Abstract：
BACKGROUND: Myocardial T1-rho (T1ρ) mapping is a promising method for identifying and quantifying myocardial injuries without contrast agents, but its clinical use is hindered by the lack of dedicated analysis tools.
OBJECTIVE: To explore the feasibility of clinically integrated artificial intelligence-driven analysis for efficient and automated myocardial T1ρ mapping.
METHODS: Retrospective.
METHODS: Five hundred seventy-three patients divided into a training (N = 500) and a test set (N = 73) including ischemic and nonischemic cases.
UNASSIGNED: Single-shot bSSFP T1ρ mapping sequence at 1.5 T.
RESULTS: The automated process included: left ventricular (LV) wall segmentation, right ventricular insertion point detection and creation of a 16-segment model for segmental T1ρ value analysis. Two radiologists (20 and 7 years of MRI experience) provided ground truth annotations. Interobserver variability and segmentation quality were assessed using the Dice coefficient with manual segmentation as reference standard. Global and segmental T1ρ values were compared. Processing times were measured.
METHODS: Intraclass correlation coefficients (ICCs) and Bland-Altman analysis (bias ±2SD); Paired Student\'s t-tests and one-way ANOVA. A P value <0.05 was considered significant.
RESULTS: The automated approach significantly reduced processing time (3 seconds vs. 1 minute 51 seconds ± 22 seconds). In the test set, automated LV wall segmentation closely matched manual results (Dice 81.9% ± 9.0) and closely aligned with interobserver segmentation (Dice 82.2% ± 6.5). Excellent ICCs were achieved on a patient basis (0.94 [95% CI: 0.91 to 0.96]) with bias of -0.93 cm2 ± 6.60. There was no significant difference in global T1ρ values between manual (54.9 msec ± 4.6; 95% CI: 53.8 to 56.0 msec, range: 46.6-70.9 msec) and automated processing (55.4 msec ± 5.1; 95% CI: 54.2 to 56.6 msec; range: 46.4-75.1 msec; P = 0.099). The pipeline demonstrated a high level of agreement with manual-derived T1ρ values at the patient level (ICC = 0.85; bias +0.52 msec ± 5.18). No significant differences in myocardial T1ρ values were found between methods across the 16 segments (P = 0.75).
CONCLUSIONS: Automated myocardial T1ρ mapping shows promise for the rapid and noninvasive assessment of heart disease.
METHODS: 3 TECHNICAL EFFICACY: Stage 1.
OBJECTIVE: To explore the feasibility of clinically integrated artificial intelligence-driven analysis for efficient and automated myocardial T1ρ mapping.
METHODS: Retrospective.
METHODS: Five hundred seventy-three patients divided into a training (N = 500) and a test set (N = 73) including ischemic and nonischemic cases.
UNASSIGNED: Single-shot bSSFP T1ρ mapping sequence at 1.5 T.
RESULTS: The automated process included: left ventricular (LV) wall segmentation, right ventricular insertion point detection and creation of a 16-segment model for segmental T1ρ value analysis. Two radiologists (20 and 7 years of MRI experience) provided ground truth annotations. Interobserver variability and segmentation quality were assessed using the Dice coefficient with manual segmentation as reference standard. Global and segmental T1ρ values were compared. Processing times were measured.
METHODS: Intraclass correlation coefficients (ICCs) and Bland-Altman analysis (bias ±2SD); Paired Student\'s t-tests and one-way ANOVA. A P value <0.05 was considered significant.
RESULTS: The automated approach significantly reduced processing time (3 seconds vs. 1 minute 51 seconds ± 22 seconds). In the test set, automated LV wall segmentation closely matched manual results (Dice 81.9% ± 9.0) and closely aligned with interobserver segmentation (Dice 82.2% ± 6.5). Excellent ICCs were achieved on a patient basis (0.94 [95% CI: 0.91 to 0.96]) with bias of -0.93 cm2 ± 6.60. There was no significant difference in global T1ρ values between manual (54.9 msec ± 4.6; 95% CI: 53.8 to 56.0 msec, range: 46.6-70.9 msec) and automated processing (55.4 msec ± 5.1; 95% CI: 54.2 to 56.6 msec; range: 46.4-75.1 msec; P = 0.099). The pipeline demonstrated a high level of agreement with manual-derived T1ρ values at the patient level (ICC = 0.85; bias +0.52 msec ± 5.18). No significant differences in myocardial T1ρ values were found between methods across the 16 segments (P = 0.75).
CONCLUSIONS: Automated myocardial T1ρ mapping shows promise for the rapid and noninvasive assessment of heart disease.
METHODS: 3 TECHNICAL EFFICACY: Stage 1.
摘要：
背景：心肌T1-rho（T1ρ）标测是一种在不使用造影剂的情况下识别和量化心肌损伤的有前途的方法，但由于缺乏专门的分析工具，其临床应用受到阻碍。
目的：探讨临床综合人工智能驱动分析用于高效和自动化心肌T1ρ映射的可行性。
方法：回顾性。
方法：573例患者分为训练组（N=500）和测试组（N=73），包括缺血性和非缺血性病例。
■1.5T处的单次bSSFPT1ρ映射序列
结果：自动化过程包括：左心室（LV）壁分割，右心室插入点检测和16段模型的创建，用于节段T1ρ值分析。两名放射科医生（20年和7年的MRI经验）提供了基本事实注释。使用Dice系数以手动分割为参考标准来评估观察者间的变异性和分割质量。比较了全局和分段T1ρ值。测量处理时间。
方法：组内相关系数（ICC）和Bland-Altman分析（偏倚±2SD）；配对学生t检验和单因素方差分析。P值<0.05被认为是显著的。
结果：自动化方法显著缩短了处理时间（3秒与1分51秒±22秒)。在测试集中，自动左心室壁分割与手动结果紧密匹配(Dice81.9%±9.0),并与观察者间分割(Dice82.2%±6.5)紧密一致。在患者基础上获得了优异的ICC(0.94[95%CI:0.91至0.96]),偏差为-0.93cm2±6.60。手动之间的全局T1ρ值没有显着差异（54.9毫秒±4.6；95％CI：53.8至56.0毫秒，范围：46.6-70.9毫秒）和自动化处理（55.4毫秒±5.1；95％CI：54.2至56.6毫秒；范围：46.4-75.1毫秒；P=0.099）。管道显示出与患者水平的手动得出的T1ρ值的高度一致性（ICC=0.85；偏差0.52毫秒±5.18）。在16个节段的方法之间未发现心肌T1ρ值的显着差异（P=0.75）。
结论：自动心肌T1ρ标测显示了快速和无创评估心脏病的前景。
方法：3技术效果：第一阶段。
目的：探讨临床综合人工智能驱动分析用于高效和自动化心肌T1ρ映射的可行性。
方法：回顾性。
方法：573例患者分为训练组（N=500）和测试组（N=73），包括缺血性和非缺血性病例。
■1.5T处的单次bSSFPT1ρ映射序列
结果：自动化过程包括：左心室（LV）壁分割，右心室插入点检测和16段模型的创建，用于节段T1ρ值分析。两名放射科医生（20年和7年的MRI经验）提供了基本事实注释。使用Dice系数以手动分割为参考标准来评估观察者间的变异性和分割质量。比较了全局和分段T1ρ值。测量处理时间。
方法：组内相关系数（ICC）和Bland-Altman分析（偏倚±2SD）；配对学生t检验和单因素方差分析。P值<0.05被认为是显著的。
结果：自动化方法显著缩短了处理时间（3秒与1分51秒±22秒)。在测试集中，自动左心室壁分割与手动结果紧密匹配(Dice81.9%±9.0),并与观察者间分割(Dice82.2%±6.5)紧密一致。在患者基础上获得了优异的ICC(0.94[95%CI:0.91至0.96]),偏差为-0.93cm2±6.60。手动之间的全局T1ρ值没有显着差异（54.9毫秒±4.6；95％CI：53.8至56.0毫秒，范围：46.6-70.9毫秒）和自动化处理（55.4毫秒±5.1；95％CI：54.2至56.6毫秒；范围：46.4-75.1毫秒；P=0.099）。管道显示出与患者水平的手动得出的T1ρ值的高度一致性（ICC=0.85；偏差0.52毫秒±5.18）。在16个节段的方法之间未发现心肌T1ρ值的显着差异（P=0.75）。
结论：自动心肌T1ρ标测显示了快速和无创评估心脏病的前景。
方法：3技术效果：第一阶段。
