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3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

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Thick slices from tomosynthesis data sets: phantom study for the evaluation of different algorithms.

Felix Diekmann¹, Henning Meyer, Susanne Diekmann

¹Department of Radiology, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany. felix.diekmann@charite.de

Journal of Digital Imaging

|October 24, 2007

Summary

Different algorithms for creating thicker slices from tomosynthesis (3D mammography) images were evaluated. The average algorithm best visualized masses, while MIP best visualized microcalcifications, suggesting thick slices improve object detection.

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Area of Science:

Medical Imaging
Radiology
Digital Signal Processing

Background:

Tomosynthesis, a 3D mammography technique, produces thin slices with high noise.
Thicker slices (1-cm) generated from tomosynthesis data may improve image interpretation by reducing noise.

Purpose of the Study:

To evaluate different postprocessing algorithms for generating thick slices from tomosynthesis source data.
To compare the effectiveness of Maximum Intensity Projection (MIP), Average (AV), and a novel softMip algorithm in visualizing simulated lesions.

Main Methods:

A homogeneous phantom with simulated microcalcifications, spiculated masses, and round masses was imaged using tomosynthesis.
Reconstructed thin slices were postprocessed using MIP, AV, and softMip to create 1-cm thick slices.
Algorithms were assessed by calculating contrast for microcalcifications and contrast-to-noise ratios (CNR) for masses.

Main Results:

The average algorithm yielded the most favorable CNR for simulated round and spiculated masses.
MIP provided the best contrast for simulated microcalcifications.
SoftMip demonstrated intermediate performance between MIP and AV for both mass and microcalcification visualization.

Conclusions:

Generating 1-cm thick slices from tomosynthesis data can enhance object visualization.
The optimal postprocessing algorithm for thick slice generation varies depending on the type of simulated lesion (masses vs. microcalcifications).
SoftMip offers a hybrid approach with performance between MIP and AV.