bartorch.nlop.MultiEcho

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bartorch.nlop.MultiEcho#

bartorch.nlop.MultiEcho(TE, shape=(), *, bounds=None, unknown=('T2',), amplitude=True, subspace=None, **scale)#

T2 or T2* from a multi-echo readout: moba -T and moba -G’s family.

The transverse decay read at a series of echo times. Which relaxation is being measured is a property of the sequence that produced the data, not of the model: a spin-echo train measures T2 and a gradient-echo train T2*, and the exponential is the same either way – which is exactly why moba has two flags for one model.

Parameters:

  • TE (sequence of float) – Echo times, in milliseconds.

  • shape (tuple of int) – The voxel shape, C order.

  • bounds (dict, optional) – {name: (low, high)}; T2 defaults to (1, 1000) ms.

  • unknown (sequence of str) – What to solve for. offset is the other thing the model exposes.

  • amplitude (bool) – Carry a complex amplitude multiplying the decay.

  • subspace (torchsim.Subspace, optional) – Solve in a temporal basis rather than in the contrasts.

  • **scale – The size of a step in a parameter left unbounded.

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