Determination of iodine detectability in different types of multiple-energy images for a photon-counting detector computed tomography system

Optimization of Low-Contrast Detectability in Abdominal Imaging: A Comparative Analysis of PCCT, DECT, and SECT Systems

BACKGROUND

Clear representation of anatomy is essential in the assessment of pathology in computed tomography (CT). With the introduction of photon-counting CT (PCCT) and more advanced iterative reconstruction (IR) algorithms into clinical practice, there is potential to improve low-contrast detectability in CT protocols. As such, it is necessary to perform task-based assessments to optimize protocols and compare image quality between PCCT and energy-integrating CT, like dual-energy CT (DECT) and single-energy CT (SECT).

PURPOSE

This work aimed to assess low-contrast detectability in abdominal protocols used in clinical PCCT, DECT, and SECT, using both model and human observers.

METHODS

Data were acquired with the standard resolution scan mode on a PCCT (NAEOTOM Alpha, Siemens Healthineers, Forchheim, Germany) and a DECT/SECT (SOMATOM Force, Siemens Healthineers, Forchheim, Germany). Detectability was investigated in the CTP 515 low-contrast module of the Catphan 600 phantom, which was surrounded by a fat annulus to simulate an abdomen and resulted in a water equivalent diameter of 298 mm. Supra-slice contrast rods with a nominal 1.0% contrast and diameters of 4, 6, 9, and 15 mm were used. Factory abdominal protocols were adjusted to acquire images with various tube potentials (70, 90, 120, and 140 kV in PCCT; 70/150Sn and 80/150Sn kV in DECT; 100 and 120 kV in SECT), virtual monoenergetic image (VMI) energy levels (40 to 140 keV in PCCT and DECT), doses (5, 10 mGy in PCCT; 10 mGy in DECT and SECT), and IR settings (Br40 kernel, no quantum IR (QIR) and QIR levels 1 to 4 in PCCT; advanced modeled IR (ADMIRE) level 3 in DECT and SECT). Mixed DECT (linear blending of the images at two tube voltages) images were also reconstructed. The noise power spectrum and task transfer function of each scan protocol were quantified; the detectability index for each protocol was also determined using in-house implementations of model observers (non-prewhitening matched filters with internal noise, NPWI, and with an eye filter and internal noise, NPWEI) and human observers (in-house four-alternative forced choice, scoring with 95% confidence intervals).

RESULTS

Results show that the image noise is minimized at a VMI energy corresponding to the applied spectrum's mean energy in PCCT and with VMI settings of 70 and 80 keV for 70/150Sn and 80/150Sn tube potential pairs, respectively, in DECT. With respect to the human observer detectability index calculations, the normalized root-mean-square error for the NPWI and NPWEI model observers was 5% and 12%, respectively. PCCT VMI improves low-contrast detectability. Additionally, detectability can be matched between PCCT protocols by increasing the QIR strength level when reducing the dose. Not only does PCCT VMI outperform DECT VMI, but also DECT VMI outperforms DECT mixed imaging in improving low-contrast detectability.

CONCLUSIONS

Low-contrast detectability is optimized when the appropriate VMI energy level is selected in PCCT and DECT to minimize image noise. PCCT improves low-contrast detectability and may allow for dose reduction in abdominal protocols compared to both DECT and SECT. The non-prewhitening model observer with internal noise better quantified low-contrast detectability without the inclusion of an eye filter.

Delineation of the brachial plexus by contrast-enhanced photon-counting detector CT and virtual monoenergetic images

OBJECTIVES

To improve the image quality of the brachial plexus in photon-counting detector CT (PCD-CT) using contrast media and virtual monoenergetic images (VMI).

MATERIALS & METHODS

We retrospectively analyzed contrast-enhanced neck PCD-CT images scanned in March-July 2023. Unenhanced and contrast-enhanced images were compared, and then 40-, 70-, and 100-keV VMIs were compared. The qualitative evaluation used a five-point Likert scale regarding overall image quality (IQ), sharpness, and noise. The quantitative evaluation used the standard deviation (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). Freidman's test and one-way ANOVA were performed.

RESULTS

Forty patients (65 years ± 17, 21 males) were included. The median scores [interquartile range, IQR] for the unenhanced and contrast-enhanced groups were IQ, 3 [2,3] and 4 [3,4] (P < 0.001); sharpness, 3 [2,3] and 4 [3,4] (P < 0.001); and noise, 3 [3,4] and 3 [3,4] (P = 0.63). Mean ± SD scores were SD, 6.7 ± 1.4 and 6.7 ± 1.0 (P = 0.95); SNR, 5.1 ± 1.2 and 5.4 ± 1.4 (P = 0.04); and CNR, 4.8 ± 1.5 and 8.1 ± 2.3 (P < 0.001). The 40-, 70-, and 100-keV groups' IQ were 2 [2,3], 4 [3,4], and 3 [3,4]; their sharpness scores were 2 [2,3], 3 [3,4], and 3 [2,3] (all, P < 0.05). Those for noise were 2 [1-3], 3 [3,4], and 4 [3,4] (all, P < 0.001 except for 70-keV vs.100-keV: P = 0.16). The SDs were 13.1 ± 2.5, 7.5 ± 1.2, and 6.0 ± 1.1. The SNRs were 4.2 ± 1.9, 5.0 ± 1.2, and 5.7 ± 1.5 (all, P < 0.001). The CNRs were 8.7 ± 4.0, 6.8 ± 2.3, and 6.5 ± 2.3 (all, P < 0.001 except for 70-keV vs.100-keV: P = 0.51).

CONCLUSION

Contrast-enhanced PCD-CT and VMIs provided good delineation of the brachial plexus.

Virtual monoenergetic images from dual-energy CT: systematic assessment of task-based image quality performance

Background

To compare task-based image quality (TB-IQ) among virtual monoenergetic images (VMI) and linear-blended images (LBI) from dual-energy CT as a function of contrast task, radiation dose, size, and lesion diameter.

Methods

A TB-IQ phantom (Mercury Phantom 4.0, Sun Nuclear Corporation) was imaged on a third-generation dual-source dual-energy CT with 100/Sn150 kVp at three volume CT dose levels (5, 10, 15 mGy). Three size sections (diameters 16, 26, 36 cm) with subsections for image noise and spatial resolution analysis were used. High-contrast tasks (e.g., calcium-containing stone and vascular lesion) were emulated using bone and iodine inserts. A low-contrast task (e.g., low-contrast lesion or hematoma) was emulated using a polystyrene insert. VMI at 40-190 keV and LBI were reconstructed. Noise power spectrum (NPS) determined the noise magnitude and texture. Spatial resolution was assessed using the task-transfer function (TTF) of the three inserts. The detectability index (d') served as TB-IQ metric.

Results

Noise magnitude increased with increasing phantom size, decreasing dose, and decreasing VMI-energy. Overall, noise magnitude was higher for VMI at 40-60 keV compared to LBI (range of noise increase, 3-124%). Blotchier noise texture was found for low and high VMIs (40-60 keV, 130-190 keV) compared to LBI. No difference in spatial resolution was observed for high contrast tasks. d' increased with increasing dose level or lesion diameter and decreasing size. For high-contrast tasks, d' was higher at 40-80 keV and lower at high VMIs. For the low-contrast task, d' was higher for VMI at 70-90 keV and lower at 40-60 keV.

Conclusions

Task-based image quality differed among VMI-energy and LBI dependent on the contrast task, dose level, phantom size, and lesion diameter. Image quality could be optimized by tailoring VMI-energy to the contrast task. Considering the clinical relevance of iodine, VMIs at 50-60 keV could be proposed as an alternative to LBI.

Computed tomography with a full FOV photon-counting detector in a clinical setting, the first experience

OBJECTIVES

to assess the feasibility of CT with an integrated photon-counting-detector system (PC-CT) in the body imaging of clinical patients.

METHODS

120 examinations using photon counting detector CT were evaluated in six groups: 1/ a standard-dose lung, 2/ low-dose lung, 3/ ultra-high resolution (UHR) lung, 4/ standard-dose abdominal, 5/ lower-dose abdominal, 6/ UHR abdominal CTA. All CT examinations were performed on a single-source prototype device equipped with a photon counting detector covering a 50 cm scan field of view. Standard dose examinations were performed with the use of detector element size of 0.4 mm, ultra-high-resolution examinations with the detector element size of 0.2 mm, respectively. The stability of the system during imaging was tested. The diagnostic quality of the acquired images was assessed based on the imaging of key structures and the noise level in five-point scale, the effective dose equivalent, dose length product and noise level, and also relation to body mass index and body surface area were compared with three similar groups of CT images made with energy integrating high end scanner. The parameters were evaluated using Wilcoxon test for independent samples, the independence was tested using Kruskal-Wallis test.

RESULTS

When PC-CT images radiation dose is compared with the similar imaging using energy integrating CT, the PC-CT shows lower dose in ultra-high resolution mode, the dose is significantly lower (p < 0.0001), the standard dose examinations were performed with the comparable radiation doses. PC-CT exhibited the significantly higher ratio between parenchyma signal and background noise both in lung and in abdominal imaging (p < 0.0001).

CONCLUSIONS

PC-CT showed imaging stability and excellent diagnostic quality at dose values that are comparable or better to the quality of energy integrating CT, the better signal and improved resolution is most important advantage of photon counting detector CT over energy integrating detector CT.

Photon-counting x-ray detectors for CT

The introduction of photon-counting detectors is expected to be the next major breakthrough in clinical x-ray computed tomography (CT). During the last decade, there has been considerable research activity in the field of photon-counting CT, in terms of both hardware development and theoretical understanding of the factors affecting image quality. In this article, we review the recent progress in this field with the intent of highlighting the relationship between detector design considerations and the resulting image quality. We discuss detector design choices such as converter material, pixel size, and readout electronics design, and then elucidate their impact on detector performance in terms of dose efficiency, spatial resolution, and energy resolution. Furthermore, we give an overview of data processing, reconstruction methods and metrics of imaging performance; outline clinical applications; and discuss potential future developments.