J. Webster Stayman

Dedicated Cone-Beam CT System for Extremity Imaging

PURPOSE To provide initial assessment of image quality and dose for a cone-beam computed tomographic (CT) scanner dedicated to extremity imaging. MATERIALS AND METHODS A prototype cone-beam CT scanner has been developed for imaging the extremities, including the weight-bearing lower extremities. Initial technical assessment included evaluation of radiation dose measured as a function of kilovolt peak and tube output (in milliampere seconds), contrast resolution assessed in terms of the signal difference-to-noise ratio (SDNR), spatial resolution semiquantitatively assessed by using a line-pair module from a phantom, and qualitative evaluation of cadaver images for potential diagnostic value and image artifacts by an expert CT observer (musculoskeletal radiologist). RESULTS The dose for a nominal scan protocol (80 kVp, 108 mAs) was 9 mGy (absolute dose measured at the center of a CT dose index phantom). SDNR was maximized with the 80-kVp scan technique, and contrast resolution was sufficient for visualization of muscle, fat, ligaments and/or tendons, cartilage joint space, and bone. Spatial resolution in the axial plane exceeded 15 line pairs per centimeter. Streaks associated with x-ray scatter (in thicker regions of the patient--eg, the knee), beam hardening (about cortical bone--eg, the femoral shaft), and cone-beam artifacts (at joint space surfaces oriented along the scanning plane--eg, the interphalangeal joints) presented a slight impediment to visualization. Cadaver images (elbow, hand, knee, and foot) demonstrated excellent visibility of bone detail and good soft-tissue visibility suitable to a broad spectrum of musculoskeletal indications. CONCLUSION A dedicated extremity cone-beam CT scanner capable of imaging upper and lower extremities (including weight-bearing examinations) provides sufficient image quality and favorable dose characteristics to warrant further evaluation for clinical use.

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.

PIRPLE: a penalized-likelihood framework for incorporation of prior images in CT reconstruction

Over the course of diagnosis and treatment, it is common for a number of imaging studies to be acquired. Such imaging sequences can provide substantial patient-specific prior knowledge about the anatomy that can be incorporated into a prior-image-based tomographic reconstruction for improved image quality and better dose utilization. We present a general methodology using a model-based reconstruction approach including formulations of the measurement noise that also integrates prior images. This penalized-likelihood technique adopts a sparsity enforcing penalty that incorporates prior information yet allows for change between the current reconstruction and the prior image. Moreover, since prior images are generally not registered with the current image volume, we present a modified model-based approach that seeks a joint registration of the prior image in addition to the reconstruction of projection data. We demonstrate that the combined prior-image- and model-based technique outperforms methods that ignore the prior data or lack a noise model. Moreover, we demonstrate the importance of registration for prior-image-based reconstruction methods and show that the prior-image-registered penalized-likelihood estimation (PIRPLE) approach can maintain a high level of image quality in the presence of noisy and undersampled projection data.

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.

Model-based iterative reconstruction for flat-panel cone-beam CT with focal spot blur, detector blur, and correlated noise

While model-based reconstruction methods have been successfully applied to flat-panel cone-beam CT (FP-CBCT) systems, typical implementations ignore both spatial correlations in the projection data as well as system blurs due to the detector and focal spot in the x-ray source. In this work, we develop a forward model for flat-panel-based systems that includes blur and noise correlation associated with finite focal spot size and an indirect detector (e.g. scintillator). This forward model is used to develop a staged reconstruction framework where projection data are deconvolved and log-transformed, followed by a generalized least-squares reconstruction that utilizes a non-diagonal statistical weighting to account for the correlation that arises from the acquisition and data processing chain. We investigate the performance of this novel reconstruction approach in both simulated data and in CBCT test-bench data. In comparison to traditional filtered backprojection and model-based methods that ignore noise correlation, the proposed approach yields a superior noise-resolution tradeoff. For example, for a system with 0.34 mm FWHM scintillator blur and 0.70 FWHM focal spot blur, using the correlated noise model instead of an uncorrelated noise model increased resolution by 42% (with variance matched at 6.9  ×  10(-8) mm(-2)). While this advantage holds across a wide range of systems with differing blur characteristics, the improvements are greatest for systems where source blur is larger than detector blur.

Volume-of-change cone-beam CT for image-guided surgery

C-arm cone-beam CT (CBCT) can provide intraoperative 3D imaging capability for surgical guidance, but workflow and radiation dose are the significant barriers to broad utilization. One main reason is that each 3D image acquisition requires a complete scan with a full radiation dose to present a completely new 3D image every time. In this paper, we propose to utilize patient-specific CT or CBCT as prior knowledge to accurately reconstruct the aspects of the region that have changed by the surgical procedure from only a sparse set of x-rays. The proposed methods consist of a 3D-2D registration between the prior volume and a sparse set of intraoperative x-rays, creating digitally reconstructed radiographs (DRRs) from the registered prior volume, computing difference images by subtracting DRRs from the intraoperative x-rays, a penalized likelihood reconstruction of the volume of change (VOC) from the difference images, and finally a fusion of VOC reconstruction with the prior volume to visualize the entire surgical field. When the surgical changes are local and relatively small, the VOC reconstruction involves only a small volume size and a small number of projections, allowing less computation and lower radiation dose than is needed to reconstruct the entire surgical field. We applied this approach to sacroplasty phantom data obtained from a CBCT test bench and vertebroplasty data with a fresh cadaver acquired from a C-arm CBCT system with a flat-panel detector. The VOCs were reconstructed from a varying number of images (10-66 images) and compared to the CBCT ground truth using four different metrics (mean squared error, correlation coefficient, structural similarity index and perceptual difference model). The results show promising reconstruction quality with structural similarity to the ground truth close to 1 even when only 15-20 images were used, allowing dose reduction by the factor of 10-20.

Intraoperative C-arm cone-beam computed tomography: quantitative analysis of surgical performance in skull base surgery.

To determine whether incorporation of intraoperative imaging via a new cone‐beam computed tomography (CBCT) image‐guidance system improves accuracy and facilitates resection in sinus and skull‐base surgery through quantification of surgical performance.

Fluence-field modulated x-ray CT using multiple aperture devices

We introduce a novel strategy for fluence field modulation (FFM) in x-ray CT using multiple aperture devices (MADs). MAD filters permit FFM by blocking or transmitting the x-ray beam on a fine (0.1-1 mm) scale. The filters have a number of potential advantages over other beam modulation strategies including the potential for a highly compact design, modest actuation speed and acceleration requirements, and spectrally neutral filtration due to their essentially binary action. In this work, we present the underlying MAD filtration concept including a design process to achieve a specific class of FFM patterns. A set of MAD filters is fabricated using a tungsten laser sintering process and integrated into an x-ray CT test bench. A characterization of the MAD filters is conducted and compared to traditional attenuating bowtie filters and the ability to flatten the fluence profile for a 32 cm acrylic phantom is demonstrated. MAD-filtered tomographic data was acquired on the CT test bench and reconstructed without artifacts associated with the MAD filter. These initial studies suggest that MAD-based FFM is appropriate for integration in clinical CT system to create patient-specific fluence field profile and reduce radiation exposures.

An analysis of coded aperture acquisition and reconstruction using multi-frame code sequences for relaxed optical design constraints

We present an investigation of the performance of coded aperture optical systems where the elements of a set of binary coded aperture masks are applied over a sequence of acquired images. In particular, we are interested in investigating code sequences and image reconstruction algorithms that reduce the optical fidelity and hardware requirements for the system. Performance is jointly tied to the mask design, the image estimation algorithm, and the inherent optical response of the system. As such, we adopt a simplified reconstruction model and consider generalized optical system aberrations in designing masks used for multi-frame reconstruction of the imagery. We also consider the case of non-Nyquist sampled (aliased) imagery. These investigations have focused on using a regularized least-squares reconstruction model and mean squared error as a performance metric. Masks are found by attempting to minimize a closed form objective that predicts the mean squared error for the reconstruction algorithm. We find that even with suboptimal solutions that binary masks can be used to improve imagery over the case of an uncoded aperture with the same aberration.

High-performance C-arm cone-beam CT guidance of thoracic surgery

Localizing sub-palpable nodules in minimally invasive video-assisted thoracic surgery (VATS) presents a significant challenge. To overcome inherent problems of preoperative nodule tagging using CT fluoroscopic guidance, an intraoperative C-arm cone-beam CT (CBCT) image-guidance system has been developed for direct localization of subpalpable tumors in the OR, including real-time tracking of surgical tools (including thoracoscope), and video-CBCT registration for augmentation of the thoracoscopic scene. Acquisition protocols for nodule visibility in the inflated and deflated lung were delineated in phantom and animal/cadaver studies. Motion compensated reconstruction was implemented to account for motion induced by the ventilated contralateral lung. Experience in CBCT-guided targeting of simulated lung nodules included phantoms, porcine models, and cadavers. Phantom studies defined low-dose acquisition protocols providing contrast-to-noise ratio sufficient for lung nodule visualization, confirmed in porcine specimens with simulated nodules (3-6mm diameter PE spheres, ~100-150HU contrast, 2.1mGy). Nodule visibility in CBCT of the collapsed lung, with reduced contrast according to air volume retention, was more challenging, but initial studies confirmed visibility using scan protocols at slightly increased dose (~4.6-11.1mGy). Motion compensated reconstruction employing a 4D deformation map in the backprojection process reduced artifacts associated with motion blur. Augmentation of thoracoscopic video with renderings of the target and critical structures (e.g., pulmonary artery) showed geometric accuracy consistent with camera calibration and the tracking system (2.4mm registration error). Initial results suggest a potentially valuable role for CBCT guidance in VATS, improving precision in minimally invasive, lungconserving surgeries, avoid critical structures, obviate the burdens of preoperative localization, and improve patient safety.