Acknowledgments. High Performance Cone-Beam CT of Acute Traumatic Brain Injury

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1 A. Sisniega et al. (presented at RSNA 214) High Performance Cone-Beam CT of Acute Traumatic Brain Injury A. Sisniega 1 W. Zbijewski 1, H. Dang 1, J. Xu 1 J. W. Stayman 1, J. Yorkston 2, N. Aygun 3 V. Koliatsos 4, J. H. Siewerdsen 1,3 1 Dept.of Biomedical Engineering, Johns Hopkins University 2 Carestream Health Inc. 3 Dept. of Radiology, Johns Hopkins University 4 Dept. of Neurology, Johns Hopkins University Acknowledgments The I-STAR Lab Imaging for Surgery, Therapy, and Radiology Carestream Health Xiaohui (Ed) Wang, Mark Shafer Bill Snyder, James Burns Dave Foos Funding Support Carestream Health NIH-CA-2R

2 A. Sisniega et al. (presented at RSNA 214) CBCT of Acute Brain Trauma Traumatic Brain Injury (TBI) 1.7M Americans suffer TBI each year (>3% of injury-related death) Prompt assessment of acute TBI is vital to guiding clinical course Detection of Acute TBI CT is front-line modality for evaluation of acute TBI High sensitivity to detection of fresh blood in the brain Hemorrhages range in size ~1 mm to > 1 cm CT poorly suited to deployment at the point-of-care Application Settings (Point-of-Care) Intensive Care Unit (ICU), Operating room (OR) Emergency Department (ED), Urgent Care Center Ambulance, locker room, sports clinic Military base hospitals, war theater Acute TBI (non-contrast-enhanced CT) CBCT of Acute Brain Trauma Aim: To develop a high-quality CBCT system for detection of TBI at the point of care Stringent image quality for TBI detection Contrast: ~5 (fresh blood in brain) Spatial Resolution: down to 1 mm Uniformity: High (~few ) Major challenge for flat-panel-detector CBCT 25 Narrow Fan-Beam (low SPR) Suboptimal Geometry (small air gap) Optimal Geometry (task-based modeling)

3 Density (g/cm 3 ) A. Sisniega et al. (presented at RSNA 214) Framework for CBCT Artifact Correction Detector Lag Veiling Glare Detector Lag: Frame-to-frame deconvolution * -1 Veiling Glare: Long-tail PSF deconvolution Beam Hardening Scatter * -1 In Press A. Sisniega et al., High-fidelity artifact correction for cone-beam CT imaging of the brain Physics in Medicine and Biology (214). MC Scatter + Beam Hardening Joseph Spital Monte Carlo + - Scatter+BH Correction in MC-GPU Initialization Beam Hardening + Segmentation Sparse Monte Carlo Pre-corrected Volume Constant Scatter Beam hardening Joseph & Spital Continuous tissue model 2 1 Air Soft Bone Threshold Value Parallel Photon Tracking GPU photon tracking Very Low N Photons Variance Reduction in GPU Forced Detection Photon splitting Angular Undersampling V Θ U MC Scatter (S MC ) De-Noised Scatter De-Noise + Sparse Views Asymmetric 3D Gaussian Kernel Corrected Volume De-noised Scatter (S MC-KS ) K KS u, v, θ

4 Difference Corrected Un-corrected A. Sisniega et al. (presented at RSNA 214) Data Acquisition kv +.2 mm Cu + 2 mm Al 72 projections 28 mas total (.39 mas/proj) 24 mgy (dose to center CTDI) No scatter grid Head Phantom Gelatin (brain) background 5 contrast simulated blood Natural skull Experimental Setup Flat-Panel Detector SDD=8 cm Object Stage SAD=58 cm X-ray Source 12 mm 1 mm 8 mm 5 mm 3 mm 1.5 mm Ventricles Superior + Peridural Brain Central Area Skull Base CBCT Imaging of TBI 3 2 Run-Time: 3.5 min/iteration x 3 iterations (Geforce GTX 78 Ti)

5 Corrected Un-corrected Corrected Un-corrected A. Sisniega et al. (presented at RSNA 214) CBCT Imaging of TBI Non-uniformity Contrast Noise Un-corrected ± Corrected ± Run-Time: 3.5 min/iteration x 3 iterations (Geforce GTX 78 Ti) CBCT Imaging of TBI Peridural Hemorrhage -6 Parenchymal (Skull Base) Hemorrhage

6 A. Sisniega et al. (presented at RSNA 214) Model-Based Reconstruction Penalized weighted least squares Weights modified to include effect of artifact correction on noise FDK C = 49.3 s = 9. CNR = PWLS C = 51.4 s = 4.8 CNR = H. Dang, et al., Cone-beam CT of traumatic brain injury using statistical reconstruction with a post-artifact-correction noise model Proc SPIE Med Imaging (214), accepted. Model-Based Reconstruction Penalized weighted least squares Weights modified to include effect of artifact correction on noise FDK C = 49.3 s = 9. CNR = PWLS C = 51.4 s = 4.8 CNR = H. Dang, et al., Cone-beam CT of traumatic brain injury using statistical reconstruction with a post-artifact-correction noise model Proc SPIE Med Imaging (214), accepted.

7 A. Sisniega et al. (presented at RSNA 214) Conclusions Artifact correction framework for brain CBCT Corrections: lag, veiling glare, BH, and scatter Iterative loop for BH and MC scatter correction MC scatter correction Combination of GPU, VR, and de-noising Run-time ~1 min (3 iteration) CBCT image quality improvements Cupping reduced by ~35 Streaks and blooming reduced by ~5 Visibility of small bleeds (3 mm, 5 ) 3x increase in CNR 2x increase in noise (resolved by PWLS with modified weights) Ongoing and future work CBCT scanner prototype, integration, and clinical studies

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