mrpro.operators.models.WASABITI

class mrpro.operators.models.WASABITI(offsets: Tensor, trec: Tensor, tp: float | Tensor = 0.005, b1_nom: float | Tensor = 3.75, gamma: float | Tensor = 42.5764, freq: float | Tensor = 127.7292)

Bases: SignalModel[Tensor, Tensor, Tensor]

WASABITI signal model.

__init__(offsets: Tensor, trec: Tensor, tp: float | Tensor = 0.005, b1_nom: float | Tensor = 3.75, gamma: float | Tensor = 42.5764, freq: float | Tensor = 127.7292) → None

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

Parameters:

• offsets – frequency offsets [Hz] with shape (offsets, …)

• trec – recovery time between offsets [s] with shape (offsets, …)

• tp – RF pulse duration [s]

• b1_nom – nominal B1 amplitude [µT]

• gamma – gyromagnetic ratio [MHz/T]

• freq – larmor frequency [MHz]

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

forward(b0_shift: Tensor, rb1: Tensor, t1: Tensor) → tuple[Tensor]

Apply WASABITI signal model.

Parameters:

• b0_shift – B0 shift [Hz] with shape (… other, coils, z, y, x)

• rb1 – relative B1 amplitude with shape (… other, coils, z, y, x)

• t1 – longitudinal relaxation time T1 [s] with shape (… other, coils, z, y, x)

Return type:

signal with shape (offsets … other, coils, z, y, x)