mrpro.operators.models.WASABITI

class mrpro.operators.models.WASABITI[source]

Bases: SignalModel[Tensor, Tensor, Tensor]

WASABITI signal model.

__init__(offsets: Tensor | float | Sequence[float], recovery_time: Tensor | float | Sequence[float], rf_duration: float | Tensor = 0.005, b1_nominal: float | Tensor = 3.75e-6, gamma: float = GYROMAGNETIC_RATIO_PROTON) → None[source]

Initialize WASABITI signal model for mapping of B0, B1 and T1 [SCH2023].

Parameters:

  • offsets (Tensor | float | Sequence[float]) – frequency offsets [Hz] with shape (offsets, ...)

  • recovery_time (Tensor | float | Sequence[float]) – recovery time between offsets [s] with shape (offsets, ...)

  • rf_duration (float | Tensor, default: 0.005) – RF pulse duration [s]

  • b1_nominal (float | Tensor, default: 3.75e-6) – nominal B1 amplitude [T]

  • gamma (float, default: GYROMAGNETIC_RATIO_PROTON) – gyromagnetic ratio [Hz/T]

References

[SCH2023]

Schuenke P, Zimmermann F, Kaspar K, Zaiss M, Kolbitsch C (2023) An Analytic Solution for the Modified WASABI Method: Application to Simultaneous B0, B1 and T1 Mapping and Correction of CEST MRI, Proceedings of the Annual Meeting of ISMRM

__call__(b0_shift: Tensor, relative_b1: Tensor, t1: Tensor) → tuple[Tensor][source]

Apply the WASABITI (Water Shift and B1 and T1) signal model.

Parameters:

  • b0_shift (Tensor) – B0 field inhomogeneity or off-resonance shift in Hz. Shape (...), for example (*other, coils, z, y, x) or (samples).

  • relative_b1 (Tensor) – Relative B1 amplitude scaling factor (actual B1 / nominal B1). Shape (...), for example (*other, coils, z, y, x) or (samples).

  • t1 (Tensor) – Longitudinal (T1) relaxation time in seconds. Shape (...), for example (*other, coils, z, y, x) or (samples).

Returns:

Signal calculated for each frequency offset and recovery time. Shape (offsets ...), for example (offsets, *other, coils, z, y, x), or (offsets, samples) where offsets is the number of frequency offsets.

forward(b0_shift: Tensor, relative_b1: Tensor, t1: Tensor) → tuple[Tensor][source]

Apply forward of WASABITI.

Note

Prefer calling the instance of the WASABITI as operator(x) over directly calling this method. See this PyTorch discussion.

__add__(other: Operator[Unpack[Tin], Tout]) → Operator[Unpack[Tin], Tout][source]
__add__(other: Tensor | complex) → Operator[Unpack[Tin], tuple[Unpack[Tin]]]

Operator addition.

Returns lambda x: self(x) + other(x) if other is a operator, lambda x: self(x) + other*x if other is a tensor

__matmul__(other: Operator[Unpack[Tin2], tuple[Unpack[Tin]]] | Operator[Unpack[Tin2], tuple[Tensor, ...]]) → Operator[Unpack[Tin2], Tout][source]

Operator composition.

Returns lambda x: self(other(x))

__mul__(other: Tensor | complex) → Operator[Unpack[Tin], Tout][source]

Operator multiplication with tensor.

Returns lambda x: self(x*other)

__radd__(other: Tensor | complex) → Operator[Unpack[Tin], tuple[Unpack[Tin]]][source]

Operator right addition.

Returns lambda x: other*x + self(x)

__rmul__(other: Tensor | complex) → Operator[Unpack[Tin], Tout][source]

Operator multiplication with tensor.

Returns lambda x: other*self(x)