# First evaluation of color K-edge image quality using spectral photon-counting CT combined with two contrast agents: A phantom study

Ramata Coulibaly, Antoine Robert, Angèle Houmeau, Christian A Hernandez, Agnieszka Gutwinska, Maria Nicole Antonuccio, Simon Rit, Joël Greffier, Salim A Si-Mohamed

<figure><img src="image_1.png" /></figure>

## ► To cite this version:

Ramata Coulibaly, Antoine Robert, Angèle Houmeau, Christian A Hernandez, Agnieszka Gutwinska, et al.. First evaluation of color K-edge image quality using spectral photon-counting CT combined with two contrast agents: A phantom study. Diagnostic and Interventional Imaging, 2025, 10.1016/j.diii.2025.11.005. hal-05399449

HAL Id: hal-05399449

https://hal.science/hal-05399449v1

Submitted on 4 Dec 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_3.png" /></figure>

Original article

# First evaluation of color K-edge image quality using spectral photon-counting CT combined with two contrast agents: A phantom study

Ramata Coulibaly<sup>a, ID</sup>, Antoine Robert<sup>a, ID</sup>, Angèle Houmeau<sup>a, ID</sup>, Christian A. Hernandez<sup>a, ID</sup>, Agnieszka Gutwinska<sup>a, ID</sup>, Maria Nicole Antonuccio<sup>b, ID</sup>, Simon Rit<sup>a, ID</sup>, Joël Greffier<sup>c, 1, ID</sup>, Salim A. Si-Mohamed<sup>a,d, 1,* ID</sup>

## ARTICLE INFO

### Keywords:

Color K-edge

Contrast agents

Image quality

Phantom study

Spectral photon counting CT

## ABSTRACT

**Purpose:** The purpose of this study was to assess the image quality of color K-edge imaging obtained with a spectral photon-counting CT (SPCCT) scanner using a spectral phantom with a mixture of iodine-based and gadolinium-based contrast agents.

**Materials and methods:** A clinical SPCCT scanner prototype was used to scan a spectral phantom. Three dedicated cavities were filled with three contrast agents including iodine alone, gadolinium alone and a mixture of both. Two concentrations of 0.5 and 2 mg/mL were evaluated using nine helical PCCT scans at 120 kVp and 150 mAs. Conventional, color iodine and color K-edge gadolinium images were obtained through a material decomposition algorithm using three basis materials (water, iodine, gadolinium). Attenuation (in Hounsfield unit [HU]), iodine and gadolinium concentrations and task-based transfer function (TTF) were measured on each cavity and image. The noise power spectrum (NPS) was calculated on the phantom's background.

**Results:** Color K-edge imaging differentiated iodine and gadolinium but underestimated their concentrations. Gadolinium concentrations were underestimated by 9.4 ± 2.2 (standard deviation [SD]) % and 9.2 ± 1.0 (SD) % for gadolinium alone, 14.9 ± 2.3 (SD) % and 11.4 ± 1.2 (SD) % for the mixture, at 0.5 and 2 mg/mL, respectively. Similar TTF values at 50 % were found for color iodine (0.43 ± 0.01 [SD] mm⁻¹) and color K-edge gadolinium (0.45 ± 0.03 (SD) mm⁻¹) images for respective cavities at 2 mg/mL but the lowest values were found for color K-edge gadolinium images (0.43 ± 0.01 [SD] mm⁻¹ vs. 0.29 ± 0.01 [SD] mm⁻¹) at 0.5 mg/mL. The value of noise magnitude was 24.75 HU, 0.06 mg/mL and 0.03 mg/mL for conventional, color iodine and color K-edge gadolinium images, respectively.

**Conclusion:** Color K-edge imaging helps distinguish between contrast agents while being associated with lownoise magnitude, high-frequency spatial noise and high spatial resolution.

## 1. Introduction

Color K-edge imaging is a new type of imaging method that can be used in combination with spectral photon-counting computed tomography (SPCCT) 1,2. This technique, often referred to as "mono-color" or

"multicolor" imaging, adds a new dimension to conventional imaging, offering specific, absolute quantification of one or several chemical elements [3–11]. For example, it can be used to differentiate two commonly-used iodine and gadolinium-based agents in a single acquisition on liver, making it possible to distinguish between arterial and

**Abbreviations:** CNR, Contrast-to-noise ratio; f₅₀, Spatial frequency corresponding to TTF at 50%; fₚeak, Spatial frequency of the NPS peak; HRB, High-resolution reconstruction kernel; HU, Hounsfield unit; NPS, Noise power spectrum; ROI, Region of interest; SD, Standard deviation; SNR, Signal-to-noise ratio; SPCCT, Spectral photon-counting computed tomography; TTF, Task-based transfer function; WL, Windows level; WW, Windows width.

* Corresponding author.

E-mail address: salim.si-mohamed@chu-lyon.fr (S.A. Si-Mohamed).

¹ Salim A. Si-Mohamed and Joël Greffier are co-last senior authors.

<footnote><sup>a</sup> Université de Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, INSERM, CREATIS UMR 5220, U1206, 69100 Villeurbanne, France</footnote>

<footnote><sup>b</sup> CT Clinical Science, Philips, 92150 Suresnes, France</footnote>

<footnote><sup>c</sup> Department of Medical Imaging, CHU Nîmes, Univ Montpellier, Nîmes Medical Imaging Group, EA 2992, 30029 Nîmes, France</footnote>

<footnote><sup>d</sup> Department of Radiology, Hôpital Louis Pradel, Hospices Civils de Lyon, 69677 Bron, France</footnote>
portal venous phases of enhancement [12]. This is explained by the specific identification of the photoelectric signature of an atom between the K-electronic layer and photons [13]. It has been developed in parallel with photon-counting detectors, which are able to individually quantify photon energy and classify it into several energy bins [1]. Additionally, color K-edge imaging benefits from other features of photon-counting detector technology such as high spatial and contrast resolution, dose efficiency, reduction of electronic noise and greater weighting of low-energy photons [1]. These combined capabilities may be devoted to emerging applications that require high-spatial resolution, specific quantitative imaging. Therefore, evaluating color K-edge image quality is essential to ensure that its unique ability is reliable for emergent applications, such as molecular, functional, diagnostic and theranostic imaging applications. Nevertheless, color K-edge imaging, like any imaging method, requires an image quality assessment to ensure accurate and efficient diagnoses.

Image quality evaluation is a specific area that assesses several technical parameters, such as spatial resolution, noise magnitude and texture, and contrast [14]. This evaluation may rely on non-specific metrics such as modulation transfer function calculated for example on a tungsten wire, and noise and contrast-to-noise ratio (CNR) measured on an image. However, these metrics are particularly suited to filtered-back projection images whereas current SPCCCT images, including color K-edge images, are reconstructed using iterative reconstruction or deep-learning de-noising algorithms, which are known to have non-linear and non-stationary properties [15,16]. To account for this, advanced metrics measuring the noise power spectrum (NPS) as a marker of noise magnitude and texture, and task-based transfer function (TTF) as a marker of spatial resolution have been validated on conventional and spectral CT images [16,17]. They present the benefit of taking into account the radiation dose and contrast in the images, which are representative of clinical conditions. Moreover, they are key prerequisites for assessing the detectability of a lesion as they support accurate and reproducible quantitative evaluation of SPCCCT technology imaging [18–20]. However, these metrics have not yet been used to evaluate color K-edge imaging using one or several contrast agents.

The purpose of this study was to assess the image quality of color K-edge imaging obtained with a SPCCCT scanner using a spectral phantom with a mixture of two humanly-approved iodine and gadolinium-based contrast agents.

## 2. Materials and method

### 2.1. Spectral phantom

A spectral custom-made 27-cm diameter section phantom ("Color iQCT") with five fillable cavities arranged in a circle on a Polystone® background was used. The Polystone® background was selected for its attenuation properties similar to adipose tissue, providing clinically relevant contrast conditions. Although it presents minor microscopic inhomogeneity, it ensures stable and reproducible spectral measurements.

Two cavities were filled with water and the remaining three cavities were filled with iodine and gadolinium-based contrast agents as follows: (i), iodine alone (I); (ii), gadolinium alone (Gd); and (iii), a mixture of iodine and gadolinium (I+Gd), in which the volumes of each agent were equal. This set was then duplicated in two ways based on the concentrations of the contrast agents. In the first configuration, the cavities were filled with low concentrations of 0.5 mg/mL per agent (i.e., I-0.5 mg/mL, Gd-0.5 mg/mL, and [Gd+I]-0.5 mg/mL). In the second configuration, the cavities were filled with medium concentrations of 2 mg/mL per agent (i.e., I-2 mg/mL, Gd-2 mg/mL, and [Gd+I]-2 mg/mL). Note that in cavity 3, the mixture contained the same concentration per agent as in cavities 1 and 2, so the overall concentration corresponded to the sum of each agent concentration.

### 2.2. Spectral photon-counting CT (SPCCT)

The SPCCCT system was a clinical prototype (Philips Healthcare) equipped with cadmium-zinc-telluride-based photon-counting detectors. This system directly converted incoming photons into energy and classified them into five specific energy bins using following thresholds: 30, 51, 62, 72 and 81 keV. It offered a 500 mm in-plane field-of-view and a pixel size of 270 × 270 µm² at the isocenter. Further details can be found in a previous study [21].

### 2.3. Acquisitions and reconstruction parameters

For each phantom configuration, nine helical scans were obtained at 120 kVp and 150 mAs with a rotation time of 0.33 s, corresponding to a volume computed tomography dose index (CTDIvol) of 15.1 mGy. A beam collimation of 64 × 0.275 mm and a pitch factor of 0.955 were set up.

Reconstruction parameters for high-resolution images were used, more specifically, a 512 × 512 mm matrix, a 300 mm field of view, 0.58 mm³ isotropic voxels, and a high-resolution reconstruction kernel (i.e., HRB, with a cut-off filter at 14.6 mm⁻¹). Level 6 of a hybrid iterative reconstruction algorithm (iDose⁴) was used for conventional images and spectral images. No pixel binning was applied to the conventional images, and two-by-two binning was applied to the spectral images. A three-material decomposition based on a maximum likelihood decomposition was used, consisting of two non-K-edge materials (water and iodine), and one K-edge material (Gd) as previously used [7,9,12].

### 2.4. Signal- (or concentration) to-noise ratio

For each configuration, the same slice was selected across the nine acquisitions and three circular regions of interest (ROIs) of 4.1 cm² were placed on the three cavities on that slice. The mean (Mean<sub>ROI</sub>) and standard deviation (SD) (Standard deviation<sub>ROI</sub>) of pixels were extracted from each ROI. HU values were extracted for conventional images and contrast agent concentration for color iodine and color K-edge gadolinium images in mg/mL. The signal-to-noise ratio (SNR) or concentration-to-noise ratio was then calculated for each acquisition using the following equation (Eq. (1)) [7]:

Signal – (or concentration) to – noise ratio

$$= \left| \frac{Mean_{ROI}}{SD_{ROI}} \right| (HU \text{ or mg/mL}) \qquad (1)$$

where Mean<sub>ROI</sub>, and SD<sub>ROI</sub> refer to the mean pixel value and the standard deviation (SD) within the selected ROI, respectively. Finally, the average value and the SD of the SNR and concentration-to-noise ratios were computed across the nine acquisitions.

### 2.5. Task-based image quality assessment

The NPS and TTF were computed using iQMetrix-CT software v1.2 [18]. For each configuration, the NPS and TTF were computed on all the data from the nine acquisitions. This choice was made to improve the accuracy of the TTF calculation by reducing the noise effect. So, for each configuration, only one NPS curve and one TTF curve per cavity studied were calculated. Therefore, it was not possible to make comparisons between NPS and TTF using a statistical test.

#### 2.5.1. Noise power spectrum

The NPS was calculated on the homogeneous section (Polystone®) of the phantom (Fig. 1). Four square ROIs of 66 × 66 pixels were placed on 54 consecutive axial slices (six slices for each of the nine acquisitions). The NPS was then computed using the following equation (Eq. (2)):
<figure><img src="image_4.png" /><figcaption>Fig. 1. Description of the regions of interest (ROIs) locations used to calculate the noise power spectrum (NPS) and the task-based transfer function (TTF) on a spectral custom-made 27-cm diameter section phantom. (A), Red squares correspond to NPS ROIs placed on the Polystone® background to assess noise magnitude and texture. (B), blue circles and red circles correspond to TTF ROIs drawn around the contrast-filled cavities (iodine, gadolinium, and iodine + gadolinium) to assess spatial resolution.</figcaption></figure>

$$NPS_{2D}(f_x, f_y) = \frac{\Delta_x \Delta_y}{L_x L_y} \frac{1}{N_{ROI}} \sum_{i=1}^{N_{ROI}} |FFT_{2D}\{ROI_i(x, y) - FIT_i(x, y)\}|^2 \quad (2)$$

where $\Delta_x$ and $\Delta_y$ are the pixel size in the x and y directions; $FFT_{2D}$ is the fast Fourier transform; $L_x$ and $L_y$ are the lengths of the ROIs in the x- and y-directions; $N_{ROI}$ is the number of ROIs; $ROI_i(x, y)$ is the mean pixel value measured of a ROI at the position $(x, y)$ and $FIT_i(x, y)$ is a 2nd order polynomial fit of $ROI_i(x, y)$.

$NPS_{1D}$ curves were computed by averaging the radial profiles of $NPS_{2D}$ in the axial plane. The square root of the area under each $NPS_{2D}$ curve was used to assess noise magnitude and the spatial frequency of the NPS peaks ($f_{peak}$) on the $NPS_{1D}$ curves to measure noise texture in all images.

### 2.5.2. Task-transfer function

For both phantom configurations, the TTF was computed on 180 consecutive axial slices (20 slices for each of the nine acquisitions) for the first configuration and 126 slices (14 slices for each of the nine acquisitions) for the second. The TTF was calculated using the circular edge technique on each of the three cavities containing the contrast agent in the conventional images (Fig. 1). The TTF was calculated only on the two iodine-containing cavities for color iodine images and the two gadolinium-containing cavities for color K-edge gadolinium images.

To quantify the loss/benefit of spatial resolution, the spatial frequency at which the TTF was reduced by 50 % ($f_{50}$) on the TTF curve was measured.

## 2.6. Statistical analysis

Statistical analysis was performed using version 4.2.2 of R software [22]. HU values, SNR, concentration-to-noise ratios and concentrations of iodine and gadolinium were expressed as means ± SD [23]. The Wilcoxon signed-rank test was used to compare HU values and agent concentrations between cavities and the two phantom configurations. A $P$ value < 0.05 was considered to indicate statistically significant difference.

# 3. Results

## 3.1. HU values and iodine or gadolinium concentrations

On conventional images, HU values were greater for 2.0 mg/mL cavities than for 0.5 mg/mL cavities ($P = 0.004$) (Table 1). For all configuration, HU values were greater for cavities containing the mixture ($[Gd+I] - 0.5$ mg/mL, and $[Gd+I] - 2$ mg/mL) than for the cavities containing only one agent (Gd-0.5 mg/mL, I-0.5 mg/mL, Gd-2 mg/mL, and I-2 mg/mL) ($P = 0.004$). HU values were greater for the cavities containing gadolinium (Gd-0.5 mg/mL, and Gd-2 mg/mL) ($P = 0.004$) than for cavities containing iodine (I-0.5 mg/mL, and I-2 mg/mL) ($P = 0.004$).

On color iodine images, iodine concentrations were underestimated by $-70.9 \pm 1.8$ (SD) % at 0.5 mg/mL, $-28.1 \pm 1.6$ (SD) % at 2.0 mg/mL for the cavity containing iodine alone and $-71.7 \pm 5.5$ (SD) % and $-21.9 \pm 1.7$ (SD) % for the cavity containing the mixtures, respectively (Table 1). Concentrations were similar ($P = 0.527$) for the cavity containing the mixture and the cavity containing iodine alone at 0.5 mg/mL (I-0.5 mg/mL) but greater ($8.6 \pm 3.7$ [SD] %) ($P = 0.009$) for the cavity containing the mixture than for the cavity containing iodine alone at 2.0 mg/mL (I-2 mg/mL).

On color K-edge gadolinium images, gadolinium concentrations were underestimated by $-9.4 \pm 2.2$ (SD) % at 0.5 mg/mL, $-9.2 \pm 1.0$ (SD) % at 2.0 mg/mL for the cavity containing gadolinium alone, and $-14.9 \pm 2.3$ (SD) % and $-11.4 \pm 1.2$ (SD) % for the cavities containing the mixture, respectively (Table 1). Concentrations were lower for cavity containing the mixture than for cavities containing gadolinium either for Gd-0.5 mg/mL ($-4.9 \pm 3.4$ [SD] %) ($P = 0.014$) and Gd-2 mg/

<figure><img src="image_5.png" /><figcaption>Fig. 2. Graphs show signal-to-noise ratio and concentration-to-noise ratio curves measured on conventional images (A), color iodine images (B), and color K-edge gadolinium images (C).</figcaption></figure>
<caption>**Table 1**</caption>

Hounsfield unit values and iodine or gadolinium concentrations measured on the cavities for all types of images and both phantom's configurations.

<table><thead><tr><th>Configurations</th><th>Cavities</th><th>Conventional images HU values</th><th>Color K-edge Gd images Gd concentration (mg/mL)</th><th>Color iodine images Iodine concentration (mg/mL)</th></tr></thead><tbody><tr><td rowspan="4">0.5 mg/mL</td><td>Iodine only (I-0.5 mg/mL)</td><td>26.6 ± 1.7</td><td>0.00 ± 0.02</td><td>0.15 ± 0.02</td></tr><tr><td>Gd only (Gd-0.5 mg/mL)</td><td>30.0 ± 1.3</td><td>0.46 ± 0.01</td><td>-0.25 ± 0.02</td></tr><tr><td>Mixture ([Gd+I]-0.5 mg/mL)</td><td>47.0 ± 1.2</td><td>0.43 ± 0.02</td><td>0.14 ± 0.02</td></tr><tr><td>Background</td><td>-94.3 ± 0.4</td><td>0.14 ± 0.00</td><td>-1.45 ± 0.01</td></tr><tr><td rowspan="4">2.0 mg/mL</td><td>Iodine only (I-2 mg/mL)</td><td>69.8 ± 1.3</td><td>-0.02 ± 0.03</td><td>1.44 ± 0.03</td></tr><tr><td>Gd only (Gd-2 mg/mL)</td><td>89.8 ± 1.7</td><td>1.82 ± 0.02</td><td>-0.24 ± 0.03</td></tr><tr><td>Mixture ([Gd+I]-2 mg/mL)</td><td>146.0 ± 1.3</td><td>1.77 ± 0.02</td><td>1.56 ± 0.03</td></tr><tr><td>Background</td><td>-95.9 ± 0.1</td><td>0.14 ± 0.01</td><td>-1.48 ± 0.00</td></tr></tbody></table>

Gd indicates gadolinium; HU indicates Hounsfield unit.

$$ \text{mL} (-2.4 \pm 1.9 \text{ [SD] \%}) (P = 0.024). $$

### 3.2. Signal- (or concentration) to-noise ratios

For all conditions, the SNRs and the concentration-to-noise ratios were significantly greater (P = 0.004) for the 2.0 mg/mL configuration than for the 0.5 mg/mL configuration (Table 2 and Fig. 2).

For conventional images and both phantom configurations, the SNRs were significantly greater for cavities containing the mixture than for the other two cavities (P = 0.004) and significantly greater for cavities containing gadolinium alone than for cavities containing iodine alone (P = 0.008 for 0.5 mg/mL, and P = 0.004 for 2 mg/mL). For color iodine images, concentration-to-noise ratios were similar between cavities containing iodine and cavities containing the mixture at 0.5 mg/mL (P > 0.999) and the same pattern was found at the 2.0 mg/mL concentration

<caption>**Table 2**</caption>

Signal or concentration-to-noise ratios measured on specific cavities for all types of images and both phantom's configurations.

<table><thead><tr><th>Configurations</th><th>Cavities</th><th>Conventional images</th><th>Color K-edge Gd images</th><th>Color iodine images</th></tr></thead><tbody><tr><td rowspan="6">0.5 mg/mL</td><td>Iodine only (I-0.5 mg/mL)</td><td>1.20 ± 0.12</td><td>NA</td><td>2.86 ± 0.62</td></tr><tr><td>Gd only (Gd-0.5 mg/mL)</td><td>1.43 ± 0.15</td><td>24.43 ± 8.12</td><td>NA</td></tr><tr><td>Mixture ([Gd+I]-0.5 mg/mL)</td><td>2.09 ± 0.15</td><td>17.15 ± 3.92</td><td>2.82 ± 0.66</td></tr><tr><td>P values Mixture vs. Gd only</td><td>0.004</td><td>0.020</td><td>-</td></tr><tr><td>P values Mixture vs. Iodine only</td><td>0.004</td><td>-</td><td>> 0.999</td></tr><tr><td>P values Gd only vs. Iodine only</td><td>0.008</td><td>-</td><td>-</td></tr><tr><td rowspan="6">2.0 mg/mL</td><td>Iodine only (I-2 mg/mL)</td><td>3.11 ± 0.29</td><td>NA</td><td>19.83 ± 2.03</td></tr><tr><td>Gd only (Gd-2 mg/mL)</td><td>4.00 ± 0.19</td><td>45.06 ± 7.13</td><td>NA</td></tr><tr><td>Mixture ([Gd+I]-2 mg/mL)</td><td>6.17 ± 0.46</td><td>41.60 ± 13.13</td><td>18.61 ± 4.17</td></tr><tr><td>P values Mixture vs. Gd-2 mg/mL</td><td>0.004</td><td>0.426</td><td>-</td></tr><tr><td>P values Mixture vs. Iodine only</td><td>0.004</td><td>-</td><td>0.496</td></tr><tr><td>P values Gd only vs. Iodine only</td><td>0.004</td><td>-</td><td>-</td></tr></tbody></table>

Gd indicates gadolinium; I indicates iodine; NA indicates not applicable. Bold indicates significant P values.

$$ (P = 0.496). $$

For color K-edge gadolinium images and at 0.5 mg/mL, the concentration-to-noise ratios were significantly greater with the cavity containing gadolinium alone at 5 mg/mL than for the cavity containing the mixture (P = 0.020) but similar at 2.0 mg/mL (P = 0.426).

### 3.3. Noise power spectrum

Noise magnitude values were 24.75 HU, 0.06 mg/mL and 0.03 mg/ mL on conventional, color iodine and color K-edge gadolinium images, respectively (Table 3).

For all image types, two NPS peaks were found on the NPS curves (Fig. 3). The spatial frequency of the first NPS peak was 0.03 mm⁻¹ for all three image types (Table 3). The second NPS peak was placed at close spatial frequencies in color iodine images (0.39 mm⁻¹) and color K-edge gadolinium images (0.41 mm⁻¹) whereas, for conventional images, it was at a lower spatial frequency (0.34 mm⁻¹).

### 3.4. Task-based transfer function

On conventional images, the f₅₀ values remained within a narrow range across all cavities for the configuration at 2 mg/mL from 0.45 to 0.46 mm⁻¹ (Table 4). At 0.5 mg/mL, the lowest f₅₀ values were found for the cavity containing iodine alone (0.40 mm⁻¹) and the greatest value for the cavity containing the mixture (0.44 mm⁻¹).

On color iodine images, the f₅₀ (TTF at 50 %) values were similar between the cavity containing the mixture and the cavity containing iodine alone at 0.5 mg/mL (0.43 mm⁻¹ vs. 0.42 mm⁻¹) but greater with the mixture than for the cavity containing iodine alone at 2 mg/mL (0.48 mm⁻¹ vs. 0.43 mm⁻¹) (Table 4, Fig. 4).

On color K-edge gadolinium images, the f₅₀ values were similar for the cavity containing the mixture and the cavity containing gadolinium alone (0.29 mm⁻¹ vs. 0.30 mm⁻¹ at 0.5 mg/mL and 0.42 mm⁻¹ vs. 0.43 mm⁻¹ at 2 mg/mL) (Fig. 4).

Improvements in spatial resolution relative to higher concentration was absent in the color iodine images, minimal in the conventional images, and greater in the color K-edge gadolinium images, where f₅₀ increased by 45.7 ± 0.7 [SD] % for both the gadolinium-only cavity

<caption>**Table 3**</caption>

Values of noise magnitude and spatial frequency of noise power spectrum peaks obtained for all types of images.

<table><thead><tr><th>Variable</th><th>Conventional images</th><th>Color K-edge Gd images</th><th>Color iodine images</th></tr></thead><tbody><tr><td>Noise magnitude</td><td>24.75 HU</td><td>0.03 mg/mL</td><td>0.06 mg/mL</td></tr><tr><td>f<sub>peak</sub> (mm<sup>-1</sup>) first peak</td><td>0.03</td><td>0.03</td><td>0.03</td></tr><tr><td>f<sub>peak</sub> (mm<sup>-1</sup>) second peak</td><td>0.34</td><td>0.41</td><td>0.39</td></tr></tbody></table>

Gd indicates gadolinium; HU indicates Hounsfield unit.
<figure><img src="image_6.png" /><figcaption>Fig. 3. Graphs show noise power spectrum (NPS) curves measured on conventional images (A), color iodine images (B), and color K-edge gadolinium images (C). The dashed line indicates the second spatial frequency of the NPS peak frequency ($f_{peak}$) for each image. HU indicates Hounsfield unit. NPS indicates noise power spectrum.</figcaption></figure>

<caption>**Table 4**</caption>

Task-based transfer function values at 50% ($f_{50}$) obtained for all types of images and both phantom's configurations.

<table><thead><tr><th>Configurations</th><th>f<sub>50</sub> (mm<sup>-1</sup>)</th><th>Conventional images</th><th>Color K-edge Gd images</th><th>Color iodine images</th></tr></thead><tbody><tr><td rowspan="3">0.5 mg/mL</td><td>Iodine only (I-0.5 mg/mL)</td><td>0.401</td><td>NA</td><td>0.420</td></tr><tr><td>Gd only (Gd-0.5 mg/mL)</td><td>0.424</td><td>0.297</td><td>NA</td></tr><tr><td>Mixture ([Gd+I]-0.5 mg/mL)</td><td>0.437</td><td>0.292</td><td>0.432</td></tr><tr><td rowspan="3">2 mg/mL</td><td>Iodine only (I-2 mg/mL)</td><td>0.450</td><td>NA</td><td>0.431</td></tr><tr><td>Gd only (Gd-2 mg/mL)</td><td>0.457</td><td>0.434</td><td>NA</td></tr><tr><td>Mixture ([Gd+I]-2 mg/mL)</td><td>0.450</td><td>0.424</td><td>0.477</td></tr></tbody></table>

Gd indicates gadolinium; NA indicates not applicable.

(from 0.297 to 0.434 mm⁻¹) and the mixture cavity (from 0.292 to 0.424 mm⁻¹) (Table 4 and Fig. 5).

## 4. Discussion

In the present study, color K-edge imaging using a clinical SPCCT prototype enabled the specific identification of two contrast agents, either mixed or alone, under clinical parameters in a spectral phantom. Task-based image quality assessment demonstrated high spatial

resolution and satisfactory noise performances in terms of magnitude and texture.

Color K-edge imaging has demonstrated its ability to distinguish between two contrast agents based on their physical properties, especially their linear attenuation coefficient [13]. However, only gadolinium is specifically identified based on its K-edge energy at 50.2 keV [7]. Photon-counting detectors quantify only transmitted photons, typically within the energy range of 40 to 120–140 keV in a clinical setting. In contrast, iodine has a K-edge energy of 33.2 keV, which is below the effective lower limit of this range. Thus, iodine is more susceptible to photon starvation effects. However, it is still possible to differentiate between a K-edge material, such as gadolinium, and a non-K-edge material, such as iodine, through three-material decomposition algorithms, as demonstrated by previous studies [9,11–13,24]. In line with the recent work of Rybertt et al. [25] and Holmes et al. [26] using different SPCCT platforms, our findings further confirm that clinical SPCCT systems can achieve material decomposition of iodine and gadolinium, thereby supporting the clinical translation of dual-contrast K-edge imaging.

In our study, the quantification of iodine and gadolinium shows that the concentration of each agent is underestimated, suggesting an imperfection in the spectral forward model. Nevertheless, their performance is consistently linear, making them suitable for clinical use under controlled circumstances. More particularly, we found that iodine quantification is associated with a more pronounced bias at a very low concentration, which may reflect a greater deviation due to the absence of a K-edge signature in iodine. In addition, this error was associated with a negative estimation of iodine concentration within the phantom's background whereas, theoretically, its concentration in iodine should be 0. This leads to an overestimation of the contrast between the phantom's

<figure><img src="image_7.png" /><figcaption>Fig. 4. Graphs show the task-based transfer function curves measured on conventional images (A), color iodine images (B), and color K-edge gadolinium images (C). Dashed lines indicate the spatial frequency corresponding to the task-based transfert function at 50%. I indicates iodine; Gd indicates gadolinium; TTF indicates task-based transfert function.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Spectral photon-counting CT images of a 27-cm spectral phantom (Color iQCT) filled with iodinated and gadolinium-based contrast agents at 0.5 mg/mL (A) and 2 mg/mL (B). Only the color iodine and color K-edge gadolinium images allow specific differentiation of each agent, whether present alone or in mixture. WW indicates windows width; WL indicates windows level.</figcaption></figure>

background and cavities of 133 % on average on color iodine image. Unlike the color iodine images, color K-edge gadolinium images show a slight positive estimation of gadolinium concentration in the phantom's background. This would also affect the difference in contrast between the background and cavities but with an underestimation. More interestingly, we found that the quantification of iodine or gadolinium is similar between mixture cavities and single-agent cavities, ensuring a comparable concentration-to-noise ratio. This contrasts with the results of the study by Rybertt et al. using a dual-source SPCCT system [25]. First, these researchers showed a roughly twofold decrease in CNR between mixtures and pure solutions on color iodine and gadolinium images. Second, they found that the CNR increased linearly with contrast concentrations, such as in the present study. Finally, they reported that the inserts were barely detectable for concentrations at 2.5 mg/mL of gadolinium and iodine (i.e., CNR values below 5), while the concentration-to-noise ratio shown in the present study was above 40 at 2 mg/mL. Despite the lack of comparability between these two figures of merit, this highlights a significantly greater image quality provided by the SPCCT technology used in the present study. Altogether, these findings clearly support the previous *in vitro* and *in vivo* proofs-of-concept using two agents simultaneously in the same voxels in combination with SPCCT systems [8,11,12].

Noise magnitude and noise texture are similar for color iodine and K-edge gadolinium images, and both are relatively low, around 0.03 mg/mL. Although spectral image noise is expected to be high due to quantum noise, no standard criteria exist for evaluating NPS in color K-edge imaging. In comparison, Rybertt et al. reported, on a dual-source SPCCT system, a noise magnitude at 0.60 ± 0.01 (SD) mg/mL, for both iodine and gadolinium images [25]. These values were obtained with different conditions by comparison with our present study (lower CTDI<sub>vol</sub> of 8 mGy, greater slice thickness, smaller phantom with a lower water equivalent diameter at 21 cm). Therefore, our findings may indicate a satisfactory and clinically relevant noise level for upcoming clinical studies that have recently been validated for first-in-human feasibility [27]. More interestingly, in our study, the frequency of the noise was approximately 12 % to 17 % greater with color iodine and color K-edge gadolinium images than with conventional images, in favor of a higher frequency texture, respectively. This may be explained by the impact of a black background in color images, contrary to the non-specific conventional images, that provides higher contrast capabilities. Also it is worth mentioning that two peaks were noticed on all the images, which

is consistent with previous studies using this clinical prototype, although their impact in clinical studies has been judged non-specific as confirmed by coronary CT angiography studies [28].

Spatial resolution increased with higher contrast agent concentrations for all types of images. This improvement was attributed to the enhanced SNR and better contour definition, as previously shown [29–31]. The effect was minimal in conventional images due to a slight increase in SNR between low and high concentrations. This effect was absent in color iodine images, which may have been hidden by the negative value of the signal found in the phantom background. Indeed, the computation of TTF and, in particular, the edge spread function, depends strongly on the difference in contrast between the object or clinical task and its background. In comparison, color K-edge images demonstrated an expected boost in TTF between the low and high concentration cavities which concurred with the boost in concentration-to-noise ratios. It is worth mentioning that the TTF value at 50 % was half that of the conventional images at 0.5 mg/mL concentration, which may be explained by the two-fold larger binning used with color K-edge images. Nevertheless, the spatial resolution obtained with a low concentration of 0.5 mg/mL may already facilitate the specific challenge of identifying micro- to millimetric oncological or inflammatory processes which typically give a low range of enhancement [11,21]. Hence, our results suggest that clinical imaging would be feasible with a spatial resolution almost similar to that of conventional images, whereas the benefit on contrast would be much greater.

This study has some limitations. The imperfections in quantification led to inaccuracies in the estimation of the TTF, particularly for the iodine agents for which a strong negative value was found in the background. This may be explained by the inorganic tissue composition of the background which could not be included in the basis materials. No statistical comparison between NPS and TTF values was available using this methodology. The image quality was not assessed using a task-based model observer. Existing model observers, like the detectability index used to estimate the radiologist's ability to detect simulated lesions, have only been validated on conventional and spectral images and are not yet applicable to color K-edge images, which require dedicated evaluation tools still under development. This offers room for methodological development by integrating objective, quantitative assessment tools to complement subjective judgements, such as the detectability index for spectral images.

In conclusion, color K-edge imaging demonstrated its feasibility for
differentiating two contrast agents whilst being associated with a low-
magnitude and high-frequency spatial noise combined with high
spatial resolution.

Human and animal rights

Not applicable for phantom studies.

Informed consent and patient details

Not applicable for phantom studies.

Funding

This work was supported by the European Research Council Starting Grant KOLOR SPCCT Imaging (N°101,118,079).

Authors contributions

All authors attest that they meet the current International Committee of Medical Journal Editors (ICMJE) criteria for Authorship.

CRediT authorship contribution statement

**Ramata Coulibaly:** Formal analysis, Investigation, Visualization, Writing – original draft, Writing – review & editing. **Antoine Robert:** Conceptualization, Methodology, Investigation, Visualization, Writing – original draft, Supervision, Writing – review & editing. **Angèle Houmeau:** Project administration, Visualization, Writing – review & editing. **Christian A. Hernandez:** Visualization, Writing – review & editing. **Agnieszka Gutwinska:** Writing – review & editing. **Maria Nicole Antonuccio:** Visualization, Writing – review & editing. **Simon Rit:** Visualization, Writing – review & editing. **Joël Greffier:** Conceptualization, Methodology, Investigation, Visualization, Validation, Formal analysis, Data curation, Writing – original draft, Supervision, Writing – review & editing. **Salim A. Si-Mohamed:** Conceptualization, Methodology, Visualization, Validation, Resources, Data curation, Funding acquisition, Writing – original draft, Supervision, Writing – review & editing.

Declaration of competing interest

Maria Nicole Antonuccio is an employee of Philips Healthcare. She had no control on the data and did not analysis them. The other authors have no conflicts of interest to disclose.

References

[1] Greffier J, Viry A, Robert A, Khorsi M, Si-Mohamed S. Photon-counting CT systems: a technical review of current clinical possibilities. Diagn Interv Imaging 2025;106: 53-9.

[2] Greffier J, Villani N, Defez D, Dabli D, Si-Mohamed S. Spectral CT imaging: technical principles of dual-energy CT and multi-energy photon-counting CT. Diagn Interv Imaging 2023;104:167-77.

[3] Cormode DP, Roessl E, Thran A, Skajaa T, Gordon RE, Schlomka JP, et al. Atherosclerotic plaque composition: analysis with multicolor CT and targeted gold nanoparticles. Radiology 2010;256:774-82.

[4] Pan D, Schirra CO, Senpan A, Schmieder AH, Stacy AJ, Roessl E, et al. An early investigation of ytterbium nanocolloids for selective and quantitative "multicolor" spectral CT imaging. ACS Nano 2012;6:3364-70.

[5] Pan D, Schirra CO, Wickline SA, Lanza GM. Multicolor computed tomographic molecular imaging with noncrystalline high-metal-density nanobeacons: metal-nanobeacons for CT imaging. Contrast Media Mol Imaging 2014;9:13-25.

[6] Cormode DP, Si-Mohamed S, Bar-Ness D, Sigovan M, Naha PC, Balegamire J, et al. Multicolor spectral photon-counting computed tomography: *in vivo* dual contrast imaging with a high count rate scanner. Sci Rep 2017;7:4784.

[7] Si-Mohamed S, Bar-Ness D, Sigovan M, Tatard-Leitman V, Cormode DP, Naha PC, et al. Multicolour imaging with spectral photon-counting CT: a phantom study. Eur Radiol Exp 2018;2:34.

[8] Cuccione E, Chhour P, Si-Mohamed S, Dumot C, Kim J, Hubert V, et al. Multicolor spectral photon counting CT monitors and quantifies therapeutic cells and their encapsulating scaffold in a model of brain damage. Nanotheranostics 2020;4: 129-41.

[9] Cosset B, Sigovan M, Boccalini S, Farhat F, Douek P, Boussel L, et al. Bicolor K-edge spectral photon-counting CT imaging for the diagnosis of thoracic endoleaks: a dynamic phantom study. Diagn Interv Imaging 2023;104:235-42.

[10] Hernandez-Fajardo CA, Lux F, Doussineau T, Dufort S, Langlois JB, Houmeau A, et al. Color K-edge angiography with a dedicated gadolinium-based contrast agent for spectral photon-counting computed tomography. Diagn Interv Imaging 2025; 106:421-429.

[11] Si-Mohamed S, Sigovan M, Hsu JC, Tatard-Leitman V, Chalabreysse L, Naha PC, et al. *In vivo* molecular K-edge imaging of atherosclerotic plaque using photon-counting CT. Radiology 2021;300:98-107.

[12] Si-Mohamed S, Tatard-Leitman V, Laugerette A, Sigovan M, Pfeiffer D, Rummeny EJ, et al. Spectral photon-counting computed tomography (SPCCT): *in vivo* single-acquisition multi-phase liver imaging with a dual contrast agent protocol. Sci Rep 2019;9:8458.

[13] Roessl E, Brendel B, Engel KJ, Schlomka JP, Thran A, Proksa R. Sensitivity of photon-counting-based K-edge imaging in X-ray computed tomography. IEEE Trans Med Imaging 2011;30:1678-90.

[14] Boccalini S, Si-Mohamed SA, Lacombe H, Diaw A, Varasteh M, Rodesch PA, et al. First In-human results of computed tomography angiography for coronary stent assessment with a spectral photon counting computed tomography. Invest Radiol 2022;57:212-21.

[15] Greffier J, Fernandez A, Macri F, Freitag C, Metge L, Beregi JP. Which dose for what image? Iterative reconstruction for CT scan. Diagn Interv Imaging 2013;94: 1117-21.

[16] Greffier J, Frandon J, Larbi A, Beregi JP, Pereira F. CT iterative reconstruction algorithms: a task-based image quality assessment. Eur Radiol 2020;30:487-500.

[17] Samei E, Bakalyar D, Boedeker KL, Brady S, Fan J, Leng S, et al. Performance evaluation of computed tomography systems: summary of AAPM Task Group 233. Med Phys 2019;46:e735-56.

[18] Greffier J, Barbotteau Y, Gardavaud F. iQMetrix-CT: new software for task-based image quality assessment of phantom CT images. Diagn Interv Imaging 2022;103: 555-62.

[19] Si-Mohamed SA, Greffier J, Miailhes J, Boccalini S, Rodesch PA, Vuillod A, et al. Comparison of image quality between spectral photon-counting CT and dual-layer CT for the evaluation of lung nodules: a phantom study. Eur Radiol 2022;32: 524-32.

[20] Si-Mohamed S, Thiviolet A, Bonnot P-E, Bar-Ness D, Képénékian V, Cormode DP, et al. Improved peritoneal cavity and abdominal organ imaging using a biphasic contrast agent protocol and spectral photon counting computed tomography K-edge imaging. Invest Radiol 2018;53:629-39.

[21] Greffier J, Si-Mohamed SA, Lacombe H, Labour J, Djabli D, Boccalini S, et al. Virtual monochromatic images for coronary artery imaging with a spectral photon-counting CT in comparison to dual-layer CT systems: a phantom and a preliminary human study. Eur Radiol 2023;33:5476-88.

[22] R Development Core Team. R: a language and environment for statistical computing. 2008.

[23] O'Shaughnessy E, Detrinidad E, Soyer P, Lecler A. An introductory guide to statistics for the radiologist. Diagn Interv Imaging 2025;106:49-52.

[24] Symons R, Krauss B, Sahbaee P, Cork TE, Lakshmanan MN, Bluemke DA, et al. Photon-counting CT for simultaneous imaging of multiple contrast agents in the abdomen: an *in vivo* study. Med Phys 2017;44:5120-7.

[25] Rybertt MV, Liu LP, Sahbaee P, Nowak T, Mathew M, Litt HI, et al. K-edge imaging using a clinical dual-source photon-counting CT system. medRxiv 2025. https:// doi.org/10.1101/2025.08.21.25333798.

[26] Holmes TW, Yin Z, Bujila R, Madhav P, Kim C, Maltz JS, et al. Ultrahigh-resolution K-edge imaging of coronary arteries with prototype deep-silicon photon-counting CT: initial results in phantoms. Radiology 2024;311:e231598.

[27] Boccalini S, Si-Mohamed S, Erhard K, Bordet M, Yagil Y, Douek P. First in-human gadolinium K-edge imaging with spectral photon-counting CT. Diagn Interv Imaging 2025;106:192-5.

[28] Si-Mohamed SA, Boccalini S, Lacombe H, Diaw A, Varasteh M, Rodesch PA, et al. Coronary CT angiography with photon-counting CT: first-in-human results. Radiology 2022;303:303-13.

[29] Rotzinger DC, Racine D, Beigelman-Aubry C, Alfudhili KM, Keller N, Monnin P, et al. Task-based model observer assessment of a partial model-based iterative reconstruction algorithm in thoracic oncologic multidetector CT. Sci Rep 2018;8: 36045.

[30] Racine D, Ryckx N, Ba A, Becc e F, Viry A, Verdun FR, et al. Task-based quantification of image quality using a model observer in abdominal CT: a multicentre study. Eur Radiol 2018;28:5203-10.

[31] Sebti Y, Si-Mohamed S, Aid R, Geinguenaud F, Chalal M, Lalatonne Y, et al. Optical and X-ray attenuation properties of hafnium oxide nanoparticles surface functionalized with fucoidan: toward the early diagnosis of atherothrombotic diseases. Mater Adv 2023;4:1011-20.