In a fetal brain screening examination, a standardized set of anatomical views is inspected and certain biometric measurements are taken in these views. Acquisition of recommended planes requires a certain level of operator expertise. 3D ultrasound has the potential to reduce the manual task to only capture a volume containing the head and to subsequently determine the standard 2D views and measurements automatically. For this purpose, a segmentation model of the fetal brain was created and trained with expert annotations. It was found that the annotations show a considerable intra- and inter-observer variability. To handle the variability, we propose a method to train the model with redundant but inconsistent reference data from many expert users. If the outlier-cleaned average of all reference annotations is considered as ground truth, errors of the automatic view detection are lower than the errors of all individual users and errors of the measurements are in the same range as user error. The resulting functionality allows the completely automated estimation of views and measurements in 3D fetal ultrasound images.
The automatic interpretation of three-dimensional fetal images poses specific challenges compared to other three-dimensional diagnostic data, especially since the orientation of the fetus in the uterus and the position of the extremities is highly variable. In this paper, we present a comprehensive articulated model of the fetal skeleton and the adaptation of the articulation for pose estimation in three-dimensional fetal images. The model is composed out of rigid bodies where the articulations are represented as rigid body transformations. Given a set of target landmarks, the model constellation can be estimated by optimization of the pose parameters. Experiments are carried out on 3D fetal MRI data yielding an average error per case of 12.03±3.36 mm between target and estimated landmark positions.
During the last years Model-Based Segmentation (MBS) techniques have been used in a broad range of medical applications. In clinical practice, such techniques are increasingly employed for diagnostic purposes and treatment decisions. However, it is not guaranteed that a segmentation algorithm will converge towards the desired solution. In specific situations as in the presence of rare anatomical variants (which cannot be represented) or for images with an extremely low quality, a meaningful segmentation might not be feasible. At the same time, an automated estimation of the segmentation reliability is commonly not available. In this paper we present an approach for the identification of segmentation failures using concepts from the field of outlier detection. The approach is validated on a comprehensive set of Computed Tomography Angiography (CTA) images by means of Receiver Operating Characteristic (ROC) analysis. Encouraging results in terms of an Area Under the ROC Curve (AUC) of up to 0.965 were achieved.
Purpose: To support intra-interventional decisions on diagnosis and treatment of cerebrovascular diseases, a
method providing quantitative information about the blood flow in the vascular system is proposed.
Method: This method combines rotational angiography to extract the 3D vessel geometry and digital subtraction
angiography (DSA) to obtain the flow observations. A physical model of blood flow and contrast agent
transport is used to predict the propagation of the contrast agent through the vascular system. In an iterative approach,
the model parameters, including the volumetric blood flow rate, are adapted until the prediction matches
the observations from the DSA. The flow estimation method was applied to patient data: For 24 patients, the
volumetric blood flow rate was determined from angiographic images and for 17 patients, results were compared
with transcranial color coded Doppler (TCCD) measurements.
Results: The agreement of the x-ray based flow estimates with TCCD was reasonable (bias ΔM = 3%,
correlation ρ = 0.76) and reproducibility was clearly better than the reproducibility of the acquired TCCD
Conclusion: Overall we conclude that it is feasible to model the contrast agent transport in patients and to
utilize the flow model to quantify their blood flow with angiographic means.
Diagnosis and treatment decisions of cerebrovascular diseases are currently based on structural information like
the endovascular lumen. In future, clinical diagnosis will increasingly be based on functional information which
gives direct information about the physiological parameters and, hence, is a direct measure for the severity of
the pathology. In this context, an important functional quantity is the volumetric blood flow over time. The
proposed flow quantification method uses contrasted X-ray images from cerebrovascular interventions and a
model of contrast agent dispersion to estimate the flow parameters from the spatial and temporal development
of the contrast agent concentration through the vascular system.
To evaluate the model-based blood flow quantification under realistic circumstances, dedicated cerebrovascular
data has been acquired during clinical interventions. To this aim, a clinical protocol for this novel procedure
has been defined and optimized. For the verification of the measured flow results ultrasound Doppler measurements
have been performed acting as reference measurements.
The clinical data available so far indicates the ability of the proposed flow model to explain the in-vivo
transport of contrast agent in blood. The flow quantification results show good correspondence of flow waveform
and mean volumetric flow rate with the accomplished ultrasound measurements before or after angiography.
For assessment of cerebrovascular diseases, it is beneficial to obtain three-dimensional (3D) information on vessel
morphology and hemodynamics. Rotational angiography is routinely used to determine 3D geometry, and we
recently outlined a method to estimate the blood flow waveform and mean volumetric flow rate from images
acquired using rotational angiography.
Our method uses a model of contrast agent dispersion to estimate the flow parameters from the spatial
and temporal progression of the contrast agent concentration, represented by a flow map. Artifacts due to the
rotation of the c-arm are overcome by using a reliability map. An attenuation calibration can be used to support
our method, but it might not be available in clinical practice. In this paper, we analyze the influence of the
attenuation calibration on our method. Furthermore, we concentrate on the validation of the proposed algorithm,
with particular emphasis on the influence of parameters such as the length of the analyzed vessel segment, the
frame rate of the acquisition, and the duration of the injection on accuracy.
For the validation, rotational angiographic image sequences from a computer simulation and from a phantom
experiment were used. With a mean error of about 10% for the mean volumetric flow rate and about 13% for
the blood flow waveform from the phantom experiments, we conclude that the method has the potential to give
quantitative estimates of blood flow parameters during cerebrovascular interventions which are accurate enough
to be clinically useful.
In coronary x-ray angiographies, the vessels supplying the heart are imaged in a number of states uniquely determined
by a combination of the respiratory intake and the heart contraction of the patient. The angiographic frames of one
sequence represent not all possible combinations of respiration and heart contraction. A couple of applications need a
continuous and dense sampling of the state-space given by the two axes 'respiration' and 'contraction', e.g. background
removal or motion-compensated catheter navigation. We present a novel method of interpolating above the twodimensional
phase-space based on pairs of angiographic frames with similar contraction, but different respiration status.
First a hypothetical model of the respiration motion is formulated, e.g. rigid transformation or rigid translation. Then the
parameters that transform a single frame into another one with similar contraction status are calculated for a number of
frames. An iterative approach is used to reconstruct the generalized transformation function from the transformation
parameters of frame pairs. Using this function, angiographic frames of arbitrary respiration status can be generated. It is
shown that the synthesized angiographies closely match real angiographies acquired at the same combination of
contraction and respiration status.