Copyright
©The Author(s) 2025.
World J Cardiol. Jul 26, 2025; 17(7): 108745
Published online Jul 26, 2025. doi: 10.4330/wjc.v17.i7.108745
Published online Jul 26, 2025. doi: 10.4330/wjc.v17.i7.108745
Table 1 Participant demographics
Male | Female | Total | |||
Participants (%) | 42 (47) | 47 (53) | 89 | ||
Patients | 26 (43) | 34 (57) | 60 | ||
Volunteers | 16 (55) | 13 (45) | 29 | ||
Ethnicity (%) | White | Black | Hispanic | Asian | Total |
Participants | 68 (76) | 15 (18) | 1 (1) | 5 (5) | 89 |
Patients | 46 (77) | 13 (21) | 1 (2) | 0 (0) | 60 |
Volunteers | 22 (77) | 2 (7) | 0 (0) | 5 (16) | 29 |
Age (year) | Male | Female | Total | ||
Participants | 57.3 (19-83) | 59.6 (18-83) | 58.4 (18-83) | ||
Patients | 62.5 (30-88) | 63.0 (18-83) | 62.8 (18-83) | ||
Volunteers | 48.6 (19-77) | 50.8 (24-71) | 49.6 (19-77) | ||
BSA (m2) | Male | Female | Total | ||
Participants | 2.04 (1.70-2.45) | 1.84 (1.31-2.87) | 1.93 (1.31-2.87) | ||
Heart rate | Male | Female | Total | ||
Participants | 66 (46-113) | 67 (47-108) | 67 (46-113) | ||
Clinical referral indication | Patients | ||||
r/o cardiotoxicity | 29 | ||||
r/o amyloidosis | 30 | ||||
r/o myocarditis | 3 | ||||
r/o cardiomyopathy | 17 | ||||
Arrhythmia | 9 | ||||
Chest pain | 9 |
Table 2 Conventional CINE and Artificial-intelligence-assisted compressed sensing CINE image parameters
C-CINE | AI-CS-CINE | |
ECG mode | Retrospective | Retrospective |
TR/TE (millisecond) | 3.57/1.75 | 2.74/1.28 |
Image matrix | 224 × 85 | 192 × 100 |
Reconstruction matrix | 2.0 | 1.5 |
Spatial resolution (mm) | 1.89 × 1.61 | 1.88 × 1.88 |
Flip angle (°) | 80 | 60 |
Bandwidth (Hz/pixel) | 1500 | 1200 |
Temporal resolution (millisecond) | 54 | 41 |
Reconstructed cardiac phases | 25 | 25 |
Field of view (mm) | 360 × 320 | 360 × 320 |
Slice thickness (mm) | 8 | 8 |
Gap (mm) | 0 | 0 |
Number of slices | 11 | 11 |
Longest breath-hold for acquisition (second) | 11 | 11 |
Number of slices acquired per 11-sec breath hold | 1 | 6 |
Number of breath-holds | 11 | 2 |
Shortest breath-hold time to acquire one slice (second) | 11 | 2 |
Total acquisition time including breath-holds (second) | 238 | 37 |
Table 3 Alignment of left ventricle and right ventricle volume and function metrics with conventional CINE vs artificial-intelligence-assisted compressed sensing CINE excluding outliers (n = 89)
mean ± SD | Bland–Altman | Intraclass coefficient | ||||
C-CINE | AI-CS-CINE | Bias ± SD | LoA | r | 95%CI | |
LVEF (%) | 56.2 ± 11.0 | 55.9 ± 11.1 | 0.28 ± 3.78 | -7.13, 7.69 | 0.94 | 0.91, 0.96 |
LVEDVi (mL/m2) | 75.9 ± 19.0 | 73.3 ± 19.0 | 2.63 ± 5.30 | -7.76, 13.01 | 0.95 | 0.93, 0.97 |
LVESVi (mL/m2) | 34.4 ± 16.0 | 33.4 ± 15.7 | 1.02 ± 3.35 | -5.55, 7.59 | 0.98 | 0.96, 0.98 |
LVSVi (mL/m2) | 41.5 ± 9.3 | 39.9 ± 9.6 | 1.61 ± 4.21 | -6.63, 9.85 | 0.89 | 0.83, 0.92 |
LVMi (mL/m2) | 58.4 ± 20.3 | 59.3 ± 20.6 | 0.91 ± 4.14 | -9.01, 7.20 | 0.98 | 0.97, 0.99 |
Relative wall thickness | 0.4 ± 0.2 | 0.4 ± 0.2 | 0.00 ± 0.05 | -0.10, 0.10 | 0.95 | 0.93, 0.97 |
RVEF (%) | 50.8 ± 10.0 | 51.5 ± 10.3 | 0.66 ± 7.32 | -15.01, 13.69 | 0.73 | 0.62, 0.82 |
RVEDVi (mL/m2) | 71.0 ± 14.8 | 69.3 ± 14.7 | 1.74 ± 6.60 | -11.21, 14.68 | 0.89 | 0.84, 0.93 |
RVESVi (mL/m2) | 35.1 ± 11.0 | 33.6 ± 10.4 | 1.51 ± 5.28 | -8.84, 11.87 | 0.87 | 0.81, 0.91 |
RVSVi (mL/m2) | 35.9 ± 9.7 | 35.7 ± 10.5 | 0.22 ± 7.12 | -13.74, 14.18 | 0.75 | 0.64, 0.83 |
Table 4 Alignment of left ventricle and right ventricle volume and function metrics with Conventional CINE vs Artificial-intelligence-assisted compressed sensing CINE in patients with cardiac amyloidosis (n = 31)
mean ± SD | Bland–Altman | Intraclass coefficient | ||||
C-CINE | AI-CS-CINE | Bias ± SD | LoA | r | 95%CI | |
LVEF (%) | 55.9 ± 13.2 | 57.0 ± 13.2 | -1.05 ± 4.56 | -10.00, 7.89 | 0.94 | 0.87, 0.97 |
LVEDVi (mL/m2) | 75.1 ± 21.1 | 72.0 ± 20.2 | 3.16 ± 7.07 | 10.70, 17.02 | 0.93 | 0.85, 0.97 |
LVESVi (mL/m2) | 34.8 ± 19.0 | 32.2 ± 17.2 | 2.55 ± 6.12 | -9.45, 14.55 | 0.93 | 0.86, 0.97 |
LVSVi (mL/m2) | 40.4 ± 10.2 | 39.8 ± 11.1 | 0.61 ± 4.52 | -8.26, 9.47 | 0.91 | 0.82, 0.95 |
LVMi (mL/m2) | 69.9 ± 29.0 | 70.4 ± 29.5 | -0.42 ± 5.81 | 11.81, 10.97 | 0.98 | 0.96, 0.99 |
Relative wall thickness | 0.5 ± 0.2 | 0.5 ± 0.2 | 0.00 ± 0.06 | -0.13, 0.12 | 0.96 | 0.91, 0.98 |
RVEF (%) | 48.9 ± 14.6 | 52.1 ± 13.0 | -3.14 ± 9.09 | 20.96, 14.68 | 0.76 | 0.56, 0.88 |
RVEDVi (mL/m2) | 70.8 ± 16.2 | 70.9 ± 15.1 | -0.02 ± 8.72 | 17.12, 17.07 | 0.84 | 0.70, 0.92 |
RVESVi (mL/m2) | 37.1 ± 16.5 | 34.5 ± 13.5 | 2.64 ± 7.65 | 12.36, 17.64 | 0.86 | 0.72, 0.93 |
RVSVi (mL/m2) | 33.7 ± 10.7 | 36.4 ± 10.5 | -2.66 ± 8.57 | 19.46, 14.13 | 0.65 | 0.40, 0.81 |
- Citation: Wang H, Schmieder A, Watkins M, Wang P, Mitchell J, Qamer SZ, Lanza G. Artificial intelligence-assisted compressed sensing CINE enhances the workflow of cardiac magnetic resonance in challenging patients. World J Cardiol 2025; 17(7): 108745
- URL: https://www.wjgnet.com/1949-8462/full/v17/i7/108745.htm
- DOI: https://dx.doi.org/10.4330/wjc.v17.i7.108745