Dual-Energy Computed Tomography for the Characterization of Intracranial Hemorrhage and Calcification: A Systematic...

The aim of this study was to develop a diagnostic framework for distinguishing calcific from hemorrhagic cerebral lesions using dual-energy computed tomography (DECT) in an anthropomorphic phantom system.

An anthropomorphic phantom was designed to mimic the CT imaging characteristics of the human head. Cylindrical lesion models containing either calcium or iron, mimicking calcification or hemorrhage, respectively, were developed to exhibit matching, and therefore indistinguishable, single-energy CT (SECT) attenuation values from 40 to 100 HU. These lesion models were fabricated at 0.5, 1, and 1.5 cm in diameter and positioned in simulated cerebrum and skull base locations within the anthropomorphic phantom. All lesion sizes were modeled in the cerebrum, while only 1.5-cm lesions were modeled in the skull base. Images were acquired using a GE 750HD CT scanner and an expansive dual-energy protocol that covered variations in dose (36.7-132.6 mGy CTDIvol, n = 12), image thickness (0.625-5 mm, n = 4), and reconstruction filter (soft, standard, detail, n = 3) for a total of 144 unique technique combinations. Images representing each technique combination were reconstructed into water and calcium material density images, as well as a monoenergetic image chosen to mimic the attenuation of a 120-kVp SECT scan. A true single-energy routine brain protocol was also included for verification of lesion SECT attenuation. Points representing the 3 dual-energy reconstructions were plotted into a 3-dimensional space (water [milligram/milliliter], calcium [milligram/milliliter], monoenergetic Hounsfield unit as x, y, and z axes, respectively), and the distribution of points analyzed using 2 approaches: support vector machines and a simple geometric bisector (GB). Each analysis yielded a plane of optimal differentiation between the calcification and hemorrhage lesion model distributions. By comparing the predicted lesion composition to the known lesion composition, we identified the optimal combination of CTDIvol, image thickness, and reconstruction filter to maximize differentiation between the lesion model types. To validate these results, a new set of hemorrhage and calcification lesion models were created, scanned in a blinded fashion, and prospectively classified using the planes of differentiation derived from support vector machine and GB methods.

Accuracy of differentiation improved with increasing dose (CTDIvol) and image thickness. Reconstruction filter had no effect on the accuracy of differentiation. Using an optimized protocol consisting of the maximum CTDIvol of 132.6 mGy, 5-mm-thick images, and a standard filter, hemorrhagic and calcific lesion models with equal SECT attenuation (Hounsfield unit) were differentiated with over 90% accuracy down to 70 HU for skull base lesions of 1.5 cm, and down to 100 HU, 60 HU, and 60 HU for cerebrum lesions of 0.5, 1.0, and 1.5 cm, respectively. The analytic method that yielded the best results was a simple GB plane through the 3-dimensional DECT space. In the validation study, 96% of unknown lesions were correctly classified across all lesion sizes and locations investigated.

We define the optimal scan parameters and expected limitations for the accurate classification of hemorrhagic versus calcific cerebral lesions in an anthropomorphic phantom with DECT. Although our proposed DECT protocol represents an increase in dose compared with routine brain CT, this method is intended as a specialized evaluation of potential brain hemorrhage and is thus counterbalanced by increased diagnostic benefit. This work provides justification for the application of this technique in human clinical trials.