(Thursday, 8/2/2018) 7:30 AM - 9:30 AM
Room: Room 202
Purpose: To develop a convolutional neural network (CNN) that can directly estimate material densities based on multi-energy images acquired with photon counting detector (PCD) CT without going through an explicit conventional material decomposition process.
Methods: A CNN was constructed with three convolutional layers, two batch-normalization layers, and two rectified linear units. The sizes of the convolutional filters in 1st, 2nd, and 3rd convolutional layers were 3×3×2×32, 3×3×32×32, and 3×3×32×2, respectively. We scanned a Gammex multi-energy CT phantom (Sun Nuclear. Corp) and a living swine (head and neck CT angiogram) on a research PCD-CT system (Siemens), using two energy bins ([30, 52] and [52, 120] keV). The phantom contained two hydroxyapatite (HA) (200 and 400 mg/cc), six Iodine (2, 5, 5, 5, 10, and 15 mg/cc), one adipose, and one solid-water inserts. Data augmentation strategies (e.g. random patches, random noise, and normalization) were used to reduce over-fitting in CNN training. The CNN was trained to predict the mass density of HA and iodine using a randomly selected 90% of the phantom data (the remaining data were used for validation). The trained CNN was tested using the animal scans for qualitative evaluation.
Results: The CNN technique accurately predicted the mass density of HA inserts (396.4±6.9 and 200.2±9.5 mg/cc) and Iodine inserts (1.72±0.45, 5.64±0.56, 5.53±0.79, 4.54±0.91, 10.8±0.58, and 15.5±0.54 mg/cc) in phantom images. The CNN technique also accurately decomposed bony structures and contrast-enhanced blood vessels in the animal images.
Conclusion: CNN technique can provide a convenient and accurate tool for material decomposition in PCD-CT.
Funding Support, Disclosures, and Conflict of Interest: No financial interests Dr. Cynthia H McCollough received research grants from Siemens Healthcare Other co-authors have nothing to disclose
Keywords
Image Analysis, Image Processing
Taxonomy
IM- CT: Dual Energy and Spectral
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