X-ray imageable, drug-loaded hydrogel that forms at body temperature for image-guided, needle- based locoregional drug delivery.

Liver cancer ranks as the fifth leading cause of cancer-related death globally. Direct intratumoral injections of anti-cancer therapeutics may improve therapeutic efficacy and mitigate adverse effects compared to intravenous injections. Some challenges of intratumoral injections are that the liquid drug formulation may not remain localized and have unpredictable volumetric distribution. Thus, drug delivery varies widely, highly-dependent upon technique. An x-ray imageable poloxamer 407 (POL)-based drug delivery gel was developed and characterized, enabling real-time feedback. Utilizing three needle devices, POL or a control iodinated contrast solution were injected into an ex vivo bovine liver. The 3D distribution was assessed with cone beam computed tomography (CBCT). The 3D distribution of POL gels demonstrated localized spherical morphologies regardless of the injection rate. In addition, the gel 3D conformal distribution could be intentionally altered, depending on the injection technique. When doxorubicin (DOX) was loaded into the POL and injected, DOX distribution on optical imaging matched iodine distribution on CBCT suggesting spatial alignment of DOX and iodine localization in tissue. The controllability and localized deposition of this formulation may ultimately reduce the dependence on operator technique, reduce systemic side effects, and facilitate reproducibility across treatments, through more predictable standardized delivery.

Smartphone Augmented Reality Outperforms Conventional CT Guidance for Composite Ablation Margins in Phantom Models.

PURPOSE: To develop and evaluate a smartphone augmented reality (AR) system for a large 50-mm liver tumor ablation with treatment planning for composite overlapping ablation zones. MATERIALS AND METHODS: A smartphone AR application was developed to display tumor, probe, projected probe paths, ablated zones, and real-time percentage of the ablated target tumor volume. Fiducial markers were attached to phantoms and an ablation probe hub for tracking. The system was evaluated with tissue-mimicking thermochromic phantoms and gel phantoms. Four interventional radiologists performed 2 trials each of 3 probe insertions per trial using AR guidance versus computed tomography (CT) guidance approaches in 2 gel phantoms. Insertion points and optimal probe paths were predetermined. On Gel Phantom 2, serial ablated zones were saved and continuously displayed after each probe placement/adjustment, enabling feedback and iterative planning. The percentages of tumor ablated for AR guidance versus CT guidance, and with versus without display of recorded ablated zones, were compared among interventional radiologists with pairwise t-tests. RESULTS: The means of percentages of tumor ablated for CT freehand and AR guidance were 36% ± 7 and 47% ± 4 (P = .004), respectively. The mean composite percentages of tumor ablated for AR guidance were 43% ± 1 (without) and 50% ± 2 (with display of ablation zone) (P = .033). There was no strong correlation between AR-guided percentage of ablation and years of experience (r < 0.5), whereas there was a strong correlation between CT-guided percentage of ablation and years of experience (r > 0.9). CONCLUSIONS: A smartphone AR guidance system for dynamic iterative large liver tumor ablation was accurate, performed better than conventional CT guidance, especially for less experienced interventional radiologists, and enhanced more standardized performance across experience levels for ablation of a 50-mm tumor.

Morphometric characterization and temporal temperature measurements during hepatic microwave ablation in swine.

PURPOSE: Heat-induced destruction of cancer cells via microwave ablation (MWA) is emerging as a viable treatment of primary and metastatic liver cancer. Prediction of the impacted zone where cell death occurs, especially in the presence of vasculature, is challenging but may be achieved via biophysical modeling. To advance and characterize thermal MWA for focal cancer treatment, an in vivo method and experimental dataset were created for assessment of biophysical models designed to dynamically predict ablation zone parameters, given the delivery device, power, location, and proximity to vessels. MATERIALS AND METHODS: MWA zone size, shape, and temperature were characterized and monitored in the absence of perfusion in ex vivo liver and a tissue-mimicking thermochromic phantom (TMTCP) at two power settings. Temperature was monitored over time using implanted thermocouples with their locations defined by CT. TMTCPs were used to identify the location of the ablation zone relative to the probe. In 6 swine, contrast-enhanced CTs were additionally acquired to visualize vasculature and absence of perfusion along with corresponding post-mortem gross pathology. RESULTS: Bench studies demonstrated average ablation zone sizes of 4.13±1.56cm2 and 8.51±3.92cm2, solidity of 0.96±0.06 and 0.99±0.01, ablations centered 3.75cm and 3.5cm proximal to the probe tip, and temperatures of 50 ºC at 14.5±13.4s and 2.5±2.1s for 40W and 90W ablations, respectively. In vivo imaging showed average volumes of 9.8±4.8cm3 and 33.2±28.4cm3 and 3D solidity of 0.87±0.02 and 0.75±0.15, and gross pathology showed a hemorrhagic halo area of 3.1±1.2cm2 and 9.1±3.0cm2 for 40W and 90W ablations, respectfully. Temperatures reached 50ºC at 19.5±9.2s and 13.0±8.3s for 40W and 90W ablations, respectively. CONCLUSION: MWA results are challenging to predict and are more variable than manufacturer-provided and bench predictions due to vascular stasis, heat-induced tissue changes, and probe operating conditions. Accurate prediction of MWA zones and temperature in vivo requires comprehensive thermal validation sets.

A Novel Magnetic Resonance Imaging/Ultrasound Fusion Prostate Biopsy Technique Using Transperineal Ultrasound: An Initial Experience.

OBJECTIVE: To report an initial experience with a novel, "fully" transperineal (TP) prostate fusion biopsy using an unconstrained ultrasound transducer placed on the perineal skin to guide biopsy needles inserted via a TP approach. METHODS: Conventional TP prostate biopsies for detection of prostate cancer have been performed with transrectal ultrasound, requiring specialized hardware, imposing limitations on needle trajectory, and contributing to patient discomfort. Seventy-six patients with known or suspected prostate cancer underwent 78 TP biopsy sessions in an academic center between June 2018 and April 2022 and were included in this study. These patients underwent TP prostate fusion biopsy using a grid or freehand device with transrectal ultrasound as well as TP prostate fusion biopsy using TP ultrasound in the same session. Per-session and per-lesion cancer detection rates were compared for conventional and fully TP biopsies using Fisher exact and McNemar's tests. RESULTS: After a refinement period in 30 patients, 92 MRI-visible prostate lesions were sampled in 46 subsequent patients, along with repeat biopsies in 2 of the 30 patients from the refinement period. Grade group ≥2 cancer was diagnosed in 24/92 lesions (26%) on conventional TP biopsy (17 lesions with grid, 7 with freehand device), and in 25/92 lesions (27%) on fully TP biopsy (P = 1.00), with a 73/92 (79%) rate of agreement for grade group ≥2 cancer between the two methods. CONCLUSION: Fully TP biopsy is feasible and may detect prostate cancer with detection rates comparable to conventional TP biopsy.

HoloLens augmented reality system for transperineal free-hand prostate procedures.

Purpose: An augmented reality (AR) system was developed to facilitate free-hand real-time needle guidance for transperineal prostate (TP) procedures and to overcome the limitations of a traditional guidance grid. Approach: The HoloLens AR system enables the superimposition of annotated anatomy derived from preprocedural volumetric images onto a patient and addresses the most challenging part of free-hand TP procedures by providing real-time needle tip localization and needle depth visualization during insertion. The AR system accuracy, or the image overlay accuracy ( n = 56 ), and needle targeting accuracy ( n = 24 ) were evaluated within a 3D-printed phantom. Three operators each used a planned-path guidance method ( n = 4 ) and free-hand guidance ( n = 4 ) to guide needles into targets in a gel phantom. Placement error was recorded. The feasibility of the system was further evaluated by delivering soft tissue markers into tumors of an anthropomorphic pelvic phantom via the perineum. Results: The image overlay error was 1.29 ± 0.57 mm , and needle targeting error was 2.13 ± 0.52 mm . The planned-path guidance placements showed similar error compared to the free-hand guidance ( 4.14 ± 1.08 mm versus 4.20 ± 1.08 mm , p = 0.90 ). The markers were successfully implanted either into or in close proximity to the target lesion. Conclusions: The HoloLens AR system can provide accurate needle guidance for TP interventions. AR support for free-hand lesion targeting is feasible and may provide more flexibility than grid-based methods, due to the real-time 3D and immersive experience during free-hand TP procedures.

Image-Guided Robotics for Standardized and Automated Biopsy and Ablation.

Image-guided robotics for biopsy and ablation aims to minimize procedure times, reduce needle manipulations, radiation, and complications, and enable treatment of larger and more complex tumors, while facilitating standardization for more uniform and improved outcomes. Robotic navigation of needles enables standardized and uniform procedures which enhance reproducibility via real-time precision feedback, while avoiding radiation exposure to the operator. Robots can be integrated with computed tomography (CT), cone beam CT, magnetic resonance imaging, and ultrasound and through various techniques, including stereotaxy, table-mounted, floor-mounted, and patient-mounted robots. The history, challenges, solutions, and questions facing the field of interventional radiology (IR) and interventional oncology are reviewed, to enable responsible clinical adoption and value definition via ergonomics, workflows, business models, and outcome data. IR-integrated robotics is ready for broader adoption. The robots are coming!

Endovascular steerable and endobronchial precurved guiding sheaths for transbronchial needle delivery under augmented fluoroscopy and cone beam CT image guidance.

BACKGROUND: Endobronchial navigation is performed in a variety of ways, none of which are meeting all the clinicians' needs required to reach diagnostic success in every patient. We sought to characterize precurved and steerable guiding sheaths (GS) in endobronchial targeting for lung biopsy using cone beam computed tomography (CBCT) based augmented fluoroscopy (AF) image guidance. METHODS: Four precurved GS (EdgeTM 45, 90, 180, 180EW, Medtronic) and two steerable GS [6.5 F Destino Twist (DT), Oscor; 6 F Morph, BioCardia] were evaluated alone and in combination with an electromagnetic tracking (EM) guide and biopsy needles in three experimental phases: (I) bench model to assess GS deflection and perform biopsy simulations; (II) ex vivo swine lung comparing 2 steerable and 2 precurved GS; and (III) in vivo male swine lung to deliver a needle (n=2 swine) or to deliver a fiducial marker (n=2 swine) using 2 steerable GS. Ex vivo and in vivo image guidance was performed with either commercial or prototype AF image guidance software (Philips) based on either prior CT or procedural CBCT. Primary outcomes were GS delivery angle (θGS) and needle delivery angle (θN) in bench evaluation and needle delivery error (mm) (mean ± se) for ex vivo and in vivo studies. RESULTS: The steerable DT had the largest range of GS delivery angles (θN: 0-114°) with either the 21 G or 19 G biopsy needle in the bench model. In ex vivo swine lung, needle delivery errors were 8.7±0.9 mm (precurved Edge 90), 5.4±1.9 mm (precurved Edge 180), 4.7±1.2 mm (steerable DT), and 5.6±2.4 mm (steerable Morph). In vivo, the needle delivery errors for the steerable GS were 6.0±1.0 mm (DT) and 15±7.0 mm (Morph). In vivo marker coil delivery was successful for both the steerable DT and morph GS. A case report demonstrated successful needle biopsy with the steerable DT. CONCLUSIONS: Endobronchial needle delivery with AF guidance is feasible without a bronchoscope with steerable GS providing comparable or improved accuracy compared to precurved GS.

Generalized chest CT and lab curves throughout the course of COVID-19.

A better understanding of temporal relationships between chest CT and labs may provide a reference for disease severity over the disease course. Generalized curves of lung opacity volume and density over time can be used as standardized references from well before symptoms develop to over a month after recovery, when residual lung opacities remain. 739 patients with COVID-19 underwent CT and RT-PCR in an outbreak setting between January 21st and April 12th, 2020. 29 of 739 patients had serial exams (121 CTs and 279 laboratory measurements) over 50 ± 16 days, with an average of 4.2 sequential CTs each. Sequential volumes of total lung, overall opacity and opacity subtypes (ground glass opacity [GGO] and consolidation) were extracted using deep learning and manual segmentation. Generalized temporal curves of CT and laboratory measurements were correlated. Lung opacities appeared 3.4 ± 2.2 days prior to symptom onset. Opacity peaked 1 day after symptom onset. GGO onset was earlier and resolved later than consolidation. Lactate dehydrogenase, and C-reactive protein peaked earlier than procalcitonin and leukopenia. The temporal relationships of quantitative CT features and clinical labs have distinctive patterns and peaks in relation to symptom onset, which may inform early clinical course in patients with mild COVID-19 pneumonia, or may shed light upon chronic lung effects or mechanisms of medical countermeasures in clinical trials.

Comparison of Smartphone Augmented Reality, Smartglasses Augmented Reality, and 3D CBCT-guided Fluoroscopy Navigation for Percutaneous Needle Insertion: A Phantom Study.

PURPOSE: To compare needle placement performance using an augmented reality (AR) navigation platform implemented on smartphone or smartglasses devices to that of CBCT-guided fluoroscopy in a phantom. MATERIALS AND METHODS: An AR application was developed to display a planned percutaneous needle trajectory on the smartphone (iPhone7) and smartglasses (HoloLens1) devices in real time. Two AR-guided needle placement systems and CBCT-guided fluoroscopy with navigation software (XperGuide, Philips) were compared using an anthropomorphic phantom (CIRS, Norfolk, VA). Six interventional radiologists each performed 18 independent needle placements using smartphone (n = 6), smartglasses (n = 6), and XperGuide (n = 6) guidance. Placement error was defined as the distance from the needle tip to the target center. Placement time was recorded. For XperGuide, dose-area product (DAP, mGy*cm2) and fluoroscopy time (sec) were recorded. Statistical comparisons were made using a two-way repeated measures ANOVA. RESULTS: The placement error using the smartphone, smartglasses, or XperGuide was similar (3.98 ± 1.68 mm, 5.18 ± 3.84 mm, 4.13 ± 2.38 mm, respectively, p = 0.11). Compared to CBCT-guided fluoroscopy, the smartphone and smartglasses reduced placement time by 38% (p = 0.02) and 55% (p = 0.001), respectively. The DAP for insertion using XperGuide was 3086 ± 2920 mGy*cm2, and no intra-procedural radiation was required for augmented reality. CONCLUSIONS: Smartphone- and smartglasses-based augmented reality reduced needle placement time and radiation exposure while maintaining placement accuracy compared to a clinically validated needle navigation platform.

Determination of disease severity in COVID-19 patients using deep learning in chest X-ray images.

PURPOSE: Chest X-ray plays a key role in diagnosis and management of COVID-19 patients and imaging features associated with clinical elements may assist with the development or validation of automated image analysis tools. We aimed to identify associations between clinical and radiographic features as well as to assess the feasibility of deep learning applied to chest X-rays in the setting of an acute COVID-19 outbreak. METHODS: A retrospective study of X-rays, clinical, and laboratory data was performed from 48 SARS-CoV-2 RT-PCR positive patients (age 60±17 years, 15 women) between February 22 and March 6, 2020 from a tertiary care hospital in Milan, Italy. Sixty-five chest X-rays were reviewed by two radiologists for alveolar and interstitial opacities and classified by severity on a scale from 0 to 3. Clinical factors (age, symptoms, comorbidities) were investigated for association with opacity severity and also with placement of central line or endotracheal tube. Deep learning models were then trained for two tasks: lung segmentation and opacity detection. Imaging characteristics were compared to clinical datapoints using the unpaired student's t-test or Mann-Whitney U test. Cohen's kappa analysis was used to evaluate the concordance of deep learning to conventional radiologist interpretation. RESULTS: Fifty-six percent of patients presented with alveolar opacities, 73% had interstitial opacities, and 23% had normal X-rays. The presence of alveolar or interstitial opacities was statistically correlated with age (P = 0.008) and comorbidities (P = 0.005). The extent of alveolar or interstitial opacities on baseline X-ray was significantly associated with the presence of endotracheal tube (P = 0.0008 and P = 0.049) or central line (P = 0.003 and P = 0.007). In comparison to human interpretation, the deep learning model achieved a kappa concordance of 0.51 for alveolar opacities and 0.71 for interstitial opacities. CONCLUSION: Chest X-ray analysis in an acute COVID-19 outbreak showed that the severity of opacities was associated with advanced age, comorbidities, as well as acuity of care. Artificial intelligence tools based upon deep learning of COVID-19 chest X-rays are feasible in the acute outbreak setting.

CT and clinical assessment in asymptomatic and pre-symptomatic patients with early SARS-CoV-2 in outbreak settings.

OBJECTIVES: The early infection dynamics of patients with SARS-CoV-2 are not well understood. We aimed to investigate and characterize associations between clinical, laboratory, and imaging features of asymptomatic and pre-symptomatic patients with SARS-CoV-2. METHODS: Seventy-four patients with RT-PCR-proven SARS-CoV-2 infection were asymptomatic at presentation. All were retrospectively identified from 825 patients with chest CT scans and positive RT-PCR following exposure or travel risks in outbreak settings in Japan and China. CTs were obtained for every patient within a day of admission and were reviewed for infiltrate subtypes and percent with assistance from a deep learning tool. Correlations of clinical, laboratory, and imaging features were analyzed and comparisons were performed using univariate and multivariate logistic regression. RESULTS: Forty-eight of 74 (65%) initially asymptomatic patients had CT infiltrates that pre-dated symptom onset by 3.8 days. The most common CT infiltrates were ground glass opacities (45/48; 94%) and consolidation (22/48; 46%). Patient body temperature (p < 0.01), CRP (p < 0.01), and KL-6 (p = 0.02) were associated with the presence of CT infiltrates. Infiltrate volume (p = 0.01), percent lung involvement (p = 0.01), and consolidation (p = 0.043) were associated with subsequent development of symptoms. CONCLUSIONS: COVID-19 CT infiltrates pre-dated symptoms in two-thirds of patients. Body temperature elevation and laboratory evaluations may identify asymptomatic patients with SARS-CoV-2 CT infiltrates at presentation, and the characteristics of CT infiltrates could help identify asymptomatic SARS-CoV-2 patients who subsequently develop symptoms. The role of chest CT in COVID-19 may be illuminated by a better understanding of CT infiltrates in patients with early disease or SARS-CoV-2 exposure. KEY POINTS: • Forty-eight of 74 (65%) pre-selected asymptomatic patients with SARS-CoV-2 had abnormal chest CT findings. • CT infiltrates pre-dated symptom onset by 3.8 days (range 1-5). • KL-6, CRP, and elevated body temperature identified patients with CT infiltrates. Higher infiltrate volume, percent lung involvement, and pulmonary consolidation identified patients who developed symptoms.

Endobronchial Navigation Guided by Cone-Beam CT-Based Augmented Fluoroscopy without a Bronchoscope: Feasibility Study in Phantom and Swine.

PURPOSE: To evaluate the accuracy of cone-beam computed tomography (CT)-based augmented fluoroscopy (AF) image guidance for endobronchial navigation to peripheral lung targets. METHODS: Prototypic endobronchial navigation AF software that superimposed segmented airways, targets, and pathways based on cone-beam CT onto fluoroscopy images was evaluated ex vivo in fixed swine lungs and in vivo in healthy swine (n = 4) without a bronchoscope. Ex vivo and in vivo (n = 3) phase 1 experiments used guide catheters and AF software version 1, whereas in vivo phase 2 (n = 1) experiments also used an endovascular steerable guiding sheath, upgraded AF software version 2, and lung-specific low-radiation-dose protocols. First-pass navigation success was defined as catheter delivery into a targeted airway segment solely using AF, with second-pass success defined as reaching the targeted segment by using updated AF image guidance based on confirmatory cone-beam CT. Secondary outcomes were navigation error, navigation time, radiation exposure, and preliminary safety. RESULTS: First-pass success was 100% (10/10) ex vivo and 19/24 (79%) and 11/15 (73%) for in vivo phases 1 and 2, respectively. Phase 2 second-pass success was 4/4 (100%). Navigation errors were 2.2 ± 1.2 mm ex vivo and 4.9 ± 3.2 mm and 4.0 ± 2.6 mm for in vivo phases 1 and 2, respectively. No major device-related complications were observed in the in vivo experiments. CONCLUSIONS: Endobronchial navigation is feasible and accurate with cone-beam CT-based AF image guidance. AF can guide endobronchial navigation with endovascular catheters and steerable guiding sheaths to peripheral lung targets, potentially overcoming limitations associated with bronchoscopy.

Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets.

Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Artificial intelligence (AI) has the potential to aid in rapid evaluation of CT scans for differentiation of COVID-19 findings from other clinical entities. Here we show that a series of deep learning algorithms, trained in a diverse multinational cohort of 1280 patients to localize parietal pleura/lung parenchyma followed by classification of COVID-19 pneumonia, can achieve up to 90.8% accuracy, with 84% sensitivity and 93% specificity, as evaluated in an independent test set (not included in training and validation) of 1337 patients. Normal controls included chest CTs from oncology, emergency, and pneumonia-related indications. The false positive rate in 140 patients with laboratory confirmed other (non COVID-19) pneumonias was 10%. AI-based algorithms can readily identify CT scans with COVID-19 associated pneumonia, as well as distinguish non-COVID related pneumonias with high specificity in diverse patient populations.

Smartphone- versus smartglasses-based augmented reality (AR) for percutaneous needle interventions: system accuracy and feasibility study.

PURPOSE: To compare the system accuracy and needle placement performance of smartphone- and smartglasses-based augmented reality (AR) for percutaneous needle interventions. METHODS: An AR platform was developed to enable the superimposition of annotated anatomy and a planned needle trajectory onto a patient in real time. The system accuracy of the AR display on smartphone (iPhone7) and smartglasses (HoloLens1) devices was evaluated on a 3D-printed phantom. The target overlay error was measured as the distance between actual and virtual targets (n = 336) on the AR display, derived from preprocedural CT. The needle overlay angle was measured as the angular difference between actual and virtual needles (n = 12) on the AR display. Three operators each used the iPhone (n = 8), HoloLens (n = 8) and CT-guided freehand (n = 8) to guide needles into targets in a phantom. Needle placement error was measured with post-placement CT. Needle placement time was recorded from needle puncture to navigation completion. RESULTS: The target overlay error of the iPhone was comparable to the HoloLens (1.75 ± 0.59 mm, 1.74 ± 0.86 mm, respectively, p = 0.9). The needle overlay angle of the iPhone and HoloLens was similar (0.28 ± 0.32°, 0.41 ± 0.23°, respectively, p = 0.26). The iPhone-guided needle placements showed reduced error compared to the HoloLens (2.58 ± 1.04 mm, 3.61 ± 2.25 mm, respectively, p = 0.05) and increased time (87 ± 17 s, 71 ± 27 s, respectively, p = 0.02). Both AR devices reduced placement error compared to CT-guided freehand (15.92 ± 8.06 mm, both p < 0.001). CONCLUSION: An augmented reality platform employed on smartphone and smartglasses devices may provide accurate display and navigation guidance for percutaneous needle-based interventions.

A Simple Flow Classification Parameter Can Discriminate Rupture Status in Intracranial Aneurysms.

BACKGROUND: A simple dimensionless aneurysm number ($An$), which depends on geometry and flow pulsatility, was previously shown to distinguish the flow mode in intracranial aneurysms (IA): vortex mode with a dynamic vortex formation/evolution if $An > 1$, and cavity mode with a steady shear layer if $An < 1$. OBJECTIVE: To hypothesize that $An\ > \ 1$ can distinguish rupture status because vortex mode is associated with high oscillatory shear index, which, in turn, is statistically associated with rupture. METHODS: The above hypothesis is tested on a retrospective, consecutively collected database of 204 patient-specific IAs. The first 119 cases are assigned to training and the remainder to testing dataset. $An$ is calculated based on the pulsatility index (PI) approximated either from the literature or solving an optimization problem (denoted as$\ \widehat {PI}$). Student's t-test and logistic regression (LR) are used for hypothesis testing and data fitting, respectively. RESULTS: $An$ can significantly discriminate ruptured and unruptured status with 95% confidence level (P < .0001). $An$ (using PI) and $\widehat {An}$ (using $\widehat {PI}$) significantly predict the ruptured IAs (for training dataset $An\!:\ $AUC = 0.85, $\widehat {An}\!:\ $AUC = 0.90, and for testing dataset $An\!:\ $sensitivity = 94%, specificity = 33%, $\widehat {An}\!:\ $sensitivity = 93.1%, specificity = 52.85%). CONCLUSION: $An > 1$ predicts ruptured status. Unlike traditional hemodynamic parameters such as wall shear stress and oscillatory shear index, $An$ has a physical threshold of one (does not depend on statistical analysis) and does not require time-consuming flow simulations. Therefore, $An$ is a simple, practical discriminator of IA rupture status.

In Vivo Characterization of the Swine Airway Morphometry and Motion Based on Computed Tomographic Imaging During Respiration.

Swine are a commonly used model in translational pulmonary research. However, in vivo airway morphometry during respiration has not been studied in extensive detail using modern imaging tools. Chest computed tomographic was performed in swine (n = 3) at multiple stages of respiration. Morphometric parameters of each airway segment at end-expiration and end-inspiration were compared as well as among matched anatomical regions (proximal and distal; ventral, lateral, and dorsal). Analysis included segment diameter, length, ellipticity, and the bifurcation angle between daughter branches. Deformation of the airway during respiration was qualitatively visualized using a point-to-point deformation map. Comparison of airway generation showed airway diameter and length were larger at end-inspiration in the fourth and seventh generations compared to end-expiration. Bifurcation angle was larger at end-inspiration compared to end-expiration. Analysis by anatomical region showed that length and bifurcation angle were larger at inspiration in the distal airway regions only. Regardless of respiratory phase, the lateral regions had larger diameters and lengths compared to the ventral and dorsal regions at similar generations and proximal regions had larger bifurcation angles. The findings that morphological changes were more prevalent in distal airways during respiration was confirmed by analysis of a deformation map. Compared to human airway models, the relative diameter may be smaller and length may be greater in swine in similar airway generations. This morphometric description of the swine airways during respiration may guide conduct of preclinical translational studies, revealing advantages and limitations of swine models for specific evaluations. Such morphometric parameters may directly determine the suitability of the swine model for the study of lung interventions, in terms of recapitulation of human morphometry dynamics.

Computer-Assisted Three-Dimensional Morphology Evaluation of Intracranial Aneurysms.

OBJECTIVE: Precise morphologic evaluation is important for intracranial aneurysm (IA) management. At present, clinicians manually measure the IA size and neck diameter on 2-dimensional (2D) digital subtraction angiographic (DSA) images and categorize the IA shape as regular or irregular on 3-dimensional (3D)-DSA images, which could result in inconsistency and bias. We investigated whether a computer-assisted 3D analytical approach could improve IA morphology assessment. METHODS: Five neurointerventionists evaluated the size, neck diameter, and shape of 39 IAs using current and computer-assisted 3D approaches. In the computer-assisted 3D approach, the size, neck diameter, and undulation index (UI, a shape irregularity metric) were extracted using semiautomated reconstruction of aneurysm geometry using 3D-DSA, followed by IA neck identification and computerized geometry assessment. RESULTS: The size and neck diameter measured using the manual 2D approach were smaller than computer-assisted 3D measurements by 2.01 mm (P < 0.001) and 1.85 mm (P < 0.001), respectively. Applying the definitions of small IAs (<7 mm) and narrow-necked IAs (<4 mm) from the reported data, interrater variation in manual 2D measurements resulted in inconsistent classification of the size of 14 IAs and the necks of 19 IAs. Visual inspection resulted in an inconsistent shape classification for 23 IAs among the raters. Greater consistency was achieved using the computer-assisted 3D approach for size (intraclass correlation coefficient [ICC], 1.00), neck measurements (ICC, 0.96), and shape quantification (UI; ICC, 0.94). CONCLUSIONS: Computer-assisted 3D morphology analysis can improve accuracy and consistency in measurements compared with manual 2D measurements. It can also more reliably quantify shape irregularity using the UI. Future application of computer-assisted analysis tools could help clinicians standardize morphology evaluations, leading to more consistent IA evaluations.

Shared and Distinct Rupture Discriminants of Small and Large Intracranial Aneurysms.

BACKGROUND AND PURPOSE: Many ruptured intracranial aneurysms (IAs) are small. Clinical presentations suggest that small and large IAs could have different phenotypes. It is unknown if small and large IAs have different characteristics that discriminate rupture. METHODS: We analyzed morphological, hemodynamic, and clinical parameters of 413 retrospectively collected IAs (training cohort; 102 ruptured IAs). Hierarchal cluster analysis was performed to determine a size cutoff to dichotomize the IA population into small and large IAs. We applied multivariate logistic regression to build rupture discrimination models for small IAs, large IAs, and an aggregation of all IAs. We validated the ability of these 3 models to predict rupture status in a second, independently collected cohort of 129 IAs (testing cohort; 14 ruptured IAs). RESULTS: Hierarchal cluster analysis in the training cohort confirmed that small and large IAs are best separated at 5 mm based on morphological and hemodynamic features (area under the curve=0.81). For small IAs (<5 mm), the resulting rupture discrimination model included undulation index, oscillatory shear index, previous subarachnoid hemorrhage, and absence of multiple IAs (area under the curve=0.84; 95% confidence interval, 0.78-0.88), whereas for large IAs (≥5 mm), the model included undulation index, low wall shear stress, previous subarachnoid hemorrhage, and IA location (area under the curve=0.87; 95% confidence interval, 0.82-0.93). The model for the aggregated training cohort retained all the parameters in the size-dichotomized models. Results in the testing cohort showed that the size-dichotomized rupture discrimination model had higher sensitivity (64% versus 29%) and accuracy (77% versus 74%), marginally higher area under the curve (0.75; 95% confidence interval, 0.61-0.88 versus 0.67; 95% confidence interval, 0.52-0.82), and similar specificity (78% versus 80%) compared with the aggregate-based model. CONCLUSIONS: Small (<5 mm) and large (≥5 mm) IAs have different hemodynamic and clinical, but not morphological, rupture discriminants. Size-dichotomized rupture discrimination models performed better than the aggregate model.

Rupture Resemblance Models May Correlate to Growth Rates of Intracranial Aneurysms: Preliminary Results.

BACKGROUND: Treatment of intracranial aneurysms (IAs) is largely guided by IA size and growth. Preliminary investigations have found a relationship between clinical factors and growth; yet, the relationship between morphologic and hemodynamic risk prediction models in IA growth is unknown. METHODS: We analyzed serial images of 5 growing and 6 stable IAs. Rupture resemblance scores (RRSs) were calculated from three-dimensional segmented images and computational fluid dynamics simulations. The morphologic (RRSM), hemodynamic (RRSH), and combination (RRSC) scores leveraged IA size ratio, wall shear stress, and oscillatory shear index. Comparisons of RRS and morphologic and hemodynamic characteristics were made between growing and stable IAs at the baseline time point and between the baseline and follow-up time points of the growing IAs. In addition, we investigated the correlation of growth rate and RRS and the hemodynamics of growing and stable regions were compared. RESULTS: Our results indicate that there is no statistical difference in IAs at the baseline time point; however, growing IAs tend to have a higher aspect ratio (P = 0.066), undulation index (P = 0.086), and RRSC (P = 0.86). In addition, we found a significant correlation between growth rate and baseline RRS of all 3 models (RRSM, r = 0.874, P < 0.001; RRSH, r = 0.727, P = 0.011; RRSC, r = 0.815, P = 0.002). We also found that growing IAs significantly increased in aspect ratio (P = 0.034), size ratio (P = 0.034), and RRSM (P = 0.034). Our results show that stable and growing regions had statistically different wall shear stress and oscillatory shear index. CONCLUSIONS: Based on this preliminary study, we conjecture that aneurysms that resemble ruptured IAs may grow faster.