithm, Matrix Inversion Tomosynthesis (MITS), uses linear algebra to solve for the blur that occurs from anatomy above and below the plane of interest in a tomosynthesis reconstruction. In order to get high quality slice images, it is imperative to use a deblurring algorithm (Fig 1). MITS performs very well at removing residual tomosynthesis image blur.
In order to optimize the image acquisition process for a particular detection task with a given reconstruction algorithm, one must determine the best total tube angle, the optimum number of projection images, and the best plane spacing for reconstructed images. We constructed the world’s first flat-panel-based tomosynthesis device
using a commercial-grade detector and a customized tube
mover (Fig 2), and conducted experiments to determine the optimum acquisition parameters. Simulated impulse response functions (and MTFs) were produced to evaluate MITS compared to conventional shift-and-add tomosynthesis for a variety of acquisition parameters. Figure 3 shows the three-dimensional impulse response for MITS and conventional shift-and-add tomosynthesis for 11 and 61 projection images. As can be seen, 61 projection images give a far smoother impulse response function. We also measured noise power spectra (NPS) of reconstructed tomosynthesis images, and found that the NPS was very consistent between MITS and shift-and-add tomosynthesis except in the mid-range frequencies. The NPS curves (Fig 4) show how the reconstructed image quality varies with different acquisition parameters.
Based on these optimization experiments, we determined that the best acquisition parameters for detection of pulmonary nodules using the MITS algorithm are 71 projection images, 20-degrees of total tube movement, and 5 mm plane spacing. Acquisition parameters optimized for other reconstruction algorithms such as filtered backprojection are slightly different.
See the review article written by our laboratory (PMB, 2003, cited below) for a general overview of tomosynthesis methods and clinical applications.