Analysis of Neural Networks in Terms of Domain Functions

Despite their success-story, artificial neural networks have one major disadvantage compared to other techniques: the inability to explain comprehensively how a trained neural network reaches its output; neural networks are not only (incorrectly) seen as a "magic tool" but possibly even more as a mysterious "black box". Although much research has already been done to "open the box," there is a notable hiatus in known publications on analysis of neural networks. So far, mainly sensitivity analysis and rule extraction methods have been used to analyze neural networks. However, these can only be applied in a limited subset of the problem domains where neural network solutions are encountered. In this paper we propose a wider applicable method which, for a given problem domain, involves identifying basic functions with which users in that domain are already familiar, and describing trained neural networks, or parts thereof, in terms of those basic functions. This will provide a comprehensible description of the neural network's function and, depending on the chosen base functions, it may also provide an insight into the neural network' s inner "reasoning." It could further be used to optimize neural network systems. An analysis in terms of base functions may even make clear how to (re)construct a superior system using those base functions, thus using the neural network as a construction advisor

Minimizing rubidium-82 tracer activity for relative PET myocardial perfusion imaging

Objectives: Recommended rubidium-82 activities for relative myocardial perfusion imaging (MPI) using present-generation PET scanners may be unnecessarily high. Our aim was to derive the minimum activity for a reliable relative PET MPI assessment. Materials and methods: We analyzed 140 scans from 28 consecutive patients who underwent rest-stress MPI-PET (Ingenuity TF). Scans of 852, 682, 511, and 341 MBq were simulated from list-mode data and compared with a reference scan using 1023 MBq. Differences in the summed rest score, total perfusion deficit, and image quality were obtained between the reference and each of the simulated rest scans. Combined stress-rest scans obtained at a selected activity of 682 MBq were diagnostically interpreted by experts and outcome was compared with the reference scan interpretation. Results: Differences in summed rest score more than or equal to 3 were found using 682, 511, and 341 MBq in two (7%), four (14%), and five (18%) patients, respectively. Differences in total perfusion deficit more than 7% were only found at 341 MBq in one patient. Image quality deteriorated significantly only for the 341 MBq scans (P<0.001). Interpretation of stress-rest scans did not differ between 682 and 1023 MBq scans. Conclusion: A significant reduction in administered Rb-82 activity is feasible in relative MPI. An activity of 682 MBq resulted in reliable diagnostic outcomes and image quality, and can therefore be considered for clinical adoption

Label-free Raman imaging of rat insulinoma INS-1E beta-cells.

<p>(A) Brightfield image of a beta-cell. (B) Raman cluster image acquired by scanning the confocal laser beam in a 64x64 raster pattern. Analysis with 3 levels of clustering illustrates the spatial variation of Raman signal within a cell by distinguishing subcellular structures. (C) Corresponding Raman cluster spectra (corrected for background) show the average signal for cytoplasm (magenta) and nucleus (red). Enlargement shows the Raman band at 520 cm-1, specific for difsulfide-bridged cysteine. Spectra are vertically offset for clear representation. Integrating over specific Raman bands shows (D) the distribution of DNA (783 cm-1, Δ = 26 cm-1) corresponding to red cluster in B, and (E) the distribution of disulfide bridges between cysteine groups (524 cm-1, Δ = 30 cm-1). Scale bar represents 10 µm. </p

Label-free Raman imaging of primary human islets of Langerhans.

<p>(A) Brightfield microscopy image of a cryosection of an islet of Langerhans. (B) Fluorescence microscopy image of the islet stained for DNA (blue), insulin (red) and glucagon (green). (C) Corresponding Raman cluster image and (D) corresponding Raman cluster spectra, acquired by scanning the confocal laser bundle in a 64x64 raster pattern. Analysis with 3 levels of clustering illustrates the spatial variation of Raman signal within the islet. Enlargement shows the disulfide band at 520 and the tryptophan band at 1552 cm-1. Spectra are vertically offset for clear representation. The bottom panel shows hyperspectral Raman images for (E) DNA (783 cm-1, Δ = 26 cm-1), (F) disulfide bridges between cysteine groups (524 cm-1, Δ = 30 cm-1) and (G) tryptophan (1545 cm-1, Δ = 21 cm-1). Minor artifacts caused by the image-stitching procedure are visible in the Raman images. Scale bar represents 50 µm. </p

Confocal Raman spectroscopy of purified hormones.

<p>(A) Raman fingerprint of insulin (a), glucagon (b), amylin (c), somatostatin (d), pancreatic polypeptide (e). Spectra are vertically offset for clear representation. Bands specific for sulfide bridges between cysteines in insulin were found at 520 and 662 cm-1. Tryptophan-specific bands at 759 and 1552 cm-1 were found in spectrum of glucagon. (B) Amino acid composition of insulin (a), containing three Raman-active sulfide bridges between cysteine groups, and glucagon (b), containing the Raman-active amino acid tryptophan. </p

Label-free detection of insulin and glucagon in human islets.

<p>The top panel shows the univariate Raman images of a cryosection of an islet of Langerhans for (A) DNA (783 cm-1, Δ = 26 cm-1), (B) disulfide bridges between cysteine groups (524 cm-1, Δ = 30 cm-1) and (C) tryptophan (1545 cm-1, Δ = 21 cm-1). The second panel shows the corresponding fluorescence microscopy images stained for (E) DNA, (F) insulin and (G) glucagon. The bottom panel shows the overlay images of the Raman image (in purple) and fluorescence image (green) for (I) DNA, (J) insulin and (K) glucagon. (D) Merged Raman spectroscopy image images of DNA (blue), difsulfide bridged cysteine (red) and tryptophan (green). (H) Merged fluorescence image of DAPI (blue), insulin (red) and glucagon (green). Scale bar represents 50 µm.</p

Development of an Ex Vivo, Beating Heart Model for CT Myocardial Perfusion

Objective. To test the feasibility of a CT-compatible, ex vivo, perfused porcine heart model for myocardial perfusion CT imaging. Methods. One porcine heart was perfused according to Langendorff. Dynamic perfusion scanning was performed with a second-generation dual source CT scanner. Circulatory parameters like blood flow, aortic pressure, and heart rate were monitored throughout the experiment. Stenosis was induced in the circumflex artery, controlled by a fractional flow reserve (FFR) pressure wire. CT-derived myocardial perfusion parameters were analysed at FFR of 1 to 0.10/0.0. Results. CT images did not show major artefacts due to interference of the model setup. The pacemaker-induced heart rhythm was generally stable at 70 beats per minute. During most of the experiment, blood flow was 0.9-1.0 L/min, and arterial pressure varied between 80 and 95mm/Hg. Blood flow decreased and arterial pressure increased by approximately 10% after inducing a stenosis with FF