TABLE 1.

Summary of recent representative studies on deep-learning-based rapid MR relaxometry

Reference	Method	Relaxation type	Network architecture	Image sequence	Training data	Testing data	Key results
Cai et al156	Deep OLED T2	T2 mapping	ResNet	Single-shot OLED planar imaging	Simulated data	Simulated phantom; in vivo brain	Reliable T2 mapping with higher accuracy and faster reconstruction than standard reconstruction method
Li et al158	MSCNN	T1ρ and T2 mapping	CNN	Magnetization-prepared angle-modulated partitioned k-space spoiled gradient echo snapshots T1ρ and T2 quantification	In vivo knee	In vivo knee	Up to 10-fold acceleration for simultaneous T1ρ and T2 maps with quantification results comparable to reference maps
Cohen et al164	MRF-DRONE	T1 and T2 mapping	FCN	Modified gradient-echo EPI MRF pulse sequence; fast imaging with steady-state precession MRF pulse sequence	Simulated data	Simulated phantom; phantom; in vivo brain	Accurate, 300 to 5000 times faster, and more robust to noise and dictionary undersampling than conventional MRF dictionary matching
Fang et al165	SCQ network	T1 and T2 mapping	FCN + U-Net	Fast imaging with steady-state precession MRF pulse sequence	In vivo brain	In vivo brain	Accurate quantification for T1 and T2 by using only 25% of time points of the original sequence
Liu et al169	MANTIS	T2 mapping	U-Net	Multi-echo spin-echo T2 mapping	In vivo knee	In vivo knee	Accurate and reliable quantification for T2 at up to eightfold acceleration, robust against k-space trajectory undersampling variation
Liu et al170	MANTIS-GAN	T2 mapping	GAN (generator, U-Net; discriminator, PatchGAN)	Multi-echo spin-echo T2 mapping	Simulated data	Simulated data	Up to eightfold acceleration for T2 mapping with accuracy and high image sharpness and texture preservation compared with the reference
Zha et al171	Relax-MANTIS	T1 mapping	U-Net	Variable flip angle spoiled gradient echo T1 mapping	In vivo lung	In vivo lung	Physics model regularized and self-supervised T1 mapping at reduced image acquisition time, robust against noise
Zibetti et al175	VN T1ρ	T1ρ mapping	VN	Modified 3D Cartesian turbo-FLASH sequence	In vivo knee	In vivo knee	Better T1ρ quantification using deep learning image reconstruction than compressed sensing
Jeelani et al177	DeepT1	T1 mapping	RCNN + U-Net	Modified Cartesian Look-Locker imaging	In vivo cardiac	In vivo cardiac	Noise-robust estimates compared with the traditional pixel-wise T1 parameter fitting at fivefold acceleration
Chaudhari et al178	MRSR	T2 mapping	CNN	DESS sequence	In vivo knee	In vivo knee	Minimally biased T2 from robust super-resolution in thin slice compared with the reference

Abbreviations:

DeepT₁, deep learning for T₁ mapping; MSCNN, model skipped convolutional neural network; Relax-MANTIS, reference-free latent map extraction MANTIS.