Computer methods and programs in biomedicine最新文献

{"title":"AI explainability and bias propagation in medical decision support","authors":"Arkadiusz Gertych , Oliver Faust","doi":"10.1016/j.cmpb.2024.108465","DOIUrl":"10.1016/j.cmpb.2024.108465","url":null,"abstract":"","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"257 ","pages":"Article 108465"},"PeriodicalIF":4.9,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142496557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Easy-to-use formulations based on the homogenization theory for vascular stent design and mechanical characterization","authors":"Dario Carbonaro ,&nbsp;Nicola Ferro ,&nbsp;Francesco Mezzadri ,&nbsp;Diego Gallo ,&nbsp;Alberto L. Audenino ,&nbsp;Simona Perotto ,&nbsp;Umberto Morbiducci ,&nbsp;Claudio Chiastra","doi":"10.1016/j.cmpb.2024.108467","DOIUrl":"10.1016/j.cmpb.2024.108467","url":null,"abstract":"<div><h3>Background and objectives</h3><div>Vascular stents are scaffolding structures implanted in the vessels of patients with obstructive disease. Stents are typically designed as cylindrical lattice structures characterized by the periodic repetition of unit cells. Their design, including geometry and material characteristics, influences their mechanical performance and, consequently, the clinical outcomes. Computational optimization frameworks have proven to be effective in assisting the design phase of vascular stents, facilitating the achievement of enhanced mechanical performances. However, the reliance on time-consuming simulations and the challenge of automating the design process limit the number of design evaluations and reduce optimization efficiency. In this context, a rapid and automated method for the mechanical characterization of vascular stents is presented, taking the stent geometry, conceived as the periodic repetition of a unit cell, and material as input and providing the mechanical response of the stent as output.</div></div><div><h3>Methods</h3><div>Vascular stents were assumed to be thin-walled hollow cylinders sharing the same macroscopic geometrical characteristics as the cylindrical lattice structure but composed of an anisotropic homogenized material. Homogenization theory was applied to average the microscopic inhomogeneities at the stent unit cell level into a homogenized material at the macro-scale, enabling the calculation of the associated homogenized material tensor. Analytical formulations were derived to relate the stent mechanical behavior to the homogenized stiffness tensor, considering linear elastic theory for thin-walled hollow cylinders and three loading scenarios of relevance for vascular stents: radial crimping; axial traction; torsion. Validation was conducted by comparing the derived analytical formulations with results obtained from finite element analyses on typical stent designs.</div></div><div><h3>Results</h3><div>Homogenized stiffness tensors were computed for the unit cells of three stent designs, revealing insights into their mechanical performance, including whether they exhibit auxetic behavior. The derived analytical formulations were successfully validated with finite element analyses, yielding low relative differences in the computed values of foreshortening, radial, axial and torsional stiffnesses for all three stents.</div></div><div><h3>Conclusions</h3><div>The proposed method offers a rapid, fully automated procedure that facilitates the assessment of the mechanical behavior of vascular stents and is suitable for effective integration into computational optimization frameworks.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"257 ","pages":"Article 108467"},"PeriodicalIF":4.9,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142564117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-modal networks for real-time monitoring of intracranial acoustic field during transcranial focused ultrasound therapy","authors":"Minjee Seo ,&nbsp;Minwoo Shin ,&nbsp;Gunwoo Noh ,&nbsp;Seung-Schik Yoo ,&nbsp;Kyungho Yoon","doi":"10.1016/j.cmpb.2024.108458","DOIUrl":"10.1016/j.cmpb.2024.108458","url":null,"abstract":"<div><h3>Background and objective:</h3><div>Transcranial focused ultrasound (tFUS) is an emerging non-invasive therapeutic technology that offers new brain stimulation modality. Precise localization of the acoustic focus to the desired brain target throughout the procedure is needed to ensure the safety and effectiveness of the treatment, but acoustic distortion caused by the skull poses a challenge. Although computational methods can provide the estimated location and shape of the focus, the computation has not reached sufficient speed for real-time inference, which is demanded in real-world clinical situations. Leveraging the advantages of deep learning, we propose multi-modal networks capable of generating intracranial pressure map in real-time.</div></div><div><h3>Methods:</h3><div>The dataset consisted of free-field pressure maps, intracranial pressure maps, medical images, and transducer placements was obtained from 11 human subjects. The free-field and intracranial pressure maps were computed using the <em>k</em>-space method. We developed network models based on convolutional neural networks and the Swin Transformer, featuring a multi-modal encoder and a decoder.</div></div><div><h3>Results:</h3><div>Evaluations on foreseen data achieved high focal volume conformity of approximately 93% for both computed tomography (CT) and magnetic resonance (MR) data. For unforeseen data, the networks achieved the focal volume conformity of 88% for CT and 82% for MR. The inference time of the proposed networks was under 0.02 s, indicating the feasibility for real-time simulation.</div></div><div><h3>Conclusions:</h3><div>The results indicate that our networks can effectively and precisely perform real-time simulation of the intracranial pressure map during tFUS applications. Our work will enhance the safety and accuracy of treatments, representing significant progress for low-intensity focused ultrasound (LIFU) therapies.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"257 ","pages":"Article 108458"},"PeriodicalIF":4.9,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142496560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Concurrent photothermal therapy and nuclear magnetic resonance imaging with plasmonic–magnetic nanoparticles: A numerical study","authors":"C. Rousseau ,&nbsp;Q.L. Vuong ,&nbsp;Y. Gossuin ,&nbsp;B. Maes ,&nbsp;G. Rosolen","doi":"10.1016/j.cmpb.2024.108453","DOIUrl":"10.1016/j.cmpb.2024.108453","url":null,"abstract":"<div><h3>Background and Objective</h3><div>: Theranostics is the combination of the diagnostic and therapeutic phases. Here we focus on simultaneous use of photothermal therapy and magnetic resonance imaging, employing a contrast-photothermal agent that converts incident light into heat and affects the transverse relaxation time, a key magnetic resonance imaging parameter. Our work considers a gold–magnetite nanoshell platform to gauge the feasibility of magnetic resonance imaging monitoring of the heating associated with phototherapy, by studying the modification of the transverse relaxation rate induced by laser illumination of a solution containing these hybrid nanoparticles.</div></div><div><h3>Methods:</h3><div>We simulate a system composed of an aqueous solution with hybrid nanoshells under continuous laser irradiation, enabling the evaluation of spatial variations of the transverse relaxation rate within the sample. We work with the hybrid nanoshell platform comprising a metal/gold shell for thermoplasmonic effects and a magnetite core for magnetic resonance imaging contrast enhancement. The optical properties of the nanoshells are first obtained through simulations using the finite element method. Next, the heating generated by the laser illumination is calculated by numerical integration. Finally, the transverse relaxation rate is obtained through the application of an analytical model. Additionally, we conduct an optimization of the nanoshell geometry to fulfill requirements of both magnetic resonance imaging and phototherapy techniques.</div></div><div><h3>Results:</h3><div>Our findings demonstrate a narrow range of nanoshell sizes exhibiting both a plasmonic absorption peak in the human biological window and a high response to laser illumination of the transverse relaxation rate. Furthermore, the illumination can induce up to a 30% modification in transverse relaxation rate compared to the non-illuminated scenario in this range of nanoshell sizes.</div></div><div><h3>Conclusions:</h3><div>In this work we establish the numerical understanding of the interplay between phototherapy and nuclear magnetic resonance imaging when employed concurrently. This allows magnetic resonance imaging monitoring of the heating associated with phototherapy.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"257 ","pages":"Article 108453"},"PeriodicalIF":4.9,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142459887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Early prediction of sudden cardiac death using multimodal fusion of ECG Features extracted from Hilbert–Huang and wavelet transforms with explainable vision transformer and CNN models","authors":"Hardik Telangore ,&nbsp;Victor Azad ,&nbsp;Manish Sharma ,&nbsp;Ankit Bhurane ,&nbsp;Ru San Tan ,&nbsp;U. Rajendra Acharya","doi":"10.1016/j.cmpb.2024.108455","DOIUrl":"10.1016/j.cmpb.2024.108455","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Sudden cardiac death (SCD) is a critical health issue characterized by the sudden failure of heart function, often caused by ventricular fibrillation (VF). Early prediction of SCD is crucial to enable timely interventions. However, current methods predict SCD only a few minutes before its onset, limiting intervention time. This study aims to develop a deep learning-based model for the early prediction of SCD using electrocardiography (ECG) signals.</div></div><div><h3>Methods:</h3><div>A multimodal explainable deep learning-based model is developed to analyze ECG signals at discrete intervals ranging from 5 to 30 min before SCD onset. The raw ECG signals, 2D scalograms generated through wavelet transform and 2D Hilbert spectrum generated through Hilbert–Huang transform (HHT) of ECG signals were applied to multiple deep learning algorithms. For raw ECG, a combination of 1D-convolutional neural networks (1D-CNN) and long short-term memory networks were employed for feature extraction and temporal pattern recognition. Besides, to extract and analyze features from scalograms and Hilbert spectra, Vision Transformer (ViT) and 2D-CNN have been used.</div></div><div><h3>Results:</h3><div>The developed model achieved high performance, with accuracy, precision, recall and F1-score of 98.81%, 98.83%, 98.81%, and 98.81% respectively to predict SCD onset 30 min in advance. Further, the proposed model can accurately classify SCD patients and normal controls with 100% accuracy. Thus, the proposed method outperforms the existing state-of-the-art methods.</div></div><div><h3>Conclusions:</h3><div>The developed model is capable of capturing diverse patterns on ECG signals recorded at multiple discrete time intervals (at 5-minute increments from 5 min to 30 min) prior to SCD onset that could discriminate for SCD. The proposed model significantly improves early SCD prediction, providing a valuable tool for continuous ECG monitoring in high-risk patients.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"257 ","pages":"Article 108455"},"PeriodicalIF":4.9,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142496558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Direct MultiSearch optimization of TPMS scaffolds for bone tissue engineering","authors":"T.H.V. Pires ,&nbsp;J.F.A. Madeira ,&nbsp;A.P.G. Castro ,&nbsp;P.R. Fernandes","doi":"10.1016/j.cmpb.2024.108461","DOIUrl":"10.1016/j.cmpb.2024.108461","url":null,"abstract":"<div><h3>Background</h3><div>Scaffolds designed for tissue engineering must consider multiple parameters, namely the permeability of the design and the wall shear stress experienced by the cells on the scaffold surface. However, these parameters are not independent from each other, with changes that improve wall shear stress, negatively impacting permeability and vice versa. This study introduces a novel multi-objective optimization framework using Direct MultiSearch (DMS) to design triply periodic minimal surface (TPMS) scaffolds for bone tissue engineering.</div></div><div><h3>Method</h3><div>The optimization algorithm focused on maximizing the permeability of the scaffolds and obtaining a desired value of average wall shear stress (which ranges between the values that promote osteogenic differentiation of 0.1 mPa and 10 mPa). Multiple fluid inlet velocities and target wall shear stress were analyzed. The DMS method successfully generated Pareto fronts for each configuration, enabling the selection of optimized scaffolds based on specific structural requirements.</div></div><div><h3>Results</h3><div>The findings reveal that increasing the target wall shear stress results in a greater number of non-dominated points on the Pareto front, highlighting a more robust optimization process. Additionally, it was also demonstrated that the tested Schwartz diamond scaffolds had a better permeability-wall shear stress relation when compared to Schoen gyroid geometries.</div></div><div><h3>Conclusions</h3><div>Direct MultiSearch was proven as an effective tool to aid in the design of tissue engineering scaffolds. This adaptable optimization framework has potential applications beyond bone tissue engineering, including cartilage tissue differentiation.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"257 ","pages":"Article 108461"},"PeriodicalIF":4.9,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the impact of ultrasound image standardization in deep learning-based segmentation of carotid plaque types","authors":"Georgia D. Liapi ,&nbsp;Christos P. Loizou ,&nbsp;Constantinos S. Pattichis ,&nbsp;Marios S. Pattichis ,&nbsp;Andrew N. Nicolaides ,&nbsp;Maura Griffin ,&nbsp;Efthyvoulos Kyriacou","doi":"10.1016/j.cmpb.2024.108460","DOIUrl":"10.1016/j.cmpb.2024.108460","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background and objective&lt;/h3&gt;&lt;div&gt;Carotid B-mode ultrasound (CBUS) imaging is often used to detect and assess atherosclerotic plaques. Doctors often need to segment plaques in the CBUS images to further examine them. Multiple studies have proposed two-dimensional CBUS plaque segmentation deep learning (DL)-based solutions, achieving promising results. In most of these studies, image standardization is not reported, while not all plaque types are represented. However, prior multiple studies have highlighted the importance of data standardization in computerized CBUS plaque classification or segmentation solutions. In this study, we propose and separately evaluate three progressive preprocessing schemes, to discover the most optimal to standardize CBUS images for DL-based carotid plaque segmentation, while we also assess the effect of each preprocessing in the segmentation performance per echodensity-based plaque type (I, II, III, IV and V).&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;We included three CBUS image datasets (276 CBUS images, from three medical centres), with which we produced 3 data folds (with the best possible equal inclusion of images from all centers per fold), to perform 3-fold cross validation-based training and evaluation of the pre-released Channel-wise Feature Pyramid Network for Medicine (CFPNet-M) model, in carotid plaque type segmentation. We included the three data folds in their original version (O), generating also three preprocessed versions of them, namely, the resolution-normalized (R), the resolution- and intensity-normalized (RN), and the resolution- and intensity-normalized combined with despeckling (RND) versions. The samples were cropped to the plaque level, and the intersection over union (IoU) and the Dice Similarity Coefficient (DSC), along with other metrics, were used to measure the model's performance. In each training round, 12 % of the images in the 2 training folds was used for internal validation (last fold was used in evaluation). Two experienced ultrasonographers manually delineated plaques in the dataset, to provide us with ground truths, while the plaque types (I to V) were extracted according to the Gray-Weale and Geroulakos classification system. We measured the mean±standard deviation of DSC within and across the three evaluated folds, per preprocessing scheme and per plaque type.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;CFPNet-M segmented the plaques in the CBUS images in all the data preprocessing versions, yielding progressively improved performances (mean DSC at 81.9 ± 9.1 %, 83.6 ± 9.0 %, 84.1 ± 8.3 %, and 84.4 ± 8.1 % for the O, R, RN and RND 3-fold cross validation processes, respectively), irrespective of the plaque type. Interestingly, CFPNet_M yielded improved performances, for all plaque types (I, II, III, IV and V), when trained and tested with the RND data versus the O version, achieving an 80.6 ± 11 % versus 77.6 ± 17 % DSC for type I, an 84.3 ± 8 % versus 81.2 ± 9 % DSC for type II","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"257 ","pages":"Article 108460"},"PeriodicalIF":4.9,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142459886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effective property method for efficient modeling of non-uniform tissue support in fluid–structure interaction simulation of blood flows","authors":"Peishuo Wu,&nbsp;Chi Zhu","doi":"10.1016/j.cmpb.2024.108457","DOIUrl":"10.1016/j.cmpb.2024.108457","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Incorporating tissue support in fluid–structure interaction analysis of cardiovascular flows is crucial for accurately representing physiological constraints, achieving realistic vessel wall motion, and minimizing artificial oscillations. The generalized Robin boundary condition, which models tissue support with a spring-damper-type force, uses elastic and damping parameters to represent the viscoelastic behavior of perivascular tissues. Using spatially distributed parameters for tissue support, rather than uniform ones, is more realistic and aligns with the varying properties of vessel walls. However, considering the spatial distribution of both can increase the complexity of preprocessing and numerical implementation. In this work, we develop an effective property method for efficient modeling of non-uniform tissue support and quantifying the contribution of tissue support to the mechanical behaviors of vessel walls.</div></div><div><h3>Methods:</h3><div>Based on the theory of linear viscoelasticity, we derive the mathematical formulas for the effective property method, integrating the parameters of generalized Robin boundary condition into vessel wall properties. The pulse wave velocity incorporating the influence of tissue support is also analyzed. Furthermore, we modify the coupled momentum method, originally formulated for elastic problems, to account for the viscoelastic properties of the vessel wall.</div></div><div><h3>Results:</h3><div>The method is verified with three-dimensional fluid–structure interaction simulations, achieving a maximum relative error of less than 2.2% for flow rate and less than 0.7% for pressure. This method shows that tissue support parameters can be integrated into vessel wall properties, resulting in increased apparent wall stiffness and viscosity, and further changing pressure, flow rate, and wave propagation.</div></div><div><h3>Conclusion:</h3><div>In this study, we develop an effective property method for quantitatively assessing the impact of tissue support and for efficiently modeling non-uniform tissue support. Moreover, this method offers further insights into clinically measured pulse wave velocity, demonstrating that it reflects the combined influence of both vessel wall properties and tissue support.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"257 ","pages":"Article 108457"},"PeriodicalIF":4.9,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Smart monitoring solution for dengue infection control: A digital twin-inspired approach","authors":"Ankush Manocha ,&nbsp;Munish Bhatia ,&nbsp;Gulshan Kumar","doi":"10.1016/j.cmpb.2024.108459","DOIUrl":"10.1016/j.cmpb.2024.108459","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>In the realm of smart healthcare, precise monitoring and prediction services are crucial for mitigating the impact of infectious diseases. This study introduces an innovative digital twin technology-inspired monitoring architecture that employs a similarity-based hybrid modeling scheme to significantly enhance accuracy in the smart healthcare domain. The research also delves into the potential of IoT technology in delivering advanced technological healthcare solutions, with a specific focus on the rapid expansion of dengue fever.</div></div><div><h3>Methods:</h3><div>The proposed digital twin-inspired healthcare system is designed to proactively combat the spread of dengue virus by enabling ubiquitous monitoring and forecasting of individuals’ susceptibility to dengue infection. The system utilizes digital twin technology to observe the status of healthcare and generate likely predictions about the vulnerability to the virus by employing k-means Clustering and Artificial Neural Networks.</div></div><div><h3>Results:</h3><div>The proposed system has been validated and its effectiveness has been demonstrated through experimental evaluation using carefully defined methods. The results of the experimental assessment confirm that the system performs optimally in terms of Temporal Delay (14.15 s), Classification Accuracy (92.86%), Sensitivity (92.43%), Specificity (91.52%), F-measure (90.86%), and Prediction Effectiveness. Moreover, by integrating a hybrid model that corrects errors in physics-based predictions employing a model for error correction driven by data, this approach has demonstrated a noteworthy 48% reduction in prediction errors, particularly in health monitoring scenarios.</div></div><div><h3>Conclusions:</h3><div>The digital twin-inspired healthcare system proposed in this study can assist healthcare providers in assessing the health vulnerability of the dengue virus, thereby reducing the likelihood of long-term or catastrophic health consequences. The integration of a hybrid modeling approach and the utilization of IoT technology has shown promising results in enhancing the accuracy and effectiveness of smart health monitoring and prediction services.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"257 ","pages":"Article 108459"},"PeriodicalIF":4.9,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142459888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"2D/3D registration based on biplanar X-ray and CT images for surgical navigation","authors":"Demin Yang,&nbsp;Haochen Shi,&nbsp;Bolun Zeng,&nbsp;Xiaojun Chen","doi":"10.1016/j.cmpb.2024.108444","DOIUrl":"10.1016/j.cmpb.2024.108444","url":null,"abstract":"<div><h3>Background and Objectives:</h3><div>Image-based 2D/3D registration is a crucial technology for fluoroscopy-guided surgical interventions. However, traditional registration methods relying on a single X-ray image into surgical navigation systems. This study proposes a novel 2D/3D registration approach utilizing biplanar X-ray images combined with computed tomography (CT) to significantly reduce registration and navigation errors. The method is successfully implemented in a surgical navigation system, enhancing its precision and reliability.</div></div><div><h3>Methods:</h3><div>First, we simultaneously register the frontal and lateral X-ray images with the CT image, enabling mutual complementation and more precise localization. Additionally, we introduce a novel similarity measure for image comparison, providing a more robust cost function for the optimization algorithm. Furthermore, a multi-resolution strategy is employed to enhance registration efficiency. Lastly, we propose a more accurate coordinate transformation method, based on projection and 3D reconstruction, to improve the precision of surgical navigation systems.<em>Results:</em> We conducted registration and navigation experiments using pelvic, spinal, and femur phantoms. The navigation results demonstrated that the feature registration errors (FREs) in the three experiments were 0.505±0.063 mm, 0.515±0.055 mm, and 0.577±0.056 mm, respectively. Compared to the point-to-point (PTP) registration method based on anatomical landmarks, our method reduced registration errors by 31.3%, 23.9%, and 26.3%, respectively.</div></div><div><h3>Conclusion:</h3><div>The results demonstrate that our method significantly reduces registration and navigation errors, highlighting its potential for application across various anatomical sites. Our code is available at: <span><span>https://github.com/SJTUdemon/2D-3D-Registration</span><svg><path></path></svg></span></div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"257 ","pages":"Article 108444"},"PeriodicalIF":4.9,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142433480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}