Accelerated CMR using zonal, parallel and prior knowledge driven imaging methods

Table 1

Parallel Imaging	Prior Knowledge Driven Imaging	Hybrids	Prior Knowledge	CMR Applications	R_nom/R_net	References
SENSE			-	cine, rt, dce, lge, qflow, cmra	1.5–8.0	[19,55,64,66-68,73,78,82-84]
SMASH			-	cine, cmra	2.0	[16,85-87]
GRAPPA			-	cine, cmra	2.0/1.6	[15,53,88]
	UNFOLD		constant x-f support	cine, rt, dce	2.0	[21,89-92]
	k-t BLAST		Measured x-f support	cine, rt, dce, qflow	5.0–8.0/4.0–5.7	[22,39,40,42,51,57,69,70,93]
	CS		sparse x-f support	cine, qflow	2.0–7.0	[23,24,94]
	TCR		temporal constraint	dce	4.0–5.0	[37]
		TSENSE	temporal correlation¹⁾	cine, rt, dce, dense	2.0–7.0	[34,52,65,75,91,95-98]
		TGRAPPA	temporal correlation²⁾	cine	2.0–4.0	[99]
		k-t GRAPPA	temporal correlation³⁾	cine, cmra	3.0–7.0/2.0–5.2	[38,100 ]
		k-t SENSE	Measured x-f support	cine, rt, dce, qflow	5.0–8.0/4.0–5.7	[22,39,41,42]
		FOCUSS	measured, sparse x-f support	cine	4.0–16.0	[24]
		UNFOLD-SENSE	constant x-f support	cine, dce	2.0–3.0/1.8–2.7	[35]

¹⁾coil sensitivity weights from temporal average of data sampled on a sheared grid (cmp. DC signal in x-f support)
²⁾auto-calibration signals (acs) from temporal average of data sampled on a sheared grid
³⁾with or without acs lines from temporal average of data sampled on a sheared grid
x: spatial position, f: temporal frequency
cine: cardiac-gated cine imaging; rt: non-gated real-time imaging; dce: dynamic contrast-enhanced imaging; lge: late gadolinium enhancement; qflow: quantitative flow imaging; cmra: coronary magnetic resonance angiography; dense: displacement-encoded imaging
R_nom: nominal reduction factor; R_net: net reduction factor taking into account calibration or training data (only reduction factors used in 2D CMR applications are reported here; in case net reduction factors are not available from literature a matrix of 200 in phase-encode direction and 11 auto-calibration or training profiles acquired interleaved are assumed).

ISSN: 1532-429X