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Ultra-Fast Monte Carlo Simulation for Cone Beam CT Imaging of Brain Trauma

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A Sisniega

A Sisniega1*, W Zbijewski1 , J Stayman1 , J Yorkston2 , N Aygun3 , V Koliatsos4 , J Siewerdsen1,3 , (1) Department of Biomedical Engineering, Johns Hopkins University, (2) Carestream Health,(3) Department of Radiology, Johns Hopkins University, (4) Department of Neurology, Johns Hopkins University.


TH-A-18C-9 Thursday 7:30AM - 9:30AM Room: 18C

Purpose: Application of cone-beam CT (CBCT) to low-contrast soft tissue imaging, such as in detection of traumatic brain injury, is challenged by high levels of scatter. A fast, accurate scatter correction method based on Monte Carlo (MC) estimation is developed for application in high-quality CBCT imaging of acute brain injury.

Methods: The correction involves MC scatter estimation executed on an NVIDIA GTX 780 GPU (MC-GPU), with baseline simulation speed of ~1e7 photons/sec. MC-GPU is accelerated by a novel, GPU-optimized implementation of variance reduction (VR) techniques (forced detection and photon splitting). The number of simulated tracks and projections is reduced for additional speed-up. Residual noise is removed and the missing scatter projections are estimated via kernel smoothing (KS) in projection plane and across gantry angles. The method is assessed using CBCT images of a head phantom presenting a realistic simulation of fresh intracranial hemorrhage (100 kVp, 180 mAs, 720 projections, source-detector distance 700 mm, source-axis distance 480 mm).

Results: For a fixed run-time of ~1 sec/projection, GPU-optimized VR reduces the noise in MC-GPU scatter estimates by a factor of 4. For scatter correction, MC-GPU with VR is executed with 4-fold angular downsampling and 1e5 photons/projection, yielding 3.5 minute run-time per scan, and de-noised with optimized KS. Corrected CBCT images demonstrate uniformity improvement of 18 HU and contrast improvement of 26 HU compared to no correction, and a 52% increase in contrast-to-noise ratio in simulated hemorrhage compared to “oracle” constant fraction correction.

Conclusion: Acceleration of MC-GPU achieved through GPU-optimized variance reduction and kernel smoothing yields an efficient (<5 min/scan) and accurate scatter correction that does not rely on additional hardware or simplifying assumptions about the scatter distribution. The method is undergoing implementation in a novel CBCT dedicated to brain trauma imaging at the point of care in sports and military applications.

Funding Support, Disclosures, and Conflict of Interest: Research grant from Carestream Health. JY is an employee of Carestream Health.

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