Browsing by Author "McCabe, Cindy"

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Open Access
Image Quality of Photon Counting and Energy Integrating Chest CT – Prospective Head-to-Head Comparison on Same Patients
(European Journal of Radiology, 2023-07) Schwartz, Fides R; Ria, Francesco; McCabe, Cindy; Zarei, Mojtaba; Rajagopal, Jayasai; Molvin, Lior; Marin, Daniele; O'Sullivan-Murphy, Bryan; Kalisz, Kevin R; Tailor, Tina D; Washington, Lacey; Henry, Travis; Samei, Ehsan
Open Access
Myelography Using Energy-Integrating Detector CT Versus Photon-Counting Detector CT for Detection of CSF-Venous Fistulas in Patients With Spontaneous Intracranial Hypotension.
(AJR. American journal of roentgenology, 2024-01) Schwartz, Fides R; Kranz, Peter G; Malinzak, Michael D; Cox, David N; Ria, Francesco; McCabe, Cindy; Harrawood, Brian; Leithe, Linda G; Samei, Ehsan; Amrhein, Timothy J
Background: CSF-venous fistulas (CVFs) are an increasingly recognized cause of spontaneous intracranial hypotension (SIH) that are often diminutive in size and exceedingly difficult to detect by conventional imaging. Objective: This study's objective was to compare EID-CT myelography and PCD-CT myelography in terms of image quality and diagnostic performance for detecting CVFs in patients with SIH. Methods: This retrospective study included 38 patients (15 men, 23 women; mean age, 55±10 years) with SIH who underwent both clinically indicated EID-CT myelography (slice thickness, 0.625 mm) and PCD-CT myelography (slice thickness, 0.2 mm; performed in ultrahigh-resolution mode) to assess for CSF leak. Three blinded radiologists reviewed examinations in random order, assessing image noise, discernibility of spinal nerve root sleeves, and overall image quality using 0-100 scales (100=highest quality), and recording locations of CVFs. Definite CVFs were defined as CVFs described in CT myelography reports using unequivocal language and showing attenuation >70 HU. Results: For all readers, PCD-CT myelography, in comparison with EID-CT myelography, showed higher image noise (reader 1: 69±19 vs 38±15; reader 2: 59±9 vs 49±13; reader 3: 57±13 vs 43±15), higher nerve root sleeve discernibility (reader 1: 84±19 vs 30±14; reader 2: 84±19 vs 70±19; reader 3: 60±13 vs 52±12), and higher overall image quality (reader 1: 84±21 vs 40±15; reader 2: 81±10 vs 72±20; reader 3: 58±11 vs 53±11) (all p<.05). Eleven patients had a definite CVF. Sensitivity and specificity for detection of definite CVF for EID-CT myelography and PCD-CT myelography for reader 1 were 45% and 96% versus 64% and 85; for reader 2 were 36% and 100% versus 55% and 96%; and for reader 3 were 45% and 100% versus 55% and 93%. For all readers, PCD-CT myelography, in comparison with EID-CT myelography, showed significantly higher sensitivity (all p<.05), without significant difference in specificity (all p>.05). Conclusion: In comparison with EID-CT myelography, PCD-CT myelography yielded significantly improved image quality with significantly higher sensitivity for CVFs without significant loss of specificity. Clinical Impact: The findings support a potential role of PCD-CT myelography in facilitating earlier diagnosis and targeted treatment of SIH, avoiding high morbidity during potentially prolonged diagnostic workups.
Open Access
Technology Characterization Through Diverse Evaluation Methodologies: Application to Thoracic Imaging in Photon-Counting Computed Tomography.
(J Comput Assist Tomogr, 2024-04-15) Rajagopal, Jayasai R; Schwartz, Fides R; McCabe, Cindy; Farhadi, Faraz; Zarei, Mojtaba; Ria, Francesco; Abadi, Ehsan; Segars, Paul; Ramirez-Giraldo, Juan Carlos; Jones, Elizabeth C; Henry, Travis; Marin, Daniele; Samei, Ehsan
OBJECTIVE: Different methods can be used to condition imaging systems for clinical use. The purpose of this study was to assess how these methods complement one another in evaluating a system for clinical integration of an emerging technology, photon-counting computed tomography (PCCT), for thoracic imaging. METHODS: Four methods were used to assess a clinical PCCT system (NAEOTOM Alpha; Siemens Healthineers, Forchheim, Germany) across 3 reconstruction kernels (Br40f, Br48f, and Br56f). First, a phantom evaluation was performed using a computed tomography quality control phantom to characterize noise magnitude, spatial resolution, and detectability. Second, clinical images acquired using conventional and PCCT systems were used for a multi-institutional reader study where readers from 2 institutions were asked to rank their preference of images. Third, the clinical images were assessed in terms of in vivo image quality characterization of global noise index and detectability. Fourth, a virtual imaging trial was conducted using a validated simulation platform (DukeSim) that models PCCT and a virtual patient model (XCAT) with embedded lung lesions imaged under differing conditions of respiratory phase and positional displacement. Using known ground truth of the patient model, images were evaluated for quantitative biomarkers of lung intensity histograms and lesion morphology metrics. RESULTS: For the physical phantom study, the Br56f kernel was shown to have the highest resolution despite having the highest noise and lowest detectability. Readers across both institutions preferred the Br56f kernel (71% first rank) with a high interclass correlation (0.990). In vivo assessments found superior detectability for PCCT compared with conventional computed tomography but higher noise and reduced detectability with increased kernel sharpness. For the virtual imaging trial, Br40f was shown to have the best performance for histogram measures, whereas Br56f was shown to have the most precise and accurate morphology metrics. CONCLUSION: The 4 evaluation methods each have their strengths and limitations and bring complementary insight to the evaluation of PCCT. Although no method offers a complete answer, concordant findings between methods offer affirmatory confidence in a decision, whereas discordant ones offer insight for added perspective. Aggregating our findings, we concluded the Br56f kernel best for high-resolution tasks and Br40f for contrast-dependent tasks.