Diffusion & Perfusion Weighted Imaging
Samantha J Holdsworth1

1Department of Anatomy and Medical Imaging, School of Medical Sciences, The University of Auckland, New Zealand

Synopsis

This lecture is devoted to the basic technological aspects of diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI) approaches used in MRI, using neuroimaging applications as examples, and with the concepts explained with minimal use of equations. PWI approaches covered are: dynamic susceptibility contrast (DSC) perfusion, dynamic contrast enhanced (DCE) perfusion, Arterial Spin Labeling (ASL), and the Intravoxel Incoherent Motion (IVIM) method.

Background

Diffusion-weighted imaging (DWI) [1-6] is one of the most important contrast mechanisms in MRI. It has revolutionised the detection of pathologic conditions physiologic mechanisms sensitive for diffusion abnormality, such as in stroke, seizure, trauma, demyelination, tumors, infection, and others. More extended diffusion-weighted scans, such as Diffusion Tensor Imaging (DTI) [7] (including extra processing of DTI to yield fiber tractography images) allow clinicians to evaluate the degree and spatial distribution of anisotropic diffusion, providing further insights into the microscopic architecture of the brain, including the orientation of large white matter pathways. DTI has been used to examine areas of subtle neural degeneration and demyelination in diseases like multiple sclerosis, and is currently part of many clinical MRI protocols.

Perfusion-weighted imaging (PWI) is an MRI technology that studies cerebral hemodynamics and blood flow [8-9]. PWI provides information on hypoperfused areas of brain tissue. It is used in the evaluation of focal brain lesions, primary tumors, in the differentiation of tumor recurrence and radionecrosis, and others. PWI is often combined with other MR techniques like magnetic resonance angiography (MRA) to assess vessel patency, and DWI to identify areas of reversible ischemia early before it progresses to permanent infarction [10-12].

Diffusion-weighted imaging

Diffusion-weighted imaging (DWI) uses the diffusion of water molecules to generate contrast in MR images. Due to the presence of cell membranes, macromolecules, fibers and other obstacles, the water molecules tend to be confined and hindered in their normal free diffusion. Water molecule diffusion patterns can therefore reveal microscopic details about tissue architecture, characterize different tissue types or pathological processes. DWI allows one to take ‘snapshots’ of this tissue water motion on a time scale of a few tens of milliseconds, using strong ‘diffusion encoding’ magnetic field gradients. On a DWI image, the grayscale pixel value is dependent on the underlying diffusivity, where voxels with high diffusion appear hypointense (e.g. CSF) and voxels with low diffusion appear hyperintense (e.g. acute stroke, Fig 1).

A DWI imaging acquisition is typically achieved with the Stejskal and Tanner method, where the strength of the diffusion encoding is represented by the ‘b-value’. A typical brain DWI sequence comprises of the acquisition of one ‘b = 0’ or T2-weighted image, followed by 3 or more diffusion-weighted ‘source’ images acquired at a b-value of 1000 (Fig. 2). These individual source images are usually combined by into a single final set for diagnosis by taking the geometric mean across these images. The combined image is known by various names: diffusion-weighted images, isotropic images, or trace images.

On both the b = 0 and b = 1000 images, the contrast not only depends upon the spatially distributed diffusion coefficient of the acquired tissues, but also depends on the T2 (and sometimes T1) values. Because of this, the ‘apparent diffusion coefficient’ or ADC is often calculated (based off the -logarithm ratio of b1000/b0), which helps to differentiate T2 shine through (or T1) effects and other artifacts from real diffusion lesions (Fig. 1).

The more advanced form of DWI, diffusion tensor imaging (DTI) (Fig. 3), looks at the degree of anisotropy of water diffusion by describing diffusion as a tensor. By acquiring six or more directions, one can model the diffusion processes as a tensor, to compute diffusion anisotropy measures such as the fractional anisotropy (FA). This allows us to visualize and assess white matter fiber tracks in which diffusion of water follows a preferred direction. By color-coding the direction of these FA maps (‘color FA’), one is able to visualize direction information. With some extra processing to follow the direction of these white matter fiber tracts, fiber tractography maps can be created, which help to look at white matter integrity.

Perfusion-weighted imaging

Perfusion-weighted imaging (PWI) methods can be categorized into two types of methods: contrast imaging by injection of MR contrast, and endogenous (and non-invasive) methods. Contrast-based methods include dynamic susceptibility contrast (DSC) perfusion [8-9] and dynamic contrast enhanced (DCE) perfusion [13-17]. Non-contrast-based methods include Arterial Spin Labeling (ASL) [18-22], and the (less commonly-used method) Intravoxel Incoherent Motion (IVIM) [23].

In DSC-MRI, a contrast agent (typically gadolinium) is injected intravenously, and a time series of fast T2*-weighted (or spin-echo) images is acquired during the first pass through the cerebral circulation. As gadolinium passes through the tissues, it produces a reduction of T2* intensity depending on the local concentration (Fig. 4). Hemodynamic maps of cerebral blood flow (CBF), cerebral blood volume (CBV) and mean transit time (MTT) or time to bolus peak (TTP) can be created by mathematical analysis of the evolution of the intensity of the signal. Both DSC-PWI and DWI are rapidly have become integral parts of the diagnostic workup in the acute stroke setting (Fig. 5).

In DCE-MRI, the contrast agent is tracked with a T1-weighted gradient echo method (Fig. 6). Since gadolinium reduces the local relaxation T1 relaxation time, such a T1-weighted image yields signal enhancement. Unlike DSC which generally assumes that the gadolinium stays in the vascular bed, DCE assumes that the contrast agent goes into tissue. This is why DCE is widely referred to as ‘permeability’ MRI, in which the signal-time-curve is thought to reflect a composite of tissue perfusion, vessel permeability, and extravascular-extracellular space. As such, by plotting the relative signal enhancement as it leaks out into the tissue versus time, one can extract qualitative measures such as wash-in, maximum enhancement, bolus arrival time, time to peak, uptake rate, and washout contrast kinetics of the tissue, thereby providing insight into the nature of the bulk tissue properties at the microvascular level (Fig. 6).

To get quantitative measurements in DCE-MRI, the easiest and most well-known model is the Tofts two-compartment (plasma space and extravascular-extracellular space) model [24]. For this approach, the signal intensity data is converted to gadolinium concentration, then the vascular input function is determined, after which pharmacokinetic modeling is performed. This yields the following metrics: the transfer constant (ktrans), a factor related to vessel permeability and tissue blood flow (the most-used constant); the fractional volume of the extravascular-extracellular space (ve), a marker of cell density; the rate constant (kep), where kep = ktrans/ve), which describes the diffusion of the contrast agent back to the plasma; and the fractional volume of the plasma space (vp).

Arterial spin labeling (ASL) uses magnetically labeled arterial blood water protons as an endogenous diffusible tracer to measure tissue blood flow. Here, flowing spins in the carotid arteries are inverted by a radiofrequency pulse. The influx of fresh, labeled protons in the tissue of interest slightly alters the magnetization and, depending on the exchange with tissue protons (therefore the T1 relaxation time of the tissue), renders this method sensitive to the local degree of microperfusion. Hence, any change in regional blood flow will be picked up by ASL via image contrast changes. The four types of ASL preparation components are: pulsed ASL (PASL), continuous ASL (CASL), pseudo-continuous ASL (pCASL) and velocity-selective ASL (VS-ASL) (Fig. 7). The primary difference among these ASL categories is the technique that magnetically tags the inflowing blood. In the past, despite being non-invasive, ASL methods have seen limited adoption clinically due to their complexity, susceptibility to motion, among other problems. However due to recent technical advances, ASL is increasingly becoming of increasing clinical interest, particularly to investigate perfusion abnormalities in cerebral stroke and patients with chronic vascular diseases (Fig. 8).

It is also possible to get MTT maps by acquiring multiple ‘post label delay’ images. These multi-delay ASL approaches can be used for any of the above ASL approaches. Multi-delay ASL is useful in cerebrovascular cases such as Moya Moya disease, in which patients have prolonged for heterogeneous blood transit times (Fig. 9). Note that these transit times can also be helpful for correcting for these transit time effects in the CBF maps.

Intravoxel incoherent motion (IVIM) imaging is a method used to assess all the microscopic motions that could contribute to the signal acquired with diffusion MRI. These motions are molecular diffusion of water and microcirculation of blood in the capillaries (i.e. perfusion, or in other words, a pseudo-diffusion process from movement of blood in the microvasculature). The IVIM model therefore infers that diffusion images may have perfusion effects (i.e. not just molecular diffusion of water in the tissue, or ‘true’ diffusion). As a consequence, this infers that the ADC could be overestimated in conventional DWI models. It also means that, using a more comprehensive DWI dataset with multiple b-values, several additional maps can be calculated, such as the ‘true’ diffusion coefficient (D), the perfusion fraction (f), the pseudo-diffusion coefficient (D*), and the ‘IVIM blood flow’ (fD*) (Figs. 10-11).

Acknowledgements

No acknowledgement found.

References

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Figures

Fig. 1 - Output from a typical brain DWI sequence.

Fig. 2 - The DWI (a.k.a. isotropic DWI, or trace image) is produced by combining at least 3 source images sensitized to diffusion along various directions. The source images may be along the laboratory x-, y-, and z-axes or in three arbitrary perpendicular orientations. However, sometimes source images are acquired in 4 (‘tetrahedral encoding’) or more directions, which boosts SNR in the final DWI. With 6 or more directions, one can also produce “diffusion tensor” images (DTI) which is used to map neural tract directional information.

Fig. 3 - State-of-the-art diffusion tensor imaging (DTI) of the human brain, acquired on a 3T GE MR system, depicts exquisite white matter structure. Data were acquired with 15 noncollinear directions with a b-value = 1000 s/mm2, 35-min total scan time.

Fig. 4 - (top) DSC-MRI. Time course of the T2*-weighted MR images during contrast material bolus passage. Due to the high concentration of contrast material the signal intensity decreases significantly during the peak of the bolus. (bottom). Plot of the signal from a T2*-weighted sequence after injection of a contrast bolus as it travels through a region of interest in the brain. In (b), the signal is converted to concentration versus time.

Fig. 5 - DWI is used to identify severely ischemic brain regions within minutes to hours after stroke onset, while PWI provides information on the hemodynamic status of the tissue and can detect impaired perfusion in both the ischemic core and the surrounding brain regions. This acute stroke patient has a clear DWI-PWI mismatch pattern in the right MCA territory. (top row) Diffusion-weighted images (b = 1000 s/mm2). The area of perfusion deficit is clearly apparent in the Tmax and MTT images. Although present, the ischemic area is less apparent on the CBF and CBV maps.

Fig. 6 - Qualitative parameters that are calculated in dynamic-contrast enhanced (DCE) perfusion MRI.

Fig. 7 - The four different ASL tagging methods used to measure CBF. Green is the imaging region and grey is the labelling region. PASL inverts spins proximal to the imaging region, while CASL and pCASL labels a narrow plane of spins continuously. VS-ASL tags spins moving at a certain velocity.

Fig. 8 - 3-year old boy with acute right MCA infarction. The clot can be seen on the T2*-weighted image (2D GRE, arrow). There is a right MCA cut off on MRA and associated decreased ASL perfusion deficit. Courtesy Kristen Yeom, Stanford University.

Fig. 9 - In ASL, one can acquire multiple pulse label delay (‘multi-delay’) images to create transit time (MTT) maps. This approach may be helpful for patients with cerebrovascular disease, such as in Moya Moya. Note that the transit time maps here quite closely matches that of the MTT calculated from the more invasive perfusion method DSC-PWI (which requires the injection of an IV contrast agent).

Fig. 10 - IVIM imaging is typically performed with about 15 different b-values. The signal from each DWI image is plotted (for each pixel) on a curve. Even curve is then fit with a bi-exponential model, where D is the true ADC, f is the unit of perfusion volume fraction, D* is the pseudo-diffusion coefficient. Here: f is thought to be related to the CBV on DSC perfusion, D* thought to be related to the MTT, and fD* to the CBF.

Fig. 11 - IVIM parametric maps of this meningioma patient closely resemble the counterpart images acquired with DSC-PWI. Courtesy of Christian Federau (ETH and University of Zürich, University hospital Basel and University of Basel).

Proc. Intl. Soc. Mag. Reson. Med. 26 (2018)