@INPROCEEDINGS{Kier2009,
  author = {Christian Kier and G{\"u}nter Seidel and Norbert Brüggemann and Johann Hagenah and Christine Klein and Til Aach and Alfred Mertins},
  title = {Ultraschall zur Früherkennung von Parkinson},
  booktitle = {Medizinische Bildverarbeitung und Mustererkennung},
  year = {2009},
  month = {Oct.},
  series = {Lecture Notes in Informatics},
  volume = {P154},
  pages = {1241–1248},
  address = {Lübeck},
  organization = {GI}
}

@INPROCEEDINGS{Kier2009a,
  author = {Christian Kier and Karsten Meyer-Wiethe and Günter Seidel and Alfred
	Mertins},
  title = {Improved Modelling of Ultrasound Contrast Agent Diminution for Blood
	Perfusion Analysis},
  booktitle = {Medical Image Computing and Computer Aided Interventions -- MICCAI},
  year = {2009}
}

@INPROCEEDINGS{Kier2009b,
  author = {Christian Kier and Karsten Meyer-Wiethe and Günter Seidel and Alfred
	Mertins},
  title = {Quality improvements for ultrasound-based cerebral perfusion analysis},
  booktitle = {Computer Assisted Radiology and Surgery -- CARS},
  year = {2009}
}

@ARTICLE{Maciak2008,
  author = {Maciak, A. and Seidel, G. and Meyer-Wiethe, K. and Kier, C. and Hofmann, U.G.},
  title = {Bolus Harmonic Imaging zur automatischen Erkennung ischämiebedingter
	Perfusionsdefizite im Gehirn},
  journal = {Ultraschall in der Medizin},
  year = {2008},
  volume = {29},
  pages = {--},
  abstract = {Zusammenfassung
	
	
	Ziel: Die Diagnose zerebrovaskulärer Erkrankungen stützt sich zunehmend
	auf den Einsatz ultraschallbasierter Verfahren. Das kontrastmittelgestützte,
	transkranielle Bolus Harmonic Imaging (BHI) hat hierbei einen hohen
	Stellenwert. Die Auswertung der aufgezeichneten Bildsequenz erfolgt
	durch geübte Ärzte jedoch manuell und ist somit zeitaufwendig. Das
	Ziel der hier beschriebenen drei Verfahren ist die sichere, vollautomatische
	Erkennung von Perfusionsdefekten des Gehirns. Material und Methoden:
	Mit dem BHI werden Ultraschallbildsequenzen aufgezeichnet, die die
	Kontrastmitteldynamik im Gehirn wiedergeben. Diese Bilder werden
	mit drei verschiedenen Verfahren automatisch ausgewertet. Zum einen
	wird ein regelbasiertes System beschrieben, welches aus den aufgezeichneten
	Ultraschallbildern Parameterbilder extrahiert und diese mithilfe
	von Expertenwissen nach perfundierten und minderperfundierten Gehirnarealen
	klassifiziert. Zum zweiten erfolgt die Klassifikation der Gehirngebiete
	unüberwacht mit dem K-Means-Verfahren. Hierzu wird jedes der Parameterbilder
	als eine Dimension des zu klassifizierenden Merkmalsraums betrachtet,
	sodass eine unüberwachte Segmentierung der minderperfundierten Gehirnbereiche
	möglich ist. Drittens wird die gesamte Bildsequenz anhand der Kontrastmitteldynamik
	pixelweise klassifiziert. Hierbei kann darauf verzichtet werden,
	Parameterbilder extrahieren zu müssen. In allen drei Fällen ist es
	im Anschluss notwendig, die beim HI auftretenden Streifenartefakte
	automatisch zu erkennen. Abschließend wird ein Aussagenbild generiert,
	in dem die gefundenen Minderperfusionen markiert sind. Ergebnisse:
	Die drei Verfahren wurden auf einem 26 Patienten umfassenden Kollektiv
	klinisch validiert. Hierbei hat sich herausgestellt, dass insbesondere
	die Segmentierung anhand der Kontrastmitteldynamik dazu geeignet
	ist, Minderperfusionen automatisch zu erkennen. Es konnte auf diesem
	Patientenkollektiv eine Sensitivität von 100 % bei einer Spezifität
	von 100 % erreicht werden. Schlussfolgerungen: Alle drei Verfahren
	erscheinen geeignet, ischämische Gehirngebiete zu erkennen. Hierbei
	liefert die Klassifikation von Gehirngebieten nach der Kontrastmitteldynamik
	die besten Ergebnisse, da sie robust gegenüber Rauschen ist. Zudem
	ist es das schnellste Verfahren, da die Extraktion von Parameterbildern
	entfällt. Es ist notwendig, die Sensitivität und Spezifität auf einem
	größeren Patientenkollektiv zu validieren. Eine sichere, vollautomatische
	Erkennung von Perfusionsdefekten direkt am Patientenbett scheint
	somit möglich zu sein.
	
	Abstract
	
	
	Purpose: The diagnosis of ischemic stroke relies increasingly on the
	usage of ultrasound-based methods. One of the recent methods is the
	transcranial, contrast agent-based Bolus Harmonic Imaging (BHI) method.
	The captured image sequence is manually examined by clinical experts
	thus resulting in a time-consuming procedure. The purpose of this
	study is to evaluate three different methods to analyze BHI image
	sequences automatically for the detection of ischemic brain tissue.
	Materials and Method: BHI captures an image sequence that provides
	information on the dynamic behavior of the ultrasound contrast agents.
	This image sequence is analyzed using three different procedures.
	First a system relying on expert knowledge is used to determine perfusion
	defects. This procedure requires parametric images, which are previously
	extracted from the image sequence. The parameter images are then
	categorized by an unsupervised classification method in well-perfused
	and ischemic tissue by regarding the parametric images as features
	describing the perfusion. Thirdly, the whole image sequence can be
	interpreted as a pixel-by-pixel behavior out of contrast agents.
	The dynamic curve of each pixel can be automatically classified as
	perfused and ischemic tissue by the K-Means method without extracting
	parametric images. In all three cases a closing step is necessary
	for the accurate interpretation of the results. Transcranial ultrasound
	imaging produces typical stripe artifacts that have to be detected
	and eliminated. A result image is then created and provides a conclusion
	about perfusion reduction in brain tissue. Results: All three methods
	have been validated on the basis of 26 patients by clinical experts.
	The segmentation on the contrast agent kinetics has proven to be
	most effective. According to our patient database, it provides the
	highest detection accuracy, resulting in a sensitivity of 100 % and
	a specificity of 100 %. Conclusion: The presented methods seem to
	be adequate for detecting ischemic brain tissue. The classification
	of contrast agent kinetics provides the best results and has further
	advantages. It is robust with respect to noise and the calculation
	is fast because the extraction of parametric images is omitted. The
	very high sensitivity and specificity must be validated in a larger
	patient population. Reliable and automated detection of perfusion
	defects at the bedside seems to be possible.},
  booktitle = {Automatic Detection of Perfusion Deficits with Bolus Harmonic Imaging},
  doi = {10.1055/s-2008-1027190},
  refid = {101055S20081027190}
}

@INPROCEEDINGS{Kier2008,
  author = {C. Kier and G. Seidel and N. Brüggemann and J. Hagenah and C. Klein
	and T. Aach and A. Mertins},
  title = {Transcranial sonography as early indicator for genetic Parkinson’s
	disease},
  booktitle = {4th European Congress for Medical and Biomedical Engineering},
  year = {2008},
  pages = {456--459},
  abstract = {Early diagnosis of Parkinson’s disease (PD) is of immense importance,
	since clinical symptoms do not occur until substantial parts of the
	brain stem have been irreparably damaged. Recent work suggests that
	by means of transcranial sonography (TCS) it is possible to determine
	the formation of monogenic forms of parkinsonism at a very early
	state. In TCS images, the mesencephalon shows a distinct hyperechogenic
	pattern in about 90% of patients with PD, despite its normal appearance
	on CT and MRI scans. At present, this pattern is manually segmented
	and the region size is used as an early PD indicator. In order to
	remove investigator dependence inherent to manual segmentation, we
	develop and validate semi-automatic features to serve as risk factors
	for PD manifestation. We show in a clinical study that some of the
	features correlate significantly with the presence of specific genetic
	mutations causing PD.},
  file = {Kier2008.pdf:christian/Kier2008.pdf:PDF},
  keywords = {parkinson},
  owner = {kier},
  timestamp = {2009.01.22}
}

@ARTICLE{Kier2007,
  author = {Christian Kier and Christina Cyrus and Günter Seidel and Ulrich G. Hofmann and Til Aach},
  title = {Segmenting the substantia nigra in ultrasound images for early diagnosis
	of Parkinson's disease},
  journal = {International Journal of Computer Assisted Radiology and Surgery},
  year = {2007},
  volume = {2},
  pages = {S83--S85},
  number = {S1},
  month = {June},
  doi = {10.1007/s11548-007-0086-4},
  keywords = {parkinson}
}

@INPROCEEDINGS{Maciak2007,
  author = {Maciak, A. and Kier, C. and Seidel, G. and Meyer-Wiethe, K. and Hofmann, U. G.},
  title = {Automatische Erkennung von Ischämien mit Bolus Harmonic Imaging},
  booktitle = {Bildverarbeitung für die Medizin 2007},
  year = {2007},
  editor = {A. Horsch, T. M. Deserno, H. Handels, H.-P. Meinzer, T. Tolxdorf},
  pages = {81-86},
  address = {München},
  month = {25-27 March},
  publisher = {Springer-Verlag},
  isbn = {3540710905}
}

@ARTICLE{Maciak2007a,
  author = {A. Maciak and C. Kier and G. Seidel and K. Meyer-Wiethe and U. G.
	Hofmann},
  title = {Detecting Stripe Artifacts in Ultrasound Images},
  journal = {Journal of Digital Imaging},
  year = {2007},
  doi = {10.1007/s10278-007-9049-0},
  url = {http://www.springerlink.com/content/rm058658xpv37ph2/?p=fbae785cac25420b93dd3c1a6b1428a9&pi=0}
}

@ARTICLE{Maciak2007b,
  author = {Adam Maciak and Christian Kier and G{\"u}nter Seidel and Karsten Meyer-Wiethe
	and Ulrich Hofmann and Til Aach},
  title = {Robuste Ermittlung parametrischer Bilder für die Ultraschall-Perfusionsbildgebung
	basierend auf einem Modell der Boluskinetik von Kontrastmitteln},
  journal = {Biomedizinische Technik. Ergänzungsband.},
  year = {2007},
  volume = {52},
  pages = {Session A2},
  address = {Berlin},
  issn = {0939-4990},
  keywords = {perfuscope}
}

@ARTICLE{Kier2007a,
  author = {C. Kier and K. Meyer-Wiethe and G. Seidel and T. Aach},
  title = {Ultrasound Cerebral Perfusion Analysis Based on a Mathematical Model
	for Diminution Harmonic Imaging},
  journal = {Methods of Information in Medicine},
  year = {2007},
  volume = {46},
  pages = {308--13},
  number = {3},
  doi = {10.1160/ME9048},
  keywords = {perfuscope}
}

@ARTICLE{Seidel2006,
  author = {G. Seidel and H. Cangür and K. Meyer-Wiethe and G. Renault and A. Herment and A. Schindler and C. Kier},
  title = {On the ability of ultrasound parametric perfusion imaging to predict
	the area of infarction in acute ischemic stroke.},
  journal = {Ultraschall Med},
  year = {2006},
  volume = {27},
  pages = {543--548},
  number = {6},
  month = {Dec},
  abstract = {PURPOSE: Cerebral perfusion deficits in acute ischemic stroke can
	be detected by means of transcranial harmonic imaging after ultrasound
	contrast agent bolus injection. We evaluated five different parameters
	of the bolus kinetics as parametric images and correlated areas of
	disturbed perfusion with the area of definite infarction. MATERIALS
	AND METHODS: Perfusion harmonic imaging after SonoVue bolus injection
	(BHI) was used to investigate 22 patients suffering from acute internal
	carotid artery infarction. For each subject, we calculated five different
	images based on the following parameters from the time-intensity
	curve in each pixel: pixelwise peak intensity (PPI), area under the
	curve (AUC), positive gradient (PG), time to peak (TTP), and a three
	factor image from the factor analysis of medical image sequences
	(FAMIS). The findings in the diencephalic imaging plane were compared
	with the definite area of infarction, as diagnosed by cranial CT.
	RESULTS: In predicting the definite area of infarction in follow-up
	CT, we found the following sensitivities and positive predictive
	values (PPV): PPI (100 \%/95 \%), AUC (100 \%/90 \%), FAMIS (89 \%/89
	\%), PG (84 \%/94 \%) and TTP (47 \%/100 \%). The areas of disturbed
	perfusion in all five types of parametric images correlated significantly
	with the area of infarction in CT. Images from the FAMIS algorithm
	and PPI images showed the highest Spearman rank correlation with
	the area of definite infarction as displayed in CT (both r = 0.76,
	p < 0.001). Images from the other parameters correlated as follows:
	PG: r = 0.62 (p = 0.003), AUC: r = 0.53 (p = 0.014), TTP: r = 0.50
	(p = 0.021). CONCLUSION: BHI can detect disturbed perfusion in acute
	hemispheric stroke. In their ability to predict the development of
	an infarction, intensity-based parameters and FAMIS were determined
	to have a high sensitivity, and TTP was found to have a high PPV
	and specificity.},
  doi = {10.1055/s-2006-927023},
  pmid = {17146746},
  url = {http://dx.doi.org/10.1055/s-2006-927023}
}

@INPROCEEDINGS{Maciak2006,
  author = {Adam Maciak and Christian Kier and G{\"u}nter Seidel and Karsten Meyer-Wiethe and Til Aach},
  title = {Parameterfreie Erkennung von Ischämien mit ultraschallbasiertem Harmonic Imaging},
  booktitle = {Proceedings BMT},
series = {ISSN 0939-4990},
  year = {2006},
  pages = {V76},
  address = {Zürich},
month = {September},
  keywords = {perfuscope}
}

@INPROCEEDINGS{Kier2006a,
  author = {Christian Kier and  Til Aach},
  title = {Predicting the benefit of sample size extension in multiclass k-NN
	classification},
  booktitle = {Pattern Recognition, 2006. ICPR 2006. 18th International Conference
	on},
  year = {2006},
  volume = {3},
  pages = {332--335},
  month = {20-24 Aug.},
  doi = {10.1109/ICPR.2006.942}
}

@Inproceedings{Kie06a,
author              = { Christian Kier and Karsten Meyer-Wiethe and G{\"u}nter Seidel
                        and Til Aach },
title               = { Ultraschall-Perfusionsbildgebung für die Schlaganfalldiagnostik auf Basis eines Modells für die Destruktionskinetik von Kontrastmittel },
pages               = { 41--45 },
month               = {March},
year                = 2006,
editor              = { H. Handels and J. Ehrhardt and A. Horsch and H.-P. Meinzer
                        and T. Tolxdoff },
publisher           = {Springer},
series              = { Informatik Aktuell },
address             = {Heidelberg},
booktitle           = { Bildverarbeitung für die Medizin 2006 }
}

@Article{Vol05a,
author              = { Antje Vollrath and Christian Kier and Karsten Meyer-Wiethe and G{\"u}nter Seidel and Til Aach },
title               = { Detecting Stripe Artefacts in Ultrasound Parametric Images },
journal             = { Biomedizinische Technik },
volume              = 50,
number              = { suppl. vol. 1 },
pages               = { 1235--1236 },
month               = {September},
year                = 2005,
publisher           = { Fachverlag Schiele und Schön },
address             = {Berlin}
}

@Inproceedings{Kie05a,
author              = { Christian Kier and Daniel Toth and Karsten Meyer-Wiethe and Angela Schindler and Hakan Cangür and Günter Seidel and Til Aach },
title               = { Cerebral Perfusion Imaging with Bolus Harmonic Imaging },
volume              = {5750},
pages               = { 437--446},
month               = {February},
year                = {2005},
editor              = { William F. Walker and Stanislav Y. Emelianov },
series              = { Proceedings of SPIE },
address             = { San Diego, CA },
booktitle           = { Ultrasonic Imaging and Signal Processing },
}

@InProceedings{Seidel05,
	title = {Sonographische Darstellung der Hirnperfusion zur Prädiktion des Hirninfarkts bei Patienten mit akutem Schlaganfall},
	author = {G Seidel and H Cang{\"o}r and K Meyer-Wiethe and G Renault and A Herment and A Schindler and C Kier and T Aach},
	year = {2005}
}

@inproceedings{Seidel04,
author          = {G{\"u}nter Seidel and Christian Kier and Karsten Meyer-Wiethe and Daniel Toth and Angela Schindler and Til Aach},
title           = {Sonografische Parameterbilder zur Darstellung der Hirnperfusion bei Patienten mit akutem Hirninfarkt},
year            = {2004}
}