Mobile Cone-Beam CT-Assisted Bronchoscopy for Peripheral Lung Lesions.

Peripheral bronchoscopy with the use of thin/ultrathin bronchoscopes and radial-probe endobronchial ultrasound (RP-EBUS) has been associated with a fair diagnostic yield. Mobile cone-beam CT (m-CBCT) could potentially improve the performance of these readily available technologies. We retrospectively reviewed the records of patients undergoing bronchoscopy for peripheral lung lesions with thin/ultrathin scope, RP-EBUS, and m-CBCT guidance. We studied the performance (diagnostic yield and sensitivity for malignancy) and safety (complications, radiation exposure) of this combined approach. A total of 51 patients were studied. The mean target size was 2.6 cm (SD, 1.3 cm) and the mean distance to the pleura was 1.5 cm (SD, 1.4 cm). The diagnostic yield was 78.4% (95 CI, 67.1-89.7%), and the sensitivity for malignancy was 77.4% (95 CI, 62.7-92.1%). The only complication was one pneumothorax. The median fluoroscopy time was 11.2 min (range, 2.9-42.1) and the median number of CT spins was 1 (range, 1-5). The mean Dose Area Product from the total exposure was 41.92 Gy·cm2 (SD, 11.35 Gy·cm2). Mobile CBCT guidance may increase the performance of thin/ultrathin bronchoscopy for peripheral lung lesions in a safe manner. Further prospective studies are needed to corroborate these findings.

Development of a fluoroscopically guided robotic assistant for instrument placement in pelvic trauma surgery.

Purpose: A method for fluoroscopic guidance of a robotic assistant is presented for instrument placement in pelvic trauma surgery. The solution uses fluoroscopic images acquired in standard clinical workflow and helps avoid repeat fluoroscopy commonly performed during implant guidance. Approach: Images acquired from a mobile C-arm are used to perform 3D-2D registration of both the patient (via patient CT) and the robot (via CAD model of a surgical instrument attached to its end effector, e.g; a drill guide), guiding the robot to target trajectories defined in the patient CT. The proposed approach avoids C-arm gantry motion, instead manipulating the robot to acquire disparate views of the instrument. Phantom and cadaver studies were performed to determine operating parameters and assess the accuracy of the proposed approach in aligning a standard drill guide instrument. Results: The proposed approach achieved average drill guide tip placement accuracy of 1.57 ± 0.47 mm and angular alignment of 0.35 ± 0.32 deg in phantom studies. The errors remained within 2 mm and 1 deg in cadaver experiments, comparable to the margins of errors provided by surgical trackers (but operating without the need for external tracking). Conclusions: By operating at a fixed fluoroscopic perspective and eliminating the need for encoded C-arm gantry movement, the proposed approach simplifies and expedites the registration of image-guided robotic assistants and can be used with simple, non-calibrated, non-encoded, and non-isocentric C-arm systems to accurately guide a robotic device in a manner that is compatible with the surgical workflow.

Evaluation of image quality and task performance for a mobile C-arm with a complementary metal-oxide semiconductor detector.

We assessed interventional radiologists' task-based image quality preferences for two- and three-dimensional images obtained with a complementary metal-oxide semiconductor (CMOS) flat-panel detector versus a hydrogenated amorphous silicon (a-Si:H) flat-panel detector. CMOS and a-Si:H detectors were implemented on identical mobile C-arms to acquire radiographic, fluoroscopic, and cone-beam computed tomography (CBCT) images of cadavers undergoing simulated interventional procedures using low- and high-dose settings. Images from both systems were displayed side by side on calibrated, diagnostic-quality displays, and three interventional radiologists evaluated task performance relevant to each image and ranked their preferences based on visibility of pertinent anatomy and interventional devices. Overall, CMOS images were preferred in fluoroscopy ( p = 0.002 ) and CBCT ( p = 0.004 ), at low-dose settings ( p = 0.001 ), and for tasks associated with high levels of spatial resolution [e.g., fine anatomical details ( p = 0.006 ) and assessment of interventional devices ( p = 0.015 )]. No significant difference was found for fluoroscopic imaging tasks emphasizing temporal resolution ( p = 0.072 ), for radiography tasks ( p = 0.825 ), when using high-dose settings ( p = 0.360 ), or tasks involving general anatomy ( p = 0.174 ). The image quality preferences are consistent with reported technical advantages of CMOS regarding finer pixel size and reduced electronic noise.

Learning-based deformable image registration: effect of statistical mismatch between train and test images.

Convolutional neural networks (CNNs) offer a promising means to achieve fast deformable image registration with accuracy comparable to conventional, physics-based methods. A persistent question with CNN methods, however, is whether they will be able to generalize to data outside of the training set. We investigated this question of mismatch between train and test data with respect to first- and second-order image statistics (e.g., spatial resolution, image noise, and power spectrum). A UNet-based architecture was built and trained on simulated CT images for various conditions of image noise (dose), spatial resolution, and deformation magnitude. Target registration error was measured as a function of the difference in statistical properties between the test and training data. Generally, registration error is minimized when the training data exactly match the statistics of the test data; however, networks trained with data exhibiting a diversity in statistical characteristics generalized well across the range of statistical conditions considered. Furthermore, networks trained on simulated image content with first- and second-order statistics selected to match that of real anatomical data were shown to provide reasonable registration performance on real anatomical content, offering potential new means for data augmentation. Characterizing the behavior of a CNN in the presence of statistical mismatch is an important step in understanding how these networks behave when deployed on new, unobserved data. Such characterization can inform decisions on whether retraining is necessary and can guide the data collection and/or augmentation process for training.

A Statistical Model for Rigid Image Registration Performance: The Influence of Soft-Tissue Deformation as a Confounding Noise Source.

Soft-tissue deformation presents a confounding factor to rigid image registration by introducing image content inconsistent with the underlying motion model, presenting non-correspondent structure with potentially high power, and creating local minima that challenge iterative optimization. In this paper, we introduce a model for registration performance that includes deformable soft tissue as a power-law noise distribution within a statistical framework describing the Cramer-Rao lower bound (CRLB) and root-mean-squared error (RMSE) in registration performance. The model incorporates both cross-correlation and gradient-based similarity metrics, and the model was tested in application to 3D-2D (CT-to-radiograph) and 3D-3D (CT-to-CT) image registration. Predictions accurately reflect the trends in registration error as a function of dose (quantum noise), and the choice of similarity metrics for both registration scenarios. Incorporating soft-tissue deformation as a noise source yields important insight on the limits of registration performance with respect to algorithm design and the clinical application or anatomical context. For example, the model quantifies the advantage of gradient-based similarity metrics in 3D-2D registration, identifies the low-dose limits of registration performance, and reveals the conditions for which the registration performance is fundamentally limited by soft-tissue deformation.

Mobile C-Arm with a CMOS detector: Technical assessment of fluoroscopy and Cone-Beam CT imaging performance.

PURPOSE: Indirect-detection CMOS flat-panel detectors (FPDs) offer fine pixel pitch, fast readout, and low electronic noise in comparison to current a-Si:H FPDs. This work investigates the extent to which these potential advantages affect imaging performance in mobile C-arm fluoroscopy and cone-beam CT (CBCT). METHODS: FPDs based on CMOS (Xineos 3030HS, 0.151 mm pixel pitch) or a-Si:H (PaxScan 3030X, 0.194 mm pixel pitch) sensors were outfitted on equivalent mobile C-arms for fluoroscopy and CBCT. Technical assessment of 2D and 3D imaging performance included measurement of electronic noise, gain, lag, modulation transfer function (MTF), noise-power spectrum (NPS), detective quantum efficiency (DQE), and noise-equivalent quanta (NEQ) in fluoroscopy (with entrance air kerma ranging 5-800 nGy per frame) and cone-beam CT (with weighted CT dose index, CTDIw , ranging 0.08-1 mGy). Image quality was evaluated by clinicians in vascular, orthopaedic, and neurological surgery in realistic interventional scenarios with cadaver subjects emulating a variety of 2D and 3D imaging tasks. RESULTS: The CMOS FPD exhibited ~2-3× lower electronic noise and ~7× lower image lag than the a-Si:H FPD. The 2D (projection) DQE was superior for CMOS at ≤50 nGy per frame, especially at high spatial frequencies (~2% improvement at 0.5 mm-1 and ≥50% improvement at 2.3 mm-1 ) and was somewhat inferior at moderate-high doses (up to 18% lower DQE for CMOS at 0.5 mm-1 ). For smooth CBCT reconstructions (low-frequency imaging tasks), CMOS exhibited ~10%-20% higher NEQ (at 0.1-0.5 mm-1 ) at the lowest dose levels (CTDIw ≤0.1 mGy), while the a-Si:H system yielded slightly (~5%) improved NEQ (at 0.1-0.5 lp/mm) at higher dose levels (CTDIw ≥0.6 mGy). For sharp CBCT reconstructions (high-frequency imaging tasks), NEQ was ~32% higher above 1 mm-1 for the CMOS system at mid-high-dose levels and ≥75% higher at the lowest dose levels (CTDIw ≤0.1 mGy). Observer assessment of 2D and 3D cadaver images corroborated the objective metrics with respect to a variety of pertinent interventional imaging tasks. CONCLUSION: Measurements of image noise, spatial resolution, DQE, and NEQ indicate improved low-dose performance for the CMOS-based system, particularly at lower doses and higher spatial frequencies. Assessment in realistic imaging scenarios confirmed improved visibility of fine details in low-dose fluoroscopy and CBCT. The results quantitate the extent to which CMOS detectors improve mobile C-arm imaging performance, especially in 2D and 3D imaging scenarios involving high-resolution tasks and low-dose conditions.

Clinical Translation of the LevelCheck Decision Support Algorithm for Target Localization in Spine Surgery.

Recent work has yielded a method for automatic labeling of vertebrae in intraoperative radiographs as an assistant to manual level counting. The method, called LevelCheck, previously demonstrated promise in phantom studies and retrospective studies. This study aims to: (#1) Analyze the effect of LevelCheck on accuracy and confidence of localization in two modes: (a) Independent Check (labels displayed after the surgeon's decision) and (b) Active Assistant (labels presented before the surgeon's decision). (#2) Assess the feasibility and utility of LevelCheck in the operating room. Two studies were conducted: a laboratory study investigating these two workflow implementations in a simulated operating environment with 5 surgeons, reviewing 62 cases selected from a dataset of radiographs exhibiting a challenge to vertebral localization; and a clinical study involving 20 patients undergoing spine surgery. In Study #1, the median localization error without assistance was 30.4% (IQR = 5.2%) due to the challenging nature of the cases. LevelCheck reduced the median error to 2.4% for both the Independent Check and Active Assistant modes (p < 0.01). Surgeons found LevelCheck to increase confidence in 91% of cases. Study #2 demonstrated accuracy in all cases. The algorithm runtime varied from 17 to 72 s in its current implementation. The algorithm was shown to be feasible, accurate, and to improve confidence during surgery.

Virtual fluoroscopy for intraoperative C-arm positioning and radiation dose reduction.

Positioning of an intraoperative C-arm to achieve clear visualization of a particular anatomical feature often involves repeated fluoroscopic views, which cost time and radiation exposure to both the patient and surgical staff. A system for virtual fluoroscopy (called FluoroSim) that could dramatically reduce time- and dose-spent "fluoro-hunting" by leveraging preoperative computed tomography (CT), encoded readout of C-arm gantry position, and automatic 3D-2D image registration has been developed. The method is consistent with existing surgical workflow and does not require additional tracking equipment. Real-time virtual fluoroscopy was achieved via mechanical encoding of the C-arm motion, C-arm geometric calibration, and patient registration using a single radiograph. The accuracy, time, and radiation dose associated with C-arm positioning were measured for FluoroSim in comparison with conventional methods. Five radiology technologists were tasked with acquiring six standard pelvic views pertinent to sacro-illiac, anterior-inferior iliac spine, and superior-ramus screw placement in an anthropomorphic pelvis phantom using conventional and FluoroSim approaches. The positioning accuracy, exposure time, number of exposures, and total time for each trial were recorded, and radiation dose was characterized in terms of entrance skin dose and in-room scatter. The geometric accuracy of FluoroSim was measured to be [Formula: see text]. There was no significant difference ([Formula: see text]) observed in the accuracy or total elapsed time for C-arm positioning. However, the total fluoroscopy time required to achieve the desired view decreased by 4.1 s ([Formula: see text] for conventional, compared with [Formula: see text] for FluoroSim, [Formula: see text]), and the total number of exposures reduced by 4.0 ([Formula: see text] for conventional, compared with [Formula: see text] for FluoroSim, [Formula: see text]). These reductions amounted to a 50% to 78% decrease in patient entrance skin dose and a 55% to 70% reduction in in-room scatter. FluoroSim was found to reduce the radiation exposure required in C-arm positioning without diminishing positioning time or accuracy, providing a potentially valuable tool to assist technologists and surgeons.

Effects of Image Quality on the Fundamental Limits of Image Registration Accuracy.

For image-guided procedures, the imaging task is often tied to the registration of intraoperative and preoperative images to a common coordinate system. While the accuracy of this registration is a vital factor in system performance, there is a relatively little work that relates registration accuracy to image quality factors, such as dose, noise, and spatial resolution. To create a theoretical model for such a relationship, we present a Fisher information approach to analyze registration performance in explicit dependence on the underlying image quality factors of image noise, spatial resolution, and signal power spectrum. The model yields analysis of the Cramer-Rao lower bound (CRLB), in registration accuracy as a function of factors governing image quality. Experiments were performed in simulation of computed tomography low-contrast soft tissue images and high-contrast bone (head and neck) images to compare the measured accuracy [root mean squared error (RMSE) of the estimated transformations] with the theoretical lower bound. Analysis of the CRLB reveals that registration performance is closely related to the signal-to-noise ratio of the cross-correlation space. While the lower bound is optimistic, it exhibits consistent trends with experimental findings and yields a method for comparing the performance of various registration methods and similarity metrics. Further analysis validated a method for determining optimal post-processing (image filtering) for registration. Two figures of merit (CRLB and RMSE) are presented that unify models of image quality with registration performance, providing an important guide to optimizing intraoperative imaging with respect to the task of registration.

Design and evaluation of a portable intra-operative unified-planning-and-guidance framework applied to distal radius fracture surgery.

PURPOSE: During a standard fracture reduction and fixation procedure of the distal radius, only fluoroscopic images are available for planning of the screw placement and monitoring of the drill bit trajectory. Our prototype intra-operative framework integrates planning and drill guidance for a simplified and improved planning transfer. METHODS: Guidance information is extracted using a video camera mounted onto a surgical drill. Real-time feedback of the drill bit position is provided using an augmented view of the planning X-rays. We evaluate the accuracy of the placed screws on plastic bones and on healthy and fractured forearm specimens. We also investigate the difference in accuracy between guided screw placement versus freehand. Moreover, the accuracy of the real-time position feedback of the drill bit is evaluated. RESULTS: A total of 166 screws were placed. On 37 plastic bones, our obtained accuracy was [Formula: see text] mm, [Formula: see text] and [Formula: see text] in tip position and orientation (azimuth and elevation), respectively. On the three healthy forearm specimens, our obtained accuracy was [Formula: see text] mm, [Formula: see text] and [Formula: see text]. On the two fractured specimens, we attained: [Formula: see text] mm, [Formula: see text] and [Formula: see text]. When screw plans were applied freehand (without our guidance system), the achieved accuracy was [Formula: see text] mm, [Formula: see text], while when they were transferred under guidance, we obtained [Formula: see text] mm, [Formula: see text]. CONCLUSIONS: Our results show that our framework is expected to increase the accuracy in screw positioning and to improve robustness w.r.t. freehand placement.

MIND Demons: Symmetric Diffeomorphic Deformable Registration of MR and CT for Image-Guided Spine Surgery.

Intraoperative localization of target anatomy and critical structures defined in preoperative MR/CT images can be achieved through the use of multimodality deformable registration. We propose a symmetric diffeomorphic deformable registration algorithm incorporating a modality-independent neighborhood descriptor (MIND) and a robust Huber metric for MR-to-CT registration. The method, called MIND Demons, finds a deformation field between two images by optimizing an energy functional that incorporates both the forward and inverse deformations, smoothness on the integrated velocity fields, a modality-insensitive similarity function suitable to multimodality images, and smoothness on the diffeomorphisms themselves. Direct optimization without relying on the exponential map and stationary velocity field approximation used in conventional diffeomorphic Demons is carried out using a Gauss-Newton method for fast convergence. Registration performance and sensitivity to registration parameters were analyzed in simulation, phantom experiments, and clinical studies emulating application in image-guided spine surgery, and results were compared to mutual information (MI) free-form deformation (FFD), local MI (LMI) FFD, normalized MI (NMI) Demons, and MIND with a diffusion-based registration method (MIND-elastic). The method yielded sub-voxel invertibility (0.008 mm) and nonzero-positive Jacobian determinants. It also showed improved registration accuracy in comparison to the reference methods, with mean target registration error (TRE) of 1.7 mm compared to 11.3, 3.1, 5.6, and 2.4 mm for MI FFD, LMI FFD, NMI Demons, and MIND-elastic methods, respectively. Validation in clinical studies demonstrated realistic deformations with sub-voxel TRE in cases of cervical, thoracic, and lumbar spine.

Utility of the LevelCheck Algorithm for Decision Support in Vertebral Localization.

STUDY DESIGN: An automatic radiographic labeling algorithm called "LevelCheck" was analyzed as a means of decision support for target localization in spine surgery. The potential clinical utility and scenarios in which LevelCheck is likely to be the most beneficial were assessed in a retrospective clinical data set (398 cases) in terms of expert consensus from a multi-reader study (three spine surgeons). OBJECTIVE: The aim of this study was to evaluate the potential utility of the LevelCheck algorithm for vertebrae localization. SUMMARY OF BACKGROUND DATA: Three hundred ninety-eight intraoperative radiographs and 178 preoperative computed tomographic (CT) images for patients undergoing spine surgery in cervical, thoracic, lumbar regions. METHODS: Vertebral labels annotated in preoperative CT image were overlaid on intraoperative radiographs via 3D-2D registration. Three spine surgeons assessed the radiographs and LevelCheck labeling according to a questionnaire evaluating performance, utility, and suitability to surgical workflow. Geometric accuracy and registration run time were measured for each case. RESULTS: LevelCheck was judged to be helpful in 42.2% of the cases (168/398), to improve confidence in 30.6% of the cases (122/398), and in no case diminished performance (0/398), supporting its potential as an independent check and assistant to decision support in spine surgery. The clinical contexts for which the method was judged most likely to be beneficial included the following scenarios: images with a lack of conspicuous anatomical landmarks; level counting across long spine segments; vertebrae obscured by other anatomy (e.g., shoulders); poor radiographic image quality; and anatomical variations/abnormalities. The method demonstrated 100% geometric accuracy (i.e., overlaid labels within the correct vertebral level in all cases) and did not introduce ambiguity in image interpretation. CONCLUSION: LevelCheck is a potentially useful means of decision support in vertebral level localization in spine surgery. LEVEL OF EVIDENCE: N/A.

Accelerated statistical reconstruction for C-arm cone-beam CT using Nesterov's method.

PURPOSE: To accelerate model-based iterative reconstruction (IR) methods for C-arm cone-beam CT (CBCT), thereby combining the benefits of improved image quality and/or reduced radiation dose with reconstruction times on the order of minutes rather than hours. METHODS: The ordered-subsets, separable quadratic surrogates (OS-SQS) algorithm for solving the penalized-likelihood (PL) objective was modified to include Nesterov's method, which utilizes "momentum" from image updates of previous iterations to better inform the current iteration and provide significantly faster convergence. Reconstruction performance of an anthropomorphic head phantom was assessed on a benchtop CBCT system, followed by CBCT on a mobile C-arm, which provided typical levels of incomplete data, including lateral truncation. Additionally, a cadaveric torso that presented realistic soft-tissue and bony anatomy was imaged on the C-arm, and different projectors were assessed for reconstruction speed. RESULTS: Nesterov's method provided equivalent image quality to OS-SQS while reducing the reconstruction time by an order of magnitude (10.0 ×) by reducing the number of iterations required for convergence. The faster projectors were shown to produce similar levels of convergence as more accurate projectors and reduced the reconstruction time by another 5.3 ×. Despite the slower convergence of IR with truncated C-arm CBCT, comparison of PL reconstruction methods implemented on graphics processing units showed that reconstruction time was reduced from 106 min for the conventional OS-SQS method to as little as 2.0 min with Nesterov's method for a volumetric reconstruction of the head. In body imaging, reconstruction of the larger cadaveric torso was reduced from 159 min down to 3.3 min with Nesterov's method. CONCLUSIONS: The acceleration achieved through Nesterov's method combined with ordered subsets reduced IR times down to a few minutes. This improved compatibility with clinical workflow better enables broader adoption of IR in CBCT-guided procedures, with corresponding benefits in overcoming conventional limits of image quality at lower dose.

3D­2D registration in mobile radiographs: algorithm development and preliminary clinical evaluation.

An image-based 3D-2D registration method is presented using radiographs acquired in the uncalibrated, unconstrained geometry of mobile radiography. The approach extends a previous method for six degree-of-freedom (DOF) registration in C-arm fluoroscopy (namely 'LevelCheck') to solve the 9-DOF estimate of geometry in which the position of the source and detector are unconstrained. The method was implemented using a gradient correlation similarity metric and stochastic derivative-free optimization on a GPU. Development and evaluation were conducted in three steps. First, simulation studies were performed that involved a CT scan of an anthropomorphic body phantom and 1000 randomly generated digitally reconstructed radiographs in posterior-anterior and lateral views. A median projection distance error (PDE) of 0.007 mm was achieved with 9-DOF registration compared to 0.767 mm for 6-DOF. Second, cadaver studies were conducted using mobile radiographs acquired in three anatomical regions (thorax, abdomen and pelvis) and three levels of source-detector distance (~800, ~1000 and ~1200 mm). The 9-DOF method achieved a median PDE of 0.49 mm (compared to 2.53 mm for the 6-DOF method) and demonstrated robustness in the unconstrained imaging geometry. Finally, a retrospective clinical study was conducted with intraoperative radiographs of the spine exhibiting real anatomical deformation and image content mismatch (e.g. interventional devices in the radiograph that were not in the CT), demonstrating a PDE = 1.1 mm for the 9-DOF approach. Average computation time was 48.5 s, involving 687 701 function evaluations on average, compared to 18.2 s for the 6-DOF method. Despite the greater computational load, the 9-DOF method may offer a valuable tool for target localization (e.g. decision support in level counting) as well as safety and quality assurance checks at the conclusion of a procedure (e.g. overlay of planning data on the radiograph for verification of the surgical product) in a manner consistent with natural surgical workflow.

Automatic localization of target vertebrae in spine surgery: clinical evaluation of the LevelCheck registration algorithm.

STUDY DESIGN: A 3-dimensional-2-dimensional (3D-2D) image registration algorithm, "LevelCheck," was used to automatically label vertebrae in intraoperative mobile radiographs obtained during spine surgery. Accuracy, computation time, and potential failure modes were evaluated in a retrospective study of 20 patients. OBJECTIVE: To measure the performance of the LevelCheck algorithm using clinical images acquired during spine surgery. SUMMARY OF BACKGROUND DATA: In spine surgery, the potential for wrong level surgery is significant due to the difficulty of localizing target vertebrae based solely on visual impression, palpation, and fluoroscopy. To remedy this difficulty and reduce the risk of wrong-level surgery, our team introduced a program (dubbed LevelCheck) to automatically localize target vertebrae in mobile radiographs using robust 3D-2D image registration to preoperative computed tomographic (CT) scan. METHODS: Twenty consecutive patients undergoing thoracolumbar spine surgery, for whom both a preoperative CT scan and an intraoperative mobile radiograph were available, were retrospectively analyzed. A board-certified neuroradiologist determined the "true" vertebra levels in each radiograph. Registration of the preoperative CT scan to the intraoperative radiograph was calculated via LevelCheck, and projection distance errors were analyzed. Five hundred random initializations were performed for each patient, and algorithm settings (viz, the number of robust multistarts, ranging 50-200) were varied to evaluate the trade-off between registration error and computation time. Failure mode analysis was performed by individually analyzing unsuccessful registrations (>5 mm distance error) observed with 50 multistarts. RESULTS: At 200 robust multistarts (computation time of ∼26 s), the registration accuracy was 100% across all 10,000 trials. As the number of multistarts (and computation time) decreased, the registration remained fairly robust, down to 99.3% registration accuracy at 50 multistarts (computation time ∼7 s). CONCLUSION: The LevelCheck algorithm correctly identified target vertebrae in intraoperative mobile radiographs of the thoracolumbar spine, demonstrating acceptable computation time, compatibility with routinely obtained preoperative CT scans, and warranting investigation in prospective studies. LEVEL OF EVIDENCE: N/A.

Low-dose preview for patient-specific, task-specific technique selection in cone-beam CT.

PURPOSE: A method is presented for generating simulated low-dose cone-beam CT (CBCT) preview images from which patient- and task-specific minimum-dose protocols can be confidently selected prospectively in clinical scenarios involving repeat scans. METHODS: In clinical scenarios involving a series of CBCT images, the low-dose preview (LDP) method operates upon the first scan to create a projection dataset that accurately simulates the effects of dose reduction in subsequent scans by injecting noise of proper magnitude and correlation, including both quantum and electronic readout noise as important components of image noise in flat-panel detector CBCT. Experiments were conducted to validate the LDP method in both a head phantom and a cadaveric torso by performing CBCT acquisitions spanning a wide dose range (head: 0.8-13.2 mGy, body: 0.8-12.4 mGy) with a prototype mobile C-arm system. After injecting correlated noise to simulate dose reduction, the projections were reconstructed using both conventional filtered backprojection (FBP) and an iterative, model-based image reconstruction method (MBIR). The LDP images were then compared to real CBCT images in terms of noise magnitude, noise-power spectrum (NPS), spatial resolution, contrast, and artifacts. RESULTS: For both FBP and MBIR, the LDP images exhibited accurate levels of spatial resolution and contrast that were unaffected by the correlated noise injection, as expected. Furthermore, the LDP image noise magnitude and NPS were in strong agreement with real CBCT images acquired at the corresponding, reduced dose level across the entire dose range considered. The noise magnitude agreed within 7% for both the head phantom and cadaveric torso, and the NPS showed a similar level of agreement up to the Nyquist frequency. Therefore, the LDP images were highly representative of real image quality across a broad range of dose and reconstruction methods. On the other hand, naïve injection ofuncorrelated noise resulted in strong underestimation of the true noise, which would lead to overly optimistic predictions of dose reduction. CONCLUSIONS: Correlated noise injection is essential to accurate simulation of CBCT image quality at reduced dose. With the proposed LDP method, the user can prospectively select patient-specific, minimum-dose protocols (viz., acquisition technique and reconstruction method) suitable to a particular imaging task and to the user's own observer preferences for CBCT scans following the first acquisition. The method could provide dose reduction in common clinical scenarios involving multiple CBCT scans, such as image-guided surgery and radiotherapy.

Soft-tissue imaging with C-arm cone-beam CT using statistical reconstruction.

The potential for statistical image reconstruction methods such as penalized-likelihood (PL) to improve C-arm cone-beam CT (CBCT) soft-tissue visualization for intraoperative imaging over conventional filtered backprojection (FBP) is assessed in this work by making a fair comparison in relation to soft-tissue performance. A prototype mobile C-arm was used to scan anthropomorphic head and abdomen phantoms as well as a cadaveric torso at doses substantially lower than typical values in diagnostic CT, and the effects of dose reduction via tube current reduction and sparse sampling were also compared. Matched spatial resolution between PL and FBP was determined by the edge spread function of low-contrast (∼ 40-80 HU) spheres in the phantoms, which were representative of soft-tissue imaging tasks. PL using the non-quadratic Huber penalty was found to substantially reduce noise relative to FBP, especially at lower spatial resolution where PL provides a contrast-to-noise ratio increase up to 1.4-2.2 × over FBP at 50% dose reduction across all objects. Comparison of sampling strategies indicates that soft-tissue imaging benefits from fully sampled acquisitions at dose above ∼ 1.7 mGy and benefits from 50% sparsity at dose below ∼ 1.0 mGy. Therefore, an appropriate sampling strategy along with the improved low-contrast visualization offered by statistical reconstruction demonstrates the potential for extending intraoperative C-arm CBCT to applications in soft-tissue interventions in neurosurgery as well as thoracic and abdominal surgeries by overcoming conventional tradeoffs in noise, spatial resolution, and dose.

Robust 3D-2D image registration: application to spine interventions and vertebral labeling in the presence of anatomical deformation.

We present a framework for robustly estimating registration between a 3D volume image and a 2D projection image and evaluate its precision and robustness in spine interventions for vertebral localization in the presence of anatomical deformation. The framework employs a normalized gradient information similarity metric and multi-start covariance matrix adaptation evolution strategy optimization with local-restarts, which provided improved robustness against deformation and content mismatch. The parallelized implementation allowed orders-of-magnitude acceleration in computation time and improved the robustness of registration via multi-start global optimization. Experiments involved a cadaver specimen and two CT datasets (supine and prone) and 36 C-arm fluoroscopy images acquired with the specimen in four positions (supine, prone, supine with lordosis, prone with kyphosis), three regions (thoracic, abdominal, and lumbar), and three levels of geometric magnification (1.7, 2.0, 2.4). Registration accuracy was evaluated in terms of projection distance error (PDE) between the estimated and true target points in the projection image, including 14 400 random trials (200 trials on the 72 registration scenarios) with initialization error up to ±200 mm and ±10°. The resulting median PDE was better than 0.1 mm in all cases, depending somewhat on the resolution of input CT and fluoroscopy images. The cadaver experiments illustrated the tradeoff between robustness and computation time, yielding a success rate of 99.993% in vertebral labeling (with 'success' defined as PDE <5 mm) using 1,718 664 ± 96 582 function evaluations computed in 54.0 ± 3.5 s on a mid-range GPU (nVidia, GeForce GTX690). Parameters yielding a faster search (e.g., fewer multi-starts) reduced robustness under conditions of large deformation and poor initialization (99.535% success for the same data registered in 13.1 s), but given good initialization (e.g., ±5 mm, assuming a robust initial run) the same registration could be solved with 99.993% success in 6.3 s. The ability to register CT to fluoroscopy in a manner robust to patient deformation could be valuable in applications such as radiation therapy, interventional radiology, and an assistant to target localization (e.g., vertebral labeling) in image-guided spine surgery.

Deformable registration of the inflated and deflated lung in cone-beam CT-guided thoracic surgery: initial investigation of a combined model- and image-driven approach.

PURPOSE: Surgical resection is the preferred modality for curative treatment of early stage lung cancer, but localization of small tumors (<10 mm diameter) during surgery presents a major challenge that is likely to increase as more early-stage disease is detected incidentally and in low-dose CT screening. To overcome the difficulty of manual localization (fingers inserted through intercostal ports) and the cost, logistics, and morbidity of preoperative tagging (coil or dye placement under CT-fluoroscopy), the authors propose the use of intraoperative cone-beam CT (CBCT) and deformable image registration to guide targeting of small tumors in video-assisted thoracic surgery (VATS). A novel algorithm is reported for registration of the lung from its inflated state (prior to pleural breach) to the deflated state (during resection) to localize surgical targets and adjacent critical anatomy. METHODS: The registration approach geometrically resolves images of the inflated and deflated lung using a coarse model-driven stage followed by a finer image-driven stage. The model-driven stage uses image features derived from the lung surfaces and airways: triangular surface meshes are morphed to capture bulk motion; concurrently, the airways generate graph structures from which corresponding nodes are identified. Interpolation of the sparse motion fields computed from the bounding surface and interior airways provides a 3D motion field that coarsely registers the lung and initializes the subsequent image-driven stage. The image-driven stage employs an intensity-corrected, symmetric form of the Demons method. The algorithm was validated over 12 datasets, obtained from porcine specimen experiments emulating CBCT-guided VATS. Geometric accuracy was quantified in terms of target registration error (TRE) in anatomical targets throughout the lung, and normalized cross-correlation. Variations of the algorithm were investigated to study the behavior of the model- and image-driven stages by modifying individual algorithmic steps and examining the effect in comparison to the nominal process. RESULTS: The combined model- and image-driven registration process demonstrated accuracy consistent with the requirements of minimally invasive VATS in both target localization (∼3-5 mm within the target wedge) and critical structure avoidance (∼1-2 mm). The model-driven stage initialized the registration to within a median TRE of 1.9 mm (95% confidence interval (CI) maximum = 5.0 mm), while the subsequent image-driven stage yielded higher accuracy localization with 0.6 mm median TRE (95% CI maximum = 4.1 mm). The variations assessing the individual algorithmic steps elucidated the role of each step and in some cases identified opportunities for further simplification and improvement in computational speed. CONCLUSIONS: The initial studies show the proposed registration method to successfully register CBCT images of the inflated and deflated lung. Accuracy appears sufficient to localize the target and adjacent critical anatomy within ∼1-2 mm and guide localization under conditions in which the target cannot be discerned directly in CBCT (e.g., subtle, nonsolid tumors). The ability to directly localize tumors in the operating room could provide a valuable addition to the VATS arsenal, obviate the cost, logistics, and morbidity of preoperative tagging, and improve patient safety. Future work includes in vivo testing, optimization of workflow, and integration with a CBCT image guidance system.

Deformable Registration of the Inflated and Deflated Lung for Cone-Beam CT-Guided Thoracic Surgery.

Intraoperative cone-beam CT (CBCT) could offer an important advance to thoracic surgeons in directly localizing subpalpable nodules during surgery. An image-guidance system is under development using mobile C-arm CBCT to directly localize tumors in the OR, potentially reducing the cost and logistical burden of conventional preoperative localization and facilitating safer surgery by visualizing critical structures surrounding the surgical target (e.g., pulmonary artery, airways, etc.). To utilize the wealth of preoperative image/planning data and to guide targeting under conditions in which the tumor may not be directly visualized, a deformable registration approach has been developed that geometrically resolves images of the inflated (i.e., inhale or exhale) and deflated states of the lung. This novel technique employs a coarse model-driven approach using lung surface and bronchial airways for fast registration, followed by an image-driven registration using a variant of the Demons algorithm to improve target localization to within ∼1 mm. Two approaches to model-driven registration are presented and compared - the first involving point correspondences on the surface of the deflated and inflated lung and the second a mesh evolution approach. Intensity variations (i.e., higher image intensity in the deflated lung) due to expulsion of air from the lungs are accounted for using an a priori lung density modification, and its improvement on the performance of the intensity-driven Demons algorithm is demonstrated. Preliminary results of the combined model-driven and intensity-driven registration process demonstrate accuracy consistent with requirements in minimally invasive thoracic surgery in both target localization and critical structure avoidance.