PurposeThe critical time between stroke onset and treatment was targeted for reduction by integrating physiological imaging into the angiography suite, potentially improving clinical outcomes. The evaluation was conducted to compare C-Arm cone beam CT perfusion (CBCTP) with multi-detector CT perfusion (MDCTP) in patients with acute ischemic stroke (AIS).ApproachThirty-nine patients with anterior circulation AIS underwent both MDCTP and CBCTP. Imaging results were compared using an in-house algorithm for CBCTP map generation and RAPID for post-processing. Blinded neuroradiologists assessed images for quality, diagnostic utility, and treatment decision support, with non-inferiority analysis (two one-sided tests for equivalence) and inter-reviewer consistency (Cohen’s kappa).ResultsThe mean time from MDCTP to angiography suite arrival was 50±34 min, and that from arrival to the first CBCTP image was 21±8 min. Stroke diagnosis accuracies were 96% [93%, 97%] with MDCTP and 91% [90%, 93%] with CBCTP. Cohen’s kappa between observers was 0.86 for MDCTP and 0.90 for CBCTP, showing excellent inter-reader consistency. CBCTP’s scores for diagnostic utility, mismatch pattern detection, and treatment decisions were noninferior to MDCTP scores (alpha = 0.05) within 20% of the range. MDCTP was slightly superior for image quality and artifact score (1.8 versus 2.3, p<0.01).ConclusionsIn this small paper, CBCTP was noninferior to MDCTP, potentially saving nearly an hour per patient if they went directly to the angiography suite upon hospital arrival.
In recent years there has been increased focus on further reducing radiation dose in CT with photon counting CT using solid-state direct-conversion photon counting detectors (PCDs) to reduce the effective dose from routine CT exams to less than 1 mSv. However, despite its noise-reducing capabilities, PCD-CT faces challenges of inaccurate CT numbers at low-dose levels: with smaller pixel areas and multiple energy channels, the number of digital counts recorded in each bin of each PCD pixel can be as low as single-digit integers leading to statistical biases in CT sinograms due to the nonlinear log transformation operation. After tomographic reconstruction, those biases lead to inaccurate CT numbers in PCD-CT images. Previous correction methods require access to the original raw PCD counts. However, in almost all commercial CT systems, raw detector counts are hidden from the end users. Additionally, some CT systems perform the logarithmic transformation of raw counts as a part of the analog-to-digital conversion process for data compression reasons. For those systems, access to the PCD counts is irretrievably lost. Even for the post-log sinogram data, they are usually not archived for each patient. These practical considerations present challenges to the offline application of CT number bias corrections. The purpose of this work was to develop a method to address the statistical bias problem in low-dose PCD-CT without requiring any access to the raw detector counts. Innovations were made in this work to enable bias correction using the post-log sinogram data or using the reconstructed, bias-contaminated PCD-CT images.
In the effort to contain the COVID-19 pandemic, quick and effective diagnosis is paramount in preventing the spread of the disease. While the reverse transcriptase polymerase chain reaction (RT-PCR) test is the gold standard method to identify COVID-19, the use of x-ray radiography (CXR) has been widely used in the clinical workup for patients suspected of infection as an additional means of diagnosis and treatment response monitoring. CXR is available in almost every medical center across the world, allowing a quick and protected means of identifying potential COVID-19 cases to subject to quarantine procedures. However, the major challenge with the use of CXR in COVID-19 diagnosis is its low sensitivity and specificity in current radiological practice due to the similarities in clinical presentation to other diseases. Machine learning methods, particularly deep learning, have been shown to perform extremely well in a variety of classification tasks, often exceeding human performance. To utilize these techniques, a large data set of over 12,000 CXR images, including over 6,000 confirmed COVID-19 positive cases, was collected to train and validate a deep learning model to differentiate COVID-19 pneumonia from other causes of CXR abnormalities. In this work we show that this deep learning method can differentiate between COVID-19 related pneumonia and non-COVID-19 pneumonia, with high sensitivity and specificity.
Flat-panel detector based cone-beam CT systems have been widely used in image-guided interventions and image-guided radiation therapy. However, several notoriously difficult challenges persist in these cone-beam CT systems: given the relatively large cone angles used in data acquisition, scatter induced artifacts significantly degrade image quality and thus the algorithms to reduce these artifacts have remained an active area of research through out the past decade. To accommodate for the limited detector dynamic range, these systems often use auto-exposure control to homogenize the noise distribution, and as a result, both kV and mA are modulated in some systems making beam hardening correction extremely difficult. Additionally, when data acquisition time is long, inadvertent motion artifacts often exacerbate the situation. In this paper, we develop a deep learning method to empirically correct these most often observed artifacts in flat-panel based CBCT images.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.