Diagnostic and procedural intraoperative ultrasound: technique, tips and tricks for optimizing results

Navigating the Future of 3D Laparoscopic Liver Surgeries: Visualization of Internal Anatomy on Laparoscopic Images With Augmented Reality

INTRODUCTION

Liver tumor resection requires precise localization of tumors and blood vessels. Despite advancements in 3-dimensional (3D) visualization for laparoscopic surgeries, challenges persist. We developed and evaluated an augmented reality (AR) system that overlays preoperative 3D models onto laparoscopic images, offering crucial support for 3D visualization during laparoscopic liver surgeries.

METHODS

Anatomic liver structures from preoperative computed tomography scans were segmented using open-source software including 3D Slicer and Maya 2022 for 3D model editing. A registration system was created with 3D visualization software utilizing a stereo registration input system to overlay the virtual liver onto laparoscopic images during surgical procedures. A controller was customized using a modified keyboard to facilitate manual alignment of the virtual liver with the laparoscopic image. The AR system was evaluated by 3 experienced surgeons who performed manual registration for a total of 27 images from 7 clinical cases. The evaluation criteria included registration time; measured in minutes, and accuracy; measured using the Dice similarity coefficient.

RESULTS

The overall mean registration time was 2.4±1.7 minutes (range: 0.3 to 9.5 min), and the overall mean registration accuracy was 93.8%±4.9% (range: 80.9% to 99.7%).

CONCLUSION

Our validated AR system has the potential to effectively enable the prediction of internal hepatic anatomic structures during 3D laparoscopic liver resection, and may enhance 3D visualization for select laparoscopic liver surgeries.

A comprehensive survey on the use of deep learning techniques in glioblastoma

Glioblastoma, characterized as a grade 4 astrocytoma, stands out as the most aggressive brain tumor, often leading to dire outcomes. The challenge of treating glioblastoma is exacerbated by the convergence of genetic mutations and disruptions in gene expression, driven by alterations in epigenetic mechanisms. The integration of artificial intelligence, inclusive of machine learning algorithms, has emerged as an indispensable asset in medical analyses. AI is becoming a necessary tool in medicine and beyond. Current research on Glioblastoma predominantly revolves around non-omics data modalities, prominently including magnetic resonance imaging, computed tomography, and positron emission tomography. Nonetheless, the assimilation of omic data-encompassing gene expression through transcriptomics and epigenomics-offers pivotal insights into patients' conditions. These insights, reciprocally, hold significant value in refining diagnoses, guiding decision- making processes, and devising efficacious treatment strategies. This survey's core objective encompasses a comprehensive exploration of noteworthy applications of machine learning methodologies in the domain of glioblastoma, alongside closely associated research pursuits. The study accentuates the deployment of artificial intelligence techniques for both non-omics and omics data, encompassing a range of tasks. Furthermore, the survey underscores the intricate challenges posed by the inherent heterogeneity of Glioblastoma, delving into strategies aimed at addressing its multifaceted nature.

Contrast enhanced ultrasound for traumatic spinal cord injury: an overview of current and future applications

STUDY DESIGN

Systematic review.

OBJECTIVE

Contrast-enhanced ultrasound (CEUS) is an imaging modality that has only recently seen neurosurgical application. CEUS uses inert microbubbles to intraoperatively visualize vasculature and perfusion of the brain and spinal cord in real time. Observation and augmentation of spinal cord perfusion is vital component of the management of traumatic spinal cord injury, yet there are limited imaging modalities to evaluate spinal cord perfusion. CEUS provides an intraoperative imaging tool to evaluate spinal cord perfusion in real time. The objective of this review is to evaluate the current literature on the various applications and benefits of CEUS in traumatic spinal cord injury.

SETTING

South Carolina, USA.

METHODS

This review was written according to the PRISMA 2020 guidelines.

RESULTS

143 articles were found in our literature search, with 46 of them being unique. After excluding articles for relevance to CEUS and spinal cord injury, we were left with 10 papers. Studies in animal models have shown CEUS to be an effective non-invasive imaging modality that can detect perfusion changes of injured spinal cords in real time.

CONCLUSION

This imaging modality can provide object perfusion data of the nidus of injury, surrounding penumbra and healthy neural tissue in a traumatized spinal cord. Investigation in its use in humans is ongoing and remains promising to be an effective diagnostic and prognostic tool for those suffering from spinal cord injury.

Intraoperative image-guidance during robotic surgery: is there clinical evidence of enhanced patient outcomes?

BACKGROUND

To date, the benefit of image guidance during robot-assisted surgery (IGS) is an object of debate. The current study aims to address the quality of the contemporary body of literature concerning IGS in robotic surgery throughout different surgical specialties.

METHODS

A systematic review of all English-language articles on IGS, from January 2013 to March 2023, was conducted using PubMed, Cochrane library's Central, EMBASE, MEDLINE, and Scopus databases. Comparative studies that tested performance of IGS vs control were included for the quantitative synthesis, which addressed outcomes analyzed in at least three studies: operative time, length of stay, blood loss, surgical margins, complications, number of nodal retrievals, metastatic nodes, ischemia time, and renal function loss. Bias-corrected ratio of means (ROM) and bias-corrected odds ratio (OR) compared continuous and dichotomous variables, respectively. Subgroup analyses according to guidance type (i.e., 3D virtual reality vs ultrasound vs near-infrared fluoresce) were performed.

RESULTS

Twenty-nine studies, based on 11 surgical procedures of three specialties (general surgery, gynecology, urology), were included in the quantitative synthesis. IGS was associated with 12% reduction in length of stay (ROM 0.88; p = 0.03) and 13% reduction in blood loss (ROM 0.87; p = 0.03) but did not affect operative time (ROM 1.00; p = 0.9), or complications (OR 0.93; p = 0.4). IGS was associated with an estimated 44% increase in mean number of removed nodes (ROM 1.44; p < 0.001), and a significantly higher rate of metastatic nodal disease (OR 1.82; p < 0.001), as well as a significantly lower rate of positive surgical margins (OR 0.62; p < 0.001). In nephron sparing surgery, IGS significantly decreased renal function loss (ROM 0.37; p = 0.002).

CONCLUSIONS

Robot-assisted surgery benefits from image guidance, especially in terms of pathologic outcomes, namely higher detection of metastatic nodes and lower surgical margins. Moreover, IGS enhances renal function preservation and lowers surgical blood loss.

A human serum albumin-indocyanine green complex offers improved tumor identification in fluorescence-guided surgery

Background

Complete tumor removal is critical for achieving a good prognosis in patients but remains challenging for surgeons. Near-infrared fluorescence-guided surgery (NIRFGS) enables surgeons to accurately localize tumors in real time and facilitates accurate resection. Indocyanine green (ICG) has been approved by the U.S. Food and Drug Administration and the National Medical Products Administration for many years. Although the application of ICG has progressed for a variety of surgeries, there are inherent limitations to ICG, including poor water solubility and photostability, short blood half-life, and aggregation in blood, resulting in poor imaging performance. We found that mixing ICG with human serum albumin (HSA) preoperatively and then injecting it can improve the imaging performance.

Methods

We prepared fluorescent probes by combining ICG with HSA and identified their optimal ratio via in vitro absorption measurement and emission spectrum characterization of ICG-HSA complex with different mixing ratios and concentration gradients. Subsequently, under the optimal ratio and clinical simulated concentration, we conducted dynamic change analysis of the fluorescence spectral properties after mixing. We then compared the uptake of ICG-HSA in vitro for two different cell types and the imaging performance of different molar ratios of ICG and HSA in mouse models.

Results

Through in vitro absorption and emission spectrum characterization of ICG-HSA mixtures with different mixing ratios and concentration gradients, the optimal ratio of the mixture was obtained (ICG:HSA =4:5). Using this ratio, clinical simulated concentration, and mixing, we completed the dynamic change analysis of the fluorescence spectrum properties. The results verified that HSA can improve the dispersion and stability of ICG in aqueous solution, reduce the proportion of free-state ICG, and thus improve the biodistribution. Moreover, the fluorescence performance of ICG was improved. ICG-HSA and ICG uptake in MDA-MB-231 cells and imaging in vivo showed that HSA increased the enrichment of ICG in tumor compared to ICG alone (ICG-HSAfluorescence intensity =237.3±10.7 vs. ICGfluorescence intensity =127.1±10.7). Compared with ICG alone, ICG-HSA provided a clearer tumor boundary and higher tumor-to-background ratio (TBR) (ICG-HSATBRmax 3.49±0.56 vs. ICGTBRmax 1.94±0.23).

Conclusions

This study suggests that ICG-HSA can achieve higher tumor-to-background contrast with shorter time and can provide an overall superior imaging performance compared to ICG alone, thus exhibiting considerable potential for clinical application.

Non-contrast ultrasound image analysis for spatial and temporal distribution of blood flow after spinal cord injury

Ultrasound technology can provide high-resolution imaging of blood flow following spinal cord injury (SCI). Blood flow imaging may improve critical care management of SCI, yet its duration is limited clinically by the amount of contrast agent injection required for high-resolution, continuous monitoring. In this study, we aim to establish non-contrast ultrasound as a clinically translatable imaging technique for spinal cord blood flow via comparison to contrast-based methods and by measuring the spatial distribution of blood flow after SCI. A rodent model of contusion SCI at the T12 spinal level was carried out using three different impact forces. We compared images of spinal cord blood flow taken using both non-contrast and contrast-enhanced ultrasound. Subsequently, we processed the images as a function of distance from injury, yielding the distribution of blood flow through space after SCI, and found the following. (1) Both non-contrast and contrast-enhanced imaging methods resulted in similar blood flow distributions (Spearman's ρ = 0.55, p < 0.0001). (2) We found an area of decreased flow at the injury epicenter, or umbra (p < 0.0001). Unexpectedly, we found increased flow at the periphery, or penumbra (rostral, p < 0.05; caudal, p < 0.01), following SCI. However, distal flow remained unchanged, in what is presumably unaffected tissue. (3) Finally, tracking blood flow in the injury zones over time revealed interesting dynamic changes. After an initial decrease, blood flow in the penumbra increased during the first 10 min after injury, while blood flow in the umbra and distal tissue remained constant over time. These results demonstrate the viability of non-contrast ultrasound as a clinical monitoring tool. Furthermore, our surprising observations of increased flow in the injury periphery pose interesting new questions about how the spinal cord vasculature reacts to SCI, with potentially increased significance of the penumbra.

[Status Quo of Surgical Navigation]

Surgical navigation, also referred to as computer-assisted or image-guided surgery, is a technique that employs a variety of methods - such as 3D imaging, tracking systems, specialised software, and robotics to support surgeons during surgical interventions. These emerging technologies aim not only to enhance the accuracy and precision of surgical procedures, but also to enable less invasive approaches, with the objective of reducing complications and improving operative outcomes for patients. By harnessing the integration of emerging digital technologies, surgical navigation holds the promise of assisting complex procedures across various medical disciplines. In recent years, the field of surgical navigation has witnessed significant advances. Abdominal surgical navigation, particularly endoscopy, laparoscopic, and robot-assisted surgery, is currently undergoing a phase of rapid evolution. Emphases include image-guided navigation, instrument tracking, and the potential integration of augmented and mixed reality (AR, MR). This article will comprehensively delve into the latest developments in surgical navigation, spanning state-of-the-art intraoperative technologies like hyperspectral and fluorescent imaging, to the integration of preoperative radiological imaging within the intraoperative setting.

Anatomy preserving GAN for realistic simulation of intraoperative liver ultrasound images

In ultrasound-guided liver surgery, the lack of large-scale intraoperative ultrasound images with important anatomical structures remains an obstacle hindering the successful application of AI to ultrasound guidance. In this case, intraoperative ultrasound (iUS) simulation should be conducted from preoperative magnetic resonance (pMR), which not only helps doctors understand the characteristics of iUS in advance, but also expands the iUS dataset from various imaging positions, thereby promoting the automatic iUS analysis in ultrasound guidance. Herein, a novel anatomy preserving generative adversarial network (ApGAN) framework was proposed to generate simulated intraoperative ultrasound (Sim-iUS) of liver with precise structure information from pMR. Specifically, the low-rank factors based bimodal fusion was first established focusing on the effective information of hepatic parenchyma. Then, a deformation field based correction module was introduced to learn and correct the slight structural distortion from surgical operations. Meanwhile, the multiple loss functions were designed to constrain the simulation of the content, structures, and style. Empirical results of clinical data showed that the proposed ApGAN obtained higher Structural Similarity (SSIM) of 0.74 and Fr´echet Inception Distance (FID) of 35.54 compared to existing methods. Furthermore, the average Hausdorff Distance (HD) error of the liver capsule structure was less than 0.25 mm, and the average relative (Euclidean Distance) ED error for polyps was 0.12 mm, indicating the high-level precision of this ApGAN in simulating the anatomical structures and focal areas.

New and effective EGFR-targeted fluorescence imaging technology for intraoperative rapid determination of lung cancer in freshly isolated tissue

PURPOSE

During lung cancer surgery, it is very important to define tumor boundaries and determine the surgical margin distance. In previous research, systemically application of fluorescent probes can help medical professionals determine the boundaries of tumors and find small tumors and metastases, thereby improving the accuracy of surgical resection. However, there are very few safe and effective probes that can be applied to clinical trials up to now, which limits the clinical application of fluorescence imaging. Here we developed a new technology that can quickly identify the tumor area in the resected lung tissue during the operation and distinguish the tumor boundary and metastatic lymph nodes.

EXPERIMENTAL DESIGN

For animal studies, a PDX model of lung cancer was established. The tumors, lungs, and peritumoral muscle tissues of tumor-bearing mice were surgically removed and incubated with a probe targeting epidermal growth factor receptor (EGFR) for 20 min, and then imaged by a closed-field near-infrared two-zone (NIR-II) fluorescence imaging system. For clinical samples, ten surgically removed lung tissues and 60 lymph nodes from 10 lung cancer patients undergoing radical resection were incubated with the targeting probe immediately after intraoperative resection and imaged to identify the tumor area and distinguish the tumor boundary and metastatic lymph nodes. The accuracy of fluorescence imaging was confirmed by HE staining and immunohistochemistry.

RESULTS

The ex vivo animal imaging experiments showed a fluorescence enhancement of tumor tissue. For clinical samples, our results showed that this new technology yielded more than 85.7% sensitivity and 100% specificity in identifying the tumor area in the resected lung tissue. The average fluorescence tumor-to-background ratio was 2.5 ± 1.3. Furthermore, we also used this technique to image metastatic lymph nodes intraoperatively and showed that metastatic lymph nodes have brighter fluorescence than normal lymph nodes, as the average fluorescence tumor-to-background signal ratio was 2.7 ± 1.1. Calculations on the results of the fluorescence signal in relation to the number of metastatic lymph nodes yielded values of 77.8% for sensitivity and 92.1% for specificity. We expect this new technology to be a useful diagnostic tool for rapid intraoperative pathological detection and margin determination.

CONCLUSIONS

By using fluorescently labeled anti-human EGFR recombinant antibody scFv fragment to incubate freshly isolated tissues during surgery, the probes can quickly accumulate in lung cancer tissues, which can be used to quickly identify tumor areas in the resected lung tissues and distinguish tumor boundaries and find metastases in lymph nodes. This technology is expected to be used for rapid intraoperative pathological detection and margin determination.

Intraoperative Evaluation of Brain-Tumor Microvascularization through MicroV IOUS: A Protocol for Image Acquisition and Analysis of Radiomic Features

Microvascular Doppler (MicroV) is a new-generation Doppler technique developed by Esaote (Esaote s.p.a., Genova, Italy), which is able to visualize small and low-flow vessels through a suppression of interfering signals. MicroV uses advanced filters that are able to differentiate tissue artifacts from low-speed blood flows; by exploiting the space-time coherence information, these filters can selectively suppress tissue components, preserving the signal coming from the microvascular flow. This technique is clinically applied to the study of the vascularization of parenchymatous lesions, often with better diagnostic accuracy than color/power Doppler techniques. The aim of this paper is to develop a reproducible protocol for the recording and collection of MicroV intraoperative ultrasound images by the use of a capable intraoperative ultrasound machine and post-processing aimed at evaluation of brain-tumor microvascularization through the analysis of radiomic features. The proposed protocol has been internally validated on eight patients and will be firstly applied to patients affected by WHO grade IV astrocytoma (glioblastoma-GBM) candidates for craniotomy and lesion removal. In a further stage, it will be generally applied to patients with primary or metastatic brain tumors. IOUS is performed before durotomy. Tumor microvascularization is evaluated using the MicroV Doppler technique and IOUS images are recorded, stored, and post-processed. IOUS images are remotely stored on the BraTIoUS database, which will promote international cooperation and multicentric analysis. Processed images and texture radiomic features are analyzed post-operatively using ImageJ, a free scientific image-analysis software based on the Sun-Java platform. Post-processing protocol is further described in-depth. The study of tumor microvascularization through advanced IOUS techniques such as MicroV could represent, in the future, a non-invasive and real-time method for intraoperative predictive evaluation of the tumor features. This evaluation could finally result in a deeper knowledge of brain-tumor behavior and in the on-going adaptation of the surgery with the improvement of surgical outcomes.

Artificial intelligence for identification of focal lesions in intraoperative liver ultrasonography

PURPOSE

Intraoperative ultrasonography (IOUS) of the liver is a crucial adjunct in every liver resection and may significantly impact intraoperative surgical decisions. However, IOUS is highly operator dependent and has a steep learning curve. We describe the design and assessment of an artificial intelligence (AI) system to identify focal liver lesions in IOUS.

METHODS

IOUS images were collected during liver resections performed between November 2020 and November 2021. The images were labeled by radiologists and surgeons as normal liver tissue versus images that contain liver lesions. A convolutional neural network (CNN) was trained and tested to classify images based on the labeling. Algorithm performance was tested in terms of area under the curves (AUCs), accuracy, sensitivity, specificity, F1 score, positive predictive value, and negative predictive value.

RESULTS

Overall, the dataset included 5043 IOUS images from 16 patients. Of these, 2576 were labeled as normal liver tissue and 2467 as containing focal liver lesions. Training and testing image sets were taken from different patients. Network performance area under the curve (AUC) was 80.2 ± 2.9%, and the overall classification accuracy was 74.6% ± 3.1%. For maximal sensitivity of 99%, the classification specificity is 36.4 ± 9.4%.

CONCLUSIONS

This study provides for the first time a proof of concept for the use of AI in IOUS and show that high accuracy can be achieved. Further studies using high volume data are warranted to increase accuracy and differentiate between lesion types.

Study protocol for a randomised controlled trial on the use of intraoperative ultrasound-guided laparoscopic ovarian cystectomy (UGLOC) as a method of fertility preservation in the management of benign ovarian cysts

INTRODUCTION

The lifetime risk of women undergoing surgery for the presence of benign ovarian pathology in the UK is 5%-10%. Despite minimally invasive surgical techniques, evidence suggests a number of healthy ovarian follicles and tissues are resected intraoperatively, resulting in subsequent decline of ovarian reserve. As such, there is an increasing demand for the implementation of fertility preservation surgery (FPS). This study will evaluate the effect on ovarian reserve following two different surgical interventions for the management of benign ovarian cysts.

METHODS AND ANALYSIS

We will conduct a two-armed randomised controlled trial comparing laparoscopic ovarian cystectomy, considered gold standard treatment as per the Royal College of Obstetricians and Gynaecologists (RCOG) Green Top guidelines for the management of benign ovarian cysts, with ultrasound-guided laparoscopic ovarian cystectomy (UGLOC), a novel method of FPS. The study commencement date was October 2021, with a completion date aimed for October 2024. The primary outcome will be the difference in anti-Müllerian hormone (AMH) (pmol/L) and antral follicle count (AFC) measured 3 and 6 months postoperatively from the preoperative baseline. Secondary outcomes include assessment of various surgical and histopathological findings, including duration of hospital stay (days), duration of surgery (minutes), presence of intraoperative cyst rupture (yes/no), presence of ovarian tissue within the resected specimen (yes/no) and the grade of follicles excised within the specimen (grade 0-4). We aim to randomise 94 patients over 3 years to achieve power of 80% at an alpha level of 0.05.

ETHICS AND DISSEMINATION

Findings will be published in peer-reviewed journals and presented at national and international conferences and scientific meetings. The Chelsea NHS Research and Ethics Committee have awarded ethical approval of the study (21/LO/036).

TRIAL REGISTRATION NUMBER

NCT05032846.

Self-supervised learning for accelerated 3D high-resolution ultrasound imaging

PURPOSE

Ultrasound (US) imaging has been widely used in diagnosis, image-guided intervention, and therapy, where high-quality three-dimensional (3D) images are highly desired from sparsely acquired two-dimensional (2D) images. This study aims to develop a deep learning-based algorithm to reconstruct high-resolution (HR) 3D US images only reliant on the acquired sparsely distributed 2D images.

METHODS

We propose a self-supervised learning framework using cycle-consistent generative adversarial network (cycleGAN), where two independent cycleGAN models are trained with paired original US images and two sets of low-resolution (LR) US images, respectively. The two sets of LR US images are obtained through down-sampling the original US images along the two axes, respectively. In US imaging, in-plane spatial resolution is generally much higher than through-plane resolution. By learning the mapping from down-sampled in-plane LR images to original HR US images, cycleGAN can generate through-plane HR images from original sparely distributed 2D images. Finally, HR 3D US images are reconstructed by combining the generated 2D images from the two cycleGAN models.

RESULTS

The proposed method was assessed on two different datasets. One is automatic breast ultrasound (ABUS) images from 70 breast cancer patients, the other is collected from 45 prostate cancer patients. By applying a spatial resolution enhancement factor of 3 to the breast cases, our proposed method achieved the mean absolute error (MAE) value of 0.90 ± 0.15, the peak signal-to-noise ratio (PSNR) value of 37.88 ± 0.88 dB, and the visual information fidelity (VIF) value of 0.69 ± 0.01, which significantly outperforms bicubic interpolation. Similar performances have been achieved using the enhancement factor of 5 in these breast cases and using the enhancement factors of 5 and 10 in the prostate cases.

CONCLUSIONS

We have proposed and investigated a new deep learning-based algorithm for reconstructing HR 3D US images from sparely acquired 2D images. Significant improvement on through-plane resolution has been achieved by only using the acquired 2D images without any external atlas images. Its self-supervision capability could accelerate HR US imaging.