Accurate and precise dynamic contrast enhanced (DCE) measurements with reduced gadolinium toxicity by lengthening pre-injection baseline
Samuel Barnes1, Thomas Ng2, and Russell Jacobs3

1Radiology, Loma Linda University Medical Center, Loma Linda, CA, United States, 2Radiology, Brigham and Women's Hospital, Boston, MA, United States, 3Caltech, Pasadena, CA, United States

Synopsis

For increased patient safety it is desirable to acquire dynamic contrast enhanced (DCE) MRI data with the lowest passable Gd contrast agent (CA) dose. However, a lower CA dose generally leads to smaller signal changes and lower quality studies. Through simulations we show that lower CA doses (up to 1/2 dose) can be compensated for by extending the pre-injection baseline acquisition time to maintain similar accuracy and precision of the fitted Ktrans values.

Objective

To evaluate whether Ktrans can be reliably estimated in dynamic contrast enhanced (DCE) MRI, with reduced contrast agent concentration, by the lengthening of pre-injection baseline time.

Introduction

DCE-MRI gives quantitative and semi-quantitative information about the integrity of the vascular system. It has been applied to the study of pathologies ranging from cancer to mild cognitive impairment. Recent studies have also shown the value of DCE-MRI in the screening of breast 1 and prostate cancer 2. In these populations, the availability of reliable and quantitative DCE-MRI parameters, especially Ktrans, would likely contribute to the efficacy of the exam. However, the promise of contrast-enhance imaging is offset by the toxicity of the infused gadolinium contrast agents, which can cause nephrogenic systemic fibrosis 3 and accumulate in the brain 4. Thus, DCE-MRI protocols that maximize the extractable quantitative information while minimizing the amount of infused contrast agent are imperative. In a prior study 5, we developed a metric, K-CNR (an estimated Ktrans contrast to noise ratio (CNR), which takes into account both the accuracy and precision of Ktrans estimation), to evaluate the effects of DCE-MRI acquisition parameters and kinetic model choice upon Ktrans estimation. Using simulations, we demonstrated that lengthening of the pre-injection baseline aids the precision and accuracy of Ktrans measurement. In this study, we evaluated whether the benefits of a lengthened baseline can offset the decreased image signal intensities obtained with reduced contrast agent concentration ([CA]).

Methods

DCE-MRI simulations were generated using the 2-compartment exchange model (2CXM) in MATLAB and fitted to extended-Tofts and Patlak models using ROCKETSHIP software 6. These simulations have been shown to reflect real-life clinical data 5. The arterial input function developed by Parker et al. 7 was modified to mimic the behavior of our clinical datasets, which uses Multihance CA. Ktrans estimation (Ktrans: 0.0005 to 0.02 min-1; vp = 0.01; ve = 0.1; SNR = 15, 30, 100; sampling time = 3.75, 15.4 s, total duration = 15 min) was performed using varying pre-injection baselines (30, 60, 300, 500 s) and at different [CA] (0.05, 0.025, 0.0125 mmol/kg or standard dose,½ dose,¼ dose, respectively). Each set of parameter combinations were simulated and fitted 100 times. The ability to reliably estimate Ktrans at differing baselines and [CA] was then evaluated using K-CNR.

Results

As expected, DCE-MRI using the highest [CA] and longest pre-injection baseline (and at shortest sampling time/highest SNR) estimated Ktrans best. Standard dose [CA]/30s baseline, ½ dose [CA]/ 60 or 300s baseline and ¼ dose [CA]/500s baseline were directly compared to examine the interplay of these two variables. Graphs comparing Ktrans(simulated) vs. Ktrans(estimated) at a sampling time of 3.75s are shown in Fig 1, and demonstrates that Ktrans were estimated well at all three conditions with both Patlak and extended-Tofts models, albeit with different variances. K-CNR vs. Ktrans plots are shown in Fig 2, highlighting the wider variance of Ktrans estimation using ¼ dose [CA]/500s compared to the standard dose [CA]/30s. For the extended-Tofts model, the ½ dose [CA]/300s achieved similar K-CNR as the standard dose [CA]/30s baseline condition, while the ½ dose [CA]/300s exhibited a higher K-CNR using the Patlak model. Other acquisition parameters can also influence the K-CNR. For example, upon decreasing the sampling time, the ½ dose [CA]/60s performs similarly to the standard dose [CA]/30s condition (Fig 3).

Discussion

In the ideal imaging situation, an adequate [CA] (high but not enough to cause dephasing effects) is desirable for accurate Ktrans estimation. However, results from this simulation study suggest that lower [CA] (up to 1/2 dose) can achieve similar Ktrans accuracy and reliability by increasing the pre-injection baseline time. This is likely secondary to the fact that concentration time curves are partially calculated as the ratio of pre-injection and post-injection signal intensities; thus reducing the variance of the baseline can reduce the variance of the post-injection time curve. This effect is also dependent on other imaging parameters, such as the sampling time, as well intrinsic variables such as ve or vp. The appropriate choice of [CA], baseline time and other acquisition variables will likely be project and site specific. K-CNR and simulations provide a framework to tailor protocols to suit individual studies.

Conclussion

Simulations demonstrate that [CA] can be reduced in DCE-MRI studies while achieving robust Ktrans estimation with appropriate adjustment of acquisition variables, such as the pre-injection baseline duration. This should be taken into account when designing protocols where nephro- or neurotoxicity due to gadolinium is a concern.

Acknowledgements

No acknowledgement found.

References

1. Kuhl CK, Schrading S, Strobel K, Schild HH, Hilgers RD, Bieling HB. Abbreviated breast magnetic resonance imaging (MRI): first postcontrast subtracted images and maximum-intensity projection-a novel approach to breast cancer screening with MRI. J Clin Oncol. 2014;32(22):2304-10.

2. Rosenkrantz AB, Kim S, Lim RP, Hindman N, Deng FM, Babb JS, et al. Prostate cancer localization using multiparametric MR imaging: comparison of Prostate Imaging Reporting and Data System (PI-RADS) and Likert scales. Radiology. 2013;269(2):482-92.

3. Daftari Besheli L, Aran S, Shaqdan K, Kay J, Abujudeh H. Current status of nephrogenic systemic fibrosis. Clin Radiol. 2014;69(7):661-8.

4. Kanda T, Fukusato T, Matsuda M, Toyoda K, Oba H, Kotoku J, et al. Gadolinium-based Contrast Agent Accumulates in the Brain Even in Subjects without Severe Renal Dysfunction: Evaluation of Autopsy Brain Specimens with Inductively Coupled Plasma Mass Spectroscopy. Radiology. 2015;276(1):228-32.

5. Barnes SR, Ng TS, Montagne A, Law M, Zlokovic BV, Jacobs RE. Optimal acquisition and modeling parameters for accurate assessment of low K blood-brain barrier permeability using dynamic contrast-enhanced MRI. Magn Reson Med. 2015.

6. Barnes SR, Ng TS, Santa-Maria N, Montagne A, Zlokovic BV, Jacobs RE. ROCKETSHIP: a flexible and modular software tool for the planning, processing and analysis of dynamic MRI studies. BMC Med Imaging. 2015;15(1):19.

7. Parker GJM, Roberts C, Macdonald A, Buonaccorsi GA, Cheung S, Buckley DL, et al. Experimentally-derived functional form for a population-averaged high-temporal-resolution arterial input function for dynamic contrast-enhanced MRI. Magn Reson Med. 2006;56(5):993-1000.

Figures

Figure 1: Ktrans estimation using Ex-tofts (a) and Patlak (b) at different [CA]/baselines, sampling time 3.74s

Figure 2: K-CNR using Ex-tofts (a) and Patlak (b) at different [CA]/baselines, sampling time = 3.75s

Figure 3: K-CNR using Ex-tofts at different [CA]/baselines, sampling time = 15.4s



Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)
3650