Comparison of enhancement quantification from virtual unenhanced images to true unenhanced images in multiphase renal Dual‐Energy computed tomography: A phantom study

Abstract Multiphase computed tomography (CT) exams are a commonly used imaging technique for the diagnosis of renal lesions and involve the acquisition of a true unenhanced (TUE) series followed by one or more postcontrast series. The difference in CT number of the mass in pre‐ and postcontrast images is used to quantify enhancement, which is an important criterion used for diagnosis. This study sought to assess the feasibility of replacing TUE images with virtual unenhanced (VUE) images derived from Dual‐Energy CT datasets in renal CT exams. Eliminating TUE image acquisition could reduce patient dose and improve clinical efficiency. A rapid kVp‐switching CT scanner was used to assess enhancement accuracy when using VUE compared to TUE images as the baseline for enhancement calculations across a wide range of clinical scenarios simulated in a phantom study. Three phantoms were constructed to simulate small, medium, and large patients, each with varying lesion size and location. Nonenhancing cystic lesions were simulated using distilled water. Intermediate (10‐20 HU [Hounsfield units]) and positively enhancing masses (≥20 HU) were simulated by filling the spherical inserts in each phantom with varied levels of iodinated contrast mixed with a blood surrogate. The results were analyzed using Bayesian hierarchical models. Posterior probabilities were used to classify enhancement measured using VUE compared to TUE images as significantly less, not significantly different, or significantly higher. Enhancement measured using TUE images was considered the ground truth in this study. For simulation of nonenhancing renal lesions, enhancement values were not significantly different when using VUE versus TUE images, with posterior probabilities ranging from 0.23‐0.56 across all phantom sizes and an associated specificity of 100%. However, for simulation of intermediate and positively enhancing lesions significant differences were observed, with posterior probabilities < 0.05, indicating significantly lower measured enhancement when using VUE versus TUE images. Positively enhancing masses were categorized accurately, with a sensitivity of 91.2%, when using VUE images as the baseline. For all scenarios where iodine was present, VUE‐based enhancement measurements classified lesions with a sensitivity of 43.2%, a specificity of 100%, and an accuracy of 78.1%. Enhancement calculated using VUE images proved to be feasible for classifying nonenhancing and highly enhancing lesions. However, differences in measured enhancement for simulation of intermediately enhancing lesions demonstrated that replacement of TUE with VUE images may not be advisable for renal CT exams.

to assess the feasibility of replacing TUE images with virtual unenhanced (VUE) images derived from Dual-Energy CT datasets in renal CT exams. Eliminating TUE image acquisition could reduce patient dose and improve clinical efficiency. A rapid kVp-switching CT scanner was used to assess enhancement accuracy when using VUE compared to TUE images as the baseline for enhancement calculations across a wide range of clinical scenarios simulated in a phantom study. Three phantoms were constructed to simulate small, medium, and large patients, each with varying lesion size and location. Nonenhancing cystic lesions were simulated using distilled water. Intermediate (10-20 HU [Hounsfield units]) and positively enhancing masses (≥20 HU) were simulated by filling the spherical inserts in each phantom with varied levels of iodinated contrast mixed with a blood surrogate. The results were analyzed using Bayesian hierarchical models. Posterior probabilities were used to classify enhancement measured using VUE compared to TUE images as significantly less, not significantly different, or significantly higher. Enhancement measured using TUE images was considered the ground truth in this study. For simulation of nonenhancing renal lesions, enhancement values were not significantly different when using VUE versus TUE images, with posterior probabilities ranging from 0.23-0.56 across all phantom sizes and an associated specificity of 100%. However, for simulation of intermediate and positively enhancing lesions significant differences were observed, with posterior probabilities < 0.05, indicating significantly lower measured enhancement when using VUE versus TUE images. Positively enhancing masses were categorized accurately, with a sensitivity of 91.2%, when using VUE images as the baseline. For all scenarios where iodine was present, VUE-based enhancement measurements classified lesions with a sensitivity of 43.2%, a specificity of 100%, and an accuracy of 78.1%. Enhancement calculated using VUE images proved to be feasible for classifying nonenhancing and highly enhancing lesions. However, differences in measured enhancement for simulation of intermediately enhancing lesions demonstrated that replacement of TUE with VUE images may not be advisable for renal CT exams. Hounsfield units (HU) or more between pre-and postcontrast images was considered positive for enhancement; however, with the advent of helical CT it has been proposed that this threshold should be increased to account for helical interpolation. 3 Enhancement is now commonly characterized by a change of 20 or more in measured CT number between TUE and postcontrast images, although this number is not universally agreed upon. 2 As a result, a mass with enhancement measuring between 10-20 HU can be considered "intermediate" and may require further evaluation. 2 Renal cell carcinoma (RCC) is the most common kidney cancer in adults, accounting for approximately 90% of renal neoplasms and 3% of all adult malignancies. 4 RCC is an aggressive disease that has a 5year survival rate of 95% for Stage 1 disease, but less than 20% for Stage 4 disease. 5 The diagnosis of RCC based on the appearance of a lesion on CT imaging can vary widely in difficulty. While the diagnosis of a simple nonenhancing cyst is straightforward, classifying complex lesions can be much more challenging. 1 Studies have shown that if the patient has an enhancing renal mass, such as RCC, the mass will have a substantial noncalcified region with a CT number measuring within a range of 20-70 HU on unenhanced CT. 6 In a postcontrast scan acquired during the corticomedullary phase, studies have shown that RCC will enhance significantly more than a benign cyst (81.4 HU vs 27.4 HU, respectively) and that a difference of >42 HU in measured enhancement during the corticomedullary phase was highly predictive of RCC with 97.1% sensitivity and 85.7% specificity. 7 Dual-Energy CT (DECT) is an extension of conventional CT in which two datasets are acquired using different photon spectra nearly simultaneously. 8 This can be achieved either by using a single X-ray tube that rapidly switches between a high and low kVp at each projection angle, scanning the patient twice using different kVp, scanning the patient with dual X-ray sources and detector arrays, or using a dual-layer detector with a single X-ray source. This work uses the rapid kV-switching technique in which the X-ray tube alternates between 80 and 140 kVp at each projection with a constant tube current of approximately 600 mA to acquire co-registered dual-energy projections. 9 Benefits of rapid kV switching include excellent temporal registration, which reduces the potential for motion artifacts, and the availability of the entire scan field of view (SFOV) for DECT image acquisition. 10 A technical challenge of this technique is the rise and fall times of the high voltage waveforms, which complicates the determination of the effective energy for the high-and low-kVp projections. 11 DECT provides the ability to exploit the attenuation properties of materials to apply material decomposition techniques. This is achievable because each material has a unique attenuation coefficient, based on a unique combination of Compton and photoelectric interaction probabilities. A basis pair of materials with a large separation in linear attenuation coefficients can be chosen, commonly water and iodine, and used for material decomposition. By assuming each voxel is a weighted combination of the basis pair, the amount of iodine in each voxel can be estimated when the object is imaged at different energies. Theoretically, material decomposition can be generalized to decompose an arbitrary number of materials 12 ; however, this work focuses on basis pair decomposition. Material decomposition is the basis for the reconstruction of virtual unenhanced (VUE) images, in which the estimated volume of iodine in each voxel is replaced by an equivalent volume of blood. 13 The use of VUE imaging provides the potential to use VUE images in place of TUE images in multiphase renal CT exams. Eliminating the precontrast phase of imaging could reduce patient dose and increase patient throughput, consequently improving clinical efficiency. Previous studies have investigated the feasibility of using VUE images in place of TUE images for patients with gastric tumors, resulting in a dose reduction of 30.5%, and in the diagnosis of patients with subarachnoid haemorrhage. 14,15 For imaging of renal lesions, it has been shown that a threshold of 2 mg/cm 3 is the most accurate in distinguishing enhancing from nonenhancing lesions using iodine density images generated from DECT. 16 Other studies have investigated the feasibility of replacing precontrast images with virtual noncontrast images in renal DECT exams. 8,[17][18][19] To our knowledge, there has not been a study conducted specifically assessing the feasibility of replacing precontrast images with VUE images for evaluation of renal masses across a wide range of clinical scenarios for the rapid kVp-switching DECT technique. The aim of this phantom study was to investigate the accuracy and sensitivity when measuring enhancement using VUE images across a variety of clinical conditions to assess the potential of replacing TUE images in diagnostic renal CT exams with VUE images derived from rapid-kVswitching DECT technology.

| ME TH ODS
The technique employed in the VUE image reconstruction is believed to utilize a two-material decomposition technique, namely water and iodine. 8 It can be assumed that iodine has displaced blood in postcontrast imaging; therefore, the amount of iodine estimated in each voxel can be replaced by an equivalent volume of blood to generate a VUE image (Fig. 1). 13 This method of VUE image reconstruction is based on the assumption that materials within each voxel mix to form an ideal solution.
Phantoms were constructed and used to compare the accuracy of measured enhancement when VUE images were used as the baseline versus TUE images across a range of simulated clinical scenarios. Several variables known to affect measured CT number were evaluated. These variables included patient size, lesion size, Gemstone Spectral Imaging (GSI) protocol used, and level of simulated enhancement.
Three elliptical cylinder phantoms were designed and constructed for this study, referred to here as the small, medium, and large phantoms. Each phantom was composed of four plates made of high density polyethylene (Fig. 2). The major/minor axes of the phantoms were | 173 facilitated by fabricating three interchangeable sets of the two interior plates for each phantom. Using these plates, each phantom could contain a 1.0, 2.0, or 3.0 cm diameter spherical insert in the periphery.
Additionally, the phantom included a 1.0-cm spherical insert and a 1.0cm-diameter cylindrical insert to the left and right of a Delrin rod, which was included to represent the spine (Fig. 3).
The phantom study was designed to simulate nonenhancing, intermediately enhancing, and highly enhancing renal lesions. A conceptual summary of all enhancement scenarios simulated is given in Table 1.
Data for each scenario were acquired in the same general fashion  Table 2, where the mAs for each GSI protocol used in the study is specifically detailed. Note that the mAs used for dual energy acquisition is linked to the GSI protocol selected.
VUE images were reconstructed from DECT datasets using the Material Suppressed Iodine (MSI) algorithm.
Zero enhancement was simulated by imaging each phantom configuration with distilled water in the phantom's inserts for pre-and postcontrast imaging. Intermediate enhancement was simulated by acquiring TUE images with a water-blood surrogate mixture in each insert to achieve precontrast CT densities of 20 and 40 HU. These values were chosen because they corresponded to the lower bound and typical value for the known RCC "danger zone" of 20-70 HU on precontrast imaging, which allows for simulation of borderline lesions. 6 Note that apple juice was used as a blood surrogate in this study, as it was found to have a similar effective atomic number, density, and CT number to blood. Iodinated contrast (Optiray 320, GE Healthcare, Waukesha, WI) was added to each insert for postcontrast imaging to simulate 10 HU (low) and 20 HU (borderline) enhancement levels for each of the baseline precontrast values (Table 1).
Previous studies have found that the typical precontrast CT number of RCC is approximately 35-40 HU, 20 and that the known unenhanced CT number range for RCC is 20-70 HU. 6 Therefore, enhancing lesions were simulated by first acquiring TUE images with a waterblood surrogate mixture to achieve a CT number of 40 HU. Enhance   Note that the credible interval (CI) is a range within which lies some predetermined percentage (e.g., 95%) of the posterior distribution of the parameters given the data and can be interpreted as the Bayesian analogue of the confidence interval.

3.A | No enhancement
For simulations of simple nonenhancing lesions, CT numbers were directly compared between TUE and VUE images (Fig. 4). Measured CT numbers matched well between VUE and TUE images (Table 3), and in all cases, the precontrast CT number measured in VUE images was not significantly different from that measured in TUE images.
Measured CT numbers were lower in VUE than TUE images for the large phantom, but the difference was not significant.

3.C | Positive enhancement
ΔVUE was significantly less than ΔTUE for simulation of positively enhancing lesions ( Fig. 6 and Table 5      For completeness, it is important to note potential differences in CT numbers measured in 120-kVp SECT and 70-keV images, which were reconstructed from a DECT dataset acquired at 80 and 140 kVp. An experiment was conducted to compare CT numbers measured in monochromatic images reconstructed from the same DECT dataset with energy ranging from 65 to 75 keV to assess the impact of choice of energy for postcontrast images on measured enhancement. The maximum difference between the CT number measured in 70-keV images and any energy in the range 65-75 keV was 3.7 HU. This implies a maximum increase of 3.7 HU in measured enhancement, which still resulted in measured enhancement being significantly lower for ΔVUE compared to ΔTUE for all simulations. This indicates that the largest contributor to differences in measured enhancement resulted from the use of VUE images as the precontrast baseline.
Limitations of this study include the use of a blood surrogate as opposed to the use of blood. The MSI algorithm has been stated to replace the estimated volume of iodine with an equivalent volume of blood. 13 In light of this, the use of blood for TUE baseline image acquisition and blood/Optiray mixtures would have been the most preferable experimental approach. However, due to limitations regarding the accessibility of blood a surrogate material was used, which may have affected the results of this study. Additionally, the phantom background was made of high density polyethylene, which has a spectral curve that is different from that of soft tissue. The CT number of the phantom background was measured to be approximately −65 HU at 120 kVp, which is lower than the CT number of soft tissue and abdominal organs.
Future work includes further phantom study with blood or investigation of material suppression techniques from other DECT technologies (e.g., dual-source DECT). An additional study should be conducted to determine the optimal postcontrast phase for VUE image reconstruction. Further work could include a more thorough investigation to determine the optimal GSI protocol for VUE image reconstruction that would most closely match the corresponding TUE image.