Publications

9 Publications visible to you, out of a total of 9

Abstract (Expand)

PURPOSE: To determine whether systematic differences were present between myocardial R2* values obtained with two different decay models: truncation and exponential + constant (Exp-C). METHODS: Single-center cohorts were used to compare black and bright blood sequences separately, and a multicenter cohort of mixed bright and black blood studies was used to assess the generalizability. Truncated exponential estimates were calculated with CMRtools, which uses a single region of interest (ROI) method. Exp-C estimates were calculated using a pixelwise approach. RESULTS: No differences could be distinguished based upon whether a white or black blood sequence was examined. The two fitting algorithms yielded similar R2* values, with R-squared values exceeding 0.997 and a coefficient of variation of 3% to 4%. Results using the pixelwise method yielded a small systematic bias ( approximately 3%) that became apparent in patients with severe iron deposition. This disparity disappeared when Exp-C fitting was used on a single ROI, suggesting that the use of pixelwise mapping was responsible for the bias. In the multicenter cohort, the strong agreement between the two fitting approaches was reconfirmed. CONCLUSION: Cardiac R2* values are independent of the signal model used for its calculation over clinically relevant ranges. Clinicians can compare results among centers using these disparate approaches with confidence.

Authors: A. Meloni, H. Y. Jr Rienhoff, A. Jones, A. Pepe, M. Lombardi, J. C. Wood

Date Published: 15th Oct 2013

Publication Type: Not specified

Abstract (Expand)

With next-generation sequencing (NGS) technologies, the life sciences face a deluge of raw data. Classical analysis processes for such data often begin with an assembly step, needing large amounts of computing resources, and potentially removing or modifying parts of the biological information contained in the data. Our approach proposes to focus directly on biological questions, by considering raw unassembled NGS data, through a suite of six command-line tools. Dedicated to ‘whole-genome assembly-free’ treatments, the Colib’read tools suite uses optimized algorithms for various analyses of NGS datasets, such as variant calling or read set comparisons. Based on the use of a de Bruijn graph and bloom filter, such analyses can be performed in a few hours, using small amounts of memory. Applications using real data demonstrate the good accuracy of these tools compared to classical approaches. To facilitate data analysis and tools dissemination, we developed Galaxy tools and tool shed repositories. With the Colib’read Galaxy tools suite, we enable a broad range of life scientists to analyze raw NGS data. More importantly, our approach allows the maximum biological information to be retained in the data, and uses a very low memory footprint.

Authors: Yvan Le Bras, Olivier Collin, Cyril Monjeaud, Vincent Lacroix, Éric Rivals, Claire Lemaitre, Vincent Miele, Gustavo Sacomoto, Camille Marchet, Bastien Cazaux, Amal Zine El Aabidine, Leena Salmela, Susete Alves-Carvalho, Alexan Andrieux, Raluca Uricaru, Pierre Peterlongo

Date Published: 1st Dec 2016

Publication Type: Not specified

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