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. 2021 Nov 11:2021:3631208.
doi: 10.1155/2021/3631208. eCollection 2021.

OS-LALM-OGM Algorithm-Based Computed Tomography Image for Characteristics and Comorbidities of Patients before Transcatheter Aortic Valve Implantation

Wenjie Sun  1 , Jin He  2 , Xianzhen Tan  1 , Yi Wang  3 , Fei Chen  4 , Dan Xu  1 , An Xie  1
Affiliations

OS-LALM-OGM Algorithm-Based Computed Tomography Image for Characteristics and Comorbidities of Patients before Transcatheter Aortic Valve Implantation

Wenjie Sun et al. J Healthc Eng. .

Abstract

Based on the ordered subsets (OS), a linear augmentation Lagrangian method (OS-LALM) was constructed, which was then combined with the optimized gradient method (OGM) to construct the OS-LALM-OGM, so as to discuss application of the computed tomography (CT) images based on OS-LALM-OGM in evaluation of clinical manifestations and complications of patients before transcatheter aortic valve implantation (TAVI). The OS-LALM-OGM was compared with the filtered back projection (FBP) and OS-LALM. In addition, it was applied to evaluate the conditions of 128 patients before TAVI. It was found that the peak signal-to-noise ratio (PSNR) of OS-LALM-OGM was greater than that of the FBP and OS-LALM when the number of iterations was 5, 20, and 40, while the root mean square error (RMSD) was the opposite (P < 0.05). The proportion of dyspnea was the highest, 38.28%, followed by angina (19.53%) and fainting (21.09%). The long diameter of the annulus and the average inner diameter of the annulus measured by the CT image based on the OS-LALM-OGM algorithm were greatly larger than the inner diameter of the aortic annulus measured by the CT based on the FBP algorithm (P < 0.05); the evaluation sensitivity (95.24%) and specificity (85.85%) of CT based on the OS-LALM-OGM algorithm were obviously greater than those of X-ray, which were 84.43% and 76.77%, respectively (P < 0.05). In short, the OS-LALM-OGM proposed had a relatively excellent effect on CT image reconstruction. The CT image based on the OS-LALM-OGM algorithm showed a better evaluation performance for patients before TAVI than the traditional FBP algorithm, showing higher sensitivity and specificity.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Comparison on PSNR of three algorithms under different iteration times. ∗ indicates that the difference was remarkable in statistics in contrast to OS-LALM-OGM (P < 0.05).
Figure 2
Figure 2
Comparison on RMSD of three algorithms under different iteration times. ∗ indicates that the difference was remarkable in statistics in contrast to OS-LALM-OGM (P < 0.05).
Figure 3
Figure 3
The aortic valve and measurement results of various indicators of patients.
Figure 4
Figure 4
CT image and index measurement of patients.
Figure 5
Figure 5
The coronary artery calcification score of patients.
Figure 6
Figure 6
The angiographic position display (left coronary tangential position) of a patient.
Figure 7
Figure 7
The clinical manifestations of patients. ∗ suggests that the difference was obvious in statistics in contrast to dyspnea (P < 0.05).
Figure 8
Figure 8
The complications of patients (1: hypertension; 2: diabetes; 3: renal insufficiency; 4: cerebrovascular disease; 5: atrial flutter and atrial fibrillation). ∗ suggests that the difference was obvious in statistics in contrast to hypertension (P < 0.05).
Figure 9
Figure 9
CT measurement results of the aortic root of the patient.
Figure 10
Figure 10
Comparison on ALD, ASD, AID, and SID of patients with bicuspid valves and trilobular valves.
Figure 11
Figure 11
Comparison on SJID, AAID, LCOH, and RCOH of patients with bicuspid valves and trilobular valves. ∗ indicates that obvious difference can be found in contrast to patients with bicuspid valves (P < 0.05).
Figure 12
Figure 12
Comparison on CT image aortic annulus measurement results based on OS-LALM-OGM algorithm and FBP algorithm. Note. 1, 2, and 3 in the figure refers to the annulus long diameter, annulus short diameter, and average inner diameter of annulus measured by CT based on the OS-LALM-OGM algorithm, respectively, and 4 represents the inner diameter of the aortic annulus measured by CT based on the FBP algorithm. ∗ indicates that visible difference could be found in contrast to 4 (P < 0.05).
Figure 13
Figure 13
Comparison on the sensitivity of CT images to patients based on OS-LALM-OGM algorithm and FBP algorithm. ∗ indicates that the different in contrast to CT results under FBP algorithm was meaningful in statistics (P < 0.05).
Figure 14
Figure 14
Comparison on the specificity of CT images to patients based on OS-LALM-OGM algorithm and FBP algorithm. ∗ indicates that the different in contrast to CT results under FBP algorithm was meaningful in statistics (P < 0.05).
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