Browsing by Subject "X-Rays"
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Item Open Access Assessment of Impact of Long-Cassette Standing X-Rays on Surgical Planning for Cervical Pathology: An International Survey of Spine Surgeons.(Neurosurgery, 2016-05) Ramchandran, Subaraman; Smith, Justin S; Ailon, Tamir; Klineberg, Eric; Shaffrey, Christopher; Lafage, Virginie; Schwab, Frank; Bess, Shay; Daniels, Alan; Scheer, Justin K; Protopsaltis, Themi S; Arnold, Paul; Haid, Regis W; Chapman, Jens; Fehlings, Michael G; Ames, Christopher P; AOSpine North America, International Spine Study GroupBackground
Understanding the role of regional segments of the spine in maintaining global balance has garnered significant attention recently. Long-cassette radiographs (LCR) are necessary to evaluate global spinopelvic alignment. However, it is unclear how LCRs impact operative decision-making for cervical spine pathology.Objective
To evaluate whether the addition of LCRs results in changes to respondents' operative plans compared to standard imaging of the involved cervical spine in an international survey of spine surgeons.Methods
Fifteen cases (5 control cases with normal and 10 test cases with abnormal global alignment) of cervical pathology were presented online with a vignette and cervical imaging. Surgeons were asked to select a surgical plan from 6 options, ranging from the least (1 point) to most (6 points) extensive. Cases were then reordered and presented again with LCRs and the same surgical plan question.Results
One hundred fifty-seven surgeons completed the survey, of which 79% were spine fellowship trained. The mean response scores for surgical plan increased from 3.28 to 4.0 (P = .003) for test cases with the addition of LCRs. However, no significant changes (P = .10) were identified for the control cases. In 4 of the test cases with significant mid thoracic kyphosis, 29% of participants opted for the more extensive surgical options of extension to the mid and lower thoracic spine when they were provided with cervical imaging only, which significantly increased to 58.3% upon addition of LCRs.Conclusion
In planning for cervical spine surgery, surgeons should maintain a low threshold for obtaining LCRs to assess global spinopelvic alignment.Item Open Access Cherenkov emissions for studying tumor changes during radiation therapy: An exploratory study in domesticated dogs with naturally-occurring cancer.(PloS one, 2020-01) Rickard, Ashlyn G; Yoshikawa, Hiroto; Palmer, Gregory M; Liu, Harrison Q; Dewhirst, Mark W; Nolan, Michael W; Zhang, XiaofengPurpose
Real-time monitoring of physiological changes of tumor tissue during radiation therapy (RT) could improve therapeutic efficacy and predict therapeutic outcomes. Cherenkov radiation is a normal byproduct of radiation deposited in tissue. Previous studies in rat tumors have confirmed a correlation between Cherenkov emission spectra and optical measurements of blood-oxygen saturation based on the tissue absorption coefficients. The purpose of this study is to determine if it is feasible to image Cherenkov emissions during radiation therapy in larger human-sized tumors of pet dogs with cancer. We also wished to validate the prior work in rats, to determine if Cherenkov emissions have the potential to act an indicator of blood-oxygen saturation or water-content changes in the tumor tissue-both of which have been correlated with patient prognosis.Methods
A DoseOptics camera, built to image the low-intensity emission of Cherenkov radiation, was used to measure Cherenkov intensities in a cohort of cancer-bearing pet dogs during clinical irradiation. Tumor type and location varied, as did the radiation fractionation scheme and beam arrangement, each planned according to institutional standard-of-care. Unmodulated radiation was delivered using multiple 6 MV X-ray beams from a clinical linear accelerator. Each dog was treated with a minimum of 16 Gy total, in ≥3 fractions. Each fraction was split into at least three subfractions per gantry angle. During each subfraction, Cherenkov emissions were imaged.Results
We documented significant intra-subfraction differences between the Cherenkov intensities for normal tissue, whole-tumor tissue, tissue at the edge of the tumor and tissue at the center of the tumor (p<0.05). Additionally, intra-subfraction changes suggest that Cherenkov emissions may have captured fluctuating absorption properties within the tumor.Conclusion
Here we demonstrate that it is possible to obtain Cherenkov emissions from canine cancers within a fraction of radiotherapy. The entire optical spectrum was obtained which includes the window for imaging changes in water and hemoglobin saturation. This lends credence to the goal of using this method during radiotherapy in human patients and client-owned pets.Item Open Access Deep learning based spectral extrapolation for dual-source, dual-energy x-ray computed tomography.(Medical physics, 2020-09) Clark, Darin P; Schwartz, Fides R; Marin, Daniele; Ramirez-Giraldo, Juan C; Badea, Cristian TPurpose
Data completion is commonly employed in dual-source, dual-energy computed tomography (CT) when physical or hardware constraints limit the field of view (FoV) covered by one of two imaging chains. Practically, dual-energy data completion is accomplished by estimating missing projection data based on the imaging chain with the full FoV and then by appropriately truncating the analytical reconstruction of the data with the smaller FoV. While this approach works well in many clinical applications, there are applications which would benefit from spectral contrast estimates over the larger FoV (spectral extrapolation)-e.g. model-based iterative reconstruction, contrast-enhanced abdominal imaging of large patients, interior tomography, and combined temporal and spectral imaging.Methods
To document the fidelity of spectral extrapolation and to prototype a deep learning algorithm to perform it, we assembled a data set of 50 dual-source, dual-energy abdominal x-ray CT scans (acquired at Duke University Medical Center with 5 Siemens Flash scanners; chain A: 50 cm FoV, 100 kV; chain B: 33 cm FoV, 140 kV + Sn; helical pitch: 0.8). Data sets were reconstructed using ReconCT (v14.1, Siemens Healthineers): 768 × 768 pixels per slice, 50 cm FoV, 0.75 mm slice thickness, "Dual-Energy - WFBP" reconstruction mode with dual-source data completion. A hybrid architecture consisting of a learned piecewise linear transfer function (PLTF) and a convolutional neural network (CNN) was trained using 40 scans (five scans reserved for validation, five for testing). The PLTF learned to map chain A spectral contrast to chain B spectral contrast voxel-wise, performing an image domain analog of dual-source data completion with approximate spectral reweighting. The CNN with its U-net structure then learned to improve the accuracy of chain B contrast estimates by copying chain A structural information, by encoding prior chain A, chain B contrast relationships, and by generalizing feature-contrast associations. Training was supervised, using data from within the 33-cm chain B FoV to optimize and assess network performance.Results
Extrapolation performance on the testing data confirmed our network's robustness and ability to generalize to unseen data from different patients, yielding maximum extrapolation errors of 26 HU following the PLTF and 7.5 HU following the CNN (averaged per target organ). Degradation of network performance when applied to a geometrically simple phantom confirmed our method's reliance on feature-contrast relationships in correctly inferring spectral contrast. Integrating our image domain spectral extrapolation network into a standard dual-source, dual-energy processing pipeline for Siemens Flash scanner data yielded spectral CT data with adequate fidelity for the generation of both 50 keV monochromatic images and material decomposition images over a 30-cm FoV for chain B when only 20 cm of chain B data were available for spectral extrapolation.Conclusions
Even with a moderate amount of training data, deep learning methods are capable of robustly inferring spectral contrast from feature-contrast relationships in spectral CT data, leading to spectral extrapolation performance well beyond what may be expected at face value. Future work reconciling spectral extrapolation results with original projection data is expected to further improve results in outlying and pathological cases.Item Open Access Spectral diffusion: an algorithm for robust material decomposition of spectral CT data.(Phys Med Biol, 2014-11-07) Clark, Darin P; Badea, Cristian TClinical successes with dual energy CT, aggressive development of energy discriminating x-ray detectors, and novel, target-specific, nanoparticle contrast agents promise to establish spectral CT as a powerful functional imaging modality. Common to all of these applications is the need for a material decomposition algorithm which is robust in the presence of noise. Here, we develop such an algorithm which uses spectrally joint, piecewise constant kernel regression and the split Bregman method to iteratively solve for a material decomposition which is gradient sparse, quantitatively accurate, and minimally biased. We call this algorithm spectral diffusion because it integrates structural information from multiple spectral channels and their corresponding material decompositions within the framework of diffusion-like denoising algorithms (e.g. anisotropic diffusion, total variation, bilateral filtration). Using a 3D, digital bar phantom and a material sensitivity matrix calibrated for use with a polychromatic x-ray source, we quantify the limits of detectability (CNR = 5) afforded by spectral diffusion in the triple-energy material decomposition of iodine (3.1 mg mL(-1)), gold (0.9 mg mL(-1)), and gadolinium (2.9 mg mL(-1)) concentrations. We then apply spectral diffusion to the in vivo separation of these three materials in the mouse kidneys, liver, and spleen.