Fast Parallel Imaging Reconstruction Method Based on SIDWT and Iterative Self-Consistency

doi:10.16183/j.cnki.jsjtu.2022.236

Abstract

Abstract:

To improve the reconstruction speed of parallel magnetic resonance imaging, an efficient reconstruction method named fSIDWT-SPIRiT is proposed based on shift-invariant discrete wavelets transform (SIDWT) and the iterative self-consistent parallel imaging reconstruction (SPIRiT) model. This method addresses the complex optimization problem containing data consistency term, calibration consistency term, and L1-norm regularization term. First, data consistency term and calibration consistency term are combined and processed, and then solved by a projected fast iterative shrinkage-thresholding algorithm to achieve fast parallel MRI reconstruction. Finally, simulation experiments are conducted using different human organ datasets. The results show that the proposed method is able to guarantee the image reconstruction quality with a faster convergence speed compared with other methods.

Key words: parallel magnetic resonance imaging, iterative self-consistent parallel imaging reconstruction (SPIRiT), image reconstruction, projected fast iterative shrinkage-thresholding algorithm (pFISTA), shift-invariant discrete wavelets transform (SIDWT)

CLC Number:

TP391

DUAN Jizhong, QIAN Qingqing. Fast Parallel Imaging Reconstruction Method Based on SIDWT and Iterative Self-Consistency[J]. Journal of Shanghai Jiao Tong University, 2023, 57(5): 582-592.

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References 24

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算法	指标	R=3	R=4	R=5	R=6	R=7
pFISTA-SPIRiT	V_SNR	29.15	27.82	26.86	25.90	25.39
	V_SSIM	0.9879	0.9843	0.9811	0.9775	0.9755
	V_HFEN	0.0574	0.0685	0.0788	0.0911	0.0980
SIDWT-SPIRiT	V_SNR	29.93	28.23	27.02	25.95	25.38
	V_SSIM	0.9902	0.9868	0.9840	0.9815	0.9800
	V_HFEN	0.0544	0.0668	0.0791	0.0913	0.0986
fSIDWT-SPIRiT	V_SNR	29.98	28.29	27.10	26.00	25.43
	V_SSIM	0.9901	0.9870	0.9842	0.9818	0.9804
	V_HFEN	0.0531	0.0658	0.0778	0.0908	0.0984

算法	指标	R=3	R=4	R=5	R=6	R=7
pFISTA-SPIRiT	V_SNR	27.74	26.26	25.34	24.45	23.79
	V_SSIM	0.9799	0.9735	0.9683	0.9635	0.9590
	V_HFEN	0.0721	0.0887	0.1035	0.1177	0.1321
SIDWT-SPIRiT	V_SNR	27.85	26.22	25.27	24.39	23.73
	V_SSIM	0.9879	0.9847	0.9826	0.9815	0.9799
	V_HFEN	0.0722	0.0900	0.1051	0.1192	0.1332
fSIDWT-SPIRiT	V_SNR	27.87	26.26	25.32	24.44	23.77
	V_SSIM	0.9881	0.9850	0.9829	0.9817	0.9805
	V_HFEN	0.0720	0.0893	0.1043	0.1184	0.1334

算法	指标	R=3	R=4	R=5	R=6	R=7
pFISTA-SPIRiT	V_SNR	22.87	21.78	20.94	20.03	19.11
	V_SSIM	0.9521	0.9411	0.9309	0.9199	0.9082
	V_HFEN	0.0644	0.0777	0.0901	0.1070	0.1253
SIDWT-SPIRiT	V_SNR	23.87	22.36	21.35	20.34	19.31
	V_SSIM	0.9517	0.9382	0.9288	0.9204	0.9073
	V_HFEN	0.0617	0.0772	0.0907	0.1076	0.1260
fSIDWT-SPIRiT	V_SNR	23.83	22.34	21.33	20.34	19.31
	V_SSIM	0.9507	0.9381	0.9278	0.9196	0.9079
	V_HFEN	0.0611	0.0767	0.0902	0.1064	0.1259

算法	指标	R=3	R=4	R=5	R=6	R=7
pFISTA-SPIRiT	V_SNR	21.10	19.76	18.93	18.29	17.66
	V_SSIM	0.9414	0.9224	0.9100	0.8963	0.8851
	V_HFEN	0.1951	0.2465	0.2801	0.3117	0.3480
SIDWT-SPIRiT	V_SNR	21.32	19.81	18.92	18.20	17.55
	V_SSIM	0.9615	0.9507	0.9434	0.9369	0.9314
	V_HFEN	0.1988	0.2538	0.2878	0.3230	0.3591
fSIDWT-SPIRiT	V_SNR	21.22	19.77	18.89	18.19	17.55
	V_SSIM	0.9609	0.9502	0.9432	0.9366	0.9315
	V_HFEN	0.2011	0.2531	0.2883	0.3219	0.3591

数据集	算法	R=3		R=4		R=5		R=6		R=7
数据集	算法	t	m	t	m	t	m	t	m	t	m
数据集1	pFISTA-SPIRiT	89	3.3	116	3.2	194	3.3	240	3.2	268	3.2
	SIDWT-SPIRiT	91	3.4	121	3.4	203	3.4	259	3.5	303	3.6
	fSIDWT-SPIRiT	27	1.0	36	1.0	59	1.0	74	1.0	85	1.0
数据集2	pFISTA-SPIRiT	148	4.4	216	3.8	281	3.3	347	3.4	426	3.6
	SIDWT-SPIRiT	128	3.8	198	3.5	274	3.3	313	3.1	390	3.3
	fSIDWT-SPIRiT	34	1.0	57	1.0	84	1.0	101	1.0	120	1.0
数据集3	pFISTA-SPIRiT	133	4.0	180	4.1	206	3.7	247	3.6	319	3.4
	SIDWT-SPIRiT	150	4.5	232	5.3	293	5.3	316	4.6	446	4.8
	fSIDWT-SPIRiT	33	1.0	44	1.0	55	1.0	68	1.0	93	1.0
数据集4	pFISTA-SPIRiT	237	3.9	294	3.5	358	3.3	431	3.3	506	3.2
	SIDWT-SPIRiT	259	4.2	343	4.0	431	4.0	515	3.9	625	3.9
	fSIDWT-SPIRiT	61	1.0	85	1.0	107	1.0	131	1.0	159	1.0