Automated Region of Interest Detection Method in Scintigraphic Glomerular Filtration Rate Estimation

Area of Science:

• Nuclear Medicine
• Medical Imaging Analysis
• Renal Physiology

Background:

• Glomerular filtration rate (GFR) is essential for assessing kidney function.
• Manual region of interest (ROI) definition in dynamic renal scintigraphy for GFR estimation is subjective and time-consuming.
• Existing automated methods lack robustness and accuracy.

Purpose of the Study:

• To develop and validate a fully automated method for kidney and background ROI detection in dynamic renal scintigraphy.
• To improve the accuracy and robustness of GFR estimation using automated ROI detection.
• To compare the performance of the proposed method against existing techniques.

Main Methods:

• Image preprocessing followed by a shape prior constrained level set (spLS) algorithm for kidney ROI delineation.
• Automated background ROI generation based on identified kidney ROIs.
• Validation using computer simulations and clinical analysis of 223 patient datasets (99mTc-diethylenetriaminepentaacetic acid scintigraphy).

Main Results:

• The spLS method demonstrated superior performance in kidney ROI detection compared to threshold and Chan-Vese level set (cvLS) methods in simulations.
• GFR estimations using spLS-derived ROIs showed high consistency and correlation (r = 0.98, p < 0.001) with physician-based reference values in clinical application.
• The automated method significantly improved upon previous ROI detection techniques.

Conclusions:

• The proposed automated ROI detection method using spLS is accurate and robust for GFR estimation.
• This technique offers significant potential to streamline and enhance the reliability of GFR assessment in clinical dynamic renal scintigraphy.
• Automating ROI detection can overcome the limitations of manual methods in clinical practice.