3D Opto-Acoustic Image Reconstruction and Motion Tracking Using Convex Optimization Algorithms
Bibliographic record
Abstract
This work involves developing techniques for improved opto-acoustic imaging (OA), with the goal of enabling enhanced visualization of vascular structures in biological tissue. This has potential application for diagnosis and detection of cancer and other diseases where blood vessels have structural and functional differences from healthy tissue. In OA systems,acoustic waves are generated by absorption of optical energy. Since hemoglobin absorbs more light than other molecules in tissue, images of the tissue’s blood distribution can be reconstructed by processing measured acoustic signals. Moreover, the wavelength-specific optical absorption of oxy- and deoxy-hemoglobin permits OA to image the blood’s oxygen saturation level. However, OA image quality is limited by the ability to localize acoustic sources in tissue, and by the ability to collect sufficient data to accurately reconstruct tissue properties. ToimproveOAimagequality,thisworkinvestigatesusingconvexmathematicaloptimizationto perform image reconstruction from transducer measurements. The proposed technique iteratively solves an inverse problem by fitting the measured data onto simulated OA signals. To accelerate computational performance, mathematical simplifications for 3D simulation and reconstruction are developed. Using multiple acquisitions to provide 3D volumetric information, a method is developedtodeterminetransducermotion from OAdataduring imagereconstruction. In addition, the ability to visualize blood oxygen saturation is characterized for a clinical OA breast imaging device, and image quality is studied using biologically-relevant tissue phantoms. Results demonstrate that reconstruction with mathematical optimization can achieve higher contrast-to-backgroundratio (CBR) andpeak-signal-to-noise ratio (PSNR) comparedto approaches involving backprojection. In addition, using a separable model for the system’s response reduces computational complexity by a factor of n in a 3D volume with n3 voxels. This potentially enables faster and more accurate image reconstruction in OA systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".