In Diffusion MRI, q-space indices are scalar quantities thatdescribe properties of the ensemble average propagator(EAP). Their values are often linked to the axonal diameter– assuming that the diffusion signal originates from insidean ensemble of parallel cylinders. However, histologicalstudies show that these assumptions are incorrect, and axonaltissue is often dispersed with various tissue compositions.Direct interpretation of these q-space indices in terms of tissuechange is therefore impossible, and we must treat themas as scalars that only give non-specific contrast – just as DTIindices. In this work, we analyze the sensitivity of q-spaceindices to tissue structure changes by simulating axonal tissuewith changing axonal diameter, dispersion and tissue compositions.Using human connectome project data we thenpredict which indices are most sensitive to tissue changes inthe brain. We show that, in both multi-shell and single-shell(DTI) data, q-space indices have higher sensitivity to tissuechanges than DTI indices in large parts of the brain. Based onthese results, it may be interesting to revisit older DTI studiesusing q-space indices as a marker for pathology.
A sensitivity analysis of Q-space indices with respect to changes in axonal diameter, dispersion and tissue composition
Zucchelli, Mauro;MENEGAZ, Gloria
2016-01-01
Abstract
In Diffusion MRI, q-space indices are scalar quantities thatdescribe properties of the ensemble average propagator(EAP). Their values are often linked to the axonal diameter– assuming that the diffusion signal originates from insidean ensemble of parallel cylinders. However, histologicalstudies show that these assumptions are incorrect, and axonaltissue is often dispersed with various tissue compositions.Direct interpretation of these q-space indices in terms of tissuechange is therefore impossible, and we must treat themas as scalars that only give non-specific contrast – just as DTIindices. In this work, we analyze the sensitivity of q-spaceindices to tissue structure changes by simulating axonal tissuewith changing axonal diameter, dispersion and tissue compositions.Using human connectome project data we thenpredict which indices are most sensitive to tissue changes inthe brain. We show that, in both multi-shell and single-shell(DTI) data, q-space indices have higher sensitivity to tissuechanges than DTI indices in large parts of the brain. Based onthese results, it may be interesting to revisit older DTI studiesusing q-space indices as a marker for pathology.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.