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Investigation of Renal CT Dose Reduction by Using Model Based Iterative Reconstruction


L Page

L Page*, V Kundra, J Rong, MD Anderson Cancer Center, Houston, TX

SU-E-I-36 Sunday 3:00PM - 6:00PM Room: Exhibit Hall

Purpose:
To evaluate the feasibility of dose reduction from using Model-Based Iterative Reconstruction (MBIR) in renal and vasculature CT studies.

Methods:
A tissue equivalent abdominal CT dose phantom (CIRS 007TE) was scanned using a GE HD750 scanner. To simulate a ureter and vasculature in CT images, tubes of varying diameters (12mm, 5mm, 2mm, and 1mm) were inserted into the center hole of the phantom. To achieve the appropriate Hounsfield Units (HU) simulating the anatomy of interest, the tubes were filled with a contrast agent (Omnipaque 350) diluted by water at concentrations of 1:8, 1:30, and 1:50. Each of these combinations were scanned using an existing renal protocol at 140 kVp(Display CTDIvol 24mGy) and repeated using reduced scan techniques to achieve lower radiation doses of 13.2, 3.6, 1.6, and 0.8mGy. These were also repeated at 120 kVp. The images were then reconstructed using MBIR. To assess image quality, the contrast-to-noise ratio (CNR) was measured for each contrast object. The average CNR was then calculated over five consecutive image slices. Based on CNR achieved at various dose levels, dose reduction factors were estimated for each combination of object sizes and contrast concentrations.

Results:
Image noises were reduced substantially over the entire dose range by using MBIR. For the large objects at lower doses, there was more noise reduction. At 0.8mGy, the noise was reduced by a factor of four. The 1mm objects at lower concentrations and low dose were not visible. For the 12mm object size group, average CNR increased by a factor of 3 over three concentrations. The dose reduction was estimated to be a factor of 15-30.

Conclusion:
It is feasible to achieve very low dose (sub-mGy) renal CT and vasculature phantoms by using MBIR. The results of this study provide guidance in our effort for clinical protocol optimization.



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