Algebraic reconstruction techniques for spectral reconstruction in diffuse optical tomography.

Appl Opt

Tomographic Imaging Systems, Philips Research, Röntgenstrasse 24, 22335 Hamburg, Germany.

Published: December 2008

Reconstruction in diffuse optical tomography (DOT) necessitates solving the diffusion equation, which is nonlinear with respect to the parameters that have to be reconstructed. Currently applied solving methods are based on the linearization of the equation. For spectral three-dimensional reconstruction, the emerging equation system is too large for direct inversion, but the application of iterative methods is feasible. Computational effort and speed of convergence of these iterative methods are crucial since they determine the computation time of the reconstruction. In this paper, the iterative methods algebraic reconstruction technique (ART) and conjugated gradients (CGs) as well as a new modified ART method are investigated for spectral DOT reconstruction. The aim of the modified ART scheme is to speed up the convergence by considering the specific conditions of spectral reconstruction. As a result, it converges much faster to favorable results than conventional ART and CG methods.

Download full-text PDF

Source
http://dx.doi.org/10.1364/ao.47.006392DOI Listing

Publication Analysis

Top Keywords

iterative methods
12
algebraic reconstruction
8
spectral reconstruction
8
reconstruction diffuse
8
diffuse optical
8
optical tomography
8
speed convergence
8
modified art
8
reconstruction
7
methods
5

Similar Publications

Objective: Deep brain stimulation (DBS) is an effective neurosurgical option for patients with treatment-resistant obsessive-compulsive disorder (OCD). Despite being more costly than neuroablative procedures of comparable efficacy, DBS has gained popularity over the years for its reversibility and adjustability. Although the cost-effectiveness of DBS has been investigated extensively in movement disorders, few economic analyses of DBS for psychiatric disorders exist.

View Article and Find Full Text PDF

Purpose: This study investigates mental health-related content to delineate potentially deficient topics for improvement in future obstetrics and gynecology (OBGYN) resident educational curriculum initiatives.

Method: In this quantitative content analysis, educational resources commonly used by OBGYN residents were selected based on a 2020 multi-institutional survey of OBGYN residents and informal group discussion with 32 OBGYN residents from a New York academic institution in April 2020. After independent screening, the authors iteratively developed, tested, and implemented a coding scheme for relevant keywords.

View Article and Find Full Text PDF

DrugAssist: a large language model for molecule optimization.

Brief Bioinform

November 2024

Department of Computer Science, Hunan University, Changsha 410008, China.

Recently, the impressive performance of large language models (LLMs) on a wide range of tasks has attracted an increasing number of attempts to apply LLMs in drug discovery. However, molecule optimization, a critical task in the drug discovery pipeline, is currently an area that has seen little involvement from LLMs. Most of existing approaches focus solely on capturing the underlying patterns in chemical structures provided by the data, without taking advantage of expert feedback.

View Article and Find Full Text PDF

Background: New methods developed to estimate when AD biomarkers became abnormal in individuals have shown considerable heterogeneity in amyloid and tau pathology onset age. This work used polygenic scores (PGS) generated from CSF Aβ and ptau GWAS, individual-level genetic data, and estimated tau onset age (ETOA) to identify genetic influences on tau onset beyond APOE.

Method: Participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) with genetic data, CSF biomarkers (Aβ and ptau), and longitudinal [F]Flortaucipir (FTP) tau PET were analyzed (N = 462).

View Article and Find Full Text PDF

Background: In Alzheimer's disease (AD), specific brain regions become vulnerable to pathology while others remain resilient. New methods of imaging such as highly multiplexed immunofluorescence (MxIF) provide an abundance of spatial information, while analytical techniques like machine learning (ML) can address questions of cellular contributors to this regional vulnerability.

Method: We performed MxIF staining for 26 markers and compared postmortem human samples from an AD-susceptible brain area, the prefrontal cortex (PFC, Brodmann's areas 9, 10 or 46) to an AD-resilient brain area, the primary visual cortex (V1, area 17).

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!