Determining x-ray projections for coil treatments of intracranial aneurysms

Flexible lateral isocenter: A novel mechanical functionality contributing to dose reduction in neurointerventional procedures

Aim of the study

A new functionality that enables vertical mobility of the lateral arm of a biplane angiographic machine is referred to as the flexible lateral isocenter. The aim of this study was to analyze the impact of the flexible lateral isocenter on the air-kerma rate under experimental conditions.

Material and methods

An anthropomorphic head-and-chest phantom with anteroposterior (AP) diameter of the chest varying from 22 cm to 30 cm simulated human bodies of different body constitutions. The angulation of the AP arm in the sagittal plane varied from 35 degrees to 55 degrees for each AP diameter. The air-kerma rate (mGy/min) values were read from the system dose display in two settings for each angle: flexible lateral isocenter and fixed lateral isocenter.

Results

The air-kerma rate was significantly lower for all AP diameters of the chest of the phantom when the flexible lateral isocenter was used: (a) For 22 cm, the p value was 0.028; (b) For 25 cm, the p value was 0.0169; (c) For 28 cm, the p value was 0.01005 and (d) For 30 cm, the p value was 0.01703.

Conclusion

Our results show that the flexible lateral isocenter contributes significantly to the reduction of the air-kerma rate, and thus to a safer environment in terms of dose lowering both for patients and staff.

Three-dimensional blood vessel quantification via centerline deformation

It is clinically important to quantify the geometric parameters of an abnormal vessel, as this information can aid radiologists in choosing appropriate treatments or apparatuses. Centerline and cross-sectional diameters are commonly used to characterize the morphology of vessel in various clinical applications. Due to the existence of stenosis or aneurysm, the associated vessel centerline is unable to truly portray the original, healthy vessel shape and may result in inaccurate quantitative measurement. To remedy such a problem, a novel method using an active tube model is proposed. In the method, a smoothened centerline is determined as the axis of a deformable tube model that is registered onto the vessel lumen. Three types of regions, normal, stenotic, and aneurysmal regions, are defined to classify the vessel segment under-analyzed by use of the algorithm of a cross-sectional-based distance field. The registration process used on the tube model is governed by different region-adaptive energy functionals associated with the classified vessel regions. The proposed algorithm is validated on the 3-D computer-generated phantoms and 3-D rotational digital subtraction angiography (DSA) datasets. Experimental results show that the deformed centerline provides better vessel quantification results compared with the original centerline. It is also shown that the registered model is useful for measuring the volume of aneurysmal regions.

Augmented vessels for quantitative analysis of vascular abnormalities and endovascular treatment planning

Endovascular treatment plays an important role in the minimally invasive treatment of patients with vascular diseases, a major cause of morbidity and mortality worldwide. Given a segmentation of an angiography, quantitative analysis of abnormal structures can aid radiologists in choosing appropriate treatments and apparatuses. However, effective quantitation is only attainable if the abnormalities are identified from the vasculature. To achieve this, a novel method is developed, which works on the simpler shape of normal vessels to identify different vascular abnormalities (viz. stenotic atherosclerotic plaque, and saccular and fusiform aneurysmal lumens) in an indirect fashion, instead of directly manipulating the complex-shaped abnormalities. The proposed method has been tested on three synthetic and 17 clinical data sets. Comparisons with two related works are also conducted. Experimental results show that our method can produce satisfactory identification of the abnormalities and approximations of the ideal post-treatment vessel lumens. In addition, it can help increase the repeatability of the measurement of clinical parameters significantly.

Tracking technique of a micro guide wire in sequential fluorograms

In this paper, we propose a tracking technique for a micro guide wire in a sequence of fluorograms. A technique in which the region-growing technique was embedded was developed. It gives the center line of a micro guide wire. A sequence of 1,024 x 1,024 x 16 bit fluorograms (111 frames) with a carotid phantom and head phantom was obtained with a CAS-8000V (Toshiba America Medical Systems, Inc., CA, USA) C-arm angiography system and a Hi-Torque Standard micro guide wire (Advanced Cardiovascular Systems, Inc., CA, USA). To evaluate the technique, we manually traced the guide wire in each sequence frame three times, and a "true" single-width micro guide wire was created from them. The number of pixels on the true guide wire and inside the two-pixel tolerance of the center line was counted in each fluorogram, and the percentage of that count based on the number of all pixels on the true guide wire was calculated as true positive (TP). In addition, the number of pixels on the center line and outside the two-pixel tolerance of the true guide wire was counted in each frame as false positive (FP). The tracking technique has a mean TP of 94.8% and a mean FP of 5.1 pixels/frame. Several frames have low TP and high FP, but the technique could continue to track the micro guide wire until the end of the sequence. We therefore concluded that we had developed an accurate automatic tracking technique for micro guide wires in fluoroscopic sequences.

A comparison of a similarity-based and a feature-based 2-D-3-D registration method for neurointerventional use

Two-dimensional (2-D)-to-three-dimensional (3-D) registration can improve visualization which may aid minimally invasive neurointerventions. Using clinical and phantom studies, two state-of-the-art approaches to rigid registration are compared quantitatively: an intensity-based algorithm using the gradient difference similarity measure; and an iterative closest point (ICP)-based algorithm. The gradient difference approach was found to be more accurate, with an average registration accuracy of 1.7 mm for clinical data, compared to the ICP-based algorithm with an average accuracy of 2.8 mm. In phantom studies, the ICP-based algorithm proved more reliable, but with more complicated clinical data, the gradient difference algorithm was more robust. Average computation time for the ICP-based algorithm was 20 s per registration, compared with 14 min and 50 s for the gradient difference algorithm.

Fusing speed and phase information for vascular segmentation of phase contrast MR angiograms

This paper presents a statistical approach to aggregating speed and phase (directional) information for vascular segmentation of phase contrast magnetic resonance angiograms (PC-MRA). Rather than relying on speed information alone, as done by others and in our own work, we demonstrate that including phase information as a priori knowledge in a Markov random field (MRF) model can improve the quality of segmentation. This is particularly true in the region within an aneurysm where there is a heterogeneous intensity pattern and significant vascular signal loss. We propose to use a Maxwell-Gaussian mixture density to model the background signal distribution and combine this with a uniform distribution for modelling vascular signal to give a Maxwell-Gaussian-uniform (MGU) mixture model of image intensity. The MGU model parameters are estimated by the modified expectation-maximisation (EM) algorithm. In addition, it is shown that the Maxwell-Gaussian mixture distribution (a) models the background signal more accurately than a Maxwell distribution, (b) exhibits a better fit to clinical data and (c) gives fewer false positive voxels (misclassified vessel voxels) in segmentation. The new segmentation algorithm is tested on an aneurysm phantom data set and two clinical data sets. The experimental results show that the proposed method can provide a better quality of segmentation when both speed and phase information are utilised.

Initialization, noise, singularities, and scale in height ridge traversal for tubular object centerline extraction

The extraction of the centerlines of tubular objects in two and three-dimensional images is a part of many clinical image analysis tasks. One common approach to tubular object centerline extraction is based on intensity ridge traversal. In this paper, we evaluate the effects of initialization, noise, and singularities on intensity ridge traversal and present multiscale heuristics and optimal-scale measures that minimize these effects. Monte Carlo experiments using simulated and clinical data are used to quantify how these "dynamic-scale" enhancements address clinical needs regarding speed, accuracy, and automation. In particular, we show that dynamic-scale ridge traversal is insensitive to its initial parameter settings, operates with little additional computational overhead, tracks centerlines with subvoxel accuracy, passes branch points, and handles significant image noise. We also illustrate the capabilities of the method for medical applications involving a variety of tubular structures in clinical data from different organs, patients, and imaging modalities.