A JAFROC study of nodule detection performance in CT images of a thorax acquired during PET/CT

接收机工作特性 成像体模 核医学 结核(地质) 放射科 模态(人机交互) 医学 计算机科学 人工智能 生物 内科学 古生物学
作者
John D. Thompson,Amy Wareing,Katy Szczepura,Sobhan Vinjamuri,Peter Hogg
出处
期刊:Radiography [Elsevier BV]
卷期号:23 (3): 191-196 被引量:1
标识
DOI:10.1016/j.radi.2017.03.001
摘要

Two types of CT images (modalities) are acquired in PET/CT: for attenuation correction (AC) and diagnosis. The purpose of the study was to compare nodule detection and localization performance between these two modalities.CT images, using both modalities, of an anthropomorphic chest phantom containing zero or more simulated spherical nodules of 5, 8, 10 and 12 mm diameters and contrasts -800, -630 and 100 HU were acquired. An observer performance study using nine observers interpreting 45 normal (zero nodules) images and 47 abnormal images (1-3 nodules; average 1.26) was conducted using the free-response receiver operating characteristic (FROC) paradigm. Data were analysed using an R software package implemented jackknife alternative FROC (JAFROC) analysis. Both empirical areas under the equally weighted AFROC curve (wAFROC) and under the highest rating inferred ROC (HR-ROC) curve were used as figures of merit (FOM). To control the probability of Type I error test alpha was set at 0.05.Nodule detection as measured by either FOM was significantly better on the diagnostic quality images (2nd modality), irrespective of the method of analysis, [reader averaged inter-modality wAFROC FOM difference = -0.07 (-0.11,-0.04); reader averaged inter-modality HR-ROC FOM difference = -0.05 (-0.09, -0.01)].Nodule detection was statistically worse on images acquired for AC; suggesting that images acquired for AC should not be used to evaluate pulmonary pathology.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
彭于晏应助阿居采纳,获得10
刚刚
领导范儿应助大大怪采纳,获得10
刚刚
遂安完成签到,获得积分10
1秒前
子车茗应助yyyyy采纳,获得30
1秒前
丘比特应助hh采纳,获得10
1秒前
香蕉觅云应助无语的麦片采纳,获得10
1秒前
joey完成签到,获得积分10
1秒前
2秒前
Evelyn完成签到,获得积分20
3秒前
3秒前
3秒前
小許要看文献完成签到,获得积分10
4秒前
4秒前
bkagyin应助豆4799采纳,获得10
5秒前
Ja12完成签到,获得积分10
5秒前
6秒前
慕新发布了新的文献求助10
6秒前
kll发布了新的文献求助10
6秒前
7秒前
7秒前
糊涂的大白完成签到,获得积分20
8秒前
DDJoy完成签到,获得积分10
8秒前
8秒前
8秒前
一胖发布了新的文献求助30
9秒前
Stella应助周亚男采纳,获得20
9秒前
SciGPT应助鞋子特大号采纳,获得10
9秒前
10秒前
柳成荫发布了新的文献求助10
10秒前
10秒前
11秒前
细心的尔容完成签到,获得积分10
11秒前
LULU发布了新的文献求助10
11秒前
11秒前
11秒前
吃橘子吗完成签到 ,获得积分10
12秒前
欣喜芷完成签到,获得积分10
12秒前
晚睡是小狗应助Ja12采纳,获得10
12秒前
科滴滴发布了新的文献求助10
12秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Handbook of pharmaceutical excipients, Ninth edition 5000
Aerospace Standards Index - 2026 ASIN2026 3000
Digital Twins of Advanced Materials Processing 2000
Polymorphism and polytypism in crystals 1000
Signals, Systems, and Signal Processing 610
Discrete-Time Signals and Systems 610
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 纳米技术 有机化学 物理 生物化学 化学工程 计算机科学 复合材料 内科学 催化作用 光电子学 物理化学 电极 冶金 遗传学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 6040568
求助须知:如何正确求助?哪些是违规求助? 7777009
关于积分的说明 16231248
捐赠科研通 5186669
什么是DOI,文献DOI怎么找? 2775483
邀请新用户注册赠送积分活动 1758574
关于科研通互助平台的介绍 1642194