Choice of the regularization parameter for perfusion quantification with MRI

Sourbron, Steven; Luypaert, Robert; Schuerbeek, Peter Van; Dujardin, Martine; Stadnik, Tadeusz

doi:10.1088/0031-9155/49/14/020

Cited by 35 publications

(73 citation statements)

References 24 publications

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“…Neglecting dispersion is known to underestimate perfusion (14,15). The magnitude of this error is significant in stroke (16), but with the present protocol it is difficult to estimate for the kidney.…”

Section: Quantification Of Rrbfmentioning

confidence: 93%

Quantification of renal perfusion and function on a voxel‐by‐voxel basis: A feasibility study

Dujardin

Sourbron

Luypaert

et al. 2005

Magnetic Resonance in Med

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The feasibility of a voxel-by-voxel deconvolution analysis of T 1 -weighted DCE data in the human kidney and its potential for obtaining quantification of perfusion and filtration was investigated. Measurements were performed on 14 normal humans and 1 transplant at 1.5 T using a Turboflash sequence. Signal time-courses were converted to tracer concentrations and deconvolved with an aorta AIF. Parametric maps of relative renal blood flow (rRBF), relative renal volume of distribution (rRVD), relative mean transit time (rMTT), and whole cortex extraction fraction (E) were obtained from the impulse response functions. For the normals average cortical rRBF, rRVD, rMTT, and E were 1.6 mL/min/mL (SD 0.8), 0.4 mL/mL (SD 0.1), 17s (SD 7), and 22.6% (SD 6.1), respectively. A gradual voxelwise rRBF increase is found from the center of two infarction zones toward the edges. Voxel IRFs showed more detail on the nefron substructure than ROI IRFs. In conclusion, quantitative voxelwise perfusion mapping based on deconvolved T 1 -DCE renal data is feasible, but absolute quantification requires inflow correction. rRBF maps and quantitative values are sufficiently sensitive to detect perfusion abnormality in pathologic areas, but further research is necessary to separate perfusion from extraction and to characterize the different compartments of the nephron on the ( Key words: renal; T-weighted; perfusion; deconvolution; quantificationDynamic contrast enhanced MRI is a promising noninvasive method for imaging renal perfusion and function (1-5), since it avoids the use of ionizing radiation and combines high temporal and spatial resolution. Moreover, Gd-DTPA is very well tolerated, not nephrotoxic, and has properties comparable to the radioisotopic 99mTc-DTPA (6,7). Tissue perfusion imaging with dynamic Gd-DTPA enhanced MRI therefore offers the potential for obtaining important information about organ viability, anatomy, and function in the normal as well as in the compromised kidney.Experiments with a rabbit model (1) have shown that Gd-DTPA enhanced bolus tracking and a deconvolution analysis of ROI signals permit quantitative measures of perfusion and filtration, independent of the effect of the shape and size of the bolus. On the other hand, results obtained with an intravascular contrast agent have shown that a pixel-by-pixel approach to perfusion quantification can be a useful tool for distinguishing normality from renal artery stenosis in dogs as well as humans (2,3).In this study we investigate the combination of both approaches: a pixel-by-pixel deconvolution analysis in the human kidney, using Gd-DTPA enhanced bolus tracking. We investigate the quality of the resulting parametric maps, evaluate to what extent they represent perfusion and extraction, and assess the potential of the technique for obtaining reliable quantification of renal blood flow and a measure of filtration. We also investigate the possibility of obtaining other parameters like the tracers' volume of distribution and mean transit time. Our first results...

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Section: Quantification Of Rrbfmentioning

confidence: 93%

Quantification of renal perfusion and function on a voxel‐by‐voxel basis: A feasibility study

Dujardin

Sourbron

Luypaert

et al. 2005

Magnetic Resonance in Med

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“…The modeling approach may also have a numerical advantage, because it constrains the number of degrees of freedom in the solution. Unconstrained model-free deconvolution is known to suffer from from low accuracy when the noise level is high (36). In (29) constraints are imposed by a polynomial representation of the residue function, which may be expected to improve accuracy and/or precision.…”

Section: Tracer Kinetic Modelingmentioning

confidence: 99%

“…Also, the method combines two tracer-kinetic approaches with contradictory assumptions (a one-compartment model and a model-free analysis), prior knowledge is required regarding the intactness of the BBB, and a one-compartment model may not fit tumor data with large blood volumes. Finally, a modelfree analysis produces severe CBF errors in data with low SNR (36), though the error may be reduced by introducing appropriate constraints in the solution (29).…”

mentioning

confidence: 99%

Quantification of cerebral blood flow, cerebral blood volume, and blood–brain‐barrier leakage with DCE‐MRI

Sourbron

Ingrisch

Siefert

et al. 2009

Magnetic Resonance in Med

233

251

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Dynamic susceptibility contrast MRI (DSC-MRI) is the current standard for the measurement of Cerebral Blood Flow (CBF) and Cerebral Blood Volume (CBV), but it is not suitable for the measurement of Extraction Flow (EF) and may not allow for absolute quantification. The objective of this study was to develop and evaluate a methodology to measure CBF, CBV, and EF from T1-weighted dynamic contrast-enhanced MRI (DCE-MRI). A twocompartment modeling approach was developed, which applies both to tissues with an intact and with a broken Blood-BrainBarrier (BBB). The approach was evaluated using measurements in normal grey matter (GM) and white matter (WM) and in tumors of 15 patients. Accuracy and precision were estimated with simulations of normal brain tissue. All tumor and normal tissue curves were accurately fitted by the model. CBF (mL/100 mL/min) was 82 ± 21 in GM and 23 ± 14 in WM, CBV (mL/100 mL) was 2.6 ± 0.8 in GM and 1.3 ± 0.4 in WM. EF (mL/100 mL/min) was close to zero in GM (−0.009 ± 0.05) and WM (−0.03 ± 0.08). Simulations show an overlap between CBF values of WM and GM, which is eliminated when Contrast-to-Noise (CNR) is improved. The model provides a consistent description of tracer kinetics in all brain tissues, and an accurate assessment of perfusion and permeability in reference tissues. The measurement sequence requires optimization to improve CNR and the precision in the perfusion parameters. With this approach, DCE-MRI presents a promising alternative to DSC-MRI for quantitative bolustracking in the brain.

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“…The 10% threshold, however, leads to a larger standard deviation, especially in white matter [29] at lower SNRs, and a higher cut-off value is normally preferable. Sourbron et al [114,115] compared two methods of threshold determination in the SVD deconvolution, namely the L-curve criterion and generalised cross-validation. They concluded that improved image contrast and increased sensitivity to pathology were obtained with both techniques compared with the use of a fixed threshold or a threshold based on SNR C .…”

Section: Model-independent Deconvolutionmentioning

confidence: 99%

Absolute quantification of perfusion using dynamic susceptibility contrast MRI: pitfalls and possibilities

Knutsson

Ståhlberg

Wirestam

2009

Magn Reson Mater Phy

104

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Absolute quantification of cerebral blood flow, cerebral blood volume and mean transit time is desirable in the determination of tissue viability thresholds and tissue at risk in acute ischaemic stroke, as well as in cases where a global reduction in cerebral blood flow is expected, for example, in patients with dementia or depressive disorders. Absolute values are also useful when comparing sequential examinations of tissue perfusion parameters, for example, in the monitoring and follow-up of various kinds of therapy. Regardless of the method employed, a number of assumptions and approximations must be made to obtain absolute measures of perfusion. Furthermore, the different stages of data acquisition and processing are associated with various degrees of uncertainty. In this review, the problems of particular relevance to absolute quantification of cerebral perfusion parameters using dynamic susceptibility contrast magnetic resonance imaging are discussed, and possible solutions are outlined.

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Choice of the regularization parameter for perfusion quantification with MRI

Cited by 35 publications

References 24 publications

Quantification of renal perfusion and function on a voxel‐by‐voxel basis: A feasibility study

Quantification of renal perfusion and function on a voxel‐by‐voxel basis: A feasibility study

Quantification of cerebral blood flow, cerebral blood volume, and blood–brain‐barrier leakage with DCE‐MRI

Absolute quantification of perfusion using dynamic susceptibility contrast MRI: pitfalls and possibilities

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