Designing sparse sensing matrix for compressive sensing to reconstruct high resolution medical images

Figures & data

Figure 1. Flowchart of proposed method to generate sensing matrix.

Table 1. Analysis of $\mathrm{\Phi }$.

Type of $\mathrm{\Phi }$	Mutual coherence	${\mathit{\mu }}_{av}$	Max eigenvalue
Bin_Phi	0.5610	0.0989	26.33
SBCM	0.4889	0.0588	4.45
SBCM_Mod (8)	0.7561	0.0932	20.08
SBCM_Mod (16)	0.7385	0.0756	10.49
SBCM_Mod (32)	0.7024	0.0638	5.50
SBCM_Mod (64)	0.6820	0.0470	3.56
SBCM_Mod (128)	0.7246	0.0105	2.08

Figure 2. Distribution of elements in Gram matrix G for various sensing matrix.

Table 2. Recovered US image quality parameters for different sensing matrix $\mathrm{\Phi }$.

Type of $\mathrm{\Phi }$	Time	MSE	SSIM	PSNR	NAE	NKR
SBCM_Mod	1.21	0.0206	0.913	32.86	0.13	0.9737
SBCM	1.44	0.5953	0.663	23.46	0.58	0.5158
Bin_Phi(BSBL_BO)	26.73	0.0481	0.703	35.57	0.24	0.9448
Bin_Phi(SL0)	7.97	0.0687	0.685	35.17	0.26	0.9176

Figure 3. US original and recovered images.

Table 3. Recovered MRI image quality parameters for different sensing matrix $\mathrm{\Phi }$.

Type of $\mathrm{\Phi }$	Time	MSE	SSIM	PSNR	NAE	NKR
SBCM_Mod	1.23	0.0035	0.8447	37.44	0.05	0.9948
SBCM	1.67	0.6006	0.4282	25.10	0.57	0.4910
Bin_Phi(BSBL_BO)	28.75	0.0077	0.6830	36.43	0.09	0.9901
Bin_Phi(SL0)	8.07	0.0138	0.6079	34.47	0.11	0.9905

Table 4. Recovered CT image quality parameters for different sensing matrix $\mathrm{\Phi }$.

Type of $\mathrm{\Phi }$	Time	MSE	SSIM	PSNR	NAE	NKR
SBCM_Mod	1.25	0.0079	0.7556	32.93	0.05	0.9921
SBCM	1.66	0.6399	0.3241	22.45	0.55	0.5199
Bin_Phi(BSBL_BO)	19.25	0.0157	0.4314	29.52	0.11	0.9814
Bin_Phi(SL0)	7.60	0.0252	0.3670	28.21	0.13	0.9828

Figure 4. MRI-ankle original and recovered images of size $256\times 256$.

Figure 5. CT-abdomen original and recovered images of size $256\times 256$.

Figure 6. US, CT, MRI image quality measure for various sensing matrix.