  Statistical iterative reconstruction is expected to improve the image quality
of megavoltage computed tomography (MVCT). However, one of the challenges of
iterative reconstruction is its large computational cost. The purpose of this
work is to develop a fast iterative reconstruction algorithm by combining
several iterative techniques and by optimizing reconstruction parameters.
Megavolt projection data was acquired from a TomoTherapy system and
reconstructed using our statistical iterative reconstruction. Total variation
was used as the regularization term and the weight of the regularization term
was determined by evaluating signal-to-noise ratio (SNR), contrast-to-noise
ratio (CNR), and visual assessment of spatial resolution using Gammex and
Cheese phantoms. Gradient decent with an adaptive convergence parameter,
ordered subset expectation maximization (OSEM), and CPU/GPU parallelization
were applied in order to accelerate the present reconstruction algorithm. The
SNR and CNR of the iterative reconstruction were several times better than that
of filtered back projection (FBP). The GPU parallelization code combined with
the OSEM algorithm reconstructed an image several hundred times faster than a
CPU calculation. With 500 iterations, which provided good convergence, our
method produced a 512$\times$512 pixel image within a few seconds. The image
quality of the present algorithm was much better than that of FBP for patient
data. An image from the iterative reconstruction in TomoTherapy can be obtained
within few seconds by fine-tuning the parameters. The iterative reconstruction
with GPU was fast enough for clinical use, and largely improve the MVCT images.
