# Advanced breast tomosynthesis reconstruction for improved cancer diagnosis

> **NIH NIH R01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2020 · $575,622

## Abstract

Digital Breast Tomosynthesis (DBT) has been shown to significantly improve the detection and
characterization of soft-tissue lesions and reduce false positive recalls in breast cancer screening. However,
DBT is still at its early stage of clinical use and continued improvement of the system design and
reconstruction methods are crucial to fully exploit its potential. Noise and resolution are major factors in
optimization of an imaging system. The noise in DBT is much higher than that in digital mammograms (DMs)
because the multiple low-dose projections increase the total detector noise. The oblique incidence to the
breast and the detector at large-angle projections further aggravates the noise problem and reduces spatial
resolution. Synthetic mammograms cannot resolve these problems because they are generated from the
DBT. It is known from CT that iterative reconstruction (IR) with properly designed regularizer can significantly
reduce noise. However, IR for CT generally does not consider spatial blur and noise correlation/aliasing.
Modeling these factors has recently started in CBCT that uses flat panel detectors. Model-based IR (MBIR)
technology has not been developed for DBT. DBT is a limited-angle tomography, which, coupled with the
very different target signals that are signs of breast cancer (microcalcifications, spiculated/ill-defined masses
and distortions) than those in CT or CBCT, makes it much more challenging to develop MBIR for DBT.
 The goal of the proposed project is to develop MBIR for DBT by accurate physics and statistics
modeling of the imaging system to improve the image quality of DBT. We will develop accelerated
reconstruction algorithms for these models to facilitate both research and eventual translation to clinical use
of such methods. Our specific aims are: (SA1) prepare three data sets for development of the MBIR method
(simulated DBT projection data, DBT projections of physical phantoms, and human subject DBT projections),
and study the impacts of various image degrading factors on the reconstructed DBT; (SA2) develop MBIR by
optimizing the design of the objective function and the iterative algorithm using the three types of data
obtained in (SA1) and a four-tier approach; and (SA3) validate the developed MBIR method by comparison
with current reconstruction techniques in terms of the detection accuracy of target signals by radiologists
(ROC study) and by computer-aided detection (CAD) systems in human subject DBT images.
 This project brings together two research teams with complementary expertise, one in imaging physics,
image analysis and lesion detection in DBT, the other in statistical iterative reconstruction for CT/SPECT/
PET/MRI, to tackle this limited-angle reconstruction problem. If successful, DBT reconstructed with the new
MBIR method is expected to improve the efficacy of early breast cancer detection and diagnosis and reduce
dose. Reducing dose and noise will also facilitate the optimization of overall DB...

## Key facts

- **NIH application ID:** 9839489
- **Project number:** 5R01CA214981-03
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** HEANG-PING CHAN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $575,622
- **Award type:** 5
- **Project period:** 2018-01-10 → 2021-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9839489

## Citation

> US National Institutes of Health, RePORTER application 9839489, Advanced breast tomosynthesis reconstruction for improved cancer diagnosis (5R01CA214981-03). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/9839489. Licensed CC0.

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