KEYWORDS: Deep convolutional neural networks, Deep learning, CT reconstruction, Reconstruction algorithms, 3D image reconstruction, X-ray computed tomography, Brain, Algorithm development
Multi-source array (MXA) Computed Tomography systems pose challenges related to sampling and x-ray scatter. We present a semi-stationary head CT system and image formation pipeline including adaptive scatter estimation and image reconstruction based on learned diffusion models. The CT was evaluated on a robotic bench system including a miniaturized carbon-nanotube x-ray source and a curved-panel detector. Scatter correction was achieved with an Adaptive Deep Scatter Estimation (ADSE) method combining geometry-invariant projection-based scatter estimation with geometry-adaptive registration and scaling. Image reconstruction followed a Diffusion Posterior Sampling method (DPS-Recon) combining an unconditional diffusion model with measured data consistency. Image quality was assessed using anthropomorphic phantoms for a semi-stationary protocol involving a 21-source MXA rotated to three positions. ADSE resulted in 118% mean increase in feature contrast accuracy, 1.75 to 13-fold improvement in CNR for variable contrast features (-337HU to 885HU), and 3.56-fold improvement in CNR for variable size features (2mm to 12mm, 110HU) compared to uncorrected reconstructions. Non-uniformity reduced 50% for the three slices. DPS-Recon reduced limited sampling artifacts and improved visualization of soft-tissue structures, particularly in less densely sampled and bony anatomy locations, and further reduced non-uniformity by 20% in the superior brain location. We present first experimental results from a semi-stationary, multi-source CT utilizing CNT x-ray sources and curved-panel detector coupled to an imaging chain that addressed the main challenges inherent to the architecture. Metrics of CT number accuracy, image uniformity, and soft-tissue visualization showed promising performance for visualization of stroke radiological markers with the proposed approach.
Tomographic systems based on stationary arrangements of compact x-ray sources coupled to curved panel detectors have shown great potential for point-of-care brain imaging, but suffer from large, non-isotropic x-ray scatter. This work presents an adaptive kernel strategy to efficiently estimate scatter in stationary multi-source CT. The adaptive scatter estimation handles non-circular geometries, by the addition of pre- and post-processing steps to projection domain scatter estimators. The method was calibrated and evaluated on simulated data for a previously presented system with 31 x-ray sources on a circular arc coupled to a curved detector. Further assessment was obtained on experimental data obtained with an imaging testbench including a compact CNT-based x-ray source and simulating the scanner geometry. The method achieved accurate air-normalized scatter distributions across x-ray source positions and detector pixels, yielding a mean absolute error of 1.98𝑥10−3 with respect to the Monte-Carlo ground truth. Air-gap compensation had the largest impact on final accuracy. Image quality for simulated data showed consistent mitigation of scatter artifacts and reduction in non-uniformity from NU = 109 HU to 24 HU, with comparable performance for variations in cranium size, ranging in length from 161 mm (NU =14 HU) to 246 mm (NU = 15 HU). The experimental data showed comparable performance with error attributable to slight simulation infidelity. This work presents an adaptive approach to scatter compensation in multi-source, non-circular geometries using warping and weighting operations coupled to kernel-based scatter estimation on a virtual circular geometry, with immediate extension to other projection-based scatter compensation strategies.
We present a multimodal catheter for characterizing airway collapse in obstructive sleep apnea (OSA) during in-vivo sleep studies. Traditionally, diagnosis focusses on identifying the presence of apnea rather than the underlying cause of obstruction, and current methods of detecting airway collapse are not able to identify a specific patient’s contributing factors. It is considered that a simple method to establish the primary site and mechanism for upper airway collapse would improve the ability of clinicians to distinguish which patients would benefit from one of the variety of treatments currently available. By introducing a newly developed manometry catheter into in-vivo studies of known OSA sufferers we can provide the means to determine the location of the site(s) of collapse, the degree of occlusion that occurs, the severity of reduced air flow, the associated anatomical features, and mechanism of collapse. The device consists of 13 discrete pressure and temperature sensing elements and a micro-video camera that collectively enable simultaneous recording of pressure, temperature, and visualization of the point of collapse. The sensors use fiber Bragg gratings (FBGs) spaced on a 10mm pitch which is sufficient to provide an accurate interpolated image of both pressure and temperature along the upper airway (above the epiglottis), whilst the use of paired FBGs effectively removes the temperature artefact. We present results from recent in-vivo studies that demonstrate the viability of the device to identify and characterize occlusive events in the upper airway and the potential to better guide subsequent therapeutic interventions.
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