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Li H, Shao G, Zhao Y, Zhong H. The value of inflow inversion recovery MRI for the diagnosis of transplant renal artery stenosis: comparison with digital subtraction angiography. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2022. [DOI: 10.1186/s43055-021-00682-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
To date, few data on the assessment of transplant renal artery stenosis (TRAS) by using inflow inversion recovery (IFIR) are available. The aims of this study was to evaluate the feasibility of IFIR in the assessment of TRAS using Digital Subtraction Angiography (DSA) as the reference.
Results
We retrospectively assessed the IFIR of 195 transplant renal arteries. The IFIR images for 194/195 arteries were judged to be of excellent, good, or moderate quality, and 1/195 was not diagnostic. There were 100 arteries with TRAS, of which 27 were subjected to DSA. The stenosis percentages were divided into five grades. Using DSA images, the TRAS in 27 patients were estimated as grade 1 (2, 7.4%), grade 2 (8, 29.6%), grade 3 (10, 37.0%), grade 4 (7, 25.9%) and grade 5 (0, 0%). In comparison, the TRAS was shown to be grade 1 (1, 3.7%), grade 2 (8, 29.6%), grade 3 (9, 33.3%), grade 4 (9, 33%) and grade 5 (0, 0%) in the IFIR images. The nonparametric Wilcoxon signed-rank test was used to compare IFIR with DSA. In addition, a Bland–Altman plot was used to estimate the agreement between IFIR and DSA measurements. There was no significant difference between IFIR and DSA measurements (p < 0.05).
Conclusions
Relative to the reference DSA, IFIR was shown to be noninvasive, accurate for the diagnosis and evaluation of TRAS.
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Deep Learning-Based Digital Subtraction Angiography Characteristics in Nursing of Maintenance Hemodialysis Patients. CONTRAST MEDIA & MOLECULAR IMAGING 2022; 2022:9356108. [PMID: 36101802 PMCID: PMC9440815 DOI: 10.1155/2022/9356108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 07/25/2022] [Accepted: 08/05/2022] [Indexed: 11/17/2022]
Abstract
This study is aimed at exploring the diagnostic value of digital subtraction angiography (DSA) based on faster region-based convolutional networks (Faster-RCNN) deep learning for maintenance hemodialysis (MHD) diseases and to provide a theoretical basis for clinical nursing. A total of 50 MHD patients who were clinically diagnosed in the Blood Purification Center were randomly divided into the control group and the experimental group (25 cases for each group). The control group was given routine nursing intervention, and the experimental group was given overall nursing intervention under the supervision of DSA. A faster RCNN multitarget detection network was constructed to analyze the average accuracy of various vascular structures in the test set. The self-rating anxiety scale (SAS) and self-rating depression scale (SDS) were used to evaluate the degree of anxiety and depression. The urine volume before and after the operation, local hematoma after a puncture, the incidence of complications, and nursing satisfaction were recorded. The results showed that the average accuracy of the vein, internal carotid artery, circle of Willis, venous sinus, and venous vessels was 0.876, 0.916, 0.994, 0.925, and 0.732, respectively. The success rate of surgery in the experiment group was higher than that in the control group, and the difference had statistical significance (P < 0.05). The SAS score and SDS score in the experimental group were significantly lower than those in the control group (P < 0.05). The total incidence rate of complications in the experimental group (16.00%) was significantly lower than that in the control group (44.00%) (P < 0.05). The satisfaction rate of the experimental group was significantly higher than that of the control group (P < 0.05). The Faster-RCNN model had the best effect in differentiating the circle of Willis and a poor effect in differentiating venous vessels. DSA based on Faster-RCNN can significantly improve the success rate of puncture in MHD patients. The implementation of holistic nursing intervention under its supervision can significantly reduce postoperative complications and improve patient satisfaction with nursing compared with routine nursing.
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Lal H, Singh P, Ponmalai K, Prasad R, Singh SP, Yadav P, Singh A, Bhadauria D, Kumar S, Agarwal V, Mishra P. Role of blood oxygen level-dependent magnetic resonance imaging in studying renal oxygenation changes in renal artery stenosis. Abdom Radiol (NY) 2022; 47:1112-1123. [PMID: 35059812 DOI: 10.1007/s00261-022-03408-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 12/31/2021] [Accepted: 01/05/2022] [Indexed: 11/29/2022]
Abstract
AIM Primary objective of this study was to compare R2* value of the post-stenotic kidney with contralateral kidney, kidneys of essential hypertensive patients, and healthy subjects using blood oxygen level-dependent magnetic resonance imaging (BOLD MRI) technique. The secondary objective was to study the effect of severity of stenosis and viability of kidneys on R2* value. METHODS We compared 4 groups of kidneys including 92 with renal artery stenosis, 37 normal contralateral kidneys of unilateral renal artery stenosis patients, 62 kidneys of essential hypertensive patients, and 40 kidneys of healthy controls using BOLD MRI. Deoxyhemoglobin level represented by R2* was calculated before and after giving furosemide and was compared among different groups. RESULTS Baseline means cortical R2* value did not differ between groups. Response to furosemide was reduced in stenotic kidneys as compared to essential hypertensive and healthy control groups (p < 0.001). The mean R2* value of the contralateral normal kidney group was not significantly different from the stenotic group. Baseline R2* value and delta R2* values did not differ between different degrees of stenosis. Higher mean cortical R2* was seen in stenotic kidneys which were small (< 7 cm) in size (24.27 ± 5.65 vs 21.7 ± 3.88; p value 0.02) or with poor corticomedullary differentiation (24.64 ± 5.8 vs 20.74 ± 3.34; p value 0.006) as compared to other stenotic kidneys. Similarly, the delta R2* value was also blunted in these small shrunken kidneys (p value < 0.001). CONCLUSION R2* values on BOLD MRI are significantly different between kidneys with and without renal artery stenosis and can potentially also predict the utility of revascularization.
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Affiliation(s)
- Hira Lal
- Department of Radiology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Raebareli Road, Lucknow, U.P., 226014, India.
- Department of Radiology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Raibareli Road, Lucknow, Uttar Pradesh, 226014, India.
| | - Priya Singh
- Department of Radiology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Raebareli Road, Lucknow, U.P., 226014, India
| | - Kaushik Ponmalai
- Department of Radiology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Raebareli Road, Lucknow, U.P., 226014, India
| | - Raghunandan Prasad
- Department of Radiology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Raebareli Road, Lucknow, U.P., 226014, India
| | - Surya Pratap Singh
- Department of Radiology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Raebareli Road, Lucknow, U.P., 226014, India
| | - Priyank Yadav
- Department of Urology and Renal Transplantation, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Raebareli Road, Lucknow, U.P., 226014, India
| | - Anuradha Singh
- Department of Radiology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Raebareli Road, Lucknow, U.P., 226014, India
| | - Dharmendra Bhadauria
- Department of Nephrology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Raebareli Road, Lucknow, U.P., 226014, India
| | - Sudeep Kumar
- Department of Cardiology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Raebareli Road, Lucknow, U.P., 226014, India
| | - Vikas Agarwal
- Department of Immunology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Raebareli Road, Lucknow, U.P., 226014, India
| | - Prabhakar Mishra
- Department of Biostatistics and Health Informatics, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Raibareli Road, Lucknow, U.P., 226014, India
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