Image quality of coronary CT angiography at ultra low tube voltage reconstructed with a deep-learning image reconstruction algorithm in patients of different weight.

Background: GE Healthcare's new generation of deep-learning image reconstruction (DLIR), the Revolution Apex CT is the first CT image reconstruction engine based on a deep neural network to be approved by the US Food and Drug Administration (FDA). It can generate high-quality CT images that restore the true texture with a low radiation dose. The aim of the present study was to assess the image quality of coronary CT angiography (CCTA) at 70 kVp with the DLIR algorithm as compared to the adaptive statistical iterative reconstruction-Veo (ASiR-V) algorithm in patients of different weight. Methods: The study group comprised 96 patients who underwent CCTA examination at 70 kVp and were subdivided by body mass index (BMI) into normal-weight patients [48] and overweight patients [48]. ASiR-V40%, ASiR-V80%, DLIR-low, DLIR-medium, and DLIR-high images were obtained. The objective image quality, radiation dose, and subjective score of the two groups of images with different reconstruction algorithms were compared and statistically analyzed. Results: In the overweight group, the noise of the DLIR image was lower than that of the routinely used ASiR-40%, and the contrast-to-noise ratio (CNR) of DLIR (H: 19.15±4.31; M: 12.68±2.91; L: 10.59±2.32) was higher than that of the ASiR-40% reconstructed image (8.39±1.46), with statistically significant differences (all P values <0.05). The subjective image quality evaluation of DLIR was significantly higher than that of ASiR-V reconstructed images (all P values <0.05), with the DLIR-H being the best. In a comparison of the normal-weight and overweight groups, the objective score of the ASiR-V-reconstructed image increased with increasing strength, but the subjective image evaluation decreased, and both differences (i.e., objective and subjective) were statistically significant (P<0.05). In general, the objective score of the DLIR reconstruction image between the two groups increased with increased noise reduction, and the DLIR-L image was the best. The difference between the two groups was statistically significant (P<0.05), but there was no significant difference in subjective image evaluation between the two groups. The effective dose (ED) of the normal-weight group and the overweight group was 1.36±0.42 and 1.59±0.46 mSv, respectively, and was significantly higher in the overweight group (P<0.05). Conclusions: As the strength of the ASiR-V reconstruction algorithm increased, the objective image quality increased accordingly, but the high-strength ASiR-V changed the noise texture of the image, resulting in a decrease in the subjective score, which affected disease diagnosis. Compared with the ASiR-V reconstruction algorithm, the DLIR reconstruction algorithm improved the image quality and diagnostic reliability for CCTA in patients with different weights, especially in heavier patients.

Feasibility of quantitative and volumetric enhancement measurement to assess tumor response in patients with breast cancer after early neoadjuvant chemotherapy.

OBJECTIVE: To evaluate the feasibility of quantitative enhancing lesion volume (ELV) for evaluating the responsiveness of breast cancer patients to early neoadjuvant chemotherapy (NAC) using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). METHODS: Seventy-five women with breast cancer underwent DCE-MRI before and after NAC. Lesions were assessed by ELV, response evaluation criteria in solid tumors 1.1 (RECIST 1.1), and total lesion volume (TLV). The diagnostic and pathological predictive performances of the methods were compared and color maps were compared with pathological results. RESULTS: ELV identified 29%, 67%, and 4% of cases with partial response, stable disease, and progressive disease, respectively. There was no significant difference in evaluation performances among the methods. The sensitivity, specificity, positive predictive value, negative predictive value (NPV), and accuracy of ELV for predicting pathologic response were 72%, 92%, 81.8%, 86.8%, and 85.3%, respectively, with the highest sensitivity, NPV, and accuracy of the three methods. The area under the receiver operating characteristic curve was also highest for ELV. Pre- and post-NAC color maps reflecting tumor activity were consistent with pathological necrosis. CONCLUSIONS: ELV may help evaluate the responsiveness of breast cancer patients to NAC, and may provide a good tumor-response indicator through the ability to indicate tumor viability.

FGFR2 mutation in a Chinese family with unusual Crouzon syndrome.

AIM: To describe the clinical characteristics with genetic lesions in a Chinese family with Crouzon syndrome. METHODS: All five patients from this family were included and received comprehensive ophthalmic and systemic examinations. Direct sequencing of the FGFR2 gene was employed for mutation identification. Crystal structure analysis was applied to analyze the structural changes associated with the substitution. RESULTS: All patients presented typical Crouzon features, including short stature, craniosynostosis, mandibular prognathism, shallow orbits with proptosis, and exotropia. Intrafamilial phenotypic diversities were observed. Atrophic optic nerves were exclusively detected in the proband and her son. Cranial magnetic resonance imaging (MRI) implied a cystic lesion in her sellar and third ventricular regions. A missense mutation, FGFR2 p.Cys342Trp, was found as disease causative. This substitution would generate conformational changes in the extracellular Ig-III domain of the FGFR-2 protein, thus altering its physical and biological properties. CONCLUSION: We describe the clinical presentations and genotypic lesions in a Chinese family with Crouzon syndrome. The intrafamilial phenotypic varieties in this family suggest that other genetic modifiers may also play a role in the pathogenesis of Crouzon syndrome.