Surgical Navigation Research Articles

A Red-Emission Fluorescent Probe for Intracellular Biothiols and Hydrogen Sulfide Imaging in Living Cells.

This research centers on the development and synthesis of a longwave fluorescence probe, labeled as 60T, designed for the simultaneous detection of hydrogen sulfide, cysteine/homocysteine, and glutathione. The probe showcases a swift response, good linearity range, and heightened sensitivity, boasting that the detection limits of the probe for Cys, Hcy, GSH and H2S were 0.140, 0.202, 0.259 and 0.396 μM, respectively. Notably, its efficacy in monitoring thiol status changes in live MCF-7 cells is underscored by a substantial decrease in fluorescence intensity upon exposure to the thiol trapping reagent, N-ethyl maleimide (NEM). With an impressive red emission signal at 630 nm and a substantial Stokes shift of 80 nm, this probe exhibits remarkable sensitivity and selectivity for biothiols and H2S, indicating promising applications in the diagnosis and surgical navigation of relevant cancers.

Comparative Analysis of Non-Rigid Registration Techniques for Liver Surface Registration.

Non-rigid surface-based soft tissue registration is crucial for surgical navigation systems, but its adoption still faces several challenges due to the large number of degrees of freedom and the continuously varying and complex surface structures present in the intra-operative data. By employing non-rigid registration, surgeons can integrate the pre-operative images into the intra-operative guidance environment, providing real-time visualization of the patient's complex pre- and intra-operative anatomy in a common coordinate system to improve navigation accuracy. However, many of the existing registration methods, including those for liver applications, are inaccessible to the broader community. To address this limitation, we present a comparative analysis of several open-source, non-rigid surface-based liver registration algorithms, with the overall goal of contrasting their strength and weaknesses and identifying an optimal solution. We compared the robustness of three optimization-based and one data-driven nonrigid registration algorithms in response to a reduced visibility ratio (reduced partial views of the surface) and to an increasing deformation level (mean displacement), reported as the root mean square error (RMSE) between the pre-and intra-operative liver surface meshed following registration. Our results indicate that the Gaussian Mixture Model - Finite Element Model (GMM-FEM) method consistently yields a lower post-registration error than the other three tested methods in the presence of both reduced visibility ratio and increased intra-operative surface displacement, therefore offering a potentially promising solution for pre- to intra-operative nonrigid liver surface registration.

ROLE AND EVOLUTION OF COMPUTER VISION IN MEDICINE

As the technologies evolve, they become more and more applicable to such fields as medicine and sciences. This article explores the transformative role of computer vision in modern medicine. It delves into the evolution and diverse applications of computer vision technology, highlighting its profound impact on medical imaging, diagnostics, and surgical procedures. Through advanced imaging techniques and machine learning algorithms, computer vision enhances diagnostic accuracy, facilitates surgical navigation, and enables real-time analysis of medical data. The article discusses key insights from industry experts and showcases notable advancements in disease detection, foreign body identification, and injury estimation. Moreover, it emphasizes the potential of computer vision to revolutionize healthcare delivery by improving patient outcomes, streamlining workflows, and enhancing clinical decision-making.

Effect of navigation endoscopy combined with three-dimensional printing technology in the treatment of orbital blowout fractures.

To explore the combined application of surgical navigation nasal endoscopy (NNE) and three-dimensional printing technology (3DPT) for the adjunctive treatment of orbital blowout fractures (OBF). Retrospective analysis was conducted on the data of patients with OBF who underwent surgical treatment at the Affiliated Eye Hospital of Nanchang University between July 2012 and November 2022. The control group consisted of patients who received traditional surgical treatment (n=43), while the new surgical group (n=52) consisted of patients who received NNE with 3DPT. The difference in therapeutic effects between the two groups was evaluated by comparing the duration of the operation, best corrected visual acuity (BCVA), enophthalmos difference, recovery rate of eye movement disorder, recovery rate of diplopia, and incidence of postoperative complications. The study included 95 cases (95 eyes), with 63 men and 32 women. The patients' age ranged from 5 to 67y (35.21±15.75y). The new surgical group and the control group exhibited no statistically significant differences in the duration of the operation, BCVA and enophthalmos difference. The recovery rates of diplopia in the new surgical group were significantly higher than those in the control group at 1mo [OR=0.03, 95%CI (0.01-0.15), P<0.0000] and 3mo [OR=0.11, 95%CI (0.03-0.36), P<0.0000] post-operation. Additionally, the recovery rates of eye movement disorders at 1 and 3mo after surgery were OR=0.08, 95%CI (0.03-0.24), P<0.0000; and OR=0.01, 95%CI (0.00-0.18), P<0.0000. The incidence of postoperative complications was lower in the new surgical group compared to the control group [OR=4.86, 95%CI (0.95-24.78), P<0.05]. The combination of NNE and 3DPT can shorten the recovery time of diplopia and eye movement disorder in patients with OBF.

Statistical segmentation model for accurate electrode positioning in Parkinson's deep brain stimulation based on clinical low-resolution image data and electrophysiology.

Deep Brain Stimulation (DBS), applying chronic electrical stimulation of subcortical structures, is a clinical intervention applied in major neurologic disorders. In order to achieve a good clinical effect, accurate electrode placement is necessary. The primary localisation is typically based on presurgical MRI imaging, often followed by intra-operative electrophysiology recording to increase the accuracy and to compensate for brain shift, especially in cases where the surgical target is small, and there is low contrast: e.g., in Parkinson's disease (PD) and in its common target, the subthalamic nucleus (STN). We propose a novel, fully automatic method for intra-operative surgical navigation. First, the surgical target is segmented in presurgical MRI images using a statistical shape-intensity model. Next, automated alignment with intra-operatively recorded microelectrode recordings is performed using a probabilistic model of STN electrophysiology. We apply the method to a dataset of 120 PD patients with clinical T2 1.5T images, of which 48 also had available microelectrode recordings (MER). The proposed segmentation method achieved STN segmentation accuracy around dice = 0.60 compared to manual segmentation. This is comparable to the state-of-the-art on low-resolution clinical MRI data. When combined with electrophysiology-based alignment, we achieved an accuracy of 0.85 for correctly including recording sites of STN-labelled MERs in the final STN volume. The proposed method combines image-based segmentation of the subthalamic nucleus with microelectrode recordings to estimate their mutual location during the surgery in a fully automated process. Apart from its potential use in clinical targeting, the method can be used to map electrophysiological properties to specific parts of the basal ganglia structures and their vicinity.

Surgical-DINO: adapter learning of foundation models for depth estimation in endoscopic surgery

PurposeDepth estimation in robotic surgery is vital in 3D reconstruction, surgical navigation and augmented reality visualization. Although the foundation model exhibits outstanding performance in many vision tasks, including depth estimation (e.g., DINOv2), recent works observed its limitations in medical and surgical domain-specific applications. This work presents a low-ranked adaptation (LoRA) of the foundation model for surgical depth estimation.MethodsWe design a foundation model-based depth estimation method, referred to as Surgical-DINO, a low-rank adaptation of the DINOv2 for depth estimation in endoscopic surgery. We build LoRA layers and integrate them into DINO to adapt with surgery-specific domain knowledge instead of conventional fine-tuning. During training, we freeze the DINO image encoder, which shows excellent visual representation capacity, and only optimize the LoRA layers and depth decoder to integrate features from the surgical scene.ResultsOur model is extensively validated on a MICCAI challenge dataset of SCARED, which is collected from da Vinci Xi endoscope surgery. We empirically show that Surgical-DINO significantly outperforms all the state-of-the-art models in endoscopic depth estimation tasks. The analysis with ablation studies has shown evidence of the remarkable effect of our LoRA layers and adaptation.ConclusionSurgical-DINO shed some light on the successful adaptation of the foundation models into the surgical domain for depth estimation. There is clear evidence in the results that zero-shot prediction on pre-trained weights in computer vision datasets or naive fine-tuning is not sufficient to use the foundation model in the surgical domain directly.

ResDAC-Net: a novel pancreas segmentation model utilizing residual double asymmetric spatial kernels

The pancreas not only is situated in a complex abdominal background but is also surrounded by other abdominal organs and adipose tissue, resulting in blurred organ boundaries. Accurate segmentation of pancreatic tissue is crucial for computer-aided diagnosis systems, as it can be used for surgical planning, navigation, and assessment of organs. In the light of this, the current paper proposes a novel Residual Double Asymmetric Convolution Network (ResDAC-Net) model. Firstly, newly designed ResDAC blocks are used to highlight pancreatic features. Secondly, the feature fusion between adjacent encoding layers fully utilizes the low-level and deep-level features extracted by the ResDAC blocks. Finally, parallel dilated convolutions are employed to increase the receptive field to capture multiscale spatial information. ResDAC-Net is highly compatible to the existing state-of-the-art models, according to three (out of four) evaluation metrics, including the two main ones used for segmentation performance evaluation (i.e., DSC and Jaccard index).Graphical abstract

Surgical Navigation in the Anterior Skull Base Using 3-Dimensional Endoscopy and Surface Reconstruction

Image guidance is an important adjunct for endoscopic sinus and skull base surgery. However, current systems require bulky external tracking equipment, and their use can interrupt efficient surgical workflow. To evaluate a trackerless surgical navigation system using 3-dimensional (3D) endoscopy and simultaneous localization and mapping (SLAM) algorithms in the anterior skull base. This interventional deceased donor cohort study and retrospective clinical case study was conducted at a tertiary academic medical center with human deceased donor specimens and a patient with anterior skull base pathology. Participants underwent endoscopic endonasal transsphenoidal dissection and surface model reconstruction from stereoscopic video with registration to volumetric models segmented from computed tomography (CT) and magnetic resonance imaging. To assess the fidelity of surface model reconstruction and accuracy of surgical navigation and surface-CT model coregistration, 3 metrics were calculated: reconstruction error, registration error, and localization error. In deceased donor models (n = 9), high-fidelity surface models of the posterior wall of the sphenoid sinus were reconstructed from stereoscopic video and coregistered to corresponding volumetric CT models. The mean (SD; range) reconstruction, registration, and localization errors were 0.60 (0.24; 0.36-0.93), 1.11 (0.49; 0.71-1.56) and 1.01 (0.17; 0.78-1.25) mm, respectively. In a clinical case study of a patient who underwent a 3D endoscopic endonasal transsphenoidal resection of a tubercular meningioma, a high-fidelity surface model of the posterior wall of the sphenoid was reconstructed from intraoperative stereoscopic video and coregistered to a volumetric preoperative fused CT magnetic resonance imaging model with a root-mean-square error of 1.38 mm. The results of this study suggest that SLAM algorithm-based endoscopic endonasal surgery navigation is a novel, accurate, and trackerless approach to surgical navigation that uses 3D endoscopy and SLAM-based algorithms in lieu of conventional optical or electromagnetic tracking. While multiple challenges remain before clinical readiness, a SLAM algorithm-based endoscopic endonasal surgery navigation system has the potential to improve surgical efficiency, economy of motion, and safety.

INTRAOPERATIVE SURGICAL NAVIGATION BASED ON LASER SCANNER FOR IMAGE-GUIDED ORAL AND MAXILLOFACIAL SURGERY

In oral and maxillofacial surgery, computer-assisted navigation technologies have been widely used to achieve intraoperative positioning. The traditional methods mainly rely on the experience of doctors and the difference between the locations of key points in the surgical area and the preoperative planning, which have certain limitations. In this paper, a new intraoperative surgical navigation framework based on mobile laser scanner is proposed, which ensures that surgery is performed accurately according to the preoperative planning. The framework mainly includes two parts. First, the real-time surface reconstruction of the anatomy should be realized during the operation. Second, the acquired image is matched to the planned image in real time. Although the most common method of surface reconstruction is to render the volume directly from raw data or render the surface from the segmented data using computed tomography/magnetic resonance (CT/MR) data, this method is too complicated for performing the real-time operation during surgery. Furthermore, a new surface registration technique is proposed for image-guided oral and maxillofacial surgery based on the point sets. To improve the registration accuracy and robustness, the point sets are modeled by Mixed Student’s t-Distribution model. In the experiments, the point sets of CT data are from 10 patients with craniomaxillofacial diseases and the surface point set is from the LRS. The TRE of 10 data was less than 1[Formula: see text]mm. Compared with the paired-point registration method and Iterative Closest Point algorithm, the results demonstrated better performance of the proposed method, the surgical situation can be displayed in real time during the surgical process, and any differences from the surgical plan can also be reflected.

Navigating the Nose—The Future of Endoscopic Endonasal Surgical Navigation

Navigating the Nose—The Future of Endoscopic Endonasal Surgical Navigation

Deformable registration of preoperative MR and intraoperative long-length tomosynthesis images for guidance of spine surgery via image synthesis

PurposeImproved integration and use of preoperative imaging during surgery hold significant potential for enhancing treatment planning and instrument guidance through surgical navigation. Despite its prevalent use in diagnostic settings, MR imaging is rarely used for navigation in spine surgery. This study aims to leverage MR imaging for intraoperative visualization of spine anatomy, particularly in cases where CT imaging is unavailable or when minimizing radiation exposure is essential, such as in pediatric surgery. MethodsThis work presents a method for deformable 3D-2D registration of preoperative MR images with a novel intraoperative long-length tomosynthesis imaging modality (viz., Long-Film [LF]). A conditional generative adversarial network is used to translate MR images to an intermediate bone image suitable for registration, followed by a model-based 3D-2D registration algorithm to deformably map the synthesized images to LF images. The algorithm’s performance was evaluated on cadaveric specimens with implanted markers and controlled deformation, and in clinical images of patients undergoing spine surgery as part of a large-scale clinical study on LF imaging. ResultsThe proposed method yielded a median 2D projection distance error of 2.0 mm (interquartile range [IQR]: 1.1–3.3 mm) and a 3D target registration error of 1.5 mm (IQR: 0.8–2.1 mm) in cadaver studies. Notably, the multi-scale approach exhibited significantly higher accuracy compared to rigid solutions and effectively managed the challenges posed by piecewise rigid spine deformation. The robustness and consistency of the method were evaluated on clinical images, yielding no outliers on vertebrae without surgical instrumentation and 3% outliers on vertebrae with instrumentation. ConclusionsThis work constitutes the first reported approach for deformable MR to LF registration based on deep image synthesis. The proposed framework provides access to the preoperative annotations and planning information during surgery and enables surgical navigation within the context of MR images and/or dual-plane LF images.

Robotic-assisted anatomic anterior cruciate ligament reconstruction: a comparative analysis of modified transtibial and anteromedial portal techniques in cadaveric knees.

Introduction: This study employed surgical robot to perform anatomic single-bundle reconstruction using the modified transtibial (TT) technique and anteromedial (AM) portal technique. The purpose was to directly compare tunnel and graft characteristics of the two techniques. Methods: Eight cadaveric knees without ligament injury were used in the study. The modified TT and AM portal technique were both conducted under surgical robotic system. Postoperative data acquisition of the tunnel and graft characteristics included tibial tunnel position, tunnel angle, tunnel length and femoral tunnel-graft angle. Results: The mean tibial tunnel length of the modified TT technique was significantly shorter than in the AM portal technique (p < 0.001). The mean length of the femoral tunnel was significantly longer for the modified TT technique than for the AM portal technique (p < 0.001). The mean coronal angle of the tibial tunnel was significantly lower for the modified TT technique than for the AM portal technique (p < 0.001). The mean coronal angle of the femoral tunnel was significantly lower for the AM portal technique than for the modified TT technique (p < 0.001). The AM portal technique resulted in a graft bending angle that was significantly more angulated in the coronal (p < 0.001) and the sagittal planes (p < 0.001) compared with the modified TT technique. Discussion: Comparison of the preoperative planning and postoperative femoral tunnel positions showed that the mean difference of the tunnel position was 1.8 ± 0.4mm. It suggested that the surgical navigation robot could make predictable tunnel position with high accuracy. The findings may support that the modified TT technique has benefits on femoral tunnel length and obliquity compared with AM portal technique. The modified TT technique showed a larger femoral tunnel angle in the coronal plane than the AM portal technique. Compared with the modified TT technique, the more horizontal trajectory of the femoral tunnel in the AM portal technique creates a shorter femoral tunnel length and a more acute graft bending angle.

Enhancing Single-Plane Fluoroscopy: A Self-Calibrating Bundle Adjustment for Distortion Modeling.

Single-plane fluoroscopy systems with image intensifiers remain commonly employed in a clinical setting. The imagery they capture is vulnerable to several types of geometric distortions introduced by the system's components and their assembly as well as interactions with the local and global magnetic fields. In this study, the application of a self-calibrating bundle adjustment is investigated as a method to correct geometric distortions in single-plane fluoroscopic imaging systems. The resulting calibrated imagery is then applied in the quantitative analysis of diaphragmatic motion and potential diagnostic applications to hemidiaphragm paralysis. The calibrated imagery is further explored and discussed in its potential impact on areas of surgical navigation. This work was accomplished through the application of a controlled experiment with three separate Philips Easy Diagnost R/F Systems. A highly redundant (~2500 to 3500 degrees-of-freedom) and geometrically strong network of 18 to 22 images of a low-cost target field was collected. The target field comprised 121 pre-surveyed tantalum beads embedded on a 25.4 mm × 25.4 mm acrylic base plate. The modeling process resulted in the estimation of five to eight distortion coefficients, depending on the system. The addition of these terms resulted in 83-85% improvement in terms of image point precision (model fit) and 85-95% improvement in 3D object reconstruction accuracy after calibration. This study demonstrates significant potential in enhancing the accuracy and reliability of fluoroscopic imaging, thereby improving the overall quality and effectiveness of medical diagnostics and treatments.

Independently Trained Multi-Scale Registration Network Based on Image Pyramid.

Image registration is a fundamental task in various applications of medical image analysis and plays a crucial role in auxiliary diagnosis, treatment, and surgical navigation. However, cardiac image registration is challenging due to the large non-rigid deformation of the heart and the complex anatomical structure. To address this challenge, this paper proposes an independently trained multi-scale registration network based on an image pyramid. By down-sampling the original input image multiple times, we can construct image pyramid pairs, and design a multi-scale registration network using image pyramid pairs of different resolutions as the training set. Using image pairs of different resolutions, train each registration network independently to extract image features from the image pairs at different resolutions. During the testing stage, the large deformation registration is decomposed into a multi-scale registration process. The deformation fields of different resolutions are fused by a step-by-step deformation method, thereby addressing the challenge of directly handling large deformations. Experiments were conducted on the open cardiac dataset ACDC (Automated Cardiac Diagnosis Challenge); the proposed method achieved an average Dice score of 0.828 in the experimental results. Through comparative experiments, it has been demonstrated that the proposed method effectively addressed the challenge of heart image registration and achieved superior registration results for cardiac images.

Comparison study of intraoperative surface acquisition methods on registration accuracy for soft-tissue surgical navigation.

To study the difference between rigid registration and nonrigid registration using two forms of digitization (contact and noncontact) in human in vivo liver surgery. A Conoprobe device attachment and sterilization process was developed to enable prospective noncontact intraoperative acquisition of organ surface data in the operating room (OR). The noncontact Conoprobe digitization method was compared against stylus-based acquisition in the context of image-to-physical registration for image-guided surgical navigation. Data from patients undergoing liver resection were analyzed under an Institutional Review Board-approved study at Memorial Sloan Kettering Cancer Center. Organ surface coverage of each surface acquisition method was compared. Registration accuracies resulting from the acquisition techniques were compared for (1)rigid registration method (RRM), (2)model-based nonrigid registration method (NRM) using surface data only, and (3)NRM with one subsurface feature (vena cava) from tracked intraoperative ultrasound (NRM-VC). Novel vessel centerline and tumor targets were segmented and compared to their registered preoperative counterparts for accuracy validation. Surface data coverage collected by stylus and Conoprobe were and , respectively. The average difference between stylus data and Conoprobe data using NRM was and using NRM-VC was , indicating the registrations to Conoprobe data performed worse than to stylus data with both NRM approaches. However, using the stylus and Conoprobe acquisition methods led to significant improvement of NRM-VC over RRM by average differences of 4.48 and 3.66mm, respectively. The first use of a sterile-field amenable Conoprobe surface acquisition strategy in the OR is reported for open liver surgery. Under clinical conditions, the nonrigid registration significantly outperformed standard-of-care rigid registration, and acquisition by contact-based stylus and noncontact-based Conoprobe produced similar registration results. The accuracy benefits of noncontact surface acquisition with a Conoprobe are likely obscured by inferior data coverage and intrinsic noise within acquisition systems.

JS-K Combined with a Melanin-Based Theranostic Agent: A Novel Sequential Delivery Strategy to Enhance the Near-Infrared Fluorescence Imaging of Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy with a 5 year survival rate less than 12%. This malignancy is closely related to the unique tumor microenvironment (TME), which is characterized by a hypovascular and hyperdense extracellular matrix, making it difficult for drugs to permeate the tumor center. Near-infrared fluorescence (NIRF) imaging, which has high sensitivity and resolution, may improve the survival rate of PDAC patients. In this study, we first used JS-K (O2-(2,4-dinitrophenyl) 1-[(4-ethoxycarbonyl) piperazine-1-yl] diazene-1-ium-1,2-diolate) to specifically dilate blood vessels within the TME of PDAC patients and subsequently injected IR820-PEG-MNPs (IPM NPs) to diagnose and treat orthotopic PDAC. We found that JS-K promoted the accumulation of IPM NPs in orthotopic Pan02 tumor-bearing mice and was able to increase the tumor signal-to-background ratio (SBR) in the orthotopic PDAC area by 41.5%. In addition, surgical navigation in orthotopic Pan02 tumor-bearing mice and complete tumor resection based on fluorescence imaging were achieved with a detection sensitivity of 81.0%. Moreover, we verified the feasibility of the combination of laparoscopy and photothermal ablation (PTA) for the treatment of PDAC. Finally, we demonstrated that IPM NPs had greater affinity for human PDAC tissues than for normal pancreatic tissues ex vivo, preliminarily highlighting the potential for clinical translation of these NPs. In conclusion, we developed and validated a novel sequential delivery strategy that promotes the accumulation of nanoagents in the tumor area and can be used for the diagnosis and treatment of PDAC.

Direct visualization of intraparotid facial nerve assisting in parotid tumor resection

Precise recognition of the intraparotid facial nerve (IFN) is crucial during parotid tumor resection. We aimed to explore the application effect of direct visualization of the IFN in parotid tumor resection. Fifteen patients with parotid tumors were enrolled in this study and underwent specific radiological scanning in which the IFNs were displayed as high-intensity images. After image segmentation, IFN could be preoperatively directly visualized. Mixed reality combined with surgical navigation were applied to intraoperatively directly visualize the segmentation results as real-time three-dimensional holograms, guiding the surgeons in IFN dissection and tumor resection. Radiological visibility of the IFN, accuracy of image segmentation and postoperative facial nerve function were analyzed. The trunks of IFN were directly visible in radiological images for all patients. Of 37 landmark points on the IFN, 36 were accurately segmented. Four patients were classified as House–Brackmann Grade I postoperatively. Two patients with malignancies had postoperative long-standing facial paralysis. Direct visualization of IFN was a feasible novel method with high accuracy that could assist in recognition of IFN and therefore potentially improve the treatment outcome of parotid tumor resection.

Multimodality Imaging for 3D Printing and Surgical Rehearsal in Complex Spine Surgery.

Surgery is the mainstay treatment of symptomatic spinal tumors. It aids in restoring functionality, managing pain and tumor growth, and improving overall quality of life. Over the past decade, advancements in medical imaging techniques combined with the use of three-dimensional (3D) printing technology have enabled improvements in the surgical management of spine tumors by significantly increasing the precision, accuracy, and safety of the surgical procedures. For complex spine surgical cases, the use of multimodality imaging is necessary to fully visualize the extent of disease, including both soft-tissue and bone involvement. Integrating the information provided by these examinations in a cohesive manner to facilitate surgical planning can be challenging, particularly when multiple surgical specialties work in concert. The digital 3-dimensional (3D) model or 3D rendering and the 3D printed model created from imaging examinations such as CT and MRI not only facilitate surgical planning but also allow the placement of virtual and physical surgical or osteotomy planes, further enhancing surgical planning and rehearsal. The authors provide practical information about the 3D printing workflow, from image acquisition to postprocessing of a 3D printed model, as well as optimal material selection and incorporation of quality management systems, to help surgeons utilize 3D printing for surgical planning. The authors also highlight the process of surgical rehearsal, how to prescribe digital osteotomy planes, and integration with intraoperative surgical navigation systems through a case-based discussion. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material.

Deep Learning-Based Medical Image Registration Algorithm: Enhancing Accuracy with Dense Connections and Channel Attention Mechanisms

In critical clinical medical image analysis applications, such as surgical navigation and tumor monitoring, image registration is crucial. Recognizing the potential for enhanced accuracy in existing unsupervised image registration techniques for single-modal imagery, this research introduces an innovative deep learning-based image registration algorithm. Its novelty resides in integrating short and long connections to create a densely connected structure, markedly refining the feature map interconnectivity within the U-Net architecture. This advancement addresses the significant semantic gap issues arising from disparities in feature map sampling depths. Moreover, the algorithm incorporates a channel attention mechanism within the U-shaped network's decoder, significantly mitigating image noise and facilitating the generation of smoother deformation fields. This enhancement not only boosts the model's detail sensitivity but also markedly increases image registration precision, particularly evident when processing single-modal brain MRI datasets, thereby proving the algorithm's efficacy and utility. Extensive clinical application-based training and testing have underscored this algorithm's substantial contributions to medical image registration accuracy enhancement. Overall, by leveraging deep learning technologies and innovative algorithmic structures, this study addresses pivotal challenges in medical image registration, offering more precise and dependable support for clinical applications like surgical navigation and tumor surveillance.

FGF19-Based Mini Probe Targeting FGFR4 for Diagnosis and Surgical Navigation of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a frequent malignancy that has a high death rate and a high rate of recurrence following surgery, owing to insufficient surgical resection. Furthermore, HCC is prone to peritoneal metastasis (HCC-PM), resulting in a significant number of tiny cancer lesions, making surgical removal more challenging. As a potential imaging target, FGFR4 is highly expressed in tumors, especially in HCC, but is less expressed in the normal liver. In this study, we used computational simulation approaches to develop peptide I0 derived from FGF19, a particular ligand of FGFR4, and labeled it with the NIRF dye, MPA, for HCC detection. In surgical navigation, the TBR was 9.31 ± 1.36 and 8.57 ± 1.15 in HepG2 in situ tumor and HCC-PM models, respectively, indicating considerable tumor uptake. As a result, peptide I0 is an excellent clinical diagnostic reagent for HCC, as well as a tool for surgically resecting HCC peritoneal metastases.