Measurement of the arterial concentration of Gd‐DTPA using MRI: A step toward quantitative perfusion imaging

Fritz‐Hansen, Thomas; Rostrup, Egill; Larsson, Henrik; Søndergaard, Lars; Ring, P.; Henriksen, O.

doi:10.1002/mrm.1910360209

Cited by 230 publications

(200 citation statements)

References 15 publications

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“…This resulted in a total of 14 patients with baseline MRI (median 2 days before NAC; range, 0-6), and 12 patients with pre-and post-treatment MRI (median 21 days after the first NAC; range, [19][20][21]. The reduction in clinical tumor diameter was evaluated after 3 or 4 cycles, median 80 days after the first NAC (range, 63-86), and ranged from 16 to 100%.…”

Section: Resultsmentioning

confidence: 99%

“…A population-based bi-exponential arterial input function previously published was used as C p in the model (21,22). To exclude curves that were not adapted to the model, voxels were discarded if the fitting algorithm did not converge, K trans or v e lay outside the range (0,10) or root mean of squared residuals of the fitting was above 30, yielding reasonably good fits as evaluated by visual inspection.…”

Section: Image Analysismentioning

confidence: 99%

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Diffusion‐weighted and dynamic contrast‐enhanced MRI in evaluation of early treatment effects during neoadjuvant chemotherapy in breast cancer patients

Jensen¹,

Garzón²,

Heldahl³

et al. 2011

Magnetic Resonance Imaging

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Purpose: To use dynamic contrast-enhanced (DCE) and diffusion-weighted (DW) MRI at 3 Tesla (T) for early evaluation of treatment effects in breast cancer patients undergoing neoadjuvant chemotherapy (NAC), and assess the reliability of DW-MRI.Materials and Methods: DW-and DCE-MRI acquisitions of 15 breast cancer patients were performed before and after one cycle of NAC. MRI tumor diameter and volume, apparent diffusion coefficient (ADC) and kinetic parameters (K trans , v e ) were derived. The reliability of ADC before NAC was assessed. Changes in MRI parameters after NAC were analyzed, and logistic regression analysis was used to find the best predictors for pathologic response. Results:The reliability for ADC values was high, with intraclass correlation coefficient of 0.84 (P ¼ 0.001). After one cycle of NAC, MRI tumor diameter (8%, P ¼ 0.005) and tumor volume (30%, P ¼ 0.008) was reduced for all patients, while ADC mean values increased (0.12 mm 2 /s, P ¼ 0.008). The best predictor for treatment response was a change in MRI tumor diameter with mean error rate of 0.167 (13% for responders, 5% for nonresponders, P ¼ 0.291).Conclusion: Changes in MRI derived tumor diameter and ADC after only one cycle of NAC could provide a valuable tool for early evaluation of treatment effects in breast cancer patients.

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Section: Resultsmentioning

confidence: 99%

Section: Image Analysismentioning

confidence: 99%

Diffusion‐weighted and dynamic contrast‐enhanced MRI in evaluation of early treatment effects during neoadjuvant chemotherapy in breast cancer patients

Jensen¹,

Garzón²,

Heldahl³

et al. 2011

Magnetic Resonance Imaging

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“…A plasma concentration-time curve was simulated as an input to the model. The amplitude and shape of the curve were based upon measurements made in the descending aorta of volunteers following bolus injection of Gd-DTPA (11). Reference data for comparison with the simulated residue curves were obtained from MR studies of human brain, breast, and prostate tumors (15).…”

Section: Data Simulationmentioning

confidence: 99%

“…Given this, the following analyses assume that the arterial input function has been measured. Although techniques to achieve this are not yet in common use, an increasing number of investigators are developing practical methods for doing so (11,12). …”

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confidence: 99%

Uncertainty in the analysis of tracer kinetics using dynamic contrast‐enhanced T₁‐weighted MRI

Buckley

2002

Magnetic Resonance in Med

255

287

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In recent years a number of physiological models have gained prominence in the analysis of dynamic contrast-enhanced T 1 -weighted MRI data. However, there remains little evidence to support their use in estimating the absolute values of tissue physiological parameters such as perfusion, capillary permeability, and blood volume. In an attempt to address this issue, data were simulated using a distributed pathway model of tracer kinetics, and three published models were fitted to the resultant concentration-time curves. Parameter estimates obtained from these fits were compared with the parameters used for the simulations. The results indicate that the use of commonly accepted models leads to systematic overestimation of the transfer constant, K trans , and potentially large underestimates of the blood plasma volume fraction, V p . In summary, proposals for a practical approach to physiological modeling using MRI data are outlined. The last decade has seen a rapid development in the use of dynamic contrast-enhanced T 1 -weighted MRI in medicine. In tandem with the technological advances that have enabled improved data acquisition, a number of investigators have employed physiological models to facilitate data interpretation. While the use of these models has found numerous applications (e.g., in studies of tumor physiology (1) and myocardial perfusion (2)), and a number of groups have assessed the potential of model parameters as surrogate markers (3,4), little has been published that addresses the direct interpretation of these results. Specifically, how do the estimates obtained using the various models compare with the physiological parameters they purport to measure? This is not a simple question to address since it is often difficult to identify a reliable "gold standard." Many investigators compare their results with those obtained with positron emission tomography (PET). However, PET shares many of the basic models employed in MRI (5). Similarly, data simulation exercises using Monte-Carlo techniques designed to assess accuracy and precision in parameter estimation often utilize the same model to both generate and analyze the data (6,7). In this way, the sensitivity of the estimates to experimental variables, such as noise and sampling frequency, is assessed but little is revealed about the physiological significance of the resultant parameter estimates.A physiological model incorporating multiple parallel pathways and heterogeneous flow was used to simulate data of a realistic nature to which simplified models were fitted. The experiment was designed to assess the accuracy of the models themselves, not the quality of the data to which they are fitted (in terms of noise, sampling frequency, etc.), since data quality is essentially an experimental variable. Furthermore, this study was restricted to those models dealing with a contrast agent that diffuses out of the vascular space (thereby incorporating capillary permeability as a model parameter). Many of the issues associated with the analysis of data from...

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“…Historically, the AIF has been measured in the vicinity of the MCA (5,(11)(12)(13)(14). This choice was partly a product of practicality, because measurements of AIF in the neck are more technically challenging and require excitation of an additional slice, while measurement of the AIF near the MCA can utilize the brain slices that are normally acquired in the perfusion scan.…”

Section: Historical Background and Signalsmentioning

confidence: 99%

Arterial input functions for dynamic susceptibility contrast MRI: Requirements and signal options

Conturo

Akbudak

Kotys

et al. 2005

Magnetic Resonance Imaging

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Cerebral perfusion imaging using dynamic susceptibility contrast (DSC) has been the subject of considerable research and shows promise for basic science and clinical use. In DSC, the MRI signals in brain tissue and feeding arteries are monitored dynamically in response to a bolus injection of paramagnetic agents, such as gadolinium (Gd) chelates. DSC has the potential to allow quantitative imaging of parameters such as cerebral blood flow (CBF) with a high signal‐to‐noise ratio (SNR) in a short scan time; however, quantitation depends critically on accurate and precise measurement of the arterial input function (AIF). We discuss many requirements and factors that make it difficult to measure the AIF. The AIF signal should be linear with respect to Gd concentration, convertible to the same concentration scale as the tissue signal, and independent of hematocrit. Complicated relationships between signal and concentration can violate these requirements. The additional requirements of a high SNR and high spatial/temporal resolution are technically challenging. AIF measurements can also be affected by signal saturation and aliasing, as well as dispersion/delay between the AIF sampling site and the tissue. We present new in vivo preliminary results for magnitude‐based (ΔR2*) and phase‐based (Δϕ) AIF measurements that show a linearity advantage of phase, and a disparity in the scaling of Δϕ AIFs, ΔR2* AIFs, and ΔR2* tissue curves. Finally, we discuss issues related to the choice of AIF signal for quantitative perfusion imaging. J. Magn. Reson. Imaging 2005. © 2005 Wiley‐Liss, Inc.

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Measurement of the arterial concentration of Gd‐DTPA using MRI: A step toward quantitative perfusion imaging

Cited by 230 publications

References 15 publications

Diffusion‐weighted and dynamic contrast‐enhanced MRI in evaluation of early treatment effects during neoadjuvant chemotherapy in breast cancer patients

Diffusion‐weighted and dynamic contrast‐enhanced MRI in evaluation of early treatment effects during neoadjuvant chemotherapy in breast cancer patients

Uncertainty in the analysis of tracer kinetics using dynamic contrast‐enhanced T₁‐weighted MRI

Arterial input functions for dynamic susceptibility contrast MRI: Requirements and signal options

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Measurement of the arterial concentration of Gd‐DTPA using MRI: A step toward quantitative perfusion imaging

Cited by 230 publications

References 15 publications

Diffusion‐weighted and dynamic contrast‐enhanced MRI in evaluation of early treatment effects during neoadjuvant chemotherapy in breast cancer patients

Diffusion‐weighted and dynamic contrast‐enhanced MRI in evaluation of early treatment effects during neoadjuvant chemotherapy in breast cancer patients

Uncertainty in the analysis of tracer kinetics using dynamic contrast‐enhanced T1‐weighted MRI

Arterial input functions for dynamic susceptibility contrast MRI: Requirements and signal options

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Uncertainty in the analysis of tracer kinetics using dynamic contrast‐enhanced T₁‐weighted MRI