Detail-on-demand visualization for lean understanding of lung abnormalities.

In some respects, the lung is an anatomical bog - having limited referential landmarks. Nonetheless, precise understanding of the abnormalities that inflict this organ is crucial to effective clinical diagnosis and treatment. However, wading interactively through a three-dimensional scan of the lung poses a visual quagmire to the radiologist, resulting in significant interpretive differences due to inter and intra observer variation. Despite the continuing progress in quantitative imaging, lack of unambiguous visualization with accurately, relevant cues severely hinders the clinical adoption of many computational tools. We address this unmet need through a lean visualization paradigm wherein information is presented hierarchically to provide an interactive macro-to-micro view of lung pathologies. At the macro level, the structural and functional information is summarized into a synoptic glyph that is readily interpreted and correlated to a priori known disease states. The glyphs are "patho-spatio-temporally" tagged to facilitate navigation through the level-of-detail scales, down to the micro level values in the image voxels, providing quantitative interpretation of tissue type and the confidence level in the quantitation. A novel volume compositing scheme is proposed to specify and guide to the optimal site for surgical lung biopsy. This intuitive, interactive interface for rapid and unambiguous navigation towards the clinical endpoint harnesses the power of bio-informatics technology to provide an efficient, clinically relevant and comprehensive summary of pulmonary disease, including precise location, spatial extent and intrinsic character.

Mapping high-fidelity volume rendering for medical imaging to CPU, GPU and many-core architectures.

Medical volumetric imaging requires high fidelity, high performance rendering algorithms. We motivate and analyze new volumetric rendering algorithms that are suited to modern parallel processing architectures. First, we describe the three major categories of volume rendering algorithms and confirm through an imaging scientist-guided evaluation that ray-casting is the most acceptable. We describe a thread- and data-parallel implementation of ray-casting that makes it amenable to key architectural trends of three modern commodity parallel architectures: multi-core, GPU, and an upcoming many-core Intel architecture code-named Larrabee. We achieve more than an order of magnitude performance improvement on a number of large 3D medical datasets. We further describe a data compression scheme that significantly reduces data-transfer overhead. This allows our approach to scale well to large numbers of Larrabee cores.

Genomic expression analysis by single-cell mRNA differential display of quiescent CD8 T cells from tumour-infiltrating lymphocytes obtained from in vivo liver tumours.

We performed a genomic study combining single-cell mRNA differential display and RNA subtractive hybridization to elucidate CD8 T-cell quiescence/ignorance. By comparing actively maintained quiescent CD8 T cells from liver tumour tumour-infiltrating lymphocytes (TILs) with quiescent T cells at the single-cell level, we identified differentially expressed candidate genes by high-throughput screening and comparative analysis of expressed sequence tags (ESTs). While genes for the T-cell receptor, tumour necrosis factor (TNF) receptor, TNF-related apoptosis inducing ligand (TRAIL) and perforin were down-regulated, key genes such as Tob, transforming growth factor (TGF)-beta, lung Krüpple-like factor (LKLF), Sno-A, Ski, Myc, Ets-2 repressor factor (ERF) and RE1-silencing transcription factor (REST/NRSF) complex were highly expressed in the quiescent TIL CD8 cells. Real-time polymerase chain reaction (PCR) further confirmed these results. A regulation model is proposed for actively maintained quiescence in CD8 T cells, including three components: up-regulation of the TGF-beta pathway, a shift in the MYC web and inhibition of the cell cycle.

Experimental and bioinformatics comparison of gene expression between T cells from TIL of liver cancer and T cells from UniGene.

BACKGROUND: The major difficulty of mapping parallel gene expression obtained from solid tumors is mainly due to contaminating cells. In this study, by applying a strategy of parallel gene expression at a cell-cluster or colony level, we have identified the gene expression pattern of T cells within tumor-infiltrating lymphocytes (TJLs) obtained from two liver cancer patients. METHODS: Here a new method was utilized to analyze the parallel gene expression. By using bioinformatics analysis, the data were also compared with T-cell gene expression present in UniGene. RESULTS: Our results demonstrated that 18 genes in specimen A and 13 genes in specimen B were highly expressed after the removal of a nonspecific TIL cDNA library, by pairing gene hybridization; the genes were expressed in CD3+ cells from peripheral blood mononuclear cells (PBMC). By using BlastN search, 17 of the 18, and 12 of the 13 sequences were exhibited, respectively, in Homo sapiens, with a range of BlastN E values of 0 to 4 x 10(-13). The LocusLink distribution in chromosomes obtained from both specimens was not significantly different; 17 of 19 putative genes (both specimen A and specimen B) were observed in the UniGene cluster in Homo sapiens, except for dihydropyrimidinase-related protein-3 and diacyglycerol kinase alpha. Interestingly, only 4 of 19 (21%) putative genes were displayed in the T-cell UniGene database (i.e., LD-78 in Hs. 73817, IL-8 in Hs. 624, TRAIL in Hs. 83429, and Fas ligand in Hs. 2007). CONCLUSIONS: By comparison with the reported data and UniGene, the parallel gene expression of T cells obtained from TIL can provide essential new insights into T-cell activity, T-cell extravasation into tumor tissues, and T-cell cytotoxicity against tumor cells.