Traditional diagnostic methods for pelvic floor dysfunction （PFD），such as the Pelvic Organ Prolapse Quantification （POP-Q） system and two-dimensional ultrasound，have limitations including strong subjectivity，inability to achieve accurate and comprehensive dynamic assessment，and lack of unified diagnostic criteria. With the advancement of three-dimensional reconstruction and digital technologies，stereoscopic models based on imaging data such as magnetic resonance imaging （MRI） have enabled visualization and precise quantification of pelvic floor structures. These intuitive graphics can significantly enhance clinicians' ability to identify anatomical changes in the pelvic floor while facilitating better understanding of the causes of functional abnormalities. Dynamic MRI combined with three-dimensional reconstruction technology utilizes various reference lines （such as the pubococcygeal line，midpubic line，H-line，M-line，etc.） in the assessment of multi-compartment prolapse. These lines are employed to evaluate organ displacement，the state of supporting structures，and the degree of prolapse in different compartments，playing a significant role in identifying anatomical defects，achieving dynamic functional assessment，and guiding individualized treatment. However，the clinical application of these reference lines faces challenges due to notable discrepancies between their evaluation results and the POP-Q scoring system，with the underlying reasons remaining unclear，which hinders widespread clinical adoption. Additionally，three-dimensional digital imaging diagnosis is susceptible to factors such as patient positioning compliance during examination，MRI equipment，proficiency in pelvic floor anatomy，and experience in image data interpretation. Currently，there is a lack of large-scale evidence-based support for this approach. In addition，the promotion of 3D reconstruction technology faces challenges such as implementation conditions，shortage of professionals，and lack of data standardization. In the future，further optimization of image fusion algorithms is needed to promote the standardization，individualization，and intelligentization of diagnostic processes.