Better image post-processing enables simplified bowel preparation with no effect on virtual colonoscopy image quality, an Italian team of radiologists found in a preliminary study published in La Radiologia Medica.
Better image post-processing enables simplified bowel preparation with no effect on virtual colonoscopy image quality, an Italian team of radiologists found in a preliminary study published in La Radiologia Medica.
The authors assessed the quality, diagnostic accuracy and patient acceptability of CT colonography performed using a simplified bowel preparation and software for post-processing digital elimination of stool and fluid data from images. They compared the results with the images obtained with conventional preparation.
Two groups of 40 asymptomatic patients aged between 48 and 72 years underwent CT colonography. In the first group, the CT scan was performed with conventional bowel preparation involving a full cathartic dose and oral contrast medium to tag any residue in the three days preceding the study. In the second group, the team did CT colonography after a reduced bowel preparation, with the oral contrast medium for residue tagging being administered only on the day of the investigation. Examination quality, diagnostic performance and patient acceptability (rated with a self-completed questionnaire) were assessed in the two groups of patients.
The team found no significant difference in examination quality (180 vs. 165 segments were free from stools and fluid) or overall diagnostic accuracy (16/17 polyps detected in the first group and 12/13 in the second).The patients appreciated the easier preparation, too, according to their questionnaires.
“The use of software for post-processing digital elimination of residue from images in conjunction with reduced bowel preparation does not reduce examination quality or diagnostic performance when compared with the conventional CT colonography technique and is more acceptable to and better tolerated by the patient,” the authors concluded.
MRI-Based AI Radiomics Model Offers 'Robust' Prediction of Perineural Invasion in Prostate Cancer
July 26th 2024A model that combines MRI-based deep learning radiomics and clinical factors demonstrated an 84.8 percent ROC AUC and a 92.6 percent precision-recall AUC for predicting perineural invasion in prostate cancer cases.
Breast MRI Study Examines Common Factors with False Negatives and False Positives
July 24th 2024The absence of ipsilateral breast hypervascularity is three times more likely to be associated with false-negative findings on breast MRI and non-mass enhancement lesions have a 4.5-fold likelihood of being linked to false-positive results, according to new research.
Can Polyenergetic Reconstruction Help Resolve Streak Artifacts in Photon Counting CT?
July 22nd 2024New research looking at photon-counting computed tomography (PCCT) demonstrated significantly reduced variation and tracheal air density attenuation with polyenergetic reconstruction in contrast to monoenergetic reconstruction on chest CT.