An Image-Domain Contrast Material Extraction Method for Dual-Energy Computed Tomography

Abstract

Objectives Conventional material decomposition techniques for dual-energy CT (DECT) assume mass or volume conservation, where the CT number of each voxel is fully assigned to predefined materials. We present an image-domain contrast material extraction process (CMEP) method that preferentially extracts contrast-producing materials while leaving the remaining image intact. Materials and Methods Image processing freeware (Fiji) is used to perform consecutive arithmetic operations on a dual-energy ratio map to generate masks, which are then applied to the original images to generate material-specific images. First, a low-energy image is divided by a high-energy image to generate a ratio map. The ratio map is then split into material-specific masks. Ratio intervals known to correspond to particular materials (e.g. iodine, calcium) are assigned a multiplier of 1, while ratio values in between these intervals are assigned linear gradients from 0 to 1. The masks are then multiplied by an original CT image to produce material-specific images. The method was tested quantitatively at Dual-Source (DSCT) and Rapid kVp-Switching CT (RSCT) with phantoms using pure and mixed formulations of tungsten, calcium and iodine. Errors were evaluated by comparing the known material concentrations with those derived from the CMEP material-specific images. Further qualitative evaluation was performed in vivo at RSCT with a rabbit model using identical CMEP parameters to the phantom. Orally administered tungsten, vascularly administered iodine, and skeletal calcium were used as the three contrast materials. Results All five material combinations; tungsten, iodine and calcium, and mixtures of tungsten-calcium and iodine-calcium, showed distinct dual-energy ratios, largely independent of material concentration at both DSCT and RSCT. The CMEP was successful in both phantoms and in vivo. For pure contrast materials in the phantom, the maximum error between the known and CMEP-derived material concentrations was 0.9 mg/mL, 24.9 mg/mL and 0.4 mg/mL for iodine, calcium and tungsten respectively. Mixtures of iodine and calcium showed the highest discrepancies, which reflected the sensitivity of iodine to the image-type chosen for the extraction of the final material-specific image. The rabbit model was able to clearly show the three extracted material phases, vascular iodine, oral tungsten and skeletal calcium. Some skeletal calcium was misassigned to the extracted iodine image, however this did not impede the depiction of the vasculature. Conclusions The CMEP is a straightforward, image domain approach to extract material signal at dual-energy CT. It has particular value for separation of experimental high-Z contrast elements from conventional iodine contrast or calcium, even when the exact attenuation coefficient profiles of desired contrast materials may be unknown. The CMEP is readily implemented in the image-domain within freeware, and can be adapted for use with images from multiple vendors.

Modified Dual-Energy Algorithm for Calcified Plaque Removal

“…Advances in dual-energy CT (DECT)-related material decomposition may allow to overcome this problem: DECT has the ability to differentiate between iodine and calcium contents by using differences in energy dependence of CT attenuation. [5][6][7][8][9] Previous studies have compared the efficiency of plaque and bone removal in DECT angiography of cervical vessels with that of conventional CTA and have found advantages for DECT regarding a reduction of reading time and radiation dose. 6,[10][11][12][13] Other studies have reported the feasibility and utility of bone and plaque removal in intracranial and cervical DECT angiography examinations compared with DSA 14,15 and the accurate classification of hemorrhagic and calcified cerebral lesions.…”

Improved Calcium Scoring at Dual-Energy Computed Tomography Angiography Using a High-Z Contrast Element and Novel Material Separation Technique

“…The Contrast Material Extraction Process (CMEP) selectively extracts contrast producing materials in the dataset based on their dual energy ratio; the ratio of CT numbers from low to high x-ray energies (25). Thresholded masks are created from the dual energy ratio map, and are subsequently applied to original CT images to generate material specific images.…”

“…While the utility of high-Z contrast agents has been shown in preclinical animal models of abdominal disease (21, 22), their performance for quantitative separation tasks such as CACS remain unknown. Also, novel algorithms to separate contrast material signal from calcium at DECT have been described (23, 24), and include the contrast material extraction process (CMEP) (25). CMEP, an image domain material separation method that offers an alternative to conventional material decomposition (MD), showed accurate separation of contrast agent signals in a phantom study (25), but has not been tested for CACS.…”

“…Also, novel algorithms to separate contrast material signal from calcium at DECT have been described (23, 24), and include the contrast material extraction process (CMEP) (25). CMEP, an image domain material separation method that offers an alternative to conventional material decomposition (MD), showed accurate separation of contrast agent signals in a phantom study (25), but has not been tested for CACS. We hypothesize that the use of a high-Z contrast material and CMEP could offer improved CACS accuracy compared to conventional iodine contrast material and MD image processing.…”

Image Decomposition Algorithm for Dual-Energy Computed Tomography via Fully Convolutional Network

“…The quality of material-specific image produced by DECT attaches great importance to the elaborated design of the basis material decomposition method. The existing decomposition methods can be divided into two main categories: projection-based [ 5 – 7 ] and image-based [ 8 – 10 ]. Projection-based methods pass the projection data through a decomposition function, followed by image reconstruction such as filtered backprojection (FBP).…”