Current status in spatiotemporal analysis of contrast‐based perfusion MRI

Abstract

In perfusion MRI, image voxels form a spatially organized network of systems, all exchanging indicator with their immediate neighbors. Yet the current paradigm for perfusion MRI analysis treats all voxels or regions‐of‐interest as isolated systems supplied by a single global source. This simplification not only leads to long‐recognized systematic errors but also fails to leverage the embedded spatial structure within the data. Since the early 2000s, a variety of models and implementations have been proposed to analyze systems with between‐voxel interactions. In general, this leads to large and connected numerical inverse problems that are intractible with conventional computational methods. With recent advances in machine learning, however, these approaches are becoming practically feasible, opening up the way for a paradigm shift in the approach to perfusion MRI. This paper seeks to review the work in spatiotemporal modelling of perfusion MRI using a coherent, harmonized nomenclature and notation, with clear physical definitions and assumptions. The aim is to introduce clarity in the state‐of‐the‐art of this promising new approach to perfusion MRI, and help to identify gaps of knowledge and priorities for future research.

Quantitative transport mapping of multi-delay arterial spin labeling MRI detects early blood perfusion alteration in Alzheimer’s disease

“…11,45 The problems with Kety flow include its violation of local mass conservation due to its use of global AIF and its use of only temporal information by ignoring the spatial transport tracer between neighboring voxels. 13 These simplifications of Kety’s model result in the loss of sensitivity of local and spatial change of blood flow, especially for patients with subtle pathophysiological changes like early stage AD.…”

“…12 This commonly known AIF problem of conventional perfusion modeling has gained attention and encouraged development of new approaches using spatiotemporal information for perfusion quantification. 13 To address this problem, we proposed to model changes in spatiotemporal tracer concentration according to the mass transport equation that utilizes spatial and temporal derivatives of the concentration without the selection of an AIF. 12 Blood flow velocity can be calculated fully automatedly from fitting four dimensional (4D) dynamic tracer imaging data to the transport equation, which is termed as quantitative transport mapping (QTM).…”

Quantitative transport mapping of multi-delay arterial spin labeling MRI detects early blood perfusion alterations in Alzheimer’s disease

“…Technically, Kety’s CBF uses a kinetic modeling with a global AIF as an input, and a lump of empirical parameters to evaluate blood perfusion in a voxelwise manner [ 12 , 49 ]. The problems with CBF include its violation of local mass conservation due to its use of global AIF and its use of only temporal information by ignoring the spatial transport tracer between neighboring voxels [ 14 ]. These simplifications of Kety’s model result in the loss of sensitivity of local and spatial changes of the blood perfusion, especially for patients with subtle pathophysiological changes like early-stage AD.…”

“…Since AIF at each voxel is not practically measurable, a single global AIF is assumed for blood perfusion to all brain regions and is known to have errors and violate the local mass conservation principle [ 13 ]. This commonly known AIF problem of conventional perfusion modeling has gained attention and encouraged the development of new approaches using spatiotemporal information for perfusion quantification [ 14 ]. To address this problem, we proposed to model changes in spatiotemporal tracer concentration according to the mass transport equation that utilizes spatial and temporal derivatives of the concentration without the selection of an AIF [ 13 ].…”

Identifiability of spatiotemporal tissue perfusion models

“…All the above implementations apply additional constraints on the reconstructed model parameters (Shalom et al 2023(Shalom et al , 2024a(Shalom et al , 2024b, for instance an assumption that diffusion is constant in space (Pellerin et al 2007), that the diffusion gradient between adjacent voxels is negligible (Fluckiger et al 2013), that parameter fields have small spatial gradients (Liu et al 2021, Zhou et al 2021, Zhang et al 2022, 2023, that transport is only radial in a lesion (Sinno et al 2021(Sinno et al , 2022, that perfusion is modeled by Darcy flow (Naevdal et al 2016), or that parameter fields are in a known relationship to each other (Naevdal et al 2016). Constraints of this type are included to reduce the computational complexity, but it is not always clear that they are physically justified, creating a risk of new biases.…”