Prospective Study
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
Figure 5
Figure 5 Artificial-intelligence-assisted compressed sensing CINE improved image quality and quantitative reliability in challenging cardiac amyloidosis cases. A: In a 75-year-old male patient diagnosed with cardiac amyloidosis and small pericardial effusion, poor image quality of the short-axis stack in conventional CINE (C-CINE) was observed due to bad breath holds and poor ECG triggering. These cardiac conditions hindered the patient’s ability to hold his breath for the required 11 seconds during C-CINE acquisition. Consequently, the endomyocardium and perimyocardium were poorly delineated, and trabeculae were not clearly visible (upper row). Such images were considered unsuitable for quantitative image analysis. However, the patient was able to hold his breath during the 2-second artificial-intelligence-assisted compressed sensing CINE (AI-CS-CINE) acquisition, enabling successful image analysis on AI-CS-CINE images only (lower row). Quantitative analysis of wall thickness on C-CINE (upper row) yielded substantially different results compared to AI-CS-CINE (lower row); B: In an 84-year-old female patient diagnosed with cardiac amyloidosis, poor ECG triggering resulted in missing imaging frames during the diastolic phase. Both C-CINE (top row) and AI-CS-CINE (bottom row) exhibited satisfactory image quality for quantitative analysis. However, quantitative metrics did not align well between C-CINE and AI-CS-CINE (right column). Due to inadequate ECG triggering, missing image frames during the diastolic phase were observed in C-CINE, with the end of the full diastolic phase not being displayed (top row). This discrepancy is evident in the 3-chamber view (middle column, arrow). In contrast, the end of the full diastolic phase was successfully displayed in AI-CS-CINE (bottom row, arrow). In this outlying case, AI-CS-CINE outperformed C-CINE, making the metrics derived from AI-CS-CINE more reliable. CINE: Conventional CINE; AI-CS-CINE: Artificial-intelligence-assisted compressed sensing CINE.