3D ultrasound-guided resection of low-grade gliomas: principles and clinical examples.

3D ultrasound (US) is a convenient tool for guiding the resection of low-grade gliomas, seemingly without deterioration in patients' quality of life. This article offers an update of the intraoperative workflow and the general principles behind the 3D US acquisition of high-quality images.The authors also provide case examples illustrating the technique in two small mesial temporal lobe lesions and in one insular glioma. Due to the ease of acquiring new images for navigation, the operations can be guided by updated image volumes throughout the entire course of surgery. The high accuracy offered by 3D US systems, based on nearly real-time images, allows for precise and safe resections. This is especially useful when an operation is performed through very narrow transcortical corridors.

A new visualization method for navigated bronchoscopy.

OBJECTIVE: In flexible endoscopy techniques, such as bronchoscopy, there is often a challenge visualizing the path from start to target based on preoperative data and accessing these during the procedure. An example of this is visualizing only the inside of central airways in bronchoscopy. Virtual bronchoscopy (VB) does not meet the pulmonologist's need to detect, define and sample the frequent targets outside the bronchial wall. Our aim was to develop and study a new visualization technique for navigated bronchoscopy. MATERIAL AND METHODS: We extracted the shortest possible path from the top of the trachea to the target along the airway centerline and a corresponding auxiliary route in the opposite lung. A surface structure between the centerlines was developed and displayed. The new technique was tested on non-selective CT data from eight patients using artificial lung targets. RESULTS: The new display technique anchored to centerline curved surface (ACCuSurf) made it easy to detect and interpret anatomical features, targets and neighboring anatomy outside the airways, in all eight patients. CONCLUSIONS: ACCuSurf can simplify planning and performing navigated bronchoscopy, meets the challenge of improving orientation and register the direction of the moving endoscope, thus creating an optimal visualization for navigated bronchoscopy.

Clinical experience with navigated 3D ultrasound angiography (power Doppler) in microsurgical treatment of brain arteriovenous malformations.

INTRODUCTION: We have previously described a method that has the potential to improve surgery of arteriovenous malformations (AVMs). In the present paper, we present our clinical results. MATERIALS AND METHODS: Of 78 patients referred for AVMs to our University Hospital from our geographical catchment region from 2005 through 2013, 31 patients were operated on with microsurgical technique. 3D MR angiography (MRA) with neuronavigation was used for planning. Navigated 3D ultrasound angiography (USA) was used to identify and clip feeders in the initial phase of the operation. None of our patients was embolized preoperatively as part of the surgical procedure. The niduses were extirpated based on the 3D USA. After extirpation, controls were done with 3D USA to verify that the AVMs were completely removed. The Spetzler three-tier classification of the patients was: A: 21, B: 6, C: 4. RESULTS: Sixty-eight feeders were identified on preoperative MRA and DSA and 67 feeders were identified and clipped by guidance of intraoperative 3D USA. Six feeders identified preoperatively were missed by 3D USA, while five preoperatively unknown feeders were found and clipped. The overall average bleeding was 440 ml. There was a significant reduction in average bleeding in the last 15 operations compared to the first 16 (340 vs. 559 ml, p = 0.019). We had no serious morbidity (GOS 3 or less). New deficits due to surgery were two patients with quadrantanopia (one class B and one class C), the latter (C) also acquired epilepsy. One patient (class A) acquired a hardly noticeable paresis in two fingers. One hundred percent angiographic cure was achieved in all patients, as evaluated by postoperative DSA. CONCLUSIONS: Navigated intraoperative 3D USA is a useful tool to identify and clip AVM feeders. Microsurgical extirpation assisted by navigated 3D USA is an effective and safe method for removing AVMs.

Intra-operative correction of brain-shift.

BACKGROUND: Brain-shift is a major source of error in neuronavigation systems based on pre-operative images. In this paper, we present intra-operative correction of brain-shift using 3D ultrasound. METHODS: The method is based on image registration of vessels extracted from pre-operative MRA and intra-operative power Doppler-based ultrasound and is fully integrated in the neuronavigation software. RESULTS: We have performed correction of brain-shift in the operating room during surgery and provided the surgeon with updated information. Here, we present data from seven clinical cases with qualitative and quantitative error measures. CONCLUSION: The registration algorithm is fast enough to provide the surgeon with updated information within minutes and accounts for large portions of the experienced shift. Correction of brain-shift can make pre-operative data like fMRI and DTI reliable for a longer period of time and increase the usefulness of the MR data as a supplement to intra-operative 3D ultrasound in terms of overview and interpretation.

Prognostic risk models for incident hypertension: A PRISMA systematic review and meta-analysis.

OBJECTIVE: Our goal was to review the available literature on prognostic risk prediction for incident hypertension, synthesize performance, and provide suggestions for future work on the topic. METHODS: A systematic search on PUBMED and Web of Science databases was conducted for studies on prognostic risk prediction models for incident hypertension in generally healthy individuals. Study-quality was assessed using the Prediction model Risk of Bias Assessment Tool (PROBAST) checklist. Three-level meta-analyses were used to obtain pooled AUC/C-statistic estimates. Heterogeneity was explored using study and cohort characteristics in meta-regressions. RESULTS: From 5090 hits, we found 53 eligible studies, and included 47 in meta-analyses. Only four studies were assessed to have results with low risk of bias. Few models had been externally validated, with only the Framingham risk model validated more than thrice. The pooled AUC/C-statistics were 0.82 (0.77-0.86) for machine learning models and 0.78 (0.76-0.80) for traditional models, with high heterogeneity in both groups (I2 > 99%). Intra-class correlations within studies were 60% and 90%, respectively. Follow-up time (P = 0.0405) was significant for ML models and age (P = 0.0271) for traditional models in explaining heterogeneity. Validations of the Framingham risk model had high heterogeneity (I2 > 99%). CONCLUSION: Overall, the quality of included studies was assessed as poor. AUC/C-statistic were mostly acceptable or good, and higher for ML models than traditional models. High heterogeneity implies large variability in the performance of new risk models. Further, large heterogeneity in validations of the Framingham risk model indicate variability in model performance on new populations. To enable researchers to assess hypertension risk models, we encourage adherence to existing guidelines for reporting and developing risk models, specifically reporting appropriate performance measures. Further, we recommend a stronger focus on validation of models by considering reasonable baseline models and performing external validations of existing models. Hence, developed risk models must be made available for external researchers.

Development of risk models of incident hypertension using machine learning on the HUNT study data.

In this study, we aimed to create an 11-year hypertension risk prediction model using data from the Trøndelag Health (HUNT) Study in Norway, involving 17 852 individuals (20-85 years; 38% male; 24% incidence rate) with blood pressure (BP) below the hypertension threshold at baseline (1995-1997). We assessed 18 clinical, behavioral, and socioeconomic features, employing machine learning models such as eXtreme Gradient Boosting (XGBoost), Elastic regression, K-Nearest Neighbor, Support Vector Machines (SVM) and Random Forest. For comparison, we used logistic regression and a decision rule as reference models and validated six external models, with focus on the Framingham risk model. The top-performing models consistently included XGBoost, Elastic regression and SVM. These models efficiently identified hypertension risk, even among individuals with optimal baseline BP (< 120/80 mmHg), although improvement over reference models was modest. The recalibrated Framingham risk model outperformed the reference models, approaching the best-performing ML models. Important features included age, systolic and diastolic BP, body mass index, height, and family history of hypertension. In conclusion, our study demonstrated that linear effects sufficed for a well-performing model. The best models efficiently predicted hypertension risk, even among those with optimal or normal baseline BP, using few features. The recalibrated Framingham risk model proved effective in our cohort.

Generating 3D images of material microstructures from a single 2D image: a denoising diffusion approach.

Three-dimensional (3D) images provide a comprehensive view of material microstructures, enabling numerical simulations unachievable with two-dimensional (2D) imaging alone. However, obtaining these 3D images can be costly and constrained by resolution limitations. We introduce a novel method capable of generating large-scale 3D images of material microstructures, such as metal or rock, from a single 2D image. Our approach circumvents the need for 3D image data while offering a cost-effective, high-resolution alternative to existing imaging techniques. Our method combines a denoising diffusion probabilistic model with a generative adversarial network framework. To compensate for the lack of 3D training data, we implement chain sampling, a technique that utilizes the 3D intermediate outputs obtained by reversing the diffusion process. During the training phase, these intermediate outputs are guided by a 2D discriminator. This technique facilitates our method's ability to gradually generate 3D images that accurately capture the geometric properties and statistical characteristics of the original 2D input. This study features a comparative analysis of the 3D images generated by our method, SliceGAN (the current state-of-the-art method), and actual 3D micro-CT images, spanning a diverse set of rock and metal types. The results shown an improvement of up to three times in the Frechet inception distance score, a typical metric for evaluating the performance of image generative models, and enhanced accuracy in derived properties compared to SliceGAN. The potential of our method to produce high-resolution and statistically representative 3D images paves the way for new applications in material characterization and analysis domains.

Correction: Teacher-student approach for lung tumor segmentation from mixed-supervised datasets.

[This corrects the article DOI: 10.1371/journal.pone.0266147.].

Automatic Detection and Tracking of Anatomical Landmarks in Transesophageal Echocardiography for Quantification of Left Ventricular Function.

OBJECTIVE: Evaluation of left ventricular (LV) function in critical care patients is useful for guidance of therapy and early detection of LV dysfunction, but the tools currently available are too time-consuming. To resolve this issue, we previously proposed a method for the continuous and automatic quantification of global LV function in critical care patients based on the detection and tracking of anatomical landmarks on transesophageal heart ultrasound. In the present study, our aim was to improve the performance of mitral annulus detection in transesophageal echocardiography (TEE). METHODS: We investigated several state-of-the-art networks for both the detection and tracking of the mitral annulus in TEE. We integrated the networks into a pipeline for automatic assessment of LV function through estimation of the mitral annular plane systolic excursion (MAPSE), called autoMAPSE. TEE recordings from a total of 245 patients were collected from St. Olav's University Hospital and used to train and test the respective networks. We evaluated the agreement between autoMAPSE estimates and manual references annotated by expert echocardiographers in 30 Echolab patients and 50 critical care patients. Furthermore, we proposed a prototype of autoMAPSE for clinical integration and tested it in critical care patients in the intensive care unit. RESULTS: Compared with manual references, we achieved a mean difference of 0.8 (95% limits of agreement: -2.9 to 4.7) mm in Echolab patients, with a feasibility of 85.7%. In critical care patients, we reached a mean difference of 0.6 (95% limits of agreement: -2.3 to 3.5) mm and a feasibility of 88.1%. The clinical prototype of autoMAPSE achieved real-time performance. CONCLUSION: Automatic quantification of LV function had high feasibility in clinical settings. The agreement with manual references was comparable to inter-observer variability of clinical experts.

Ultrasound imaging in neurosurgery: approaches to minimize surgically induced image artefacts for improved resection control.

BACKGROUND: Intraoperative ultrasound imaging is used in brain tumor surgery to identify tumor remnants. The ultrasound images may in some cases be more difficult to interpret in the later stages of the operation than in the beginning of the operation. The aim of this paper is to explain the causes of surgically induced ultrasound artefacts and how they can be recognized and reduced. METHODS: The theoretical reasons for artefacts are addressed and the impact of surgery is discussed. Different setups for ultrasound acquisition and different acoustic coupling fluids to fill up the resection cavity are evaluated with respect to improved image quality. RESULTS: The enhancement artefact caused by differences in attenuation of the resection cavity fluid and the surrounding brain is the most dominating surgically induced ultrasound artefact. The influence of the artefact may be reduced by inserting ultrasound probes with small footprint into the resection cavity for a close-up view of the areas with suspected tumor remnants. A novel acoustic coupling fluid developed for use during ultrasound imaging in brain tumor surgery has the potential to reduce surgically induced ultrasound artefacts to a minimum. CONCLUSIONS: Surgeons should be aware of artefacts in ultrasound images that may occur during brain tumor surgery. Techniques to identify and reduce image artefacts are useful and should be known to users of ultrasound in brain tumor surgery.

Learning deep abdominal CT registration through adaptive loss weighting and synthetic data generation.

PURPOSE: This study aims to explore training strategies to improve convolutional neural network-based image-to-image deformable registration for abdominal imaging. METHODS: Different training strategies, loss functions, and transfer learning schemes were considered. Furthermore, an augmentation layer which generates artificial training image pairs on-the-fly was proposed, in addition to a loss layer that enables dynamic loss weighting. RESULTS: Guiding registration using segmentations in the training step proved beneficial for deep-learning-based image registration. Finetuning the pretrained model from the brain MRI dataset to the abdominal CT dataset further improved performance on the latter application, removing the need for a large dataset to yield satisfactory performance. Dynamic loss weighting also marginally improved performance, all without impacting inference runtime. CONCLUSION: Using simple concepts, we improved the performance of a commonly used deep image registration architecture, VoxelMorph. In future work, our framework, DDMR, should be validated on different datasets to further assess its value.

Automated estimation of mitral annular plane systolic excursion by artificial intelligence from 3D ultrasound recordings.

Perioperative monitoring of cardiac function is beneficial for early detection of cardiovascular complications. The standard of care for cardiac monitoring performed by trained cardiologists and anesthesiologists involves a manual and qualitative evaluation of ultrasound imaging, which is a time-demanding and resource-intensive process with intraobserver- and interobserver variability. In practice, such measures can only be performed a limited number of times during the intervention. To overcome these difficulties, this study presents a robust method for automatic and quantitative monitoring of cardiac function based on 3D transesophageal echocardiography (TEE) B-mode ultrasound recordings of the left ventricle (LV). Such an assessment obtains consistent measurements and can produce a near real-time evaluation of ultrasound imagery. Hence, the presented method is time-saving and results in increased accessibility. The mitral annular plane systolic excursion (MAPSE), characterizing global LV function, is estimated by landmark detection and cardiac view classification of two-dimensional images extracted along the long-axis of the ultrasound volume. MAPSE estimation directly from 3D TEE recordings is beneficial since it removes the need for manual acquisition of cardiac views, hence decreasing the need for interference by physicians. Two convolutional neural networks (CNNs) were trained and tested on acquired ultrasound data of 107 patients, and MAPSE estimates were compared to clinically obtained references in a blinded study including 31 patients. The proposed method for automatic MAPSE estimation had low bias and low variability in comparison to clinical reference measures. The method accomplished a mean difference for MAPSE estimates of (-0.16±1.06) mm. Thus, the results did not show significant systematic errors. The obtained bias and variance of the method were comparable to inter-observer variability of clinically obtained MAPSE measures on 2D TTE echocardiography. The novel pipeline proposed in this study has the potential to enhance cardiac monitoring in perioperative- and intensive care settings.

Teacher-student approach for lung tumor segmentation from mixed-supervised datasets.

PURPOSE: Cancer is among the leading causes of death in the developed world, and lung cancer is the most lethal type. Early detection is crucial for better prognosis, but can be resource intensive to achieve. Automating tasks such as lung tumor localization and segmentation in radiological images can free valuable time for radiologists and other clinical personnel. Convolutional neural networks may be suited for such tasks, but require substantial amounts of labeled data to train. Obtaining labeled data is a challenge, especially in the medical domain. METHODS: This paper investigates the use of a teacher-student design to utilize datasets with different types of supervision to train an automatic model performing pulmonary tumor segmentation on computed tomography images. The framework consists of two models: the student that performs end-to-end automatic tumor segmentation and the teacher that supplies the student additional pseudo-annotated data during training. RESULTS: Using only a small proportion of semantically labeled data and a large number of bounding box annotated data, we achieved competitive performance using a teacher-student design. Models trained on larger amounts of semantic annotations did not perform better than those trained on teacher-annotated data. Our model trained on a small number of semantically labeled data achieved a mean dice similarity coefficient of 71.0 on the MSD Lung dataset. CONCLUSIONS: Our results demonstrate the potential of utilizing teacher-student designs to reduce the annotation load, as less supervised annotation schemes may be performed, without any real degradation in segmentation accuracy.

Deep learning for image-based liver analysis - A comprehensive review focusing on malignant lesions.

Deep learning-based methods, in particular, convolutional neural networks and fully convolutional networks are now widely used in the medical image analysis domain. The scope of this review focuses on the analysis using deep learning of focal liver lesions, with a special interest in hepatocellular carcinoma and metastatic cancer; and structures like the parenchyma or the vascular system. Here, we address several neural network architectures used for analyzing the anatomical structures and lesions in the liver from various imaging modalities such as computed tomography, magnetic resonance imaging and ultrasound. Image analysis tasks like segmentation, object detection and classification for the liver, liver vessels and liver lesions are discussed. Based on the qualitative search, 91 papers were filtered out for the survey, including journal publications and conference proceedings. The papers reviewed in this work are grouped into eight categories based on the methodologies used. By comparing the evaluation metrics, hybrid models performed better for both the liver and the lesion segmentation tasks, ensemble classifiers performed better for the vessel segmentation tasks and combined approach performed better for both the lesion classification and detection tasks. The performance was measured based on the Dice score for the segmentation, and accuracy for the classification and detection tasks, which are the most commonly used metrics.

Intra-operative imaging with 3D ultrasound in neurosurgery.

In recent years the quality of ultrasound (US) imaging has improved considerably. The integration of three dimensional (3D) US with neuronavigation technology has created an efficient and inexpensive tool for intra-operative imaging in neurosurgery. Our experience is based on more than 900 operations with the intra-operative 3D ultrasound equipment SonoWand® and some operations with the research equipment Custux X. The technology has been applied to improve surgery of intraparencymal brain tumours, but has also been found to be useful in a wide range of other procedures, such as operations for cavernomas, skull base tumours, medulla lesions, arteriovenous malformations (AVMs) and for endoscopy guidance. Compared to intraoperative magnetic resonance imaging (ioMRI), 3D US technology is advantageous in different ways: it is flexible and can be used in any operation theatre. There is no need for special instruments, and no need for radiologists or technicians. It adds very little extra time to the operation, and the investment-costs are considerably lower than for ioMRI.

Automatic segmentation tool for 3D digital rocks by deep learning.

Obtaining an accurate segmentation of images obtained by computed microtomography (micro-CT) techniques is a non-trivial process due to the wide range of noise types and artifacts present in these images. Current methodologies are often time-consuming, sensitive to noise and artifacts, and require skilled people to give accurate results. Motivated by the rapid advancement of deep learning-based segmentation techniques in recent years, we have developed a tool that aims to fully automate the segmentation process in one step, without the need for any extra image processing steps such as noise filtering or artifact removal. To get a general model, we train our network using a dataset made of high-quality three-dimensional micro-CT images from different scanners, rock types, and resolutions. In addition, we use a domain-specific augmented training pipeline with various types of noise, synthetic artifacts, and image transformation/distortion. For validation, we use a synthetic dataset to measure accuracy and analyze noise/artifact sensitivity. The results show a robust and accurate segmentation performance for the most common types of noises present in real micro-CT images. We also compared the segmentation of our method and five expert users, using commercial and open software packages on real rock images. We found that most of the current tools fail to reduce the impact of local and global noises and artifacts. We quantified the variation on human-assisted segmentation results in terms of physical properties and observed a large variation. In comparison, the new method is more robust to local noises and artifacts, outperforming the human segmentation and giving consistent results. Finally, we compared the porosity of our model segmented images with experimental porosity measured in the laboratory for ten different untrained samples, finding very encouraging results.

Navigated resection of giant intracranial meningiomas based on intraoperative 3D ultrasound.

BACKGROUND: Surgical resection of giant meningiomas may pose different challenges. Normal brain tissue is often compressed to the limit and is vulnerable to further traction. In addition, severe intraoperative bleeding may be a problem as many giant meningiomas are vascularised with deep feeding vessels entering from the skull base. Neuronavigation based on preoperative imaging can be of limited use as there may be extensive brain shifts during surgery. METHOD: We have retrospectively evaluated navigated resection based on intraoperative 3D ultrasound in a series of 15 giant meningiomas with a diameter of more than 5 cm. A pre- and postoperative MRI was preformed in all patients. Preoperative and postoperative neurological function was assessed. FINDINGS: We were able to safely perform ultrasound-guided intracapsular gross total resection of tumour tissue in all patients. Twelve out of 15 patients were radically operated (Simpson grade I and II). Major feeding arteries and adjacent normal arteries could be identified by ultrasound power Doppler angiography. In one patient we were not able to indentify important venous structures. All patients experienced postoperative improvement of their symptoms. Postoperative MRIs did not reveal significant ischemic changes in adjacent normal brain tissue. The mean duration of hospitalisation after surgery was 4.9 days. CONCLUSION: We present a method of ultrasound-guided resection of giant meningiomas. The method enables image-guided resection through narrow approaches that minimise traction. Power Doppler angiography allows the identification of feeding vessels that may be coagulated to limit bleeding. Likewise, normal arteries can be avoided during surgery. The tumour capsule is often surprisingly easy to remove from the arachnoid membrane after gross intracapsular tumour reduction.

Automatic Intraoperative Correction of Brain Shift for Accurate Neuronavigation.

BACKGROUND: Unreliable neuronavigation owing to inaccurate patient-to-image registration and brain shift is a major problem in conventional magnetic resonance imaging-guided neurosurgery. We performed a prospective intraoperative validation of a system for fully automatic correction of this inaccuracy based on intraoperative three-dimensional ultrasound and magnetic resonance imaging-to-ultrasound registration. METHODS: The system was tested intraoperatively in 13 tumor resection cases, and performance was evaluated intraoperatively and postoperatively. RESULTS: Intraoperatively, the system was accurate enough for tumor resection guidance in 9 of 13 cases. Manually placed anatomic landmarks showed improvement of alignment from 5.12 mm to 2.72 mm (median) after intraoperative correction. Postoperatively, the limitations of the current system were identified and modified for the system to be sufficiently accurate in all cases. CONCLUSIONS: Automatic and accurate correction of spatially unreliable neuronavigation is feasible within the constraints of surgery. The current limitations of the system were also identified and addressed.

Laboratory test of Single Landmark registration method for ultrasound-based navigation in laparoscopy using an open-source platform.

PURPOSE: Test the feasibility of the novel Single Landmark image-to-patient registration method for use in the operating room for future clinical trials. The algorithm is implemented in the open-source platform CustusX, a computer-aided intervention research platform dedicated to intraoperative navigation and ultrasound, with an interface for laparoscopic ultrasound probes. METHODS: The Single Landmark method is compared to fiducial landmark on an IOUSFAN (Kyoto Kagaku Co., Ltd., Japan) soft tissue abdominal phantom and T2 magnetic resonance scans of it. RESULTS: The experiments show that the accuracy of the Single Landmark registration is good close to the registered point, increasing with the distance from this point (12.4 mm error at 60 mm away from the registered point). In this point, the registration accuracy is mainly dominated by the accuracy of the user when clicking on the ultrasound image. In the presented set-up, the time required to perform the Single Landmark registration is 40% less than for the FLRM. CONCLUSION: The Single Landmark registration is suitable for being integrated in a laparoscopic workflow. The statistical analysis shows robustness against translational displacements of the patient and improvements in terms of time. The proposed method allows the clinician to accurately register lesions intraoperatively by clicking on these in the ultrasound image provided by the ultrasound transducer. The Single Landmark registration method can be further combined with other more accurate registration approaches improving the registration at relevant points defined by the clinicians.

Freehand 3D ultrasound reconstruction algorithms--a review.

Three-dimensional (3D) ultrasound (US) is increasingly being introduced in the clinic, both for diagnostics and image guidance. Although dedicated 3D US probes exist, 3D US can also be acquired with the still frequently used two-dimensional (2D) US probes. Obtaining 3D volumes with 2D US probes is a two-step process. First, a positioning sensor must be attached to the probe; second, a reconstruction of a 3D volume can be performed into a regular voxel grid. Various algorithms have been used for performing 3D reconstruction based on 2D images. Up till now, a complete overview of the algorithms, the way they work and their benefits and drawbacks due to various applications has been missing. The lack of an overview is made clear by confusions about algorithm and group names in the existing literature. This article is a review aimed at explaining and categorizing the various algorithms into groups, according to algorithm implementation. The algorithms are compared based on published data and our own laboratory results. Positive and practical uses of the various algorithms for different applications are discussed, with a focus on image guidance.