1
|
Saito M, Saito T. A simple algorithm to derive virtual non-contrast electron density from dual-energy computed tomography data for radiotherapy treatment planning. Med Phys 2025; 52:3107-3116. [PMID: 39865311 DOI: 10.1002/mp.17648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2024] [Revised: 12/28/2024] [Accepted: 01/09/2025] [Indexed: 01/28/2025] Open
Abstract
BACKGROUND The use of iodinated contrast-enhancing agents in computed tomography (CT) improves the visualization of relevant structures for radiotherapy treatment planning (RTP). However, it can lead to dose calculation errors by incorrectly converting a CT number to electron density. PURPOSE This study aimed to propose an algorithm for deriving virtual non-contrast (VNC) electron density from dual-energy CT (DECT) data. This algorithm was developed by extending the formula previously developed by Saito, which enables the calculation of the electron density of human tissue through weighted subtraction of CT numbers acquired from DECT scans. METHODS To investigate the feasibility of the proposed VNC algorithm, we performed analytical DECT image simulations at 90 and 150 kV/Sn on virtual phantoms consisting of various tissue/iodine surrogates with known mass densities and elemental compositions. Two different shapes of phantoms made of water-mimicking surrogates were generated as inputs: a circular phantom (33 cm diameter) for calibration and an elliptical phantom (33 cm width and 28 cm height) for validation. The circular phantom was equipped with inserts of human-tissue-mimicking substitutes, pure water, and iodine-enhanced soft-tissue substitutes (2, 5, 10, and 15 mg/mL iodine). The elliptical phantom contained inserts of reference human tissues, iodine-enhanced soft-tissue substitutes (2, 2.5, 5, 7.5, 10, 15, and 20 mg/mL iodine), and a 10-mm-diameter core of 4 mg/mL iodine surrounded by a blood-mimicking base material. The performance of the proposed algorithm was evaluated by comparing the accuracy of VNC electron densities with those of non-contrast (NC) base materials (water- or blood-mimicking surrogates). RESULTS The derived algorithm enabled the calculation of VNC electron density in a manner similar to that of unenhanced human tissues by adapting a VNC-specific weighting factor, thereby eliminating the intermediate step of converting CT numbers to electron density. The simulated results showed that the VNC algorithm could almost completely remove the contrast in the electron density image between iodine-enhanced and base materials. The relative deviations of simulated VNC electron density values from the corresponding pre-contrast value were within ± 0.4% for all tested materials, with a root-mean-square error (RMSE) of 0.2%. CONCLUSIONS Within the limits of the analytical DECT image simulation used in this study, the simple VNC algorithm could effectively provide accurate VNC electron densities for iodine-enhanced materials. This may allow the contrast agent to be used for CT scans during RTP without compromising dose calculation accuracy.
Collapse
Affiliation(s)
- Masatoshi Saito
- Department of Radiological Technology, School of Health Sciences, Faculty of Medicine, Niigata University, Niigata, Japan
| | - Tatsuki Saito
- Department of Chemistry, Faculty of Science, Hokkaido University, Sapporo, Hokkaido, Japan
| |
Collapse
|
2
|
Kim J, Lee J, Kim B, Kim S, Jin H, Jung S. Generation of deep learning based virtual non-contrast CT using dual-layer dual-energy CT and its application to planning CT for radiotherapy. PLoS One 2024; 19:e0316099. [PMID: 39775325 PMCID: PMC11684624 DOI: 10.1371/journal.pone.0316099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2024] [Accepted: 12/05/2024] [Indexed: 01/11/2025] Open
Abstract
This paper presents a novel approach for generating virtual non-contrast planning computed tomography (VNC-pCT) images from contrast-enhanced planning CT (CE-pCT) scans using a deep learning model. Unlike previous studies, which often lacked sufficient data pairs of contrast-enhanced and non-contrast CT images, we trained our model on dual-energy CT (DECT) images, using virtual non-contrast CT (VNC CT) images as outputs instead of true non-contrast CT images. We used a deterministic method to convert CE-pCT images into pseudo DECT images for model application. Model training and evaluation were conducted on 45 patients. The performance of our model, 'VNC-Net', was evaluated using various metrics, demonstrating high scores for quantitative performance. Moreover, our model accurately replicated target VNC CT images, showing close correspondence in CT numbers. The versatility of our model was further demonstrated by applying it to pseudo VNC DECT generation, followed by conversion to VNC-pCT. CE-pCT images of ten liver cancer patients and ten left-sided breast cancer patients were used. A quantitative comparison with true non-contrast planning CT (TNC-pCT) images validated the accuracy of the generated VNC-pCT images. Furthermore, dose calculations on CE-pCT and VNC-pCT images from patients undergoing volumetric modulated arc therapy for liver and breast cancer treatment showed the clinical relevance of our approach. Despite the model's overall good performance, limitations remained, particularly in maintaining CT numbers of bone and soft tissue less influenced by contrast agent. Future research should address these challenges to further improve the model's accuracy and applicability in radiotherapy planning. Overall, our study highlights the potential of deep learning models to improve imaging protocols and accuracy in radiotherapy planning.
Collapse
Affiliation(s)
- Jungye Kim
- Department of Biomedical Engineering, Korea University, Seoul, Republic of Korea
| | - Jimin Lee
- Department of Nuclear Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea
- Graduate School of Artificial Intelligence, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea
| | - Bitbyeol Kim
- Department of Radiation Oncology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Sangwook Kim
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Hyeongmin Jin
- Department of Radiation Oncology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Seongmoon Jung
- Department of Radiation Oncology, Seoul National University Hospital, Seoul, Republic of Korea
- Division of Biomedical Metrology, Ionizing Radiation Group, Korea Research Institute of Standards and Science, Daejeon, Republic of Korea
| |
Collapse
|
3
|
Beikali Soltani M, Bouchard H. Dual virtual non-contrast imaging: a Bayesian quantitative approach to determine radiotherapy quantities from contrast-enhanced DECT images. Phys Med Biol 2024; 69:245008. [PMID: 39577082 DOI: 10.1088/1361-6560/ad965f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Accepted: 11/22/2024] [Indexed: 11/24/2024]
Abstract
Objective.Contrast agents in computed tomography (CT) scans can compromise the accuracy of dose calculations in radiation therapy planning, especially for particle therapy. This often requires an additional non-contrast CT scan, increasing radiation exposure and introducing potential registration errors. Our goal is to resolve these issues by accurately estimating radiotherapy parameters from dual virtual non-contrast (dual-VNC) images generated by contrast-enhanced dual-energy CT (DECT) scans, while accounting for noise and variability in tissue composition.Approach.A new Bayesian model is introduced to estimate dual-VNC Hounsfield units from contrast-enhanced DECT data. The model defines a prior distribution that describes tissue variations in terms of elemental compositions and mass densities. Multiple reference tissues are used to estimate variations across human tissues. A likelihood distribution is also defined to model the noise contained in CT data. The model is thoroughly validated in a simulated environment including 12 virtual patients under low and high iodine uptake scenarios, while incorporating noise and beam hardening effects. The eigentissue decomposition technique is used to derive elemental compositions and parameters critical for radiotherapy from the dual-VNC images, such as electron density (ρe), particle stopping power (SPR), and photon energy absorption coefficient (EAC).Main results.The proposed method yields accurate voxelwise estimations forρe, SPR, and EAC, with root mean square errors of 3.09%, 3.14%, and 1.34% for highly-enhanced tissues, compared to 5.93%, 6.39%, and 17.11% when the presence of contrast agent is ignored. It also demonstrates robustness to systematic shifts in tissue composition and bandwidth variations in the prior distribution, resulting in overall uncertainties down to 1.13%, 1.33%, and 0.86% forρe, SPR, and EAC in soft tissues; 1.17%, 1.32%, and 1.34% in enhanced soft tissues; and 4.34%, 4.00%, and 2.50% in bones.Significance.The proposed method accurately derives radiotherapy parameters from contrast-enhanced DECT data and demonstrates robustness against systematic errors in reference data, highlighting its potential for clinical use.
Collapse
Affiliation(s)
- Mohsen Beikali Soltani
- Département de physique, Université de Montréal, Campus MIL, 1375 Av. Thérèse-Lavoie-Roux, Montréal, QC, H2V 0B3, Canada
- Centre de recherche du Centre hospitalier de l'Université de Montréal, 900 Rue Saint-Denis, Montréal, QC, H2X 0A9, Canada
| | - Hugo Bouchard
- Département de physique, Université de Montréal, Campus MIL, 1375 Av. Thérèse-Lavoie-Roux, Montréal, QC, H2V 0B3, Canada
- Centre de recherche du Centre hospitalier de l'Université de Montréal, 900 Rue Saint-Denis, Montréal, QC, H2X 0A9, Canada
- Département de radio-oncologie, Centre hospitalier de l'Université de Montréal, 1051 Rue Sanguinet, Montréal, QC H2X 3E4, Canada
| |
Collapse
|
4
|
Afifah M, Bulthuis MC, Goudschaal KN, Verbeek-Spijkerman JM, Rosario TS, den Boer D, Hinnen KA, Bel A, van Kesteren Z. Virtual unenhanced dual-energy computed tomography for photon radiotherapy: The effect on dose distribution and cone-beam computed tomography based position verification. Phys Imaging Radiat Oncol 2024; 29:100545. [PMID: 38369991 PMCID: PMC10869258 DOI: 10.1016/j.phro.2024.100545] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 01/24/2024] [Accepted: 01/29/2024] [Indexed: 02/20/2024] Open
Abstract
Background and Purpose Virtual Unenhanced images (VUE) from contrast-enhanced dual-energy computed tomography (DECT) eliminate manual suppression of contrast-enhanced structures (CES) or pre-contrast scans. CT intensity decreases in high-density structures outside the CES following VUE algorithm application. This study assesses VUE's impact on the radiotherapy workflow of gynecological tumors, comparing dose distribution and cone-beam CT-based (CBCT) position verification to contrast-enhanced CT (CECT) images. Materials and Methods A total of 14 gynecological patients with contrast-enhanced CT simulation were included. Two CT images were reconstructed: CECT and VUE. Volumetric Modulated Arc Therapy (VMAT) plans generated on CECT were recalculated on VUE using both the CECT lookup table (LUT) and a dedicated VUE LUT. Gamma analysis assessed 3D dose distributions. CECT and VUE images were retrospectively registered to daily CBCT using Chamfer matching algorithm.. Results Planning target volume (PTV) dose agreement with CECT was within 0.35% for D2%, Dmean, and D98%. Organs at risk (OARs) D2% agreed within 0.36%. A dedicated VUE LUT lead to smaller dose differences, achieving a 100% gamma pass rate for all subjects. VUE imaging showed similar translations and rotations to CECT, with significant but minor translation differences (<0.02 cm). VUE-based registration outperformed CECT. In 24% of CBCT-CECT registrations, inadequate registration was observed due to contrast-related issues, while corresponding VUE images achieved clinically acceptable registrations. Conclusions VUE imaging in the radiotherapy workflow is feasible, showing comparable dose distributions and improved CBCT registration results compared to CECT. VUE enables automated bone registration, limiting inter-observer variation in the Image-Guided Radiation Therapy (IGRT) process.
Collapse
Affiliation(s)
- Maryam Afifah
- Amsterdam UMC, Location Vrije Universiteit, Department of Radiation Oncology, De Boelelaan 1118, Amsterdam, the Netherlands
| | - Marloes C. Bulthuis
- Amsterdam UMC, Location University of Amsterdam, Department of Radiation Oncology, Meibergdreef 9, Amsterdam, the Netherlands
| | - Karin N. Goudschaal
- Amsterdam UMC, Location University of Amsterdam, Department of Radiation Oncology, Meibergdreef 9, Amsterdam, the Netherlands
| | - Jolanda M. Verbeek-Spijkerman
- Amsterdam UMC, Location University of Amsterdam, Department of Radiation Oncology, Meibergdreef 9, Amsterdam, the Netherlands
| | - Tezontl S. Rosario
- Amsterdam UMC, Location Vrije Universiteit, Department of Radiation Oncology, De Boelelaan 1118, Amsterdam, the Netherlands
| | - Duncan den Boer
- Amsterdam UMC, Location Vrije Universiteit, Department of Radiation Oncology, De Boelelaan 1118, Amsterdam, the Netherlands
| | - Karel A. Hinnen
- Amsterdam UMC, Location University of Amsterdam, Department of Radiation Oncology, Meibergdreef 9, Amsterdam, the Netherlands
| | - Arjan Bel
- Amsterdam UMC, Location University of Amsterdam, Department of Radiation Oncology, Meibergdreef 9, Amsterdam, the Netherlands
| | - Zdenko van Kesteren
- Amsterdam UMC, Location University of Amsterdam, Department of Radiation Oncology, Meibergdreef 9, Amsterdam, the Netherlands
| |
Collapse
|
5
|
Noid G, Godfrey G, Hall W, Shah J, Paulson E, Knechtges P, Erickson B, Allen Li X. Predicting Treatment Response From Extracellular Volume Fraction for Chemoradiation Therapy of Pancreatic Cancer. Int J Radiat Oncol Biol Phys 2023; 115:803-808. [PMID: 36210026 DOI: 10.1016/j.ijrobp.2022.09.084] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 09/15/2022] [Accepted: 09/27/2022] [Indexed: 11/06/2022]
Abstract
PURPOSE Dual-energy computed tomography (DECT) data can be used to calculate the extracellular volume fraction (ECVf) in tumors, which has been correlated with treatment outcome. This study sought to find a correlation between ECVf and treatment response as measured by the change in cancer antigen (CA) 19 to 9 during chemoradiation therapy (CRT) for pancreatic cancer. METHODS AND MATERIALS Dual-energy CT data acquired during the late arterial contrast phase in the standard radiation therapy simulation on a dual-source DECT simulator for 25 patients with pancreatic cancer, along with their CA19-9 and hematocrit data, were analyzed. Each patient underwent preoperative CRT with a prescription of 50.4 Gy in 28 fractions. The patients were chosen based on the presence of a solid tumor in the pancreas that could be clearly delineated. A region of interest (ROI) was placed in the tumor and in the aorta. From the ratio of the iodine density calculated from the DECT in the ROI and the hematocrit taken at the time of simulation, the ECVf was calculated. The ECVf was then compared with the change in CA19-9 before and after the CRT. Distant metastases as the cause of CA19-9 elevation were ruled out on subsequent restaging images before surgery. The DECT-derived iodine ratio was validated using a phantom study. RESULTS The DECT-derived iodine concentration agreed with the phantom measurements (R2, 1.0). The average hematocrit, ECVf, and change in CA19-9 during the treatment for the 25 patients was 35.6 ± 5.4%, 7.3 ± 4.9%, and -4.6 ± 21.8 respectively. A linear correlation was found between the ECVf and the change in CA19-9, with an R2 of 0.7: ΔCA19-9 = 3.63 × ECVf - 31.1. The correlation was statistically significant (P = .006). CONCLUSIONS The calculated ECV fraction based on iodine maps from dual-source DECT may be used to predict treatment response after neoadjuvant chemoradiation therapy for pancreatic cancer.
Collapse
Affiliation(s)
- George Noid
- Medical College of Wisconsin, Milwaukee, Wisconsin
| | | | - William Hall
- Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Jainil Shah
- Siemens Medical Solutions USA, Inc, Malvern, Pennsylvania
| | - Eric Paulson
- Medical College of Wisconsin, Milwaukee, Wisconsin
| | | | | | - X Allen Li
- Medical College of Wisconsin, Milwaukee, Wisconsin.
| |
Collapse
|
6
|
Yang M, Wohlfahrt P, Shen C, Bouchard H. Dual- and multi-energy CT for particle stopping-power estimation: current state, challenges and potential. Phys Med Biol 2023; 68. [PMID: 36595276 DOI: 10.1088/1361-6560/acabfa] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022]
Abstract
Range uncertainty has been a key factor preventing particle radiotherapy from reaching its full physical potential. One of the main contributing sources is the uncertainty in estimating particle stopping power (ρs) within patients. Currently, theρsdistribution in a patient is derived from a single-energy CT (SECT) scan acquired for treatment planning by converting CT number expressed in Hounsfield units (HU) of each voxel toρsusing a Hounsfield look-up table (HLUT), also known as the CT calibration curve. HU andρsshare a linear relationship with electron density but differ in their additional dependence on elemental composition through different physical properties, i.e. effective atomic number and mean excitation energy, respectively. Because of that, the HLUT approach is particularly sensitive to differences in elemental composition between real human tissues and tissue surrogates as well as tissue variations within and among individual patients. The use of dual-energy CT (DECT) forρsprediction has been shown to be effective in reducing the uncertainty inρsestimation compared to SECT. The acquisition of CT data over different x-ray spectra yields additional information on the material elemental composition. Recently, multi-energy CT (MECT) has been explored to deduct material-specific information with higher dimensionality, which has the potential to further improve the accuracy ofρsestimation. Even though various DECT and MECT methods have been proposed and evaluated over the years, these approaches are still only scarcely implemented in routine clinical practice. In this topical review, we aim at accelerating this translation process by providing: (1) a comprehensive review of the existing DECT/MECT methods forρsestimation with their respective strengths and weaknesses; (2) a general review of uncertainties associated with DECT/MECT methods; (3) a general review of different aspects related to clinical implementation of DECT/MECT methods; (4) other potential advanced DECT/MECT applications beyondρsestimation.
Collapse
Affiliation(s)
- Ming Yang
- The University of Texas MD Anderson Cancer Center, Department of Radiation Physics, 1515 Holcombe Blvd Houston, TX 77030, United States of America
| | - Patrick Wohlfahrt
- Massachusetts General Hospital and Harvard Medical School, Department of Radiation Oncology, Boston, MA 02115, United States of America
| | - Chenyang Shen
- University of Texas Southwestern Medical Center, Department of Radiation Oncology, 2280 Inwood Rd Dallas, TX 75235, United States of America
| | - Hugo Bouchard
- Département de physique, Université de Montréal, Complexe des sciences, 1375 Avenue Thérèse-Lavoie-Roux, Montréal, Québec H2V0B3, Canada.,Centre de recherche du Centre hospitalier de l'Université de Montréal, 900 Rue Saint-Denis, Montréal, Québec, H2X 0A9, Canada.,Département de radio-oncologie, Centre hospitalier de l'Université de Montréal, 1051 Rue Sanguinet, Montréal, Québec H2X 3E4, Canada
| |
Collapse
|
7
|
Kauw F, Ding VY, Dankbaar JW, van Ommen F, Zhu G, Boothroyd DB, Wolman DN, Molvin L, de Jong HWAM, Kappelle LJ, Velthuis BK, Heit JJ, Wintermark M. Detection of Early Ischemic Changes with Virtual Noncontrast Dual-Energy CT in Acute Ischemic Stroke: A Noninferiority Analysis. AJNR Am J Neuroradiol 2022; 43:1259-1264. [PMID: 35953275 PMCID: PMC9451625 DOI: 10.3174/ajnr.a7600] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 06/17/2022] [Indexed: 01/26/2023]
Abstract
BACKGROUND AND PURPOSE Dual-energy virtual NCCT has the potential to replace conventional NCCT to detect early ischemic changes in acute ischemic stroke. In this study, we evaluated whether virtual NCCT is noninferior compared with standard linearly blended NCCT, a surrogate of conventional NCCT, regarding the detection of early ischemic changes with ASPECTS. MATERIALS AND METHODS Adult patients who presented with suspected acute ischemic stroke and who underwent dual-energy NCCT and CTA and brain MR imaging within 48 hours were included. Standard linearly blended images were reconstructed to match a conventional NCCT. Virtual NCCT images were reconstructed from CTA. ASPECTS was evaluated on conventional NCCT, virtual NCCT, and DWI, which served as the reference standard. Agreement between CT assessments and the reference standard was evaluated with the Lin concordance correlation coefficient. Noninferiority was assessed with bootstrapped estimates of the differences in ASPECTS between conventional and virtual NCCT with 95% CIs. RESULTS Of the 193 included patients, 100 patients (52%) had ischemia on DWI. Compared with the reference standard, the ASPECTS concordance correlation coefficient for conventional and virtual NCCT was 0.23 (95% CI, 0.15-0.32) and 0.44 (95% CI, 0.33-0.53), respectively. The difference in the concordance correlation coefficient between virtual and conventional NCCT was 0.20 (95% CI, 0.01-0.39) and did not cross the prespecified noninferiority margin of -0.10. CONCLUSIONS Dual-energy virtual NCCT is noninferior compared with conventional NCCT for the detection of early ischemic changes with ASPECTS.
Collapse
Affiliation(s)
- F Kauw
- From the Departments of Radiology (F.K., F.v.O., G.Z., D.N.W., L.M., J.J.H., M.W.)
- Departments of Radiology (F.K., J.W.D., F.v.O., H.W.A.M.d.J., B.K.V.)
- Neurology (F.K., L.J.K.), University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - V Y Ding
- Medicine (V.Y.D., D.B.B.), Stanford University, Stanford, California
| | - J W Dankbaar
- Departments of Radiology (F.K., J.W.D., F.v.O., H.W.A.M.d.J., B.K.V.)
| | - F van Ommen
- From the Departments of Radiology (F.K., F.v.O., G.Z., D.N.W., L.M., J.J.H., M.W.)
- Departments of Radiology (F.K., J.W.D., F.v.O., H.W.A.M.d.J., B.K.V.)
| | - G Zhu
- From the Departments of Radiology (F.K., F.v.O., G.Z., D.N.W., L.M., J.J.H., M.W.)
| | - D B Boothroyd
- Medicine (V.Y.D., D.B.B.), Stanford University, Stanford, California
| | - D N Wolman
- From the Departments of Radiology (F.K., F.v.O., G.Z., D.N.W., L.M., J.J.H., M.W.)
| | - L Molvin
- From the Departments of Radiology (F.K., F.v.O., G.Z., D.N.W., L.M., J.J.H., M.W.)
| | - H W A M de Jong
- Departments of Radiology (F.K., J.W.D., F.v.O., H.W.A.M.d.J., B.K.V.)
| | - L J Kappelle
- Neurology (F.K., L.J.K.), University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - B K Velthuis
- Departments of Radiology (F.K., J.W.D., F.v.O., H.W.A.M.d.J., B.K.V.)
| | - J J Heit
- From the Departments of Radiology (F.K., F.v.O., G.Z., D.N.W., L.M., J.J.H., M.W.)
| | - M Wintermark
- From the Departments of Radiology (F.K., F.v.O., G.Z., D.N.W., L.M., J.J.H., M.W.)
| |
Collapse
|
8
|
Spiral Computed Tomography Imaging Analysis of Positioning of Lumbar Spinal Nerve Anesthesia under the Concept of Enhanced Recovery after Surgery. CONTRAST MEDIA & MOLECULAR IMAGING 2022; 2022:1703250. [PMID: 35711532 PMCID: PMC9187486 DOI: 10.1155/2022/1703250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 05/02/2022] [Accepted: 05/04/2022] [Indexed: 11/17/2022]
Abstract
The objective of this research was to explore the effect of perioperative anesthesia management for patients based on the concept of enhanced recovery after surgery (ERAS) and the application value of the computed tomography (CT) localization method in lumbar spinal nerve anesthesia, reducing the damage caused by anesthesia. One hundred and twenty patients who underwent the lumbar spinal anesthesia in lower limb surgery were selected as the research subjects. According to puncture positioning and nursing intention, the patients were classified into the control group with 30 patients (method of anatomical landmarks), CT group with 50 patients (the CT localization), and ERAS group with 40 patients (the CT localization and the ERAS management). The effects of the anesthesia positioning method and the ERAS management were compared and analyzed. The results showed that d (0.32) and r (0.27) of exponential filtering function were notably smaller than those of R-L filtering function (d = 0.40, r = 0.39) and of S-R filtering function (d = 0.37, r = 0.36) (P < 0.05). Puncture time ((9.23 ± 0.32) min vs. (13.11 ± 0.45) min), puncture direction change (20% vs. 33.33%), abnormal puncture sensation (22% vs. 40%), and nerve root touch (4% vs. 23.33%) in the CT group were all lower than those in the control group. The proportion of Degree I anesthesia effect (94%) of the CT group was greatly higher than that of the control group (76.67%) (P < 0.05). The VAS score, time of activity and gastrointestinal function recovery, and the incidence of adverse reactions (2.5% vs. 28%) in the ERAS group were lower than those in the CT group (P < 0.05). All in all, the CT localization method can improve the difficulty of anesthesia puncture and improve the anesthetic effect; the ERAS nursing concept can improve the postoperative pain of patients and contribute to the prognosis of patients and have a good clinical value.
Collapse
|