Health AI Central
Peer-Reviewed Publication
Eur Radiol2026October 9, 2026Journal Article

Subtraction iodine maps combined with deep learning reconstruction for improved evaluation of bony invasion in temporal bone lesions.

Tong Su1, Zhe Zhang1,2, Xu Tian3, Yu Chen4, Min Xu5, Jian Wang5, Zhuhua Zhang1, Guodong Feng6, Zhengyu Jin7
1Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
2Department of Radiology, Beijing Changping District Shahe Hospital, Beijing, China.
3Department of Otorhinolaryngology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
4Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China. bjchenyu@126.com.
5Canon Medical Systems (China), Beijing, China.
6Department of Otorhinolaryngology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China. fengguodong2013@163.com.
7Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China. jinzy@pumch.cn.
PubMed DOI

Abstract

OBJECTIVES: To assess the capability of subtraction iodine maps (SIM) combined with deep learning reconstruction (DLR) to evaluate the identification of bone involvement in patients with temporal bone lesions, compared with conventional enhanced CT with hybrid iterative reconstruction (HIR). MATERIALS AND METHODS: This prospective study continuously recruited seventy-seven patients (mean age: 46.…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free AccountSign In

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.