Repeatability and reproducibility of ADC measurements: a prospective multicenter whole-body-MRI study.

OBJECTIVES: Multicenter oncology trials increasingly include MRI examinations with apparent diffusion coefficient (ADC) quantification for lesion characterization and follow-up. However, the repeatability and reproducibility (R&R) limits above which a true change in ADC can be considered relevant are poorly defined. This study assessed these limits in a standardized whole-body (WB)-MRI protocol. METHODS: A prospective, multicenter study was performed at three centers equipped with the same 3.0-T scanners to test a WB-MRI protocol including diffusion-weighted imaging (DWI). Eight healthy volunteers per center were enrolled to undergo test and retest examinations in the same center and a third examination in another center. ADC variability was assessed in multiple organs by two readers using two-way mixed ANOVA, Bland-Altman plots, coefficient of variation (CoV), and the upper limit of the 95% CI on repeatability (RC) and reproducibility (RDC) coefficients. RESULTS: CoV of ADC was not influenced by other factors (center, reader) than the organ. Based on the upper limit of the 95% CI on RC and RDC (from both readers), a change in ADC in an individual patient must be superior to 12% (cerebrum white matter), 16% (paraspinal muscle), 22% (renal cortex), 26% (central and peripheral zones of the prostate), 29% (renal medulla), 35% (liver), 45% (spleen), 50% (posterior iliac crest), 66% (L5 vertebra), 68% (femur), and 94% (acetabulum) to be significant. CONCLUSIONS: This study proposes R&R limits above which ADC changes can be considered as a reliable quantitative endpoint to assess disease or treatment-related changes in the tissue microstructure in the setting of multicenter WB-MRI trials. KEY POINTS: • The present study showed the range of R&R of ADC in WB-MRI that may be achieved in a multicenter framework when a standardized protocol is deployed. • R&R was not influenced by the site of acquisition of DW images. • Clinically significant changes in ADC measured in a multicenter WB-MRI protocol performed with the same type of MRI scanner must be superior to 12% (cerebrum white matter), 16% (paraspinal muscle), 22% (renal cortex), 26% (central zone and peripheral zone of prostate), 29% (renal medulla), 35% (liver), 45% (spleen), 50% (posterior iliac crest), 66% (L5 vertebra), 68% (femur), and 94% (acetabulum) to be detected with a 95% confidence level.

Augmented Reality-Assisted Neurosurgical Drain Placement (ARANED): Technical Note.

BACKGROUND: Many surgical procedures, such as placement of intracranial drains, are currently being performed blindly, relying on anatomical landmarks. As a result, accuracy results still have room for improvement. Neuronavigation could address this issue, but its application in an urgent setting is often impractical. Augmented reality (AR) provided through a head-worn device has the potential to tackle this problem, but its implementation should meet physicians' needs. METHODS: The Surgical Augmented Reality Assistance (SARA) project aims to develop an AR solution that is suitable for preoperative planning, intraoperative visualisation and navigational support in an everyday clinical setting, using a Microsoft HoloLens. RESULTS: Proprietary hardware and software adaptations and dedicated navigation algorithms are applied to the Microsoft HoloLens to optimise it specifically for neurosurgical navigation. This includes a pipeline with an additional set of advanced, semi-automated algorithms responsible for image processing, hologram-to-patient registration and intraoperative tracking using infrared depth-sensing. A smooth and efficient workflow while maintaining high accuracy is prioritised. The AR solution provides a fully integrated and completely mobile navigation setup. Initial preclinical and clinical validation tests applying the solution to intracranial drain placement are described. CONCLUSION: AR has the potential to vastly increase accuracy of everyday procedures that are frequently performed without image guidance, but could still benefit from navigational support, such as intracranial drain placements. Technical development should go hand in hand with preclinical and clinical validation in order to demonstrate improvements in accuracy and clinical outcomes.

The effect of augmented reality on the accuracy and learning curve of external ventricular drain placement.

OBJECTIVE: The traditional freehand technique for external ventricular drain (EVD) placement is most frequently used, but remains the primary risk factor for inaccurate drain placement. As this procedure could benefit from image guidance, the authors set forth to demonstrate the impact of augmented-reality (AR) assistance on the accuracy and learning curve of EVD placement compared with the freehand technique. METHODS: Sixteen medical students performed a total of 128 EVD placements on a custom-made phantom head, both before and after receiving a standardized training session. They were guided by either the freehand technique or by AR, which provided an anatomical overlay and tailored guidance for EVD placement through inside-out infrared tracking. The outcome was quantified by the metric accuracy of EVD placement as well as by its clinical quality. RESULTS: The mean target error was significantly impacted by either AR (p = 0.003) or training (p = 0.02) in a direct comparison with the untrained freehand performance. Both untrained (11.9 ± 4.5 mm) and trained (12.2 ± 4.7 mm) AR performances were significantly better than the untrained freehand performance (19.9 ± 4.2 mm), which improved after training (13.5 ± 4.7 mm). The quality of EVD placement as assessed by the modified Kakarla scale (mKS) was significantly impacted by AR guidance (p = 0.005) but not by training (p = 0.07). Both untrained and trained AR performances (59.4% mKS grade 1 for both) were significantly better than the untrained freehand performance (25.0% mKS grade 1). Spatial aptitude testing revealed a correlation between perceptual ability and untrained AR-guided performance (r = 0.63). CONCLUSIONS: Compared with the freehand technique, AR guidance for EVD placement yielded a higher outcome accuracy and quality for procedure novices. With AR, untrained individuals performed as well as trained individuals, which indicates that AR guidance not only improved performance but also positively impacted the learning curve. Future efforts will focus on the translation and evaluation of AR for EVD placement in the clinical setting.

Multi-atlas segmentation of the skeleton from whole-body MRI-Impact of iterative background masking.

PURPOSE: To improve multi-atlas segmentation of the skeleton from whole-body MRI. In particular, we study the effect of employing the atlas segmentations to iteratively mask tissues outside of the region of interest to improve the atlas alignment and subsequent segmentation. METHODS: An improved atlas registration scheme is proposed. Starting from a suitable initial alignment, the alignment is refined by introducing additional stages of deformable registration during which the image sampling is limited to the dilated atlas segmentation label mask. The performance of the method was demonstrated using leave-one-out cross-validation using atlases of 10 whole-body 3D-T1 images of prostate cancer patients with bone metastases and healthy male volunteers, and compared to existing state of the art. Both registration accuracy and resulting segmentation quality, using four commonly used label fusion strategies, were evaluated. RESULTS: The proposed method showed significant improvement in registration and segmentation accuracy with respect to the state of the art for all validation criteria and label fusion strategies, resulting in a Dice coefficient of 0.887 (STEPS label fusion). The average Dice coefficient for the multi-atlas segmentation showed over 11% improvement with a decrease of false positive rate from 28.3% to 13.2%. For this application, repeated application of the background masking did not lead to significant improvement of the segmentation result. CONCLUSIONS: A registration strategy, relying on the use of atlas segmentations as mask during image registration was proposed and evaluated for multi-atlas segmentation of whole-body MRI. The approach significantly improved registration and final segmentation accuracy and may be applicable to other structures of interest.

Registration strategies for multi-modal whole-body MRI mosaicing.

PURPOSE: To test and compare different registration approaches for performing whole-body diffusion-weighted (wbDWI) image station mosaicing, and its alignment to corresponding anatomical T1 whole-body image. METHODS: Four different registration strategies aiming at mosaicing of diffusion-weighted image stations, and their alignment to the corresponding whole-body anatomical image, were proposed and evaluated. These included two-step approaches, where diffusion-weighted stations are first combined in a pairwise (Strategy 1) or groupwise (Strategy 2) manner and later non-rigidly aligned to the anatomical image; a direct pairwise mapping of DWI stations onto the anatomical image (Strategy 3); and simultaneous mosaicing of DWI and alignment to the anatomical image (Strategy 4). Additionally, different images driving the registration were investigated. Experiments were performed for 20 whole-body images of patients with bone metastases. RESULTS: Strategies 1 and 2 showed significant improvement in mosaicing accuracy with respect to the non-registered images (P < 0.006). Strategy 2 based on ADC images increased the alignment accuracy between DWI stations and the T1 whole-body image (P = 0.0009). CONCLUSIONS: A two-step registration strategy, relying on groupwise mosaicing of the ADC stations and subsequent registration to T1 , provided the best compromise between whole-body DWI image quality and multi-modal alignment. Magn Reson Med 79:1684-1695, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Development and validation of an automated and marker-free CT-based spatial analysis method (CTSA) for assessment of femoral hip implant migration: In vitro accuracy and precision comparable to that of radiostereometric analysis (RSA).

BACKGROUND AND PURPOSE: We developed a marker-free automated CT-based spatial analysis (CTSA) method to detect stem-bone migration in consecutive CT datasets and assessed the accuracy and precision in vitro. Our aim was to demonstrate that in vitro accuracy and precision of CTSA is comparable to that of radiostereometric analysis (RSA). MATERIAL AND METHODS: Stem and bone were segmented in 2 CT datasets and both were registered pairwise. The resulting rigid transformations were compared and transferred to an anatomically sound coordinate system, taking the stem as reference. This resulted in 3 translation parameters and 3 rotation parameters describing the relative amount of stem-bone displacement, and it allowed calculation of the point of maximal stem migration. Accuracy was evaluated in 39 comparisons by imposing known stem migration on a stem-bone model. Precision was estimated in 20 comparisons based on a zero-migration model, and in 5 patients without stem loosening. RESULTS: Limits of the 95% tolerance intervals (TIs) for accuracy did not exceed 0.28 mm for translations and 0.20° for rotations (largest standard deviation of the signed error (SD(SE)): 0.081 mm and 0.057°). In vitro, limits of the 95% TI for precision in a clinically relevant setting (8 comparisons) were below 0.09 mm and 0.14° (largest SD(SE): 0.012 mm and 0.020°). In patients, the precision was lower, but acceptable, and dependent on CT scan resolution. INTERPRETATION: CTSA allows detection of stem-bone migration with an accuracy and precision comparable to that of RSA. It could be valuable for evaluation of subtle stem loosening in clinical practice.

Predictive value of pattern classification 24 hours after radiofrequency ablation of liver metastases on CT and positron emission tomography/CT.

PURPOSE: To assess a classification scheme for predicting local tumor progression (LTP) after radiofrequency (RF) ablation of liver metastases, using predefined patterns on contrast-enhanced computed tomography (CT) and positron emission tomography (PET) combined with CT (PET/CT) acquired 24 hours after RF ablation. MATERIALS AND METHODS: There were 45 metastases in 20 patients treated. After 24 hours, imaging of the ablation zones was performed with contrast-enhanced PET/CT. Three independent radiologists prospectively assessed contrast-enhanced CT and combined PET/CT images to identify three patterns: pattern I, no tissue enhancement or fluorodeoxyglucose uptake between the ablation zone and the liver parenchyma; pattern II, a rimlike pattern; and pattern III, a peripheral nodule. PET/CT images obtained after 8-10 weeks were evaluated for LTP. The patterns were analyzed for their sensitivity, specificity, positive predictive value, and negative predictive value for predicting LTP. RESULTS: Pattern I was most frequently observed (81% for contrast-enhanced CT and 61% for PET/CT) as well as for ablation zones that showed LTP (52% and 37%, respectively). Conversely, pattern II was observed for tumors that were completely ablated (6% and 29%, respectively). Patterns II and III together had the highest sensitivity for predicting LTP (48% and 63%, respectively); pattern III had the highest specificity (94% and 95%, respectively). For nodular patterns, test characteristics were better for PET/CT compared with contrast-enhanced CT, but the difference was not significant. Nodular patterns > 1 cm achieved high positive predictive value (both 100%). CONCLUSIONS: Inflammation and hyperemia can hinder interpretation on imaging 24 hours after RF ablation, especially on PET/CT. Nodular patterns around the ablation zone on early contrast-enhanced CT and PET/CT have a high predictive value for LTP and should be taken into account for disease management.

STRUCTURE-FUNCTION IN SMOKERS : WHEN A SMALL AIRWAYS TEST REALLY REFLECTS THE SMALL AIRWAYS.

INTRODUCTION: If Multiple Breath Washout (MBW) derived acinar ventilation heterogeneity (Sacin) really represents peripheral units, the N2 phase-III of the first MBW exhalation should be curvilinear. This is due to the superposed effect of gas diffusion and convection resulting in an equilibration of N2 concentrations between neighbouring lung units throughout exhalation. We investigated this in smokers with CT-proven functional small airway disease. METHODS: Instantaneous N2slopes were computed over 40ms intervals throughout phase-III and normalized by mean phase-III N2 concentration. N2phase-III (concave) curvilinearity was quantified as the rate at which the instantaneous N2 slope decreases past the phase-II peak over a 1s interval; for a linear N2phase-III unaffected by diffusion, this rate would amount to 0L-1/s. N2phase-III curvilinearity was obtained on the experimental curves and on existing model simulations of N2curves from a normal peripheral lung model and one with missing terminal bronchioles (either 50% or 30% TB left). RESULTS: In forty-six smokers (66 (+8) years; 49 (+26) packyears) with CT-evidence of peripheral lung destruction, instantaneous N2 slope decrease was compared between those with (fSAD+fEmphys)>20% (-0.26+0.14(SD) L-1/s;n=24) and those with (fSAD+fEmphys)<20% (-0.16+0.12(SD)L-1/s;n=22) (P=0.014). Experimental values fell in the range predicted by a realistic peripheral lung model with progressive reduction of terminal bronchioles: values of instantaneous N2-slope decrease obtained from model simulations were -0.09L-1/s (normal lung;100%TB left), -0.17L-1/s (normal lung 50%TB left) and -0.29L-1/s (30%TB left). DISCUSSION : In smokers with CT-based evidence of functional small airways alterations, it is possible to demonstrate that Sacin really does represent the most peripheral airspaces.

Determination of rotation center and diameter of femoral heads using off-the-shelf augmented reality hardware for navigation.

In total hip arthroplasty (THA), determining the center of rotation (COR) and diameter of the hip joint (acetabulum and femoral head) is essential to restore patient biomechanics. This study investigates on-the-fly determination of hip COR and size, using off-the-shelf augmented reality (AR) hardware. An AR head-mounted device (HMD) was configured with inside-out infrared tracking enabling the determination of surface coordinates using a handheld stylus. Two investigators examined 10 prosthetic femoral heads and cups, and 10 human femurs. The HMD calculated the diameter and COR through sphere fitting. Results were compared to data obtained from either verified prosthetic geometry or post-hoc CT analysis. Repeated single-observer measurements showed a mean diameter error of 0.63 mm ± 0.48 mm for the prosthetic heads and 0.54 mm ± 0.39 mm for the cups. Inter-observer comparison yielded mean diameter errors of 0.28 mm ± 0.71 mm and 1.82 mm ± 1.42 mm for the heads and cups, respectively. Cadaver testing found a mean COR error of 3.09 mm ± 1.18 mm and a diameter error of 1.10 mm ± 0.90 mm. Intra- and inter-observer reliability averaged below 2 mm. AR-based surface mapping using HMD proved accurate and reliable in determining the diameter of THA components with promise in identifying COR and diameter of osteoarthritic femoral heads.

Modality redundancy for MRI-based glioblastoma segmentation.

PURPOSE: Automated glioblastoma segmentation from magnetic resonance imaging is generally performed on a four-modality input, including T1, contrast T1, T2 and FLAIR. We hypothesize that information redundancy is present within these image combinations, which can possibly reduce a model's performance. Moreover, for clinical applications, the risk of encountering missing data rises as the number of required input modalities increases. Therefore, this study aimed to explore the relevance and influence of the different modalities used for MRI-based glioblastoma segmentation. METHODS: After the training of multiple segmentation models based on nnU-Net and SwinUNETR architectures, differing only in their amount and combinations of input modalities, each model was evaluated with regard to segmentation accuracy and epistemic uncertainty. RESULTS: Results show that T1CE-based segmentation (for enhanced tumor and tumor core) and T1CE-FLAIR-based segmentation (for whole tumor and overall segmentation) can reach segmentation accuracies comparable to the full-input version. Notably, the highest segmentation accuracy for nnU-Net was found for a three-input configuration of T1CE-FLAIR-T1, suggesting the confounding effect of redundant input modalities. The SwinUNETR architecture appears to suffer less from this, where said three-input and the full-input model yielded statistically equal results. CONCLUSION: The T1CE-FLAIR-based model can therefore be considered as a minimal-input alternative to the full-input configuration. Addition of modalities beyond this does not statistically improve and can even deteriorate accuracy, but does lower the segmentation uncertainty.

The STOIC2021 COVID-19 AI challenge: Applying reusable training methodologies to private data.

Challenges drive the state-of-the-art of automated medical image analysis. The quantity of public training data that they provide can limit the performance of their solutions. Public access to the training methodology for these solutions remains absent. This study implements the Type Three (T3) challenge format, which allows for training solutions on private data and guarantees reusable training methodologies. With T3, challenge organizers train a codebase provided by the participants on sequestered training data. T3 was implemented in the STOIC2021 challenge, with the goal of predicting from a computed tomography (CT) scan whether subjects had a severe COVID-19 infection, defined as intubation or death within one month. STOIC2021 consisted of a Qualification phase, where participants developed challenge solutions using 2000 publicly available CT scans, and a Final phase, where participants submitted their training methodologies with which solutions were trained on CT scans of 9724 subjects. The organizers successfully trained six of the eight Final phase submissions. The submitted codebases for training and running inference were released publicly. The winning solution obtained an area under the receiver operating characteristic curve for discerning between severe and non-severe COVID-19 of 0.815. The Final phase solutions of all finalists improved upon their Qualification phase solutions.

Development and Validation of a Predictive Model for Metastatic Melanoma Patients Treated with Pembrolizumab Based on Automated Analysis of Whole-Body [18F]FDG PET/CT Imaging and Clinical Features.

BACKGROUND: Antibodies that inhibit the programmed cell death protein 1 (PD-1) receptor offer a significant survival benefit, potentially cure (i.e., durable disease-free survival following treatment discontinuation), a substantial proportion of patients with advanced melanoma. Most patients however fail to respond to such treatment or acquire resistance. Previously, we reported that baseline total metabolic tumour volume (TMTV) determined by whole-body [18F]FDG PET/CT was independently correlated with survival and able to predict the futility of treatment. Manual delineation of [18F]FDG-avid lesions is however labour intensive and not suitable for routine use. A predictive survival model is proposed based on automated analysis of baseline, whole-body [18F]FDG images. METHODS: Lesions were segmented on [18F]FDG PET/CT using a deep-learning approach and derived features were investigated through Kaplan-Meier survival estimates with univariate logrank test and Cox regression analyses. Selected parameters were evaluated in multivariate Cox survival regressors. RESULTS: In the development set of 69 patients, overall survival prediction based on TMTV, lactate dehydrogenase levels and presence of brain metastases achieved an area under the curve of 0.78 at one year, 0.70 at two years. No statistically significant difference was observed with respect to using manually segmented lesions. Internal validation on 31 patients yielded scores of 0.76 for one year and 0.74 for two years. CONCLUSIONS: Automatically extracted TMTV based on whole-body [18F]FDG PET/CT can aid in building predictive models that can support therapeutic decisions in patients treated with immune-checkpoint blockade.

Computer-aided diagnosis of skeletal metastases in multi-parametric whole-body MRI.

The confident detection of metastatic bone disease is essential to improve patients' comfort and increase life expectancy. Multi-parametric magnetic resonance imaging (MRI) has been successfully used for monitoring of metastatic bone disease, allowing for comprehensive and holistic evaluation of the total tumour volume and treatment response assessment. The major challenges of radiological reading of whole-body MRI come from the amount of data to be reviewed and the scattered distribution of metastases, often of complex shapes. This makes bone lesion detection and quantification demanding for a radiologist and prone to error. Additionally, whole-body MRI are often corrupted with multiple spatial and intensity distortions, which further degrade the performance of image reading and image processing algorithms. In this work we propose a fully automated computer-aided diagnosis system for the detection and segmentation of metastatic bone disease using whole-body multi-parametric MRI. The system consists of an extensive image preprocessing pipeline aiming at enhancing the image quality, followed by a deep learning framework for detection and segmentation of metastatic bone disease. The system outperformed state-of-the-art methodologies, achieving a detection sensitivity of 63% with a mean of 6.44 false positives per image, and an average lesion Dice coefficient of 0.53. A provided ablation study performed to investigate the relative importance of image preprocessing shows that introduction of region of interest mask and spatial registration have a significant impact on detection and segmentation performance in whole-body MRI. The proposed computer-aided diagnosis system allows for automatic quantification of disease infiltration and could provide a valuable tool during radiological examination of whole-body MRI.

Comparison of intra- and inter-patient intensity standardization methods for multi-parametric whole-body MRI.

Objective.To test and compare different intensity standardization approaches for whole-body multi-parametric MR images, aiming to compensate voxel intensity differences between scans. These differences, common for magnetic resonance imaging, pose problems in image quantification, assessment of changes between a baseline and follow-up scan, and hinder performance of image processing and machine learning algorithms.Approach.In this work, we present a comparison on the accuracy of intensity standardization approaches with increasing complexity, for intra- and inter-patient multi-parametric whole-body MRI. Several approaches were used: z-scoring of the intensities, piecewise linear mapping and deformable mapping of intensity distributions into established reference intensity space. For each method, the impact on standardization algorithm on the use of single image or average population distribution reference; as well as, whole image and region of interest were additionally investigated. All methods were validated on a data set of 18 whole-body anatomical and diffusion-weighted MR scans consisting of baseline and follow-up examinations acquired from advanced prostate cancer patients and healthy volunteers.Main results.The piecewise linear intensity standardisation approach provided the best compromise between standardization accuracy and method stability, with average deviations in intensity profile of 0.011-0.027 and mean absolute difference of 0.29-0.37 standard score (intra-patient) and 0.014-0.056 (inter-patient), depending on the type of used MR modality.Significance.Linear piecewise approaches showed the overall best performance across multiple validation metrics, mostly because of its robustness. The inter-patient standardization proved to perform better when using population average reference image; in contrary to intra-patient approach, where the best results were achieved by standardizing towards a reference image taken as the baseline scan.

Computer-vision based analysis of the neurosurgical scene - A systematic review.

Introduction: With increasing use of robotic surgical adjuncts, artificial intelligence and augmented reality in neurosurgery, the automated analysis of digital images and videos acquired over various procedures becomes a subject of increased interest. While several computer vision (CV) methods have been developed and implemented for analyzing surgical scenes, few studies have been dedicated to neurosurgery. Research question: In this work, we present a systematic literature review focusing on CV methodologies specifically applied to the analysis of neurosurgical procedures based on intra-operative images and videos. Additionally, we provide recommendations for the future developments of CV models in neurosurgery. Material and methods: We conducted a systematic literature search in multiple databases until January 17, 2023, including Web of Science, PubMed, IEEE Xplore, Embase, and SpringerLink. Results: We identified 17 studies employing CV algorithms on neurosurgical videos/images. The most common applications of CV were tool and neuroanatomical structure detection or characterization, and to a lesser extent, surgical workflow analysis. Convolutional neural networks (CNN) were the most frequently utilized architecture for CV models (65%), demonstrating superior performances in tool detection and segmentation. In particular, mask recurrent-CNN manifested most robust performance outcomes across different modalities. Discussion and conclusion: Our systematic review demonstrates that CV models have been reported that can effectively detect and differentiate tools, surgical phases, neuroanatomical structures, as well as critical events in complex neurosurgical scenes with accuracies above 95%. Automated tool recognition contributes to objective characterization and assessment of surgical performance, with potential applications in neurosurgical training and intra-operative safety management.

Combined evaluation of blood flow and tissue perfusion in diabetic feet by intra-arterial dynamic 4DCT imaging.

Critical limb ischemia is associated with high mortality and major amputations. Intra-arterial digital subtraction angiography (IADSA) has been the reference standard but has some shortcomings including the two-dimensional projection and the lack of tissue perfusion information. The aim of this exploratory study is to examine four-dimensional computed tomography (4DCT) angiography and perfusion imaging using low-volume intra-arterial contrast injections for an improved anatomic and hemodynamic assessment in patients with foot ulcers. Three patients underwent a low-volume (2 mL) intra-arterial contrast-enhanced 4DCT examination combined with a diagnostic IADSA. An automated assessment of blood flow and tissue perfusion from the 4DCT data was performed. Vascular structures and corresponding blood flows were successfully assessed and correlated well with the IADSA results. Perfusion values of the affected tissue were significantly higher compared to the unaffected tissue. The proposed 4DCT protocol combined with the minimal usage of contrast agent (2 mL) provides superior images compared to IADSA as three phases (arterial, perfusion, and venous) are captured. The obtained parameters could allow for an improved diagnosis of critical limb ischemia as both the proximal vasculature and the extent of the perfusion deficit in the microvasculature can be assessed.Relevance statementIntra-arterial 4DCT allows for assessing three phases (arterial, perfusion and venous) using minimal contrast (2 mL). This method could lead to an improved diagnosis of critical limb ischemia as both proximal vasculature and the extent of the perfusion deficit are assessed.Trial registrationISRCTN, ISRCTN95737449. Registered 14 March 2023-retrospectively registered, https://www.isrctn.com/ISRCTN95737449 Key points• Three phases (arterial, perfusion, and venous) are obtained from 2 mL intra-arterial 4DCT.• The obtained hemodynamic parameters correlated well with the IADSA findings.• 4DCT surpassed IADSA in terms of assessment of venous blood flow and inflammatory hyperperfusion.• The assessment of tissue perfusion could lead to optimizing the revascularization strategy.

Neuro-oncological augmented reality planning for intracranial tumor resection.

Background: Before starting surgery for the resection of an intracranial tumor, its outlines are typically marked on the skin of the patient. This allows for the planning of the optimal skin incision, craniotomy, and angle of approach. Conventionally, the surgeon determines tumor borders using neuronavigation with a tracked pointer. However, interpretation errors can lead to important deviations, especially for deep-seated tumors, potentially resulting in a suboptimal approach with incomplete exposure. Augmented reality (AR) allows displaying of the tumor and critical structures directly on the patient, which can simplify and improve surgical preparation. Methods: We developed an AR-based workflow for intracranial tumor resection planning deployed on the Microsoft HoloLens II, which exploits the built-in infrared-camera for tracking the patient. We initially performed a phantom study to assess the accuracy of the registration and tracking. Following this, we evaluated the AR-based planning step in a prospective clinical study for patients undergoing resection of a brain tumor. This planning step was performed by 12 surgeons and trainees with varying degrees of experience. After patient registration, tumor outlines were marked on the patient's skin by different investigators, consecutively using a conventional neuronavigation system and an AR-based system. Their performance in both registration and delineation was measured in terms of accuracy and duration and compared. Results: During phantom testing, registration errors remained below 2.0 mm and 2.0° for both AR-based navigation and conventional neuronavigation, with no significant difference between both systems. In the prospective clinical trial, 20 patients underwent tumor resection planning. Registration accuracy was independent of user experience for both AR-based navigation and the commercial neuronavigation system. AR-guided tumor delineation was deemed superior in 65% of cases, equally good in 30% of cases, and inferior in 5% of cases when compared to the conventional navigation system. The overall planning time (AR = 119 ± 44 s, conventional = 187 ± 56 s) was significantly reduced through the adoption of the AR workflow (p < 0.001), with an average time reduction of 39%. Conclusion: By providing a more intuitive visualization of relevant data to the surgeon, AR navigation provides an accurate method for tumor resection planning that is quicker and more intuitive than conventional neuronavigation. Further research should focus on intraoperative implementations.

Exploring technology acceptance of head-mounted device-based augmented reality surgical navigation in orthopaedic surgery.

BACKGROUND: This study used the Unified Theory of Acceptance and Use of Technology (UTAUT) to investigate the acceptance of HMD-based AR surgical navigation. METHODS: An experiment was conducted in which participants drilled 12 predefined holes using freehand drilling, proprioceptive control, and AR assistance. Technology acceptance was assessed through a survey and non-participant observations. RESULTS: Participants' intention to use AR correlated (p < 0.05) with social influence (Spearman's rho (rs) = 0.599), perceived performance improvement (rs = 0.592) and attitude towards AR (rs = 0.542). CONCLUSIONS: While most participants acknowledged the potential of AR, they also highlighted persistent barriers to adoption, such as issues related to user-friendliness, time efficiency and device discomfort. To overcome these challenges, future AR surgical navigation systems should focus on enhancing surgical performance while minimising disruptions to workflows and operating times. Engaging orthopaedic surgeons in the development process can facilitate the creation of tailored solutions and accelerate adoption.

Quantitative hemodynamic assessment of stenotic below-the-knee arteries using spatio-temporal bolus tracking on 4D-CT angiography.

BACKGROUND: Peripheral arterial disease (PAD) is a chronic occlusive disease that restricts blood flow in the lower limbs, causing partial or complete blockages of the blood flow. While digital subtraction angiography (DSA) has traditionally been the preferred method for assessing blood flow in the lower limbs, advancements in wide beam Computed Tomography (CT), allowing successive acquisition at high frame rate, might enable hemodynamic measurements. PURPOSE: To quantify the arterial blood flow in stenotic below-the-knee (BTK) arteries. To this end, we propose a novel method for contrast bolus tracking and assessment of quantitative hemodynamic parameters in stenotic arteries using 4D-CT. METHODS: Fifty patients with suspected PAD underwent 4D-CT angiography in addition to the clinical run-off computed tomography angiography (CTA). From these dynamic acquisitions, the BTK arteries were segmented and the region of maximum blood flow was extracted. Time attenuation curves (TAC) were estimated using 2D spatio-temporal B-spline regression, enforcing both spatial and temporal smoothness. From these curves, quantitative hemodynamic parameters, describing the shape of the propagating contrast bolus were automatically extracted. We evaluated the robustness of the proposed TAC fitting method with respect to interphase delay and imaging noise and compared it to commonly used approaches. Finally, to illustrate the potential value of 4D-CT, we assessed the correlation between the obtained hemodynamic parameters and the presence of PAD. RESULTS: 280 out of 292 arteries were successfully segmented, with failures mainly due to a delayed contrast arrival. The proposed method led to physiologically plausible hemodynamic parameters and was significantly more robust compared to 1D temporal regression. A significant correlation between the presence of proximal stenoses and several hemodynamic parameters was found. CONCLUSIONS: The proposed method based on spatio-temporal bolus tracking was shown to lead to stable and physiologically plausible estimation of quantitative hemodynamic parameters, even in the case of stenotic arteries. These parameters may provide valuable information in the evaluation of PAD and contribute to its diagnosis.

Dynamic CT scanning of the knee: Combining weight bearing with real-time motion acquisition.

BACKGROUND: Imaging the lower limb during weight-bearing conditions is essential to acquire advanced functional joint information. The horizontal bed position of CT systems however hinders this process. The purpose of this study was to validate and test a device to simulate realistic knee weight-bearing motion in a horizontal position during dynamic CT acquisition and process the acquired images. METHODS: "Orthostatic squats" was compared to "Horizontal squats" on a device with loads between 35% and 55% of the body weight (%BW) in 20 healthy volunteers. Intraclass Correlation Coefficient (ICC), and standard error of measurement (SEM), were computed as measures of the reliability of curve kinematic and surface EMG (sEMG) data. Afterwards, the device was tested during dynamic CT acquisitions on three healthy volunteers and three patients with patellofemoral pain syndrome. The respective images were processed to extract Tibial-Tuberosity Trochlear-Groove distance, Bisect Offset and Lateral Patellar Tilt metrics. RESULTS: For sEMG, the highest average ICCs (SEM) of 0.80 (6.9), was found for the load corresponding to 42%BW. Kinematic analysis showed ICCs were the highest for loads of 42%BW during the eccentric phase (0.79-0.87) and from maximum flexion back to 20° (0.76). The device proved to be safe and reliable during the acquisition of dynamic CT images and the three metrics were computed, showing preliminary differences between healthy and pathological participants. CONCLUSIONS: This device could simulate orthostatic squats in a horizontal position with good reliability. It also successfully provided dynamic CT scan images and kinematic parameters of healthy and pathological knees during weight-bearing movement.