A technique for multi-dimensional optimization of

A technique for multi-dimensional optimization of radiation dose, contrast dose, and image quality in CT imaging Pooyan Sahbaeea,b, Ehsan Abadib,d, Jeremiah Sandersb,c, Marc Becchettib,c, Yakun Zhangb, Greeshma Agasthyab, Paul Segarsb,c, Ehsan Sameib,c,d a

b

Department of Physics, North Carolina State University, Raleigh, NC 27695 Ravin Advanced Imaging Laboratories, Dept. of Radiology, Duke University, Durham, NC 27705 c Medical Physics Graduate Program, Duke University, Durham, NC 27705 d Department of Electrical and Computer Engineering, Duke University, Durham, NC 27705 ABSTRACT

The purpose of this study was to substantiate the interdependency of image quality, radiation dose, and contrast material dose in CT towards the patient-specific optimization of the imaging protocols. The study deployed two phantom platforms. First, a variable sized phantom containing an iodinated insert was imaged on a representative CT scanner at multiple CTDI values. The contrast and noise were measured from the reconstructed images for each phantom diameter. Linearly related to iodine-concentration, contrast to noise ratio (CNR), was calculated for different iodine-concentration levels. Second, the analysis was extended to a recently developed suit of 58 virtual human models (5D-XCAT) with added contrast dynamics. Emulating a contrast-enhanced abdominal image procedure and targeting a peak-enhancement in aorta, each XCAT phantom was “imaged” using a CT simulation platform. 3D surfaces for each patient/size established the relationship between iodine-concentration, dose, and CNR. The Sensitivity of Ratio (SR), defined as ratio of change in iodine-concentration versus dose to yield a constant change in CNR was calculated and compared at high and low radiation dose for both phantom platforms. The results show that sensitivity of CNR to iodine concentration is larger at high radiation dose (up to 73%). The SR results were highly affected by radiation dose metric; CTDI or organ dose. Furthermore, results showed that the presence of contrast material could have a profound impact on optimization results (up to 45%). Keywords: Computed Tomography, Contrast Material, Compartmental Model, Contrast Enhanced, Patient Specific, Iodine, Radiation dose, CTDI, Image Quality 1. INTRODUCTION With increasing attention to the potential risk from CT radiation to the patient’s body due to the expanding use of computed tomography (CT), there is a need to manage patient dose at CT examinations. Majority (over 60%) of CT imaging involves the use of contrast material; the presence of which has not been fully considered in most CT optimization studies of dose and image quality. Recent studies have also shown that the administration of contrast material results in increase in radiation dose. Also, the use of contrast material can also increase the level of radiation induced DNA damage. [1-5] A recent study of a population of patients undergoing a contrast material-enhanced chest CT examination indicated a substantial increase (107% ± 19) in the number of phosphorylated histone H2AX foci per lymphocyte.[6] Patient-specific variability such as anatomy, size, age, and gender can have a notable effect on CT radiation dose.[79] Prior studies have also shown that the variability across different patients results in a significant difference in the circulation of contrast material within the patient’s body and hence significant disparity in contrast enhancement in the organs across individual patients. Therefore, it is important to characterize the impact of dose associated with contrast medium in the context of individual patients. Email: [email protected] Medical Imaging 2016: Physics of Medical Imaging, edited by Despina Kontos, Thomas G. Flohr, Joseph Y. Luo, Proc. of SPIE Vol. 9783, 97833F · © 2016 SPIE · CCC code: 1605-7422/16/$18 · doi: 10.1117/12.2216516

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In our previous work, we developed a series of anatomically variable contrast enhanced phantoms, which enabled us to study the effect of contrast material on CT dosimetry. [10-12] However, to conduct comprehensive evaluation and optimization studies in CT imaging techniques the image quality, radiation dose, and contrast material correlations need to be taken into account. The purpose of this study was to develop a patient-specific strategy to investigate the interdependency of iodine concentration (IC), radiation dose, and image quality in a routine abdomen CT scan. 2. MATERIALS AND METHODS Two phantom platforms were deployed in this study: (1) variable sized phantom (Mercury-3.0, Duke University) with an iodinated insert and (2) virtual contrast enhanced human models (5D-XCAT). Contrast to noise ratio (CNR) and radiation dose calculation methods in the phantom platforms are described in the following sections (II.A and II.B). 2.1 IQ and dose in Mercury phantom A variable sized (12, 18, 23, 30, 37 cm) physical phantom (Mercury-3.0) with an iodinated insert (8.5 mgI/ml) was imaged on a Siemens (FLASH) CT scanner at multiple CTDI values (0.7-22.6 mGy) at 120 kVp. The contrast and noise were measured from the reconstructed images for each phantom diameter. Contrast was calculated as the difference between the image values averaged over two contrast-enhanced and un-enhanced selected ROIs. Noise was calculated as the standard deviation of adjacent background. Finally, knowing the fact that the iodine concentration (IC) is linearly proportional to contrast enhancement (CE)[10, 12, 13], i.e., CE = 26.18 x IC at 120 kVp, CNR was calculated for 16 iodine-concentration levels (0-8.5 mgI/ml). 2.2 IQ and dose in 5D XCAT models To estimate the image quality across our previously developed contrast-enhanced XCAT models, 58 models were implemented in a CT simulation tool, computer assisted tomography simulation (CATSim, GE Global Research). Simulating a contrast-enhanced abdominal image procedure and targeting a peak-enhancement in aorta, each XCAT phantom was “imaged” at 120 kVp and multiple mAs values (100-500). Then the CT images were reconstructed for different iodine concentration and mAs values across different patients. ROI was selected within the liver. Contrast was calculated from the difference between two identical ROIs from contrast enhanced and un-enhanced images. Noise was calculated also from the standard deviation of a background. Finally, CNR was calculated across different patients. For the same contrast enhanced XCAT models, in order to calculate the radiation dose delivered to liver, we used our previously validated Monte Carlo simulation code to model the geometry of a GE CT scanner (light speed VCT) to simulate a real abdomen CT scan. [7, 10-12] The simulation included the helical and axial motion as well as bowtie filter. The liver doses calculated in mGy per 100 mAs were converted to organ dose in mGy at different corresponding mAs values used in our image quality estimation. 2.3 Sensitivity ratio In order to capture and quantify the relative importance of iodine load versus radiation safety on CNR, a new optimization metric, sensitivity ratio (SR) of CNR was introduced. We defined and calculated a new optimization metric, the Sensitivity Ratio (SR) as SR = ΔCNR IC /ΔCNR Dose

(1.a)

ΔCNR IC = ΔCNR /ΔIC

(1.b)

in which and ΔCNR Dose = ΔCNR /ΔDose

(1.c)


Then SR at low dose level (5-20 mGy), SRlow, and high dose level (23-58 mGy), SRhigh, were calculated. To calculate the SR, two different radiation dose metrics were used; (1) organ dose or liver dose, (2) CTDIvol. SR was calculated using organ dose with and without considering the impact of iodine on radiation dose. 2.4 Evaluation and comparisons 3D surface for each patient/size was plotted to show the relationship between iodine-concentration, dose, and CNR. Depending on the radiation dose metric used in SR calculation, 3 different comparisons were made: (1) the SR computed from XCAT models using liver dose were compared to SR computed using corresponding CTDIvol. (2) SR results calculated from XCAT models using CTDIvol were compared with SR calculated from Mercury phantom. (3) SR results calculated from XCAT models using organ dose considering the effect of iodine on dose were compared with SR calculated using organ dose without considering the effect of iodine on dose. 3. RESULTS Figure 1.a shows the reconstructed cross sectional images of our physical phantom, Mercury phantom, with different diameters (12, 18, 23, 30, 37 cm) acquired from Siemens (FLASH) CT scanner at 22.6 CTDI values at 120 kVp. Figure 1.b shows the reconstructed image of one of the XCAT models (“normal 1”) using the CT scan simulator, CATSim, modeling the geometry of a GE CT scanner. Figure 6.2 shows the 3D image quality (CNR) surfaces with respect to iodine concentration and radiation dose for a) Mercury and b) XCAT models, in which each surface represents a phantom diameter for Mercury phantom or an individual XCAT model. CNR results increased as the patient’s size decreased. Figure 6.2 showing the CNR contours with respect to organ dose and iodine concentration helped us to better understand the impact of iodine and dose on CNR. For desired image-quality values, the iso-image quality contour lines reflected the trade off between contrast material and radiation doses. The horizontal behavior of the contours shows the higher sensitivity of CNR on iodine comparing to the organ dose. Figure 6.3 shows the SR results calculated from XCAT models using a) organ dose, b) CTDIvol , and c) organ dose without effect of iodine, and finally d) from Mercury phantom using CTDIvol. Comparisons across SR results using different dose metrics showed that: first, the significant impact of dose metric, whether organ dose or CTDIvol , used in SR calculation (up to 46% higher); second, the difference between optimization results using physical phantom and patient models (up to 110%); and finally, the importance of the impact of contrast material on radiation dose, and hence the optimization results (up to 45%).


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Figure 0.1. Reconstructed images from a) real CT scan data of Mercury phantom, and b) simulated CT data from 5D XCAT models used in this study. The Mercury phantom was imaged in a Siemens (FLASH) CT scanner and computational models were imaged using a CT scan simulator modeling the geometry of a GE CT scanner.


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Figure 0.2. 3D CNR surfaces (left) and contours (right) with respect to iodine concentration and radiation dose for a) Mercury and b) 4 XCAT models (2 normal and 2 obese).


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4. DISCUSSION As described in the Introduction 6.1, our significant advancement in the human modeling platform via adding the realistic model of contrast perfusion to the current computational phantoms enabled us to take an important steps towards our ultimate goal of this study which is optimization of medical imaging and administration techniques. Applying our previously validated contrast-enhanced XCAT models (5D XCAT),[10, 12] as well as our previously validated Monte Carlo simulation software to compute the patient-specific organ dose from 5D XCAT models, along


with a CT scan simulation tool, CATSim, to calculate the image quality from reconstructed images, for the first time, we could achieve a better understanding of the inter-correlation between these three factors altogether. The 3D CNR results for a given iodine concentration calculated from both physical and computational models increased as the patient diameter decreased. This can be explained by the fact that the noise is highly dependent on the patient’s size and increases with the patient’s size. The CNR contours’ pattern showed that the CNR is relatively more sensitive to iodine concentration at higher radiation dose, i.e., in higher dose a small change in iodine concentration resulted in a change in CNR level, which explains horizontal behavior of the lines at higher radiation dose. The calculation of sensitivity ratio provided us a better picture of correlation between image quality, radiation dose, and iodine concentration. The optimization results acquired from both phantom platforms showed that for a given patient, the SR is larger at higher radiation dose. This simply implies that at higher dose level, the change in iodine has more effect on CNR than change in dose, consistent with the CNR contours pattern described above. Furthermore, comparison of SR results using different dose metrics, demonstrated the importance of the dose metric in our SR computation. In other word, SR results were highly affected by radiation dose metric (up to 46%). Finally, the comparison of the results from un-enhanced versus enhanced XCAT models showed a significant difference (up to 45%) in SR, which highlights the importance of considering the contrast material in optimization studies. Although this study offered a novel method to investigate the correlation between image quality, radiation dose, and contrast dose, it has several limitations. First, the image quality metric used in this study, contrast to noise ratio, was calculated from the ratio of difference between enhanced and un-enhanced images and noise. Although, in the absence of heterogeneity and organ’s texture CNR can be a good representative of image quality, but moving towards the development of more realistic virtual models by adding the texture to the organs, the offered method needs to be extended to task-specific image quality metrology. Second, this study only included 4 patients which cannot be a sufficient representative of a patient population. However, the patients were selected from different size categories. Third, in this study the physical phantom was scanned using a Siemens scanner, while the results from computational models were acquired from a software modeling the GE scanner geometry, which directly might affect the CNR results. However, this limitation does not affect the optimization metric defined based on the relative difference of the CNR, and hence comparison results across different cases. Finally, this study was based on the fixed-tube-current examination, i.e., the tube-current-modulation was not explicitly modeled in our simulation. Future work should include the technology of tube current modulation. 5. CONCLUSION The iodine-concentration, image quality, and radiation dose are highly interdependent. Quantified interdependency of contrast dose, radiation dose, and image quality. The understanding of the relationships between iodineconcentration, image quality, and radiation dose will allow for a more comprehensive optimization of CT imaging devices and techniques, providing the methodology to balance iodine-concentration and dose based on patient’s attributes.

REFERENCE 1. 2. 3.

Geisel, D., J.T. Heverhagen, M. Kalinowski, and H.-J. Wagner, DNA Double-Strand Breaks after Percutaneous Transluminal Angioplasty 1. Radiology, 2008. 248(3): p. 852-859. Grudzenski, S., M.A. Kuefner, M.B. Heckmann, M. Uder, and M. Löbrich, Contrast Medium–enhanced Radiation Damage Caused by CT Examinations 1. Radiology, 2009. 253(3): p. 706-714. Pathe, C., K. Eble, D. Schmitz-Beuting, B. Keil, B. Kaestner, M. Voelker, B. Kleb, K.J. Klose, and J.T. Heverhagen, The presence of iodinated contrast agents amplifies DNA radiation damage in computed tomography. Contrast media & molecular imaging, 2011. 6(6): p. 507-513.


4. 5. 6. 7. 8. 9. 10. 11. 12. 13.

Löbrich, M., N. Rief, M. Kühne, M. Heckmann, J. Fleckenstein, C. Rübe, and M. Uder, In vivo formation and repair of DNA double-strand breaks after computed tomography examinations. Proceedings of the National Academy of Sciences of the United States of America, 2005. 102(25): p. 8984-8989. Rothkamm, K. and M. Löbrich, Evidence for a lack of DNA double-strand break repair in human cells exposed to very low x-ray doses. Proceedings of the National Academy of Sciences, 2003. 100(9): p. 50575062. Piechowiak, E.I., J.-F.W. Peter, B. Kleb, K.J. Klose, and J.T. Heverhagen, Intravenous Iodinated Contrast Agents Amplify DNA Radiation Damage at CT. Radiology, 2015. Sahbaee, P., W.P. Segars, and E. Samei, Patient-based estimation of organ dose for a population of 58 adult patients across 13 protocol categories. Medical physics, 2014. 41(7): p. 072104. Li, X., E. Samei, W.P. Segars, G.M. Sturgeon, J.G. Colsher, G. Toncheva, T.T. Yoshizumi, and D.P. Frush, Patient-specific radiation dose and cancer risk estimation in CT: Part II. Application to patients. Medical physics, 2011. 38(1): p. 408-419. Tian, X., X. Li, W.P. Segars, D.P. Frush, E.K. Paulson, and E. Samei, Dose coefficients in pediatric and adult abdominopelvic CT based on 100 patient models. Physics in medicine and biology, 2013. 58(24): p. 8755. Sahbaee, P., Y. Lin, P.P. Segars, D. Marin, R. Nelson, and E. Samei. "Determination of contrast media administration to achieve a targeted contrast enhancement in computed tomography." Journal of Medical Imaging 3.1 (2016): 013501-013501. Sahbaee, P., E. Samei, and W. Segars, SU-C-12A-03: The Impact of Contrast Medium On Radiation Dose in CT: A Systematic Evaluation Across 58 Patient Models. Medical Physics, 2014. 41(6): p. 106-106. Sahbaee, P., W.P. Segars, and E. Samei, Multi-phase CT: Impact of Contrast Medium Propagation on Radiation Dose across a Population of Patient Models. 2014. Bae, K.T., Intravenous Contrast Medium Administration and Scan Timing at CT: Considerations and Approaches 1. Radiology, 2010. 256(1): p. 32-61.
