Adaptive statistical iterative reconstruction improves image quality without affecting perfusion CT quantitation in...

Area of Science:

• Radiology and Imaging
• Oncology
• Medical Physics

Background:

• Perfusion CT (pCT) is crucial for assessing colorectal cancer characteristics.
• Image quality in pCT impacts diagnostic accuracy.
• Adaptive Statistical Iterative Reconstruction (ASIR) is an advanced image processing technique.

Purpose of the Study:

• To evaluate the impact of varying Adaptive Statistical Iterative Reconstruction (ASIR) levels on perfusion CT (pCT) image quality.
• To assess the effect of ASIR on quantitative pCT parameters in primary colorectal cancer.
• To determine optimal ASIR settings for colorectal cancer pCT.

Main Methods:

• A prospective observational study involving 32 patients with colorectal adenocarcinoma.
• pCT scans were acquired and reconstructed using ASIR percentages from 0% to 100%.
• Image noise, contrast-to-noise ratio (CNR), and pCT parameters (BF, BV, MTT, PS) were quantified and analyzed.

Main Results:

• Increasing ASIR percentages significantly reduced image noise (33%) and increased CNR (61%).
• Tumor CNR exceeded 1.5 at 60% ASIR and higher.
• Mean differences in perfusion parameters (BF, BV, MTT, PS) across ASIR levels were minimal (<3%).

Conclusions:

• ASIR effectively improves image noise and CNR in colorectal cancer pCT.
• ASIR can be applied without substantially altering critical perfusion parameter quantitation.
• ASIR offers a valuable tool for enhancing diagnostic quality in colorectal cancer imaging.