PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 15950895

Extracting and visualizing physiological parameters using dynamic contrast-enhanced magnetic resonance imaging of the breast.

Paul Armitage·Christian Behrenbruch·Michael Brady·Niall Moore
View original source ↗Export metadata ↓

Abstract

An analysis procedure is presented that enables the acquisition and visualization of physiologically relevant parameters using dynamic contrast-enhanced magnetic resonance imaging. The first stage of the process involves the use of a signal model that relates the measured magnetic resonance signal to the contrast agent concentration. Since the model requires knowledge of the longitudinal relaxation time T(1), a novel optimization scheme is presented which ensures a reliable measurement. Pharmacokinetic modelling of the observed contrast agent uptake is then performed to obtain physiological parameters relating to microvessel leakage permeability and volume fraction and the assumptions made in the derivation of these parameters are discussed. A simple colour representation is utilized that enables the relevant physiological information to be conveyed to the clinician in a visually efficient and meaningful manner. A second representation, based on vector maps, is also devised and it is demonstrated how this can be used for malignant tumour segmentation. Finally, the procedure is applied to 14 pre- and post-chemotherapy breast cases to demonstrate the clinical value of the technique. In particular, the apparent improved representation of tissue vascularity when compared to conventional methods and the implications for this in treatment assessment are discussed.

Explore related subjects

AlgorithmsArea Under CurveBayes TheoremBreast NeoplasmsContrast MediaDiagnosis, DifferentialFemaleHumansImage Processing, Computer-AssistedImaging, Three-DimensionalMagnetic Resonance ImagingPhantoms, ImagingJournal ArticleResearch Support, Non-U.S. Gov't

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Paul Armitage, Christian Behrenbruch, Michael Brady, Niall Moore. 2005-04-21. Extracting and visualizing physiological parameters using dynamic contrast-enhanced magnetic resonance imaging of the breast.. https://doi.org/10.1016/j.media.2005.01.001

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2026

A note on a generalized single step theory for any number of hierarchical genomic matrices.

BACKGROUND: The Single Step algorithm allows combining information from genotyped and un-genotyped individuals, provided they are connected by a pedigree. However, current single step theory is limited to a single list of markers. RESULTS: We present a generalized single step (GSS) method that can accommodate any number of hierarchical molecular datasets (e.g. sequence, high and low density arrays) and pedigree, avoiding imputation. We prove that a similar efficient inversion algorithm exists. The method is recursive, starting with the highest marker density scenario. We illustrate the method with simulation and show that GSS can increase predictive accuracy compared to standard single step. R code is provided so that custom scenarios can be easily compared, either with simulated or real data. CONCLUSION: The method developed generalizes extant single step theory to any number of hierarchical molecular relationship matrices, broadening the scenarios where single step can be applied. A topic of particular interest can be ecology field data or human populations where pedigree is not available, but where samples sequenced and genotyped at different densities can exist. GSS can also be a useful tool to optimize allocation of genotyping and / or sequencing resources.

Algorithms↗
PubMed2026

cgDist: Nucleotide-level distance calculation from cgMLST allelic profiles.

Bacterial genomic surveillance requires balancing computational efficiency with genetic resolution for effective cluster investigation. cgMLST distance calculations treat all allelic differences as equivalent units, obscuring nucleotide-level variation. Furthermore, single nucleotide polymorphism-based pipelines provide finer resolution at substantially higher computational cost, which limits their routine deployment in surveillance laboratories. We present cgDist, an algorithm that calculates nucleotide-level distances directly from cgMLST allelic profiles, providing finer resolution than allele-count distances by leveraging within-allele nucleotide variation. The cache architecture stores alignment statistics, enabling distance calculation modes without computation and supporting both dataset-specific and schema-complete cache generation. This design enables incremental surveillance analysis, with performance benefits as laboratories accumulate alignment data. cgDist functions as a precision 'zoom lens' for the investigation of clusters identified through initial cgMLST screening. Rather than restructuring population relationships, this targeted approach concentrates enhanced resolution where it is most informative. The algorithm ensures that cgDist distances are greater than or equal to corresponding cgMLST distances, preserving epidemiological interpretability while adding genetic discrimination. By increasing resolution within identified clusters, cgDist may also support outbreak investigation, a potential application that remains to be evaluated on outbreak-derived data.

Algorithms↗
PubMed2026

Theseus: fast and optimal affine-gap sequence-to-graph alignment.

MOTIVATION: Sequence-to-graph alignment is a central problem in bioinformatics, with applications in multiple sequence alignment (MSA) and pangenome analysis, among others. However, current algorithms for optimal affine-gap alignment impose high memory and computational requirements, limiting their scalability to aligning long sequences to complex graphs. Practical solutions partially address this problem using heuristic strategies that ultimately trade off optimality for speed. RESULTS: This work presents Theseus, a novel, fast, and optimal affine-gap sequence-to-graph alignment algorithm. Theseus leverages similarities between genomic sequences to accelerate the alignment computation and reduces the overall memory requirements without compromising optimality. To that end, Theseus processes only a subset of the dynamic programming cells, using a sparse-data strategy that enables efficient sequence-to-graph alignment. Moreover, our algorithm supports optimal affine-gap alignment on arbitrary directed graphs, including those with cycles. We evaluate Theseus on two key problems: MSA and pangenome read mapping. For MSA, we compare it against SPOA, abPOA, and POASTA. Theseus is 1.6× to 17.6× faster than POASTA, and 7.3× faster, on average, than SPOA, both optimal aligners. Compared with abPOA, Theseus ensures optimality and scales to the largest problems. For pangenome read mapping, we benchmark Theseus against the alignment stage of the mapping tool vg map, along with the alignment kernels of SPOA, abPOA, and POASTA. Theseus outperforms the other methods, showing a 1.9× to 16.9× speedup on short reads. Moreover, Theseus is 1.5× to 36.3× faster than vg when aligning against synthetic cyclic graphs. AVAILABILITY AND IMPLEMENTATION: Theseus code and documentation are publicly available at https://github.com/albertjimenezbl/theseus-lib.

Algorithms↗