Physics in medicine and biology
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Quality assurance of data prior to use in automated pipelines and image analysis would assist in safeguarding against biases and incorrect interpretation of results. Automation of quality assurance steps would further improve robustness and efficiency of these methods, motivating widespread adoption of techniques. Previous work by our group demonstrated the ability of convolutional neural networks (CNN) to efficiently classify head and neck (H&N) computed-tomography (CT) images for the presence of dental artifacts (DA) that obscure visualization of structures and the accuracy of Hounsfield units. ⋯ Despite these promising results, transfer learning did not improve AUC when utilizing small resampling grids or small datasets. Our work demonstrates the potential of our previously developed automated quality assurance methods to generalize to external datasets. Additionally, we showed that transfer learning with fine-tuning using small portions of external datasets can be used to fine-tune models for improved performance when large variations in images are present.
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To improve the reconstruction condition and alleviate the noise amplification of GRAPPA reconstruction by aggregating the phase conjugated and nonlinear kernel mapped coils with the original physical coil. Nonlinear GRAPPA (NL-GRAPPA) is a kernel-based non-iterative approach which can reduce noise-induced error in GRAPPA reconstruction. And virtual conjugate coil (VCC) embeds the conjugate symmetric property of k-space into GRAPPA data synthesis to improve reconstruction condition. ⋯ The proposed method illustrated improved visual image quality evidenced by reduced retrospective RMSE and prospective g-factor comparing with conventional GRAPPA and the recently proposed iterative SENSE-LORAKS reconstructions. Although a larger amount of calibration data and smaller kernel size were required to stabilize the calibration of fourfold extended kernel for the proposed method, it was non-iterative and relatively insensitive to parameter adjustment in the applications. The proposed NL-VCC-extension to conventional GRAPPA brings visible improvements for imaging scenarios accelerated by the widely available uniform undersampling schemes in a practically efficient manner without iteration.
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Percutaneous screw fixation in pelvic trauma surgery is a challenging procedure that often requires long fluoroscopic exposure times and trial-and-error insertion attempts along narrow bone corridors of the pelvis. We report a method to automatically plan surgical trajectories using preoperative CT and assist device placement by augmenting the fluoroscopic scene with planned trajectories. A pelvic shape atlas was formed from 40 CT images and used to construct a statistical shape model (SSM). ⋯ The approach yields a method for both automatic planning of pelvic fracture fixation and augmentation of fluoroscopy for improved surgical precision and safety. The method does not require segmentation of the patient CT, operates without additional hardware (e.g. tracking systems), and is consistent with common workflow in fluoroscopically guided procedures. The approach has the potential to reduce operating time and radiation dose by minimizing trial-and-error attempts in percutaneous screw placement.
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Radio-guided occult lesion localisation using iodine-125 seeds (ROLLIS) for surgical removal of impalpable breast lesions has been increasingly used as an alternative to hook-wire localisation. This technique involves implanting a low-level radioactive seed into the lesion, followed by excision of the lesion guided using a hand-held gamma probe. Although the level of radiation exposure from this procedure is low, there has not been an appropriate method to quantify the organ dose. ⋯ The calculated dose rate factors from the ROLLIS procedure ranged from 0.0097 to 0.0477 mGy · h-1 · MBq-1 depending on many factors including breast size, lesion size, seed number, seed activity and the time in situ. Lookup tables were compiled to allow a convenient way to calculate the mean absorbed dose for future ROLLIS patients. Based on clinical data collected at our institution, the mean absorbed dose to the breast for a typical ROLLIS patient would be approximately 0.50 mGy.
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Respiratory and cardiac motion can strongly impair cardiac PET image quality and tracer uptake quantification. Standard gating techniques can minimize these motion artefacts but suffer from low signal-to-noise ratio because only a small percentage of the total data is utilized. Motion correction approaches have been proposed to overcome this problem but require accurate knowledge of such physiological motion. ⋯ The average increase in uptake values using MCIR was 23% ± 10% (p < 0.0001), with values of 28% ± 11% (p < 0.0001) for basal, 21% ± 8% (p < 0.0001) for mid-cavity and 17% ± 7% (p < 0.0001) for apical segments. With the proposed scheme we could ensure high PET image quality and improve local PET uptake quantification by up to 30%. Attenuation correction and motion information was obtained from the same PET-MR raw data, which was obtained during free-breathing to minimize scan times and to increase patient comfort.