Utilization of virtual low-keV monoenergetic images generated using dual-layer spectral detector computed tomography for the assessment of peritoneal seeding from ovarian cancer.

ABSTRACT

This study aimed to compare the quality of virtual low-keV monoenergetic images vs conventional images reconstructed from dual-layer spectral detector computed tomography (SDCT) for the detection of peritoneal implants of ovarian cancer.Fifty ovarian cancer patients who underwent abdominopelvic SDCT scans were included in this retrospective study. Virtual monoenergetic images at 40 (VMI40) and 50 keV (VMI50), and two conventional images were reconstructed using filtered back projection (FBP) and iterative model reconstruction (IMR) protocols. The mean attenuation of the peritoneal implant, signal-to-noise ratio (SNR), contrast-to-noise ratio relative to ascites (CNRA) and adjacent reference tissues (e.g., bowel wall, hepatic, or splenic parenchyma [CNRB]) were calculated and compared using paired t tests. Qualitative image analysis regarding overall image quality, image noise, image blurring, lesion conspicuity, was performed by two radiologists. A subgroup analysis according to the peritoneal implant region was also conducted.VMI40 yielded significantly higher mean attenuation (183.35) of SNR and CNR values (SNR 11.69, CNRA 7.39, CNRB 2.68), compared to VMI50, IR, and FBP images (P < .001). The mean attenuation (129.65), SNR and CNR values (SNR 9.37, CNRA 5.72, CNRB 2.02) of VMI50 were also significantly higher than those of IR and FBP images (P < .001). In the subgroup analysis, all values were significantly higher on VMI40 regardless of the peritoneal implant region (P < .05). In both readers, overall image quality and image blurring showed highest score in VMI50, while image noise and lesion conspicuity showed best score in IMR and VMI40 respectively. Inter-reader agreements are moderate to almost perfect in every parameter.The low-keV VMIs improved both quantitative assessment and lesion conspicuity of peritoneal implants from ovarian cancer compared to conventional images.