Advanced Perfusion Settings (Advanced Options)

The Analysis Tab

Raw dynamic curve analysis:
When no AIF deconvolution is applied, both CBV and CBF are only determined in a relative sense based on the properties of the first-pass tissue response curve. From this curve, the perfusion parameters can be estimated in different ways. The relative CBV is always estimated from the area under the first-pass curve (AUC), but relative perfusion and mean transit time can be estimated in two different ways.

• MTT from AUC/peak: The most common approximation is to use the 'height-area' relationship to estimate MTT: With this approach, the relative blood flow is simply estimated as the peak height of the first - pass curve (Cpeak). Relative MTT is then determined as rMTT =  AUC/Cpeak.
• MTT from first moment: With this approach, MTT is estimated from the normalized first moment of the first-pass curve: rMTT = fmAUC/AUC and rBF=AUC/rMTT. Where fmAUC is the first moment of the area under the first-pass curve.

Note that, in the absence of AIF deconvolution, the resulting ‘blood flow’ map is only a very crude approximation, since the dispersion of the tissue response due to a finite duration of the AIF is not accounted for.

The Segmentation Tab

Tissue segmentation is used for various purposes as part of advanced perfusion analysis. All segmentation described here are based on segmentation of the raw perfusion data using different properties of the dynamic signal response or baseline signal intensity to group tissues into different clusters based on predefined properties.

Vessel segmentation

Number of clusters used for the analysis can be specified. You can also choose to remove areas with high MTT values and/or high baseline signal before the clustering analysis is done.

• High MTT: Exclude high MTT pixels from vessel segmentation. This option will only have an effect if the highly vascularized tissue has elevated MTT values compared to vessels.
• High baseline SI: Exclude tissue with high relative baseline signal intensity. This option will only have effect if the signal intensity of the highly vascularized tissue has high SI relative to vessels. This is typically the case if the baseline images are T2/T2* weighted.

Brain mask

Select which area you would like to use as the brain mask; white matter, grey matter or both. This choice will be reflected in the Brain Mask output map selected in Perfusion Settings.

Normalization

Choose reference tissue, either white matter, grey matter or both. The resulting mask is used as normal tissue for the normalization of CBV and CBF values to normal tissue. If ‘remove non-brain from ref mask’ is checked, then bright regions (assumed to represent pathology) in the raw baseline data (before contrast agent arrival) is assumed to represent pathology and will be clustered out. Note that this option is only applied to T2/T2*-weighted DSC sequences, which are assumed to be sufficiently T2-weighted to render pathology hyperintense relative to normal tissue. If no pathology is present, then this option may result in undesirable effects by removing bright regions of normal brain tissue, and the option should therefore be used with caution.

The CA Leakage Correction Tab

The underlying kinetic model used in the perfusion module assumes that the contrast agent is contained in the intravascular space for the duration of the dynamic acquisition. If the blood brain barrier is severely compromised due to pathology this assumption may no longer be valid. When <Apply apply contrast agent leakage correction> is checked in the main menu, contrast agent leakage from the intravascular to extracellular space is corrected for using one of the two methods available in the Advanced Perfusion Settings menu. Note that the leakage correction algorithms uses both WM and GM as reference mask, regardless of the brain mask / normalization settings.

Two methods for leakage correction exist:

• The Weisskoff method (Weisskoff et al, 1994). In short, this method corrects for extravasation by comparing the dynamic response of each voxel to a reference curve derived from the mean of all ‘normal appearing’ voxels (see ‘normalization’ and ‘brain mask’ above). The deviation in shape of a given dynamic curve relative to the reference curve can then be used to derive relative extravasation, using a simplified kinetic model. The original method as proposed by Weisskoff only account for ‘negative’ (T1-dominant) leakage effects (resulting in under-estimation of CBV in T2/T2*-weighted DSC). In nordicICE, both T1- and T2/T2*-dominant leakage effects can be corrected for, as described below.
• Residue function based leakage correction This method was developed to overcome the MTT-sensitivity of the Weisskoff method (see in Bjornerud et al. J Cereb Blood Flow Metab. 2011 Oct;31(10):2041-53 and Emblem et al. J Cereb Blood Flow Metab. 2011 Oct;31(10):2054-64). In short, the method performs AIF deconvolution and uses the shape of the resulting pixel-wise residue function to directly correct for extravasation. The method works regardless of AIF definition and if no AIF is specifically defined, a global AIF will be automatically estimated. When residue-based deconvolution is selected, two additional options are available:
• Parametric residue fit: when selected, a parametric function (based on the kinetic model used) is fitted to the pixel-wise residue function, which may provide a better estimate of the leakage properties in some cases.
• Global residue correction: depending on the AIF, a systematic global ‘leakage effect’ may be artificially present in the residue function even in the absence of extravasation. When this option is selected, this global effect is estimated and removed from the pixel-wise effect. This option cannot be combined with parametric fit (above).

When leakage correction is enabled an additional output image can be generated ('Leakage map' or K2-map). This is a 'pseudo Ktrans' where the pixel intensity is proportional to the rate of contrast leakage from the intra- to the extravascular space. 

Detect both positive (T2) and negative (T1) K2 values: When set, the leakage correction includes leakage causing both T1 and T2 effects and hence does not depend on the 'tail' of the dynamic curve being negative in leaky pixels.

K2 cutoff: Leakage values (in terms of the leakage constant K2) with an absolute value below the specified limit will be set to zero. 

The AIF and deconvolution Tab

AIF deconvolution analysis

When AIF (arterial input function) deconvolution is applied the cerebral blood volume can be estimated in two different ways as specified below: 

· rBV from normalized AUC:  The blood volume is estimated by the normalized area under the dynamic curve; that is the area under the tissue response curve divided by the area under the AIF. This is the most common method for estimating the BV when the AIF is known. 

· rBV from Residue AUC: Selecting this method, the BV is estimated from the area under the residue function curve where the residue function is estimated by AIF deconvolution. This method of determining BV may be theoretically more correct but is also more sensitive to noise and to the choice of deconvolution method. 

· Iterative Tikhonov regularization

o Here you specify the number of iterations to run when the optimal singular value filter factor is determined iteratively for Tikhonov regularization.  The iterative procedure searches for the 'optimal' trade-off between a 'correct' solution and an oscillating solution. For details of the method, see Hansen HC (SIAM Journal on Scientific Computing 1993;14(6): 1487 – 1503). 

· No. of iterations:  This is the total number of iterations used to determine the optimal regularization value. Note that if a large number of iterations are used, the deconvolution analysis is significantly slower than when a fixed threshold in used. 

Auto-AIF detection

Specify the number of iterations when the AIF is found automatically.

AIF K-means Cluster Parameters

Here, you can specify the criteria to be used when performing an automatic search for AIF. Changes to these settings should be done with caution, but some optimization may be required for cases where default settings fail to provide good estimation.

Population based AIF

Two different shaped population-based AIFs are included. When 'Population based AIF' is selected in the main menu of the perfusion module, the application will use the AIF specified here. A ‘population based AIF’ as used here is a standard AIF derived from a multi-compartment kinetic model of the expected shape of a contrast bolus in the brain following a rapid injection in a ‘standard’ person. The kinetic model used to obtain the standard AIF is described in van Osch MJ et al. Magn Reson Med. 2003 Sep; 50(3): 614-22. The two different pre-defined AIFs differ in sharpness and maximum amplitude.  

Additionally, you can define your own AIF as a binary file and use this in the analysis. If specifying your own AIF from a binary file, the number of entries in the file must match the number of dynamic time-points in the raw data. You also need to specify the time-resolution of the supplied AIF. If different from that of the dynamic data, the provided AIF will be re-sampled to match the resolution of the imaging data. The amplitude values of the AIF entries are assumed to reflect the concentration of the contrast agent at each time-point (in units of mM).

For all predefined AIFs as defined above, the relaxivity in blood (in units of mM-1s-1) of the contrast agent used need to be specified since the AIFs here are represented in units of contrast agent concentration (mM) and need to be converted in the relaxation rates (1/R1 or 1/R2,R2*, depending on acquisition type). Default relaxivity values are provided based on ‘standard’ gadolinium chelates at 3.0 T. For T2/T2* based perfusion imaging, the actual in vivo blood relaxivity is more complex, and some studies suggest a quadratic dose-response. An option of model a quadratic dose-response (as described in vanOsch et al Magn Reson Med 49(6): 1067-1076).