Quantitative cerebral perfusion using the PRESTO acquisition scheme

Improved reliability of perfusion estimation in dynamic susceptibility contrast MRI by using the arterial input function from dynamic contrast enhanced MRI

The arterial input function (AIF) plays a crucial role in estimating quantitative perfusion properties from dynamic susceptibility contrast (DSC) MRI. An important issue, however, is that measuring the AIF in absolute contrast-agent concentrations is challenging, due to uncertainty in relation to the measuredR 2 ∗ -weighted signal, signal depletion at high concentration, and partial-volume effects. A potential solution could be to derive the AIF from separately acquired dynamic contrast enhanced (DCE) MRI data. We aim to compare the AIF determined from DCE MRI with the AIF from DSC MRI, and estimated perfusion coefficients derived from DSC data using a DCE-driven AIF with perfusion coefficients determined using a DSC-based AIF. AIFs were manually selected in branches of the middle cerebral artery (MCA) in both DCE and DSC data in each patient. In addition, a semi-automatic AIF-selection algorithm was applied to the DSC data. The amplitude and full width at half-maximum of the AIFs were compared statistically using the Wilcoxon rank-sum test, applying a 0.05 significance level. Cerebral blood flow (CBF) was derived with different AIF approaches and compared further. The results showed that the AIFs extracted from DSC scans yielded highly variable peaks across arteries within the same patient. The semi-automatic DSC-AIF had significantly narrower width compared with the manual AIFs, and a significantly larger peak than the manual DSC-AIF. Additionally, the DCE-based AIF provided a more stable measurement of relative CBF and absolute CBF values estimated with DCE-AIFs that were compatible with previously reported values. In conclusion, DCE-based AIFs were reproduced significantly better across vessels, showed more realistic profiles, and delivered more stable and reasonable CBF measurements. The DCE-AIF can, therefore, be considered as an alternative AIF source for quantitative perfusion estimations in DSC MRI.

Assessment of the clinical feasibility of detecting subtle blood-brain barrier leakage in cerebral small vessel disease using dynamic susceptibility contrast MRI

PURPOSE

Cerebral small vessel disease (cSVD) involves several pathologies affecting the small vessels, including blood-brain barrier (BBB) impairment. Dynamic susceptibility contrast (DSC) MRI is sensitive to both blood perfusion and BBB leakage, and correction methods may be crucial for obtaining reliable perfusion measures. These methods might also be applicable to detect BBB leakage itself. This study investigated to what extent DSC-MRI can measure subtle BBB leakage in a clinical feasibility setting.

METHODS

In vivo DCE and DSC data were collected from fifteen cSVD patients (71 (±10) years, 6F/9M) and twelve elderly controls (71 (±10) years, 4F/8M). DSC-derived leakage fractions were obtained using the Boxerman-Schmainda-Weisskoff method (K2). K2 was compared with the DCE-derived leakage rate K_i, obtained from Patlak analysis. Subsequently, differences were assessed between white matter hyperintensities (WMH), cortical gray matter (CGM), and normal-appearing white matter (NAWM). Additionally, computer simulations were performed to assess the sensitivity of DSC-MRI to BBB leakage.

RESULTS

K2 showed significant differences between tissue regions (P < 0.001 for CGM-NAWM and CGM-WMH, and P = 0.001 for NAWM-WMH). Conversely, according to the computer simulations the DSC sensitivity was insufficient to measure subtle BBB leakage, as the K2 values were below the derived limit of quantification (4∙10^-3 min^-1). As expected, K_i was elevated in the WMH compared to CGM and NAWM (P < 0.001).

CONCLUSIONS

Although clinical DSC-MRI seems capable to detect subtle BBB leakage differences between WMH and normal-appearing brain tissue it is not recommended. K2 as a direct measure for subtle BBB leakage remains ambiguous as its signal effects are due to mixed T₁- and T₂^∗-weighting. Further research is warranted to better disentangle perfusion from leakage effects.

Applying machine learning classifiers to automate quality assessment of paediatric dynamic susceptibility contrast (DSC-) MRI data

OBJECTIVE

Investigate the performance of qualitative review (QR) for assessing dynamic susceptibility contrast (DSC-) MRI data quality in paediatric normal brain and develop an automated alternative to QR.

METHODS

1027 signal-time courses were assessed by Reviewer 1 using QR. 243 were additionally assessed by Reviewer 2 and % disagreements and Cohen's κ (κ) were calculated. The signal drop-to-noise ratio (SDNR), root mean square error (RMSE), full width half maximum (FWHM) and percentage signal recovery (PSR) were calculated for the 1027 signal-time courses. Data quality thresholds for each measure were determined using QR results. The measures and QR results trained machine learning classifiers. Sensitivity, specificity, precision, classification error and area under the curve from a receiver operating characteristic curve were calculated for each threshold and classifier.

RESULTS

Comparing reviewers gave 7% disagreements and κ = 0.83. Data quality thresholds of: 7.6 for SDNR; 0.019 for RMSE; 3 s and 19 s for FWHM; and 42.9 and 130.4% for PSR were produced. SDNR gave the best sensitivity, specificity, precision, classification error and area under the curve values of 0.86, 0.86, 0.93, 14.2% and 0.83. Random forest was the best machine learning classifier, giving sensitivity, specificity, precision, classification error and area under the curve of 0.94, 0.83, 0.93, 9.3% and 0.89.

CONCLUSION

The reviewers showed good agreement. Machine learning classifiers trained on signal-time course measures and QR can assess quality. Combining multiple measures reduces misclassification.

ADVANCES IN KNOWLEDGE

A new automated quality control method was developed, which trained machine learning classifiers using QR results.

Dynamic susceptibility-contrast magnetic resonance imaging with contrast agent leakage correction aids in predicting grade in pediatric brain tumours: a multicenter study

BACKGROUND

Relative cerebral blood volume (rCBV) measured using dynamic susceptibility-contrast MRI can differentiate between low- and high-grade pediatric brain tumors. Multicenter studies are required for translation into clinical practice.

OBJECTIVE

We compared leakage-corrected dynamic susceptibility-contrast MRI perfusion parameters acquired at multiple centers in low- and high-grade pediatric brain tumors.

MATERIALS AND METHODS

Eighty-five pediatric patients underwent pre-treatment dynamic susceptibility-contrast MRI scans at four centers. MRI protocols were variable. We analyzed data using the Boxerman leakage-correction method producing pixel-by-pixel estimates of leakage-uncorrected (rCBVuncorr) and corrected (rCBVcorr) relative cerebral blood volume, and the leakage parameter, K2. Histological diagnoses were obtained. Tumors were classified by high-grade tumor. We compared whole-tumor median perfusion parameters between low- and high-grade tumors and across tumor types.

RESULTS

Forty tumors were classified as low grade, 45 as high grade. Mean whole-tumor median rCBVuncorr was higher in high-grade tumors than low-grade tumors (mean ± standard deviation [SD] = 2.37±2.61 vs. -0.14±5.55; P<0.01). Average median rCBV increased following leakage correction (2.54±1.63 vs. 1.68±1.36; P=0.010), remaining higher in high-grade tumors than low grade-tumors. Low-grade tumors, particularly pilocytic astrocytomas, showed T1-dominant leakage effects; high-grade tumors showed T2*-dominance (mean K2=0.017±0.049 vs. 0.002±0.017). Parameters varied with tumor type but not center. Median rCBVuncorr was higher (mean = 1.49 vs. 0.49; P=0.015) and K2 lower (mean = 0.005 vs. 0.016; P=0.013) in children who received a pre-bolus of contrast agent compared to those who did not. Leakage correction removed the difference.

CONCLUSION

Dynamic susceptibility-contrast MRI acquired at multiple centers helped distinguish between children's brain tumors. Relative cerebral blood volume was significantly higher in high-grade compared to low-grade tumors and differed among common tumor types. Vessel leakage correction is required to provide accurate rCBV, particularly in low-grade enhancing tumors.

Longitudinal, leakage corrected and uncorrected rCBV during the first-line treatment of glioblastoma: a prospective study

PURPOSE

Dynamic susceptibility contrast (DSC) MR-perfusion is becoming a standard of care for the monitoring of glioblastoma. Yet, technical standards are lacking and measurements without leakage correction are still common. Also, data on leakage corrected measurements during stable disease is scarce. In this study we hypothesized that basic leakage correction would significantly enhance data quality during stable disease and improve progress detection. We furthermore investigated whether longitudinal data could increase diagnostic performance.

METHODS

Patients with histologically proven glioblastoma undergoing first-line therapy were prospectively recruited. We conducted DSC perfusion measurements without prebolus administration in 6-week intervals from the end of radiotherapy until progression. Maximum relative cerebral volume values (rCBV_max) with and without leakage correction were calculated using Philips IntelliSpace®.

RESULTS

We recruited 16 patients and conducted 82 MRI scans with a mean follow up of 7.2 month. During stable disease, corrected rCBV_max was significantly more stable than uncorrected rCBV_max. Detection of progression with a rCBV_max cutoff was better for corrected (specificity 86%) than for uncorrected rCBV_max (specificity 41%). Interestingly, the increase of corrected rCBV_max upon progression also had a good diagnostic performance with a combination of both cutoffs delivering the best result (sensitivity/specificity 89%/93%).

CONCLUSION

Corrected rCBV_max supports the imaging finding of a stable disease and large increases during longitudinal observation support the diagnosis of tumor progression. rCBV values without prebolus or leakage correction are not reliable to monitor glioblastomas. Further studies to investigate the value of longitudinal rCBV dynamics for the differentiation of real tumor progression from pseudoprogression are warranted.

Imaging vascular and hemodynamic features of the brain using dynamic susceptibility contrast and dynamic contrast enhanced MRI

In the context of neurologic disorders, dynamic susceptibility contrast (DSC) and dynamic contrast enhanced (DCE) MRI provide valuable insights into cerebral vascular function, integrity, and architecture. Even after two decades of use, these modalities continue to evolve as their biophysical and kinetic basis is better understood, with improvements in pulse sequences and accelerated imaging techniques and through application of more robust and automated data analysis strategies. Here, we systematically review each of these elements, with a focus on how their integration improves kinetic parameter accuracy and the development of new hemodynamic biomarkers that provide sub-voxel sensitivity (e.g., capillary transit time and flow heterogeneity). Regarding contrast mechanisms, we discuss the dipole-dipole interactions and susceptibility effects that give rise to simultaneous T₁, T₂ and T₂^∗ relaxation effects, including their quantification, influence on pulse sequence parameter optimization, and use in methods such as vessel size and vessel architectural imaging. The application of technologic advancements, such as parallel imaging, simultaneous multi-slice, undersampled k-space acquisitions, and sliding window strategies, enables improved spatial and/or temporal resolution of DSC and DCE acquisitions. Such acceleration techniques have also enabled the implementation of, clinically feasible, simultaneous multi-echo spin- and gradient echo acquisitions, providing more comprehensive and quantitative interrogation of T₁, T₂ and T₂^∗ changes. Characterizing these relaxation rate changes through different post-processing options allows for the quantification of hemodynamics and vascular permeability. The application of different biophysical models provides insight into traditional hemodynamic parameters (e.g., cerebral blood volume) and more advanced parameters (e.g., capillary transit time heterogeneity). We provide insight into the appropriate selection of biophysical models and the necessary post-processing steps to ensure reliable measurements while minimizing potential sources of error. We show representative examples of advanced DSC- and DCE-MRI methods applied to pathologic conditions affecting the cerebral microcirculation, including brain tumors, stroke, aging, and multiple sclerosis. The maturation and standardization of conventional DSC- and DCE-MRI techniques has enabled their increased integration into clinical practice and use in clinical trials, which has, in turn, spurred renewed interest in their technological and biophysical development, paving the way towards a more comprehensive assessment of cerebral hemodynamics.

Utilization of MR angiography in perfusion imaging for identifying arterial input function

OBJECTIVE

This research utilizes magnetic resonance angiography (MRA) to identify arterial locations during the parametric evaluation of concentration time curves (CTCs), and to prevent shape distortions in arterial input function (AIF).

MATERIALS AND METHODS

We carried out cluster analysis with the CTC parameters of voxels located within and around the middle cerebral artery (MCA). Through MRA, we located voxels that meet the AIF criteria and those with distorted CTCs. To minimize partial volume effect, we re-scaled the time integral of CTCs by the time integral of venous output function (VOF). We calculated the steady-state value to area under curve ratio (SS:AUC) of VOF and used it as a reference in selecting AIF. CTCs close to this reference value (selected AIF) and those far from it were used (eliminated AIF) to compute cerebral blood flow (CBF).

RESULTS

Eliminated AIFs were found to be either on or anterior to MCA, whereas selected AIFs were located superior, inferior, posterior, or anterior to MCA. If the SS:AUC of AIF was far from the reference value, CBF was either under- or over-estimated by a maximum of 41.1 ± 14.3 and 36.6 ± 19.2%, respectively.

CONCLUSION

MRA enables excluding voxels on the MCA during cluster analysis, and avoiding the risk of shape distortions.

Influence of blood/tissue differences in contrast agent relaxivity on tracer-based MR perfusion measurements

PURPOSE

Perfusion assessment by monitoring the transport of a tracer bolus depends critically on conversion of signal intensity into tracer concentration. Two main assumptions are generally applied for this conversion; (1) contrast agent relaxivity is identical in blood and tissue, (2) change in signal intensity depends only on the primary relaxation effect. The purpose of the study was to assess the validity and influence of these assumptions.

MATERIALS AND METHODS

Blood and cerebral tissue relaxivities r1, r2, and r2* for gadodiamide were measured in four pigs at 1.5 T. Gadolinium concentration was determined by inductively coupled plasma atomic emission spectroscopy. Influence of the relaxivities, secondary relaxation effects and choice of singular value decomposition (SVD) regularization threshold was studied by simulations.

RESULTS

In vivo relaxivities relative to blood concentration [in s(-1) mM(-1) for blood, gray matter (GM), white matter (WM)] were for r1 (2.614 ± 1.061, 0.010 ± 0.001, 0.004 ± 0.002), r2 (5.088 ± 0.952, 0.091 ± 0.008, 0.059 ± 0.014), and r2* (13.292 ± 3.928, 1.696 ± 0.157, 0.910 ± 0.139). Although substantial, by a nonparametric test for paired samples, the differences were not statistically significant. The GM to WM blood volume ratio was estimated to 2.6 ± 0.9 by r1, 1.6 ± 0.3 by r2, and 1.9 ± 0.2 by r2*. Secondary relaxation was found to reduce the tissue blood flow, as did the SVD regularization threshold.

CONCLUSION

Contrast agent relaxivity is not identical in blood and tissue leading to substantial errors. Further errors are introduced by secondary relaxation effects and the SVD regularization.

Quantification of cerebral perfusion using dynamic quantitative susceptibility mapping

PURPOSE

The purpose of this study is to develop a dynamic quantitative susceptibility mapping (QSM) technique with sufficient temporal resolution to map contrast agent concentration in cerebral perfusion imaging.

METHODS

The dynamic QSM used a multiecho three-dimensional (3D) spoiled gradient echo golden angle interleaved spiral sequence during contrast bolus injection. Four-dimensional (4D) space-time resolved magnetic field reconstruction was performed using the temporal resolution acceleration with constrained evolution reconstruction method. Deconvolution of the gadolinium-induced field was performed at each time point with the morphology enabled dipole inversion method to generate a 4D gadolinium concentration map, from which three-dimensional spatial distributions of cerebral blood volume and cerebral blood flow were computed.

RESULTS

Initial in vivo brain imaging demonstrated the feasibility of using dynamic QSM for generating quantitative 4D contrast agent maps and imaging three-dimensional perfusion. The cerebral blood flow obtained with dynamic QSM agreed with that obtained using arterial spin labeling.

CONCLUSION

Dynamic QSM can be used to perform 4D mapping of contrast agent concentration in contrast-enhanced magnetic resonance imaging. The perfusion parameters derived from this 4D contrast agent concentration map were in good agreement with those obtained using arterial spin labeling.

The 39 steps: evading error and deciphering the secrets for accurate dynamic susceptibility contrast MRI

Dynamic susceptibility contrast (DSC) MRI is the most commonly used MRI method to assess cerebral perfusion and other related haemodynamic parameters. Although the technique is well established and used routinely in clinical centres, there are still many problems that impede accurate perfusion quantification. In this review article, we present 39 steps which guide the reader through the theoretical principles, practical decisions, potential problems, current limitations and latest advances in DSC-MRI. The 39 steps span the collection, analysis and interpretation of DSC-MRI data, expounding issues and possibilities relating to the contrast agent, the acquisition of DSC-MRI data, data pre-processing, the contrast concentration-time course, the arterial input function, deconvolution, common perfusion parameters, post-processing possibilities, patient studies, absolute versus relative quantification and automated analysis methods.

The PRESTO technique for fMRI

In the early days of BOLD fMRI, the acquisition of T(2)(*) weighted data was greatly facilitated by rapid scan techniques such as EPI. The latter, however, was only available on a few MRI systems that were equipped with specialized hardware that allowed rapid switching of the imaging gradients. For this reason, soon after the invention of fMRI, the scan technique PRESTO was developed to make rapid T(2)(*) weighted scanning available on standard clinical scanners. This method combined echo shifting, which allows for echo times longer than the sequence repetition time, with acquisition of multiple k-space lines per excitation. These two concepts were combined in order to achieve a method fast enough for fMRI, while maintaining a sufficiently long echo time for optimal contrast. PRESTO has been primarily used for 3D scanning, which minimized the contribution of large vessels due to inflow effects. Although PRESTO is still being used today, its appeal has lessened somewhat due to increased gradient performance of modern MRI scanners. Compared to 2D EPI, PRESTO may have somewhat reduced temporal stability, which is a disadvantage for fMRI that may not outweigh the advantage of reduced inflow effects provided by 3D scanning. In this overview, the history of the development of the PRESTO is presented, followed by a qualitative comparison with EPI.

Baseline CBF, and BOLD, CBF, and CMRO2 fMRI of visual and vibrotactile stimulations in baboons

Neurovascular coupling associated with visual and vibrotactile stimulations in baboons anesthetized sequentially with isoflurane and ketamine was evaluated using multimodal functional magnetic resonance imaging (fMRI) on a clinical 3-Tesla scanner. Basal cerebral blood flow (CBF), and combined blood-oxygenation-level-dependent (BOLD) and CBF fMRI of visual and somatosensory stimulations were measured using pseudo-continuous arterial spin labeling. Changes in stimulus-evoked cerebral metabolic rate of oxygen (CMRO(2)) were estimated using calibrated fMRI. Arterial transit time for vessel, gray matter (GM), and white matter (WM) were 250, 570, and 823 ms, respectively. Gray matter and WM CBF, respectively, were 107.8±7.9 and 47.8±3.8 mL per 100 g per minute under isoflurane, and 108.8±10.3 and 48.7±4.2 mL per 100 g per minute under ketamine (mean±s.e.m., N=8 sessions, five baboons). The GM/WM CBF ratio was not statistically different between the two anesthetics, averaging 2.3±0.1. Hypercapnia evoked global BOLD and CBF increases. Blood-oxygenation-level-dependent, CBF, and CMRO(2) signal changes by visual and vibrotactile stimulations were 0.19% to 0.22%, 18% to 23%, and 4.9% to 6.7%, respectively. The CBF/CMRO(2) ratio was 2.9 to 4.7. Basal CBF and fMRI responses were not statistically different between the two anesthetics. This study establishes a multimodal fMRI protocol to probe clinically relevant functional, physiological and metabolic information in large nonhuman primates.

New criterion to aid manual and automatic selection of the arterial input function in dynamic susceptibility contrast MRI

Dynamic susceptibility contrast-MRI requires an arterial input function (AIF) to obtain cerebral blood flow, cerebral blood volume, and mean transit time. The current AIF selection criteria discriminate venous, capillary, and arterial profiles based on shape and timing characteristics of the first passage. Unfortunately, partial volume effects can lead to shape errors in the bolus passage, including a narrower and higher peak, which might be selected as a "correct" AIF. In this study, a new criterion is proposed that detects shape errors based on tracer kinetic principles for computing cerebral blood volume. This criterion uses the ratio of the steady-state value to the area-under-the-curve of the first passage, which should result in an equal value for tissue and arterial responses. By using a reference value from tissue, partial volume effects-induced shape errors of the AIF measurement can be detected. Different factors affecting the ratio were investigated using simulations. These showed that the new criterion should only be used in studies with T(1) -insensitive acquisition. In vivo data were used to evaluate the proposed approach. The data showed that the new criterion enables detection of shape errors, although false positives do occur, which could be easily avoided when combined with current AIF selection criteria.

Diffusion tensor and perfusion MRI of non-human primates

This paper reviews recent non-human primate (NHP) neuroimaging literature using MRI in macaque, baboon and chimpanzee. It describes general challenges and limitations for NHP MRI studies, and reviews recent applications of anatomical, diffusion tensor, cerebral blood flow MRI. Applications to NHP stroke is discussed in some detail.

Optimal location for arterial input function measurements near the middle cerebral artery in first-pass perfusion MRI

One of the main difficulties in obtaining quantitative perfusion values from dynamic susceptibility contrast-magnetic resonance imaging is a correct arterial input function (AIF) measurement, as partial volume effects can lead to an erroneous shape and amplitude of the AIF. Cerebral blood flow and volume scale linearly with the area under the AIF, but shape changes of the AIF can lead to large, nonlinear errors. Current manual and automated AIF selection procedures do not guarantee the exclusion of partial volume effects from AIF measurements. This study uses a numerical model, validated by phantom experiments, for predicting the optimal location for AIF measurements in the vicinity of the middle cerebral artery (MCA). Three different sequences were investigated and evaluated on a voxel-by-voxel basis by comparison with the ground truth. Subsequently, the predictions were evaluated in an in vivo example. The findings are fourfold: AIF measurements should be performed in voxels completely outside the artery, here a linear relation should be assumed between DeltaR*2 and the concentration contrast agent, the exact optimal location differs per acquisition type, and voxels including a small MCA yield also correct AIF measurements for segmented echo planar imaging when a short echo time was used.

Nonlinear DeltaR*2 effects in perfusion quantification using bolus-tracking MRI

Dynamic susceptibility contrast MRI involves injection of a contrast agent, whose concentration is estimated from DeltaR*2 changes. However, measurement of contrast-agent concentration is prone to various sources of error; in particular, the commonly assumed linear relationship between contrast agent concentration and DeltaR*2 in arterial blood is known to be invalid. In this study, we characterized the associated perfusion errors. Large errors were found when the linear assumption is used; these errors were highly dependent on the choice of tissue relaxivity. The errors were greatly reduced when using the quadratic model, and were further reduced when quantifying perfusion as a relative measure. This study suggests the linear assumption should be abandoned in favor of the quadratic model. Thus, the errors are minimized leading to improved quantification that will enable perfusion MRI to continue to play an important role in quantifying perfusion in brain diseases (e.g., acute stroke).

Quantitative mapping of hepatic perfusion index using MR imaging: a potential reproducible tool for assessing tumour response to treatment with the antiangiogenic compound BIBF 1120, a potent triple angiokinase inhibitor

Hepatic metastases are arterially supplied, resulting in an elevated hepatic perfusion index (HPI). The purpose of this study was to use dynamic contrast-enhanced (DCE) MR imaging to quantify the HPI of metastases and the liver before and after treatment with a novel antiangiogenic drug. Ten patients with known metastatic liver disease underwent DCE-MR studies. HPIs of metastases and whole liver were derived using regions of interest (ROIs) and calculated on a pixel-by-pixel basis from quantified changes in gadopentetate dimeglumine (Gd-DTPA) concentration. The HPI measurement error prior to treatment was derived by the Bland-Altman analysis. The median HPI before and after treatment with antiangiogenic drug BIBF 1120 were compared using the Wilcoxon signed rank test. Prior to treatment, the median HPI of metastases, 0.75 +/- 0.14, was significantly higher than that of the whole liver, 0.66 +/- 0.16 (p < 0.01). Bland-Altman reproducibility coefficients of the median HPI from metastases and whole liver were 13.0 and 5.1% respectively. The median HPI of metastases decreased significantly at 28 days after treatment with BIBF 1120 (p < 0.05). This pilot study demonstrates that HPI determined using quantified Gd-DTPA concentration is reproducible and may be useful for monitoring antiangiogenic treatment response of hepatic metastases.

Contrast agent concentration measurements affecting quantification of bolus-tracking perfusion MRI

Measurement of the concentration of the contrast agent using dynamic susceptibility contrast MRI relies on field inhomogeneities caused by the presence of the paramagnetic agent. The usual method for calculation of the concentration from dynamic T2*-weighted images is based on two key assumptions: 1) a linear relation between the change in R2* and the contrast agent concentration, and 2) a negligible effect on the MR signal due to concurrent T1 changes. In this study the effect of inaccuracies in these two assumptions on perfusion measurements was investigated using simulations and in vivo data. The results of the simulations provide a quantitative characterization of the magnitude of these effects for various experimental conditions (e.g., when a 1-sec TR is used with TE=20 ms, the T1 effects can introduce up to 40% cerebral blood flow underestimation depending on the flip angle). These findings can be used as a guide to estimate the errors in specific practical implementations, as well as to optimize the sequence parameters to minimize their effect. In summary, this study shows that the arterial input function measurement should be corrected for nonlinear R2* effects and that care should be taken in the study design to avoid introducing significant T1 effects in perfusion quantification.

Quantitative basal CBF and CBF fMRI of rhesus monkeys using three-coil continuous arterial spin labeling

A three-coil continuous arterial-spin-labeling technique with a separate neck labeling coil was implemented on a Siemens 3T Trio for quantitative cerebral blood flow (CBF) and CBF fMRI measurements in non-human primates (rhesus monkeys). The optimal labeling power was 2 W, labeling efficiency was 92+/-2%, and optimal post-labeling delay was 0.8 s. Gray matter (GM) and white matter (WM) were segmented based on T1 maps. Quantitative CBF were obtained in 3 min with 1.5-mm isotropic resolution. Whole-brain average DeltaS/S was 1.0-1.5%. GM CBF was 104+/-3 ml/100 g/min (n = 6, SD) and WM CBF was 45+/-6 ml/100 g/min in isoflurane-anesthetized rhesus monkeys, with the CBF GM/WM ratio of 2.3+/-0.2. Combined CBF and BOLD (blood-oxygenation-level-dependent) fMRI associated with hypercapnia and hyperoxia were made with 8-s temporal resolution. CBF fMRI responses to 5% CO2 were 59+/-10% (GM) and 37+/-4% (WM); BOLD fMRI responses were 2.0+/-0.4% (GM) and 1.2+/-0.4% (WM). CBF fMRI responses to 100% O2 were -9.4+/-2% (GM) and -3.9+/-2.6% (WM); BOLD responses were 2.4+/-0.7% (GM) and 0.8+/-0.2% (WM). The use of a separate neck coil for spin labeling significantly increased CBF signal-to-noise ratio and the use of small receive-only surface coil significantly increased signal-to-noise ratio and spatial resolution. This study sets the stage for quantitative perfusion imaging and CBF fMRI for neurological diseases in anesthetized and awake monkeys.