1,673 research outputs found
Monitoring mouse brain perfusion with hybrid magnetic resonance optoacoustic tomography
Progress in brain research critically depends on the development of next-generation multi-modal imaging tools capable of capturing transient functional events and multiplexed contrasts noninvasively and concurrently, thus enabling a holistic view of dynamic events in vivo. Here we report on a hybrid magnetic resonance and optoacoustic tomography (MROT) system for murine brain imaging, which incorporates an MR-compatible spherical matrix array transducer and fiber-based light illumination into a 9.4 T small animal scanner. An optimized radiofrequency coil has further been devised for whole-brain interrogation. System's utility is showcased by acquiring complementary angiographic and soft tissue anatomical contrast along with simultaneous dual-modality visualization of contrast agent dynamics in vivo
-Metric: An N-Dimensional Information-Theoretic Framework for Groupwise Registration and Deep Combined Computing
This paper presents a generic probabilistic framework for estimating the
statistical dependency and finding the anatomical correspondences among an
arbitrary number of medical images. The method builds on a novel formulation of
the -dimensional joint intensity distribution by representing the common
anatomy as latent variables and estimating the appearance model with
nonparametric estimators. Through connection to maximum likelihood and the
expectation-maximization algorithm, an information\hyp{}theoretic metric called
-metric and a co-registration algorithm named -CoReg
are induced, allowing groupwise registration of the observed images with
computational complexity of . Moreover, the method naturally
extends for a weakly-supervised scenario where anatomical labels of certain
images are provided. This leads to a combined\hyp{}computing framework
implemented with deep learning, which performs registration and segmentation
simultaneously and collaboratively in an end-to-end fashion. Extensive
experiments were conducted to demonstrate the versatility and applicability of
our model, including multimodal groupwise registration, motion correction for
dynamic contrast enhanced magnetic resonance images, and deep combined
computing for multimodal medical images. Results show the superiority of our
method in various applications in terms of both accuracy and efficiency,
highlighting the advantage of the proposed representation of the imaging
process
A Spatiotemporal Volumetric Interpolation Network for 4D Dynamic Medical Image
Dynamic medical imaging is usually limited in application due to the large
radiation doses and longer image scanning and reconstruction times. Existing
methods attempt to reduce the dynamic sequence by interpolating the volumes
between the acquired image volumes. However, these methods are limited to
either 2D images and/or are unable to support large variations in the motion
between the image volume sequences. In this paper, we present a spatiotemporal
volumetric interpolation network (SVIN) designed for 4D dynamic medical images.
SVIN introduces dual networks: first is the spatiotemporal motion network that
leverages the 3D convolutional neural network (CNN) for unsupervised parametric
volumetric registration to derive spatiotemporal motion field from two-image
volumes; the second is the sequential volumetric interpolation network, which
uses the derived motion field to interpolate image volumes, together with a new
regression-based module to characterize the periodic motion cycles in
functional organ structures. We also introduce an adaptive multi-scale
architecture to capture the volumetric large anatomy motions. Experimental
results demonstrated that our SVIN outperformed state-of-the-art temporal
medical interpolation methods and natural video interpolation methods that have
been extended to support volumetric images. Our ablation study further
exemplified that our motion network was able to better represent the large
functional motion compared with the state-of-the-art unsupervised medical
registration methods.Comment: 10 pages, 8 figures, Conference on Computer Vision and Pattern
Recognition (CVPR) 202
Automatic Spatiotemporal Analysis of Cardiac Image Series
RÉSUMÉ
Ă€ ce jour, les maladies cardiovasculaires demeurent au premier rang des principales causes de
décès en Amérique du Nord. Chez l’adulte et au sein de populations de plus en plus jeunes,
la soi-disant épidémie d’obésité entraînée par certaines habitudes de vie tels que la mauvaise
alimentation, le manque d’exercice et le tabagisme est lourde de conséquences pour les personnes
affectées, mais aussi sur le système de santé. La principale cause de morbidité et de
mortalité chez ces patients est l’athérosclérose, une accumulation de plaque à l’intérieur des
vaisseaux sanguins à hautes pressions telles que les artères coronaires. Les lésions athérosclérotiques
peuvent entraîner l’ischémie en bloquant la circulation sanguine et/ou en provoquant
une thrombose. Cela mène souvent à de graves conséquences telles qu’un infarctus. Outre les
problèmes liés à la sténose, les parois artérielles des régions criblées de plaque augmentent la
rigidité des parois vasculaires, ce qui peut aggraver la condition du patient. Dans la population
pédiatrique, la pathologie cardiovasculaire acquise la plus fréquente est la maladie de
Kawasaki. Il s’agit d’une vasculite aigüe pouvant affecter l’intégrité structurale des parois des
artères coronaires et mener à la formation d’anévrismes. Dans certains cas, ceux-ci entravent
l’hémodynamie artérielle en engendrant une perfusion myocardique insuffisante et en activant
la formation de thromboses.
Le diagnostic de ces deux maladies coronariennes sont traditionnellement effectués à l’aide
d’angiographies par fluoroscopie. Pendant ces examens paracliniques, plusieurs centaines de
projections radiographiques sont acquises en séries suite à l’infusion artérielle d’un agent de
contraste. Ces images révèlent la lumière des vaisseaux sanguins et la présence de lésions
potentiellement pathologiques, s’il y a lieu. Parce que les séries acquises contiennent de l’information
très dynamique en termes de mouvement du patient volontaire et involontaire (ex.
battements cardiaques, respiration et déplacement d’organes), le clinicien base généralement
son interprétation sur une seule image angiographique où des mesures géométriques sont effectuées
manuellement ou semi-automatiquement par un technicien en radiologie. Bien que
l’angiographie par fluoroscopie soit fréquemment utilisé partout dans le monde et souvent
considéré comme l’outil de diagnostic “gold-standard” pour de nombreuses maladies vasculaires,
la nature bidimensionnelle de cette modalité d’imagerie est malheureusement très
limitante en termes de spécification géométrique des différentes régions pathologiques. En effet,
la structure tridimensionnelle des sténoses et des anévrismes ne peut pas être pleinement
appréciée en 2D car les caractéristiques observées varient selon la configuration angulaire de
l’imageur. De plus, la présence de lésions affectant les artères coronaires peut ne pas refléter
la véritable santé du myocarde, car des mécanismes compensatoires naturels (ex. vaisseaux----------ABSTRACT
Cardiovascular disease continues to be the leading cause of death in North America. In adult
and, alarmingly, ever younger populations, the so-called obesity epidemic largely driven by
lifestyle factors that include poor diet, lack of exercise and smoking, incurs enormous stresses
on the healthcare system. The primary cause of serious morbidity and mortality for these
patients is atherosclerosis, the build up of plaque inside high pressure vessels like the coronary
arteries. These lesions can lead to ischemic disease and may progress to precarious blood
flow blockage or thrombosis, often with infarction or other severe consequences. Besides
the stenosis-related outcomes, the arterial walls of plaque-ridden regions manifest increased
stiffness, which may exacerbate negative patient prognosis. In pediatric populations, the
most prevalent acquired cardiovascular pathology is Kawasaki disease. This acute vasculitis
may affect the structural integrity of coronary artery walls and progress to aneurysmal lesions.
These can hinder the blood flow’s hemodynamics, leading to inadequate downstream
perfusion, and may activate thrombus formation which may lead to precarious prognosis.
Diagnosing these two prominent coronary artery diseases is traditionally performed using
fluoroscopic angiography. Several hundred serial x-ray projections are acquired during selective
arterial infusion of a radiodense contrast agent, which reveals the vessels’ luminal
area and possible pathological lesions. The acquired series contain highly dynamic information
on voluntary and involuntary patient movement: respiration, organ displacement and
heartbeat, for example. Current clinical analysis is largely limited to a single angiographic
image where geometrical measures will be performed manually or semi-automatically by a
radiological technician. Although widely used around the world and generally considered
the gold-standard diagnosis tool for many vascular diseases, the two-dimensional nature of
this imaging modality is limiting in terms of specifying the geometry of various pathological
regions. Indeed, the 3D structures of stenotic or aneurysmal lesions may not be fully appreciated
in 2D because their observable features are dependent on the angular configuration of
the imaging gantry. Furthermore, the presence of lesions in the coronary arteries may not
reflect the true health of the myocardium, as natural compensatory mechanisms may obviate
the need for further intervention. In light of this, cardiac magnetic resonance perfusion
imaging is increasingly gaining attention and clinical implementation, as it offers a direct
assessment of myocardial tissue viability following infarction or suspected coronary artery
disease. This type of modality is plagued, however, by motion similar to that present in fluoroscopic
imaging. This issue predisposes clinicians to laborious manual intervention in order
to align anatomical structures in sequential perfusion frames, thus hindering automation o
Automatic Spatiotemporal Analysis of Cardiac Image Series
RÉSUMÉ
Ă€ ce jour, les maladies cardiovasculaires demeurent au premier rang des principales causes de
décès en Amérique du Nord. Chez l’adulte et au sein de populations de plus en plus jeunes,
la soi-disant épidémie d’obésité entraînée par certaines habitudes de vie tels que la mauvaise
alimentation, le manque d’exercice et le tabagisme est lourde de conséquences pour les personnes
affectées, mais aussi sur le système de santé. La principale cause de morbidité et de
mortalité chez ces patients est l’athérosclérose, une accumulation de plaque à l’intérieur des
vaisseaux sanguins à hautes pressions telles que les artères coronaires. Les lésions athérosclérotiques
peuvent entraîner l’ischémie en bloquant la circulation sanguine et/ou en provoquant
une thrombose. Cela mène souvent à de graves conséquences telles qu’un infarctus. Outre les
problèmes liés à la sténose, les parois artérielles des régions criblées de plaque augmentent la
rigidité des parois vasculaires, ce qui peut aggraver la condition du patient. Dans la population
pédiatrique, la pathologie cardiovasculaire acquise la plus fréquente est la maladie de
Kawasaki. Il s’agit d’une vasculite aigüe pouvant affecter l’intégrité structurale des parois des
artères coronaires et mener à la formation d’anévrismes. Dans certains cas, ceux-ci entravent
l’hémodynamie artérielle en engendrant une perfusion myocardique insuffisante et en activant
la formation de thromboses.
Le diagnostic de ces deux maladies coronariennes sont traditionnellement effectués à l’aide
d’angiographies par fluoroscopie. Pendant ces examens paracliniques, plusieurs centaines de
projections radiographiques sont acquises en séries suite à l’infusion artérielle d’un agent de
contraste. Ces images révèlent la lumière des vaisseaux sanguins et la présence de lésions
potentiellement pathologiques, s’il y a lieu. Parce que les séries acquises contiennent de l’information
très dynamique en termes de mouvement du patient volontaire et involontaire (ex.
battements cardiaques, respiration et déplacement d’organes), le clinicien base généralement
son interprétation sur une seule image angiographique où des mesures géométriques sont effectuées
manuellement ou semi-automatiquement par un technicien en radiologie. Bien que
l’angiographie par fluoroscopie soit fréquemment utilisé partout dans le monde et souvent
considéré comme l’outil de diagnostic “gold-standard” pour de nombreuses maladies vasculaires,
la nature bidimensionnelle de cette modalité d’imagerie est malheureusement très
limitante en termes de spécification géométrique des différentes régions pathologiques. En effet,
la structure tridimensionnelle des sténoses et des anévrismes ne peut pas être pleinement
appréciée en 2D car les caractéristiques observées varient selon la configuration angulaire de
l’imageur. De plus, la présence de lésions affectant les artères coronaires peut ne pas refléter
la véritable santé du myocarde, car des mécanismes compensatoires naturels (ex. vaisseaux----------ABSTRACT
Cardiovascular disease continues to be the leading cause of death in North America. In adult
and, alarmingly, ever younger populations, the so-called obesity epidemic largely driven by
lifestyle factors that include poor diet, lack of exercise and smoking, incurs enormous stresses
on the healthcare system. The primary cause of serious morbidity and mortality for these
patients is atherosclerosis, the build up of plaque inside high pressure vessels like the coronary
arteries. These lesions can lead to ischemic disease and may progress to precarious blood
flow blockage or thrombosis, often with infarction or other severe consequences. Besides
the stenosis-related outcomes, the arterial walls of plaque-ridden regions manifest increased
stiffness, which may exacerbate negative patient prognosis. In pediatric populations, the
most prevalent acquired cardiovascular pathology is Kawasaki disease. This acute vasculitis
may affect the structural integrity of coronary artery walls and progress to aneurysmal lesions.
These can hinder the blood flow’s hemodynamics, leading to inadequate downstream
perfusion, and may activate thrombus formation which may lead to precarious prognosis.
Diagnosing these two prominent coronary artery diseases is traditionally performed using
fluoroscopic angiography. Several hundred serial x-ray projections are acquired during selective
arterial infusion of a radiodense contrast agent, which reveals the vessels’ luminal
area and possible pathological lesions. The acquired series contain highly dynamic information
on voluntary and involuntary patient movement: respiration, organ displacement and
heartbeat, for example. Current clinical analysis is largely limited to a single angiographic
image where geometrical measures will be performed manually or semi-automatically by a
radiological technician. Although widely used around the world and generally considered
the gold-standard diagnosis tool for many vascular diseases, the two-dimensional nature of
this imaging modality is limiting in terms of specifying the geometry of various pathological
regions. Indeed, the 3D structures of stenotic or aneurysmal lesions may not be fully appreciated
in 2D because their observable features are dependent on the angular configuration of
the imaging gantry. Furthermore, the presence of lesions in the coronary arteries may not
reflect the true health of the myocardium, as natural compensatory mechanisms may obviate
the need for further intervention. In light of this, cardiac magnetic resonance perfusion
imaging is increasingly gaining attention and clinical implementation, as it offers a direct
assessment of myocardial tissue viability following infarction or suspected coronary artery
disease. This type of modality is plagued, however, by motion similar to that present in fluoroscopic
imaging. This issue predisposes clinicians to laborious manual intervention in order
to align anatomical structures in sequential perfusion frames, thus hindering automation o
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