Back

Enhancing PET/CT Assessment with Porous 3D Printed Grids: A Pilot Study

Nalla, S. K. K.; Maronnier, Q.; Hazan, T. P.; Kennedy, J.; Caselles, O.

2025-04-01 radiology and imaging
10.1101/2025.04.01.25325022 medRxiv
Show abstract

ObjectivePhantom experiments are widely used for standardisation in positron emission tomography (PET), but current practices to do not necessarily reflect clinical reality and require meticulous phantom preparation for repeatability. 3D printing can reduce these limitations by optimizing preparatory methods and improving phantom features. This work proposes employing 3D-printed porous grids as an alternative mechanism to emulate targets with contrast. ApproachAcrylonitrile butadiene styrene (ABS) cubic grids (4 cm/side) with varying design characteristics and targets were printed. Grids were immersed in a [18F]FDG solution with soap within a conventional phantom. Five consecutive acquisitions were repeated on five different days (Day 0, 1,4-6) using a Discovery MI PET/CT. Target representation index(TRI) and dilution coefficient (DC) were the metrics used for analysis. Friedman test was utilized to study global statistical significance across days. Main ResultsPET images resulted in clear demarcation of various contrast regions produced by the dilution grid. Quantitative metrics showed consistent results across trials, confirming robustness. Dilutions achieved (mean{+/-}std. dev.) were 1.93{+/-}0.14, 2.44{+/-}0.2, and 3.01{+/-}0.08 vs. 2, 2.5 and 3.33 (theoretical), respectively. Observed TRI were in range of 0.4 - 1.2. Correlation across days was strong (p[≥]0.67), and days 1 and 5 had the best pairwise comparable results. Significance3D-printed grids offer a reliable, reproducible alternative for PET/CT assessment. Dozens of targets with background were produced with a single tracer administration. CT attenuation throughout the phantom mimicked water, giving good PET representation of wall-free targets.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.