Phantom-based standardization of CT angiography images for spot sign detection

Morotti, Andrea; Romero, Javier; Jessel, Michael J.; Hernandez, Andrew M.; Vashkevich, Anastasia; Schwab, Kristin; Burns, Joseph D.; Shah, Qaisar; Bergman, Thomas; Suri, M. Fareed K.; Ezzeddine, Mustapha A.; Kirmani, Jawad F.; Agarwal, Sachin; Shapshak, Angela Hays; Messé, Steven R.; Venkatasubramanian, Chitra; Palmieri, Katherine; Lewandowski, Christopher; Chang, Tiffany R.; Chang, Ira; Rose, David Z.; Smith, Wade S.; Hsu, Chung Y.; Liu, Chun Lin; Lien, Li Ming; Hsiao, Chen Yu; Iwama, Toru; Afzal, Mohammad Rauf; Cassarly, Christy; Greenberg, Steven M.; Martin, Renée; Qureshi, Adnan I.; Rosand, Jonathan; Boone, John M.; Goldstein, Joshua N.

doi:10.1007/s00234-017-1857-4

Cited by 1 publication

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“…This specific iodine concentration was chosen to simulate a contrast of ∼130 HU with respect to the acrylic background consistent with the lower range of contrast enhancement thresholds recommended for stenotic lesions throughout the body. [20][21][22] The relationship between HU and iodine concentration was determined a priori by scanning a custom iodine calibration phantom, previously reported, 23 on the Aquilion Precision (Canon Medical Systems, Otawara, Japan) high-resolution CT scanner (explained in detail in the following section) at 120 kV . The calibration phantom is composed of small plugs of iodine of various concentrations (0, 2, 5, 10, 15 and 20 mg/cc iodine) all encased within a thin plastic rod (CIRS Inc., Norfolk, VA).…”

Section: Stenotic Lesion Phantom Designmentioning

confidence: 99%

Performance of high‐resolution CT for detection and discrimination tasks related to stenotic lesions – A phantom study using model observers

et al. 2023

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Background Accurate detection and grading of atheromatous stenotic lesions within the cardiac, renal, and intracranial vasculature is imperative for early recognition of disease and guiding treatment strategies. Purpose In this work, a stenotic lesion phantom was used to compare high resolution and normal resolution modes on the same CT scanner in terms of detection and size discrimination performance. Materials and Methods The phantom is comprised of three acrylic cylinders (each 15.0 cm in diameter and 1.3 cm thick) with a matching array of holes in each module. The outer two modules contain holes that are slightly larger than the corresponding hole in the central module to simulate stenotic narrowing in vasculature. The stack of modules was submerged in an iodine solution simulating contrast‐enhanced stenotic lesions with a range of lumen diameters (1.32–10.08 mm) and stenosis severity (0%, 50%, 60%, 70%, and 80%). The phantom was imaged on the Canon Aquilion Precision high‐resolution CT scanner in high‐resolution (HR) mode (0.25 mm × 0.50 mm detector element size) and normal‐resolution (NR) mode (0.50 mm × 0.50 mm) using 120 kV and two dose levels (14 and 21 mGy SSDE) with 30 repeat scans acquired for each combination. Filtered back‐projection (FBP) and a hybrid‐iterative reconstruction (AIDR) were used with the FC18 kernel, as well as a deep learning algorithm (AiCE) which is only available for HR. A non‐prewhitening model observer with an eye filter was implemented to quantify performance for detection and size discrimination tasks in the axial plane. Results Detection performance improved with increasing diameter, dose, and for AIDR in comparison to FBP for a fixed resolution mode. Performance in the HR mode was generally higher than NR for the smaller lumen diameters (1–5 mm) with decreasing differences as the diameter increased. Performance in NR mode surpassed HR mode for lumen diameters greater than ∼4 mm and ∼5 mm for 14 mGy and 21 mGy, respectively. AiCE provided consistently higher detection performance compared with AIDR‐FC18 (48% higher for a 6 mm lumen diameter). Discrimination performance increased with increasing nominal diameter, dose, and for larger differences in stenosis severity. When comparing discrimination performance in HR to NR modes, the largest relative differences occur at the smallest nominal diameters and smallest differences in stenosis severity. The AiCE reconstruction algorithm produced the highest overall discrimination performance values, and these were significantly higher than AIDR‐FC18 for nominal diameters of 7.14 and 10.08 mm. Conclusions HR mode outperforms NR for detection up to a specific diameter and the results improve with AiCE and for higher dose levels. For the task of size discrimination, HR mode consistently outperforms NR if AIDR‐FC18 is used for dose levels of at least 21 mGy, and the results improve with AiCE and for the smallest differences in stenosis severity investigated (50% vs. 60%). High‐resolution CT appears to be beneficial for detecting s...

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