Radiation Dose Reduction in Contrast-Enhanced Abdominal CT: Comparison of Photon-Counting Detector CT with 2nd Generation Dual-Source Dual-Energy CT in an oncologic cohort.

RATIONAL AND OBJECTIVES: Comparison of radiation dose and image quality in routine abdominal and pelvic contrast-enhanced computed tomography (CECT) between a photon-counting detector CT (PCD-CT) and a dual energy dual source CT (DSCT). MATERIALS AND METHODS: 70 oncologic patients (mean age 66 ± 12 years, 29 females) were prospectively enrolled between November 2021 and February 2022. Abdominal CECT were clinically indicated and performed first on a 2nd-generation DSCT and at follow-up on a 1st-generation dual-source PCD-CT. The same contrast media (Imeron 350, Bracco imaging) and pump protocol was used for both scans. For both scanners, polychromatic images were reconstructed with 3mm slice thickness and comparable kernel (I30f[DSCT] and Br40f[PCD-CT]); for PCD-CT data from all counted events above the lowest energy threshold at 20 keV ("T3D") were used. Results were compared in terms of radiation dose metrics of CT dose index (CTDIvol), dose length product (DLP) and size-specific dose estimation (SSDE), objective and subjective measurements of image quality were scored by two emergency radiologists including lesion conspicuity. RESULTS: Median time interval between the scans was 4 months (IQR: 3-6). CNRvessel and SNRvessel of T3D reconstructions from PCD-CT were significantly higher than those of DSCT (all, p < 0.05). Qualitative image noise analysis from PCD-CT and DSCT yielded a mean of 4 each. Lesion conspicuity was rated significantly higher in PCD-CT (Q3 strength) compared to DSCT images. CTDI, DLP and SSDE mean values for PCD-CT and DSCT were 7.98 ± 2.56 mGy vs. 14.11 ± 2.92 mGy, 393.13 ± 153.55 mGy*cm vs. 693.61 ± 185.76 mGy*cm and 9.98 ± 2.41 vs. 14.63 ± 1.63, respectively, translating to a dose reduction of around 32% (SSDE). CONCLUSION: PCD-CT enables oncologic abdominal CT with a significantly reduced dose while keeping image quality similar to 2nd-generation DSCT.

Effects of different virtual monoenergetic CT image data on chest wall post-processing "unfolded ribs" and proposal of an algorithm improvement.

PURPOSE: To find out if the use of different virtual monoenergetic data sets enabled by DECT technology might have a negative impact on post-processing applications, specifically in case of the "unfolded ribs" algorithm. Metal or beam hardening artifacts are suspected to generate image artifacts and thus reduce diagnostic accuracy. This paper tries to find out how the generation of "unfolded rib" CT image reformates is influenced by different virtual monoenergetic CT images and looks for possible improvement of the post-processing tool. MATERIAL AND METHODS: Between March 2021 and April 2021, thin-slice dual-energy CT image data of the chest were used creating "unfolded rib" reformates. The same data sets were analyzed in three steps: first the gold standard with the original algorithm on mixed image data sets followed by the original algorithm on different keV levels (40-120 keV) and finally using a modified algorithm which in the first step used segmentation based on mixed image data sets, followed by segmentation based on different keV levels. Image quality (presence of artifacts), lesion and fracture detectability were assessed for all series. RESULTS: Both, the original and the modified algorithm resulted in more artifact-free image data sets compared to the gold standard. The modified algorithm resulted in significantly more artifact-free image data sets at the keV-edges (40-120 keV) compared the original algorithm. Especially "black artifacts" and pseudo-lesions, potentially inducing false positive findings, could be reduced in all keV level with the modified algorithm. Detection of focal sclerotic, lytic or mixed (k = 0.990-1.000) lesions was very good for all keV levels. The Fleiss-kappa test for detection of fresh and old rib fractures was ≥ 0.997. CONCLUSION: The use of different virtual monoenergetic keVs for the "unfolded rib" algorithm is generating different artifacts. Segmentation-based artifacts could be eliminated by the proposed new algorithm, showing the best results at 70-80 keV.

Reduced versus standard dose contrast volume for contrast-enhanced abdominal CT in overweight and obese patients using photon counting detector technology vs. second-generation dual-source energy integrating detector CT.

PURPOSE: To compare image quality of contrast-enhanced abdominal-CT using 1st-generation Dual Source Photon-Counting Detector CT (DS-PCD-CT) versus 2nd-generation Dual-Source Energy Integrating-Detector CT (DS-EID-CT) in patients with BMI ≥ 25, applying two different contrast agent volumes, vendor proposed protocols and different virtual monoenergetic images (VMI). METHOD: 68 overweight (BMI ≥ 25 kgm2) patients (median age: 65 years; median BMI 33.3 kgm2) who underwent clinically indicated, portal-venous contrast-enhanced abdominal-CT on a commercially available 1st-generation DS-PCD-CT were prospectively included if they already have had a pre-exam on 2nd-generation DS-EID-CT using a standardized exam protocol. Obesity were defined by BMI-calculation (overweight: 25-29.9, obesity grade I: 30-34.9; obesity grade II: 35-39.9; obesity grade III: > 40) and by the absolute weight value. Body weight adapted contrast volume (targeted volume of 1.2 mL/kg for the 1st study and 0.8 mL/kg for the 2nd study) was applied in both groups. Dual Energy mode was used for both the DS-PCD-CT and the DS-EID-CT. Polychromatic images and VMI (40 keV and 70 keV) were reconstructed for both the DS-EID-CT and the DS-PCD-CT data (termed T3D). Two radiologists assessed subjective image quality using a 5-point Likert-scale. Each reader drew ROIs within parenchymatous organs and vascular structures to analyze image noise, contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR). RESULTS: Median time interval between scans was 12 months (Min: 6 months; Max: 36 months). BMI classification included overweight (n = 10, 14.7 %), obesity grade I (n = 38, 55.9 %), grade II (n = 13, 19.1 %) and grade III (n = 7, 10.3 %). The SNR achieved with DS-PCD-CT at QIR level 3was 12.61 vs. 11.47 (QIR 2) vs. 10.53 (DS-EID-CT), irrespective of parenchymatous organs. For vessels, the SNR were 16.73 vs. 14.20 (QIR 2) vs. 12.07 (DS-EID-CT). Moreover, the obtained median noise at QIR level 3 was as low as that of the DS-EID-CT (8.65 vs. 8.65). Both radiologists rated the image quality higher for DS-PCD-CT data sets (p < 0.05). The highest CNR was achieved at 40 keV for both scanners. T3D demonstrated significantly higher SNR and lower noise level compared to 40 keV and 70 keV. Median CTDIvol and DLP values for DS-PCD-CT and DS-EID-CT were 10.90 mGy (IQR: 9.31 - 12.50 mGy) vs. 16.55 mGy (IQR: 15.45 - 18.17 mGy) and 589.50 mGy * cm (IQR: 498.50 - 708.25 mGy * cm) vs. 848.75 mGy * cm (IQR: 753.43 - 969.58 mGy * cm) (p < 0.001). CONCLUSION: Image quality can be maintained while significantly reducing the contrast volume and the radiation dose (27% and 34% lower DLP and 31% lower CDTIvol) for abdominal contrast-enhanced CT using a 1st-generation DS-PCD-CT. Moreover, polychromatic reconstruction T3D on a DS-PCD-CT enables sufficient diagnostic image quality for oncological imaging.

Image Quality and Radiation Dose of Contrast-Enhanced Chest-CT Acquired on a Clinical Photon-Counting Detector CT vs. Second-Generation Dual-Source CT in an Oncologic Cohort: Preliminary Results.

Our aim was to compare the image quality and patient dose of contrast-enhanced oncologic chest-CT of a first-generation photon-counting detector (PCD-CT) and a second-generation dual-source dual-energy CT (DSCT). For this reason, one hundred consecutive oncologic patients (63 male, 65 ± 11 years, BMI: 16−42 kg/m2) were prospectively enrolled and evaluated. Clinically indicated contrast-enhanced chest-CT were obtained with PCD-CT and compared to previously obtained chest-DSCT in the same individuals. The median time interval between the scans was three months. The same contrast media protocol was used for both scans. PCD-CT was performed in QuantumPlus mode (obtaining full spectral information) at 120 kVp. DSCT was performed using 100 kV for Tube A and 140 kV for Tube B. "T3D" PCD-CT images were evaluated, which emulate conventional 120 keV polychromatic images. For DSCT, the convolution algorithm was set at I31f with class 1 iterative reconstruction, whereas comparable Br40 kernel and iterative reconstruction strengths (Q1 and Q3) were applied for PCD-CT. Two radiologists assessed image quality using a five-point Likert scale and performed measurements of vessels and lung parenchyma for signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and in the case of pulmonary metastases tumor-to-lung parenchyma contrast ratio. PCD-CT CNRvessel was significantly higher than DSCT CNRvessel (all, p < 0.05). Readers rated image contrast of mediastinum, vessels, and lung parenchyma significantly higher in PCD-CT than DSCT images (p < 0.001). Q3 PCD-CT CNRlung_parenchyma was significantly higher than DSCT CNRlung_parenchyma and Q1 PCD-CT CNRlung_parenchyma (p < 0.01). The tumor-to-lung parenchyma contrast ratio was significantly higher on PCD-CT than DSCT images (0.08 ± 0.04 vs. 0.03 ± 0.02, p < 0.001). CTDI, DLP, SSDE mean values for PCD-CT and DSCT were 4.17 ± 1.29 mGy vs. 7.21 ± 0.49 mGy, 151.01 ± 48.56 mGy * cm vs. 288.64 ± 31.17 mGy * cm and 4.23 ± 0.97 vs. 7.48 ± 1.09, respectively. PCD-CT enables oncologic chest-CT with a significantly reduced dose while maintaining image quality similar to a second-generation DSCT for comparable protocol settings.

Weight bias and linguistic body representation in anorexia nervosa: Findings from the BodyTalk project.

OBJECTIVE: This study provides a comprehensive assessment of own body representation and linguistic representation of bodies in general in women with typical and atypical anorexia nervosa (AN). METHODS: In a series of desktop experiments, participants rated a set of adjectives according to their match with a series of computer generated bodies varying in body mass index, and generated prototypic body shapes for the same set of adjectives. We analysed how body mass index of the bodies was associated with positive or negative valence of the adjectives in the different groups. Further, body image and own body perception were assessed. RESULTS: In a German-Italian sample comprising 39 women with AN, 20 women with atypical AN and 40 age matched control participants, we observed effects indicative of weight stigmatization, but no significant differences between the groups. Generally, positive adjectives were associated with lean bodies, whereas negative adjectives were associated with obese bodies. DISCUSSION: Our observations suggest that patients with both typical and atypical AN affectively and visually represent body descriptions not differently from healthy women. We conclude that overvaluation of low body weight and fear of weight gain cannot be explained by generally distorted perception or cognition, but require individual consideration.