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Loktyushin A, Herz K, Dang N, Glang F, Deshmane A, Weinmüller S, Doerfler A, Schölkopf B, Scheffler K, Zaiss M. MRzero - Automated discovery of MRI sequences using supervised learning. Magn Reson Med 2021; 86:709-724. [PMID: 33755247 DOI: 10.1002/mrm.28727] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 01/15/2021] [Accepted: 01/20/2021] [Indexed: 12/30/2022]
Abstract
PURPOSE A supervised learning framework is proposed to automatically generate MR sequences and corresponding reconstruction based on the target contrast of interest. Combined with a flexible, task-driven cost function this allows for an efficient exploration of novel MR sequence strategies. METHODS The scanning and reconstruction process is simulated end-to-end in terms of RF events, gradient moment events in x and y, and delay times, acting on the input model spin system given in terms of proton density, T 1 and T 2 , and Δ B 0 . As a proof of concept, we use both conventional MR images and T 1 maps as targets and optimize from scratch using the loss defined by data fidelity, SAR penalty, and scan time. RESULTS In a first attempt, MRzero learns gradient and RF events from zero, and is able to generate a target image produced by a conventional gradient echo sequence. Using a neural network within the reconstruction module allows arbitrary targets to be learned successfully. Experiments could be translated to image acquisition at the real system (3T Siemens, PRISMA) and could be verified in the measurements of phantoms and a human brain in vivo. CONCLUSIONS Automated MR sequence generation is possible based on differentiable Bloch equation simulations and a supervised learning approach.
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Affiliation(s)
- A Loktyushin
- Magnetic Resonance Center, Max-Planck Institute for Biological Cybernetics, Tübingen, Germany
- Empirical Inference, Max-Planck Institute for Intelligent Systems, Tübingen, Germany
| | - K Herz
- Magnetic Resonance Center, Max-Planck Institute for Biological Cybernetics, Tübingen, Germany
- University of Tübingen, Tübingen, Germany
| | - N Dang
- Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Neuroradiology, University Clinic Erlangen, Erlangen, Germany
| | - F Glang
- Magnetic Resonance Center, Max-Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - A Deshmane
- Magnetic Resonance Center, Max-Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - S Weinmüller
- Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Neuroradiology, University Clinic Erlangen, Erlangen, Germany
| | - A Doerfler
- Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Neuroradiology, University Clinic Erlangen, Erlangen, Germany
| | - B Schölkopf
- Empirical Inference, Max-Planck Institute for Intelligent Systems, Tübingen, Germany
| | - K Scheffler
- Magnetic Resonance Center, Max-Planck Institute for Biological Cybernetics, Tübingen, Germany
- University of Tübingen, Tübingen, Germany
| | - M Zaiss
- Magnetic Resonance Center, Max-Planck Institute for Biological Cybernetics, Tübingen, Germany
- Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Neuroradiology, University Clinic Erlangen, Erlangen, Germany
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Kopp M, Harmeling S, Schütz G, Schölkopf B, Fähnle M. Towards denoising XMCD movies of fast magnetization dynamics using extended Kalman filter. Ultramicroscopy 2015; 148:115-122. [DOI: 10.1016/j.ultramic.2014.10.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2014] [Revised: 09/16/2014] [Accepted: 10/05/2014] [Indexed: 10/24/2022]
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Abstract
We report on the development and online testing of an electroencephalogram-based brain-computer interface (BCI) that aims to be usable by completely paralysed users-for whom visual or motor-system-based BCIs may not be suitable, and among whom reports of successful BCI use have so far been very rare. The current approach exploits covert shifts of attention to auditory stimuli in a dichotic-listening stimulus design. To compare the efficacy of event-related potentials (ERPs) and steady-state auditory evoked potentials (SSAEPs), the stimuli were designed such that they elicited both ERPs and SSAEPs simultaneously. Trial-by-trial feedback was provided online, based on subjects' modulation of N1 and P3 ERP components measured during single 5 s stimulation intervals. All 13 healthy subjects were able to use the BCI, with performance in a binary left/right choice task ranging from 75% to 96% correct across subjects (mean 85%). BCI classification was based on the contrast between stimuli in the attended stream and stimuli in the unattended stream, making use of every stimulus, rather than contrasting frequent standard and rare 'oddball' stimuli. SSAEPs were assessed offline: for all subjects, spectral components at the two exactly known modulation frequencies allowed discrimination of pre-stimulus from stimulus intervals, and of left-only stimuli from right-only stimuli when one side of the dichotic stimulus pair was muted. However, attention modulation of SSAEPs was not sufficient for single-trial BCI communication, even when the subject's attention was clearly focused well enough to allow classification of the same trials via ERPs. ERPs clearly provided a superior basis for BCI. The ERP results are a promising step towards the development of a simple-to-use, reliable yes/no communication system for users in the most severely paralysed states, as well as potential attention-monitoring and -training applications outside the context of assistive technology.
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Affiliation(s)
- N J Hill
- Wadsworth Center, New York State Department of Health, Albany, NY, USA.
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Gomez-Rodriguez M, Peters J, Hill J, Schölkopf B, Gharabaghi A, Grosse-Wentrup M. Closing the sensorimotor loop: haptic feedback facilitates decoding of motor imagery. J Neural Eng 2011; 8:036005. [PMID: 21474878 DOI: 10.1088/1741-2560/8/3/036005] [Citation(s) in RCA: 138] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
The combination of brain-computer interfaces (BCIs) with robot-assisted physical therapy constitutes a promising approach to neurorehabilitation of patients with severe hemiparetic syndromes caused by cerebrovascular brain damage (e.g. stroke) and other neurological conditions. In such a scenario, a key aspect is how to reestablish the disrupted sensorimotor feedback loop. However, to date it is an open question how artificially closing the sensorimotor feedback loop influences the decoding performance of a BCI. In this paper, we answer this issue by studying six healthy subjects and two stroke patients. We present empirical evidence that haptic feedback, provided by a seven degrees of freedom robotic arm, facilitates online decoding of arm movement intention. The results support the feasibility of future rehabilitative treatments based on the combination of robot-assisted physical therapy with BCIs.
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Martens SMM, Mooij JM, Hill NJ, Farquhar J, Schölkopf B. A graphical model framework for decoding in the visual ERP-based BCI speller. Neural Comput 2010; 23:160-82. [PMID: 20964540 DOI: 10.1162/neco_a_00066] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
We present a graphical model framework for decoding in the visual ERP-based speller system. The proposed framework allows researchers to build generative models from which the decoding rules are obtained in a straightforward manner. We suggest two models for generating brain signals conditioned on the stimulus events. Both models incorporate letter frequency information but assume different dependencies between brain signals and stimulus events. For both models, we derive decoding rules and perform a discriminative training. We show on real visual speller data how decoding performance improves by incorporating letter frequency information and using a more realistic graphical model for the dependencies between the brain signals and the stimulus events. Furthermore, we discuss how the standard approach to decoding can be seen as a special case of the graphical model framework. The letter also gives more insight into the discriminative approach for decoding in the visual speller system.
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Abstract
This paper provides an introduction to support vector machines, kernel Fisher discriminant analysis, and kernel principal component analysis, as examples for successful kernel-based learning methods. We first give a short background about Vapnik-Chervonenkis theory and kernel feature spaces and then proceed to kernel based learning in supervised and unsupervised scenarios including practical and algorithmic considerations. We illustrate the usefulness of kernel algorithms by discussing applications such as optical character recognition and DNA analysis.
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Martens SMM, Hill NJ, Farquhar J, Schölkopf B. Overlap and refractory effects in a brain–computer interface speller based on the visual P300 event-related potential. J Neural Eng 2009; 6:026003. [PMID: 19255462 DOI: 10.1088/1741-2560/6/2/026003] [Citation(s) in RCA: 89] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Abstract
MOTIVATION Eukaryotic pre-mRNAs are spliced to form mature mRNA. Pre-mRNA alternative splicing greatly increases the complexity of gene expression. Estimates show that more than half of the human genes and at least one-third of the genes of less complex organisms, such as nematodes or flies, are alternatively spliced. In this work, we consider one major form of alternative splicing, namely the exclusion of exons from the transcript. It has been shown that alternatively spliced exons have certain properties that distinguish them from constitutively spliced exons. Although most recent computational studies on alternative splicing apply only to exons which are conserved among two species, our method only uses information that is available to the splicing machinery, i.e. the DNA sequence itself. We employ advanced machine learning techniques in order to answer the following two questions: (1) Is a certain exon alternatively spliced? (2) How can we identify yet unidentified exons within known introns? RESULTS We designed a support vector machine (SVM) kernel well suited for the task of classifying sequences with motifs having positional preferences. In order to solve the task (1), we combine the kernel with additional local sequence information, such as lengths of the exon and the flanking introns. The resulting SVM-based classifier achieves a true positive rate of 48.5% at a false positive rate of 1%. By scanning over single EST confirmed exons we identified 215 potential alternatively spliced exons. For 10 randomly selected such exons we successfully performed biological verification experiments and confirmed three novel alternatively spliced exons. To answer question (2), we additionally used SVM-based predictions to recognize acceptor and donor splice sites. Combined with the above mentioned features we were able to identify 85.2% of skipped exons within known introns at a false positive rate of 1%. AVAILABILITY Datasets, model selection results, our predictions and additional experimental results are available at http://www.fml.tuebingen.mpg.de/~raetsch/RASE SUPPLEMENTARY INFORMATION: http://www.fml.tuebingen.mpg.de/raetsch/RASE.
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Affiliation(s)
- G Rätsch
- Friedrich Miescher Laboratory of the Max Planck Society Max Planck, Spemannstrasse 35, Tübingen, Germany.
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Affiliation(s)
- S. Romdhani
- University of Basel, Bernoullistrasse 16, 4056 Basel, Switzerland
| | - P. Torr
- Oxford Brookes University, Department of Computing, Oxford OX33 1HX, UK
| | - B. Schölkopf
- Max–Planck–Institutes for Biological Cybernetics, Spemannstraβe 38, 72076 Tübingen, Germany
| | - A. Blake
- Microsoft Research Ltd, 7 J J Thomson Avenue, Cambridge CB3 0FB, UK
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Abstract
Suppose you are given some data set drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S equals some a priori specified value between 0 and 1. We propose a method to approach this problem by trying to estimate a function f that is positive on S and negative on the complement. The functional form of f is given by a kernel expansion in terms of a potentially small subset of the training data; it is regularized by controlling the length of the weight vector in an associated feature space. The expansion coefficients are found by solving a quadratic programming problem, which we do by carrying out sequential optimization over pairs of input patterns. We also provide a theoretical analysis of the statistical performance of our algorithm. The algorithm is a natural extension of the support vector algorithm to the case of unlabeled data.
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Affiliation(s)
- B Schölkopf
- Microsoft Research Ltd, Cambridge CB2 3NH, U.K
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Zien A, Rätsch G, Mika S, Schölkopf B, Lengauer T, Müller KR. Engineering support vector machine kernels that recognize translation initiation sites. Bioinformatics 2000; 16:799-807. [PMID: 11108702 DOI: 10.1093/bioinformatics/16.9.799] [Citation(s) in RCA: 145] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION In order to extract protein sequences from nucleotide sequences, it is an important step to recognize points at which regions start that code for proteins. These points are called translation initiation sites (TIS). RESULTS The task of finding TIS can be modeled as a classification problem. We demonstrate the applicability of support vector machines for this task, and show how to incorporate prior biological knowledge by engineering an appropriate kernel function. With the described techniques the recognition performance can be improved by 26% over leading existing approaches. We provide evidence that existing related methods (e.g. ESTScan) could profit from advanced TIS recognition.
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Affiliation(s)
- A Zien
- GMD.SCAI, Schloss Birlinghoven, 53754 Sankt Augustin, Germany.
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Abstract
Besides the familiar moon illusion [e.g. Hershenson, 1989 The Moon Illusion (Hillsdale, NJ: Lawrence Erlbaum Associates)], wherein the moon appears bigger when it is close to the horizon, there is a less known illusion which causes the moon's illuminated side to appear turned away from the direction of the sun. An experiment documenting the effect is described, and a possible explanation is put forward.
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Affiliation(s)
- B Schölkopf
- Max-Planck-Institut für biologische Kybernetik, Tübingen, Germany.
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Schölkopf B, Mika S, Burges CC, Knirsch P, Müller KR, Rätsch G, Smola AJ. Input space versus feature space in kernel-based methods. ACTA ACUST UNITED AC 1999; 10:1000-17. [PMID: 18252603 DOI: 10.1109/72.788641] [Citation(s) in RCA: 798] [Impact Index Per Article: 31.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Blanz V, Schölkopf B, Bülthoff H, Burges C, Vapnik V, Vetter T. Comparison of view-based object recognition algorithms using realistic 3D models. Artificial Neural Networks — ICANN 96 1996. [DOI: 10.1007/3-540-61510-5_45] [Citation(s) in RCA: 58] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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