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Objective. Surgical guidewires are commonly used in placing fixation implants to stabilize fractures. Accurate positioning of these instruments is challenged by difficulties in 3D reckoning from 2D fluoroscopy. This work aims to enhance the accuracy and reduce exposure times by providing 3D navigation for guidewire placement from as little as two fluoroscopic images. Approach. Our approach combines machine learning-based segmentation with the geometric model of the imager to determine the 3D poses of guidewires. Instrument tips are encoded as individual keypoints, and the segmentation masks are processed to estimate the trajectory. Correspondence between detections in multiple views is established using the pre-calibrated system geometry, and the corresponding features are backprojected to obtain the 3D pose. Guidewire 3D directions were computed using both an analytical and an optimization-based method. The complete approach was evaluated in cadaveric specimens with respect to potential confounding effects from the imaging geometry and radiographic scene clutter due to other instruments. Main results. The detection network identified the guidewire tips within 2.2 mm and guidewire directions within 1.1°, in 2D detector coordinates. Feature correspondence rejected false detections, particularly in images with other instruments, to achieve 83% precision and 90% recall. Estimating the 3D direction via numerical optimization showed added robustness to guidewires aligned with the gantry rotation plane. Guidewire tips and directions were localized in 3D world coordinates with a median accuracy of 1.8 mm and 2.7°, respectively. Significance. The paper reports a new method for automatic 2D detection and 3D localization of guidewires from pairs of fluoroscopic images. Localized guidewires can be virtually overlaid on the patient's pre-operative 3D scan during the intervention. Accurate pose determination for multiple guidewires from two images offers to reduce radiation dose by minimizing the need for repeated imaging and provides quantitative feedback prior to implant placement. [ABSTRACT FROM AUTHOR]

Purpose. Target localization in pulmonary interventions (e.g. transbronchial biopsy of a lung nodule) is challenged by deformable motion and may benefit from fluoroscopic overlay of the target to provide accurate guidance. We present and evaluate a 3D–2D image registration method for fluoroscopic overlay in the presence of tissue deformation using a multi-resolution/multi-scale (MRMS) framework with an objective function that drives registration primarily by soft-tissue image gradients. Methods. The MRMS method registers 3D cone-beam CT to 2D fluoroscopy without gating of respiratory phase by coarse-to-fine resampling and global-to-local rescaling about target regions-of-interest. A variation of the gradient orientation ( GO) similarity metric (denoted G O ′ ) was developed to downweight bone gradients and drive registration via soft-tissue gradients. Performance was evaluated in terms of projection distance error at isocenter (PDEiso). Phantom studies determined nominal algorithm parameters and capture range. Preclinical studies used a freshly deceased, ventilated porcine specimen to evaluate performance in the presence of real tissue deformation and a broad range of 3D–2D image mismatch. Results. Nominal algorithm parameters were identified that provided robust performance over a broad range of motion (0–20 mm), including an adaptive parameter selection technique to accommodate unknown mismatch in respiratory phase. The G O ′ metric yielded median PDEiso = 1.2 mm, compared to 6.2 mm for conventional GO. Preclinical studies with real lung deformation demonstrated median PDEiso = 1.3 mm with MRMS + G O ′ registration, compared to 2.2 mm with a conventional transform. Runtime was 26 s and can be reduced to 2.5 s given a prior registration within ∼5 mm as initialization. Conclusions. MRMS registration via soft-tissue gradients achieved accurate fluoroscopic overlay in the presence of deformable lung motion. By driving registration via soft-tissue image gradients, the method avoided false local minima presented by bones and was robust to a wide range of motion magnitude. [ABSTRACT FROM AUTHOR]

Purpose: A modular phantom for dosimetry and imaging performance evaluation in cone‐beam computed tomography (CBCT) is reported, providing a tool for quantitative technical assessment that can be adapted to a broad variety of CBCT imaging configurations and clinical applications. Methods: The phantom presents a set of modules that can be ordered in various configurations suitable to a particular CBCT system. Modules include slabs containing a uniform medium, low‐contrast inserts, line‐spread features, and disk features suitable to measurement of image uniformity, noise, noise‐power spectrum (NPS), contrast, contrast‐to‐noise ratio (CNR), Hounsfield (HU) accuracy, linearity, spatial resolution modulation transfer function (MTF), and magnitude of cone‐beam artifact. Automated software recognizes the phantom configuration in DICOM images and provides structured reporting of such test measures. In any modular configuration, the phantom permits measurement of air kerma in central and peripheral locations with an air ionization chamber (e.g., Farmer chamber). The utility and adaptability of the phantom were demonstrated across a spectrum of CBCT systems, including scanners for orthopaedic imaging (Carestream OnSight 3D, Rochester NY), breast imaging (Doheny prototype, UC Davis), image‐guided surgery (IGS, Medtronic O‐arm, Littleton MA), angiography (Siemens Artis Zeego, Forcheim Germany), and image‐guided radiation therapy (IGRT, Elekta Synergy XVI, Stockholm Sweden). Results: The phantom provided a consistent platform for quantitative assessment of dose and imaging performance compatible with a broad spectrum of CBCT systems. The purpose of the survey was not to obtain head‐to‐head performance comparison of systems designed for such distinct clinical applications. Rather, the survey demonstrated the suitability of the phantom to a broad spectrum of systems in a manner that provides characterization pertinent to disparate applications and imaging tasks. For example: the orthopaedic CBCT system (pertinent clinical tasks relating to high‐resolution bone imaging) was shown to achieve MTF consistent with imaging of high‐contrast trabecular bone structures (i.e., the MTF reduced to 10% at spatial frequency, f10 = 1.2 mm−1); the breast system (even higher‐resolution imaging of microcalcifications) exhibited f10 = 2.2 mm−1; the IGS system (tasks including both bone and soft‐tissue contrast resolution) provided f10 = 0.9 mm−1 and soft‐tissue CNR = 1.64; the angiography system (soft‐tissue body interventions) demonstrated CNR = 1.2 in soft tissues approximating liver lesions; and the IGRT system (pertinent tasks emphasizing HU linearity and image uniformity) showed linear response with HU values (R2 = 1), with a cupping artifact (tcup = 5.8%) due to x‐ray scatter. Conclusions: The phantom provides an adaptable, quantitative basis for CBCT dosimetry and imaging performance evaluation suitable to a broad variety of CBCT systems. The dosimetry and image quality metrics are consistent with up‐to‐date methods for rigorous, quantitative, physics testing and should be suitable to emerging standards for CBCT quality assurance. [ABSTRACT FROM AUTHOR]