Lyu Peijie, Liu Nana, Harrawood Brian, Solomon Justin, Wang Huixia, Chen Yan, Rigiroli Francesca, Ding Yuqin, Schwartz Fides Regina, Jiang Hanyu, Lowry Carolyn, Wang Luotong, Samei Ehsan, Gao Jianbo, Marin Daniele
Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1 East Jianshe Road, Zhengzhou, 450052, Henan Province, China.
Department of Radiology, Duke University Medical Center, 2301 Erwin Rd, Box 3808, Durham, NC, 27710, USA.
Eur Radiol. 2023 Mar;33(3):1629-1640. doi: 10.1007/s00330-022-09206-3. Epub 2022 Nov 3.
To compare the image quality and hepatic metastasis detection of low-dose deep learning image reconstruction (DLIR) with full-dose filtered back projection (FBP)/iterative reconstruction (IR).
A contrast-detail phantom consisting of low-contrast objects was scanned at five CT dose index levels (10, 6, 3, 2, and 1 mGy). A total of 154 participants with 305 hepatic lesions who underwent abdominal CT were enrolled in a prospective non-inferiority trial with a three-arm design based on phantom results. Data sets with full dosage (13.6 mGy) and low dosages (9.5, 6.8, or 4.1 mGy) were acquired from two consecutive portal venous acquisitions, respectively. All images were reconstructed with FBP (reference), IR (control), and DLIR (test). Eleven readers evaluated phantom data sets for object detectability using a two-alternative forced-choice approach. Non-inferiority analyses were performed to interpret the differences in image quality and metastasis detection of low-dose DLIR relative to full-dose FBP/IR.
The phantom experiment showed the dose reduction potential from DLIR was up to 57% based on the reference FBP dose index. Radiation decreases of 30% and 50% resulted in non-inferior image quality and hepatic metastasis detection with DLIR compared to full-dose FBP/IR. Radiation reduction of 70% by DLIR performed inferiorly in detecting small metastases (< 1 cm) compared to full-dose FBP (difference: -0.112; 95% confidence interval [CI]: -0.178 to 0.047) and full-dose IR (difference: -0.123; 95% CI: -0.182 to 0.053) (p < 0.001).
DLIR enables a 50% dose reduction for detecting low-contrast hepatic metastases while maintaining comparable image quality to full-dose FBP and IR.
• Non-inferiority study showed that deep learning image reconstruction (DLIR) can reduce the dose to oncological patients with low-contrast lesions without compromising the diagnostic information. • Radiation dose levels for DLIR can be reduced to 50% of full-dose FBP and IR for detecting low-contrast hepatic metastases, while maintaining comparable image quality. • The reduction of radiation by 70% by DLIR is clinically acceptable but insufficient for detecting small low-contrast hepatic metastases (< 1 cm).
比较低剂量深度学习图像重建(DLIR)与全剂量滤波反投影(FBP)/迭代重建(IR)的图像质量和肝转移灶检测情况。
使用包含低对比度物体的对比细节模体在五个CT剂量指数水平(10、6、3、2和1 mGy)下进行扫描。根据模体结果,采用三臂设计进行前瞻性非劣效性试验,纳入了154名有305个肝病灶的接受腹部CT检查的参与者。分别从连续两次门静脉期扫描中获取全剂量(13.6 mGy)和低剂量(9.5、6.8或4.1 mGy)的数据集。所有图像均采用FBP(参考)、IR(对照)和DLIR(试验)进行重建。11名阅片者采用二选迫选法评估模体数据集的物体可检测性。进行非劣效性分析以解释低剂量DLIR相对于全剂量FBP/IR在图像质量和转移灶检测方面的差异。
模体实验表明,基于参考FBP剂量指数,DLIR的剂量降低潜力高达57%。与全剂量FBP/IR相比,DLIR降低30%和50%的辐射剂量时,图像质量和肝转移灶检测具有非劣效性。与全剂量FBP(差异:-0.112;95%置信区间[CI]:-0.178至0.047)和全剂量IR(差异:-0.123;95% CI:-0.182至0.053)相比,DLIR降低70%的辐射剂量在检测小转移灶(<1 cm)方面表现较差(p<0.001)。
DLIR在检测低对比度肝转移灶时可降低50%的辐射剂量,同时保持与全剂量FBP和IR相当的图像质量。
• 非劣效性研究表明,深度学习图像重建(DLIR)可降低患有低对比度病灶的肿瘤患者的辐射剂量,同时不影响诊断信息。• 在检测低对比度肝转移灶时,DLIR的辐射剂量水平可降低至全剂量FBP和IR的50%,同时保持相当的图像质量。• DLIR降低70%的辐射剂量在临床上是可接受的,但不足以检测小的低对比度肝转移灶(<1 cm)。