An adaptive segmentation algorithm for time-of-flight MRA data

Multi-Level Residual Dual Attention Network for Major Cerebral Arteries Segmentation in MRA Toward Diagnosis of Cerebrovascular Disorders

Segmentation of major brain vessels is very important for the diagnosis of cerebrovascular disorders and subsequent surgical planning. Vessel segmentation is an important preprocessing step for a wide range of algorithms for the automatic diagnosis or treatment of several vascular pathologies and as such, it is valuable to have a well-performing vascular segmentation pipeline. In this article, we propose an end-to-end multiscale residual dual attention deep neural network for resilient major brain vessel segmentation. In the proposed network, the encoder and decoder blocks of the U-Net are replaced with the multi-level atrous residual blocks to enhance the learning capability by increasing the receptive field to extract the various semantic coarse- and fine-grained features. Dual attention block is incorporated in the bottleneck to perform effective multiscale information fusion to obtain detailed structure of blood vessels. The methods were evaluated on the publicly available TubeTK data set. The proposed method outperforms the state-of-the-art techniques with dice of 0.79 on the whole-brain prediction. The statistical and visual assessments indicate that proposed network is robust to outliers and maintains higher consistency in vessel continuity than the traditional U-Net and its variations.

Attention-Assisted Adversarial Model for Cerebrovascular Segmentation in 3D TOF-MRA Volumes

Cerebrovascular segmentation in time-of-flight magnetic resonance angiography (TOF-MRA) volumes is essential for a variety of diagnostic and analytical applications. However, accurate cerebrovascular segmentation in 3D TOF-MRA is faced with multiple issues, including vast variations in cerebrovascular morphology and intensity, noisy background, and severe class imbalance between foreground cerebral vessels and background. In this work, a 3D adversarial network model called A-SegAN is proposed to segment cerebral vessels in TOF-MRA volumes. The proposed model is composed of a segmentation network A-SegS to predict segmentation maps, and a critic network A-SegC to discriminate predictions from ground truth. Based on this model, the aforementioned issues are addressed by the prevailing visual attention mechanism. First, A-SegS is incorporated with feature-attention blocks to filter out discriminative feature maps, though the cerebrovascular has varied appearances. Second, a hard-example-attention loss is exploited to boost the training of A-SegS on hard samples. Further, A-SegC is combined with an input-attention layer to attach importance to foreground cerebrovascular class. The proposed methods were evaluated on a self-constructed voxel-wise annotated cerebrovascular TOF-MRA segmentation dataset, and experimental results indicate that A-SegAN achieves competitive or better cerebrovascular segmentation results compared to other deep learning methods, effectively alleviating the above issues.

Efficient Johnson-SB Mixture Model for Segmentation of CT Liver Image

To overcome the problem that the traditional Gaussian mixture model (GMM) cannot well describe the skewness distribution of the gray-level histogram of a liver CT slice, we propose a novel segmentation method for liver CT images by introducing the Johnson-SB mixture model (J_SBMM). The Johnson-SB model not only has a flexible asymmetrical distribution but also covers a variety of other distributions as well. In this article, the parameter optimization formulas for J_SBMM were derived by employing the expectation-maximization (EM) algorithm and maximum likelihood. The implementation process of the J_SBMM-based segmentation algorithm is provided in detail. To make better use of the skewness of Johnson-SB and improve the segmentation accuracy, we devise an idea to divide the histogram into two parts and calculate the segmentation threshold for each part, respectively, which is called J_SBMM-TDH. By analyzing and comparing the segmentation thresholds with different cluster numbers, it is illustrated that the segmentation threshold of J_SBMM-TDH will tend to be stable with the increasing of cluster number, while that of GMM is sensitive to different cluster numbers. The proposed J_SBMM-TDH is applied to segment four randomly obtained abdominal CT image sequences, and the segmentation results and robustness have been compared between J_SBMM-TDH and GMM. It is verified that J_SBMM-TDH has preferable segmentation results and better robustness than GMM for the segmentation of liver CT images.

Cerebral oxygen extraction fraction (OEF): Comparison of challenge-free gradient echo QSM+qBOLD (QQ) with 15O PET in healthy adults

We aimed to validate oxygen extraction fraction (OEF) estimations by quantitative susceptibility mapping plus quantitative blood oxygen-level dependence (QSM+qBOLD, or QQ) using ¹⁵O-PET. In ten healthy adult brains, PET and MRI were acquired simultaneously on a PET/MR scanner. PET was acquired using C[¹⁵O], O[¹⁵O], and H₂[¹⁵O]. Image-derived arterial input functions and standard models of oxygen metabolism provided quantification of PET. MRI included T1-weighted imaging, time-of-flight angiography, and multi-echo gradient-echo imaging that was processed for QQ. Region of interest (ROI) analyses compared PET OEF and QQ OEF. In ROI analyses, the averaged OEF differences between PET and QQ were generally small and statistically insignificant. For whole brains, the average and standard deviation of OEF was 32.8 ± 6.7% for PET; OEF was 34.2 ± 2.6% for QQ. Bland-Altman plots quantified agreement between PET OEF and QQ OEF. The interval between the 95% limits of agreement was 16.9 ± 4.0% for whole brains. Our validation study suggests that respiratory challenge-free QQ-OEF mapping may be useful for non-invasive clinical assessment of regional OEF impairment.

A Parallel Cerebrovascular Segmentation Algorithm Based on Focused Multi-Gaussians Model and Heterogeneous Markov Random Field

A complete and detailed cerebrovascular image segmented from time-of-flight magnetic resonance angiography (TOF-MRA) data is essential for the diagnosis and therapy of the cerebrovascular diseases. In recent years, three-dimensional cerebrovascular segmentation algorithms based on statistical models have been widely used, but the existed methods always perform poorly on stenotic vessels and are not robust enough. In this paper, we propose a parallel cerebrovascular segmentation algorithm based on focused multi-Gaussians model and heterogeneous Markov random field. Specifically, we present a focused multi-Gaussians (FMG) model with local fitting region to model the vascular tissue more accurately and introduce the chaotic oscillation particle swarm optimization (CO-PSO) algorithm to improve the global optimization capability in the parameter estimation. Furthermore, we design a heterogeneous Markov Random Field (MRF) in the three-dimensional neighborhood system to incorporate precise local character of image. Finally, the algorithm has been performed parallel optimization based on GPUs and obtain about 60 times speedup compared to serial execution. The experiments show that the proposed algorithm can produce more detailed segmentation result in shorter time and performs well on the stenotic vessels robustly.

Quantification of morphometry and intensity features of intracranial arteries from 3D TOF MRA using the intracranial artery feature extraction (iCafe): A reproducibility study

BACKGROUND

Accurate and reliable vascular features extracted from 3D time-of-flight (TOF) magnetic resonance angiography (MRA) can help evaluate cerebral vascular diseases and conditions. The goal of this study was to evaluate the reproducibility of an intracranial artery feature extraction (iCafe) algorithm for quantitative analysis of intracranial arteries from TOF MRA.

METHODS

Twenty-four patients with known intracranial artery stenosis were recruited and underwent two separate MRA scans within 2 weeks of each other. Each dataset was blinded to associated imaging and clinical data and then processed independently using iCafe. Inter-scan reproducibility analysis was performed on the 24 pairs of scans while intra-/inter-operator reproducibility and stenosis detection were assessed on 8 individual MRA scans. After tracing the vessels visualized on TOF MRA, iCafe was used to automatically extract the locations with stenosis and eight other vascular features. The vascular features included the following six morphometry and two signal intensity features: artery length (total, distal, and proximal), volume, number of branches, average radius of the M1 segment of the middle cerebral artery, and average normalized intensity of all arteries and large vertical arteries. A neuroradiologist independently reviewed the images to identify locations of stenosis for the reference standard. Reproducibility of stenosis detection and vascular features was assessed using Cohen's kappa, the intra-class correlation coefficient (ICC), and within-subject coefficient of variation (CV).

RESULTS

The segment-based sensitivity of iCafe for stenosis detection ranged from 83.3-91.7% while specificity was 97.4%. Kappa values for inter-scan and intra-operator reproducibility were 0.73 and 0.77, respectively. All vascular features demonstrated excellent inter-scan and intra-operator reproducibility (ICC = 0.91-1.00, and CV = 1.21-8.78% for all markers), and good to excellent inter-operator reproducibility (ICC = 0.76-0.99, and CV = 3.27-15.79% for all markers).

CONCLUSION

Intracranial artery features can be reliably quantified from TOF MRA using iCafe to provide both clinical diagnostic assistance and facilitate future investigative quantitative analyses.

Multiple Hidden Markov Model for Pathological Vessel Segmentation

One of the obstacles that prevent the accurate delineation of vessel boundaries is the presence of pathologies, which results in obscure boundaries and vessel-like structures. Targeting this limitation, we present a novel segmentation method based on multiple Hidden Markov Models. This method works with a vessel axis + cross-section model, which constrains the classifier around the vessel. The vessel axis constraint gives our method the potential to be both physiologically accurate and computationally effective. Focusing on pathological vessels, we reap the benefits of the redundant information embedded in multiple vessel-specific features and the good statistical properties coming with Hidden Markov Model, to cover the widest possible spectrum of complex situations. The performance of our method is evaluated on synthetic complex-structured datasets, where we achieve a 91% high overlap ratio. We also validate the proposed method on a real challenging case, segmentation of pathological abdominal arteries. The performance of our method is promising, since our method yields better results than two state-of-the-art methods on both synthetic datasets and real clinical datasets.

Axis-Guided Vessel Segmentation Using a Self-Constructing Cascade-AdaBoost-SVM Classifier

One major limiting factor that prevents the accurate delineation of vessel boundaries has been the presence of blurred boundaries and vessel-like structures. Overcoming this limitation is exactly what we are concerned about in this paper. We describe a very different segmentation method based on a cascade-AdaBoost-SVM classifier. This classifier works with a vessel axis + cross-section model, which constrains the classifier around the vessel. This has the potential to be both physiologically accurate and computationally effective. To further increase the segmentation accuracy, we organize the AdaBoost classifiers and the Support Vector Machine (SVM) classifiers in a cascade way. And we substitute the AdaBoost classifier with the SVM classifier under special circumstances to overcome the overfitting issue of the AdaBoost classifier. The performance of our method is evaluated on synthetic complex-structured datasets, where we obtain high overlap ratios, around 91%. We also validate the proposed method on one challenging case, segmentation of carotid arteries over real clinical datasets. The performance of our method is promising, since our method yields better results than two state-of-the-art methods on both synthetic datasets and real clinical datasets.

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.

Quantitative hemodynamic PET imaging using image-derived arterial input function and a PET/MR hybrid scanner

Positron emission tomography (PET) with ¹⁵O-tracers is commonly used to measure brain hemodynamic parameters such as cerebral blood flow, cerebral blood volume, and cerebral metabolic rate of oxygen. Conventionally, the absolute quantification of these parameters requires an arterial input function that is obtained invasively by sampling blood from an artery. In this work, we developed and validated an image-derived arterial input function technique that avoids the unreliable and burdensome arterial sampling procedure for full quantitative ¹⁵O-PET imaging. We then compared hemodynamic PET imaging performed on a PET/MR hybrid scanner against a conventional PET only scanner. We demonstrated the proposed imaging-based technique was able to generate brain hemodynamic parameter measurements in strong agreement with the traditional arterial sampling based approach. We also demonstrated that quantitative ¹⁵O-PET imaging can be successfully implemented on a PET/MR hybrid scanner.

Vascular segmentation of head phase-contrast magnetic resonance angiograms using grayscale and shape features

BACKGROUND AND OBJECTIVE

In neurosurgery planning, vascular structures must be predetermined, which can guarantee the security of the operation carried out in the case of avoiding blood vessels. In this paper, an automatic algorithm of vascular segmentation, which combined the grayscale and shape features of the blood vessels, is proposed to extract 3D vascular structures from head phase-contrast magnetic resonance angiography dataset.

METHODS

First, a cost function of mis-segmentation is introduced on the basis of traditional Bayesian statistical classification, and the blood vessel of weak grayscale that tended to be misclassified into background will be preserved. Second, enhanced vesselness image is obtained according to the shape-based multiscale vascular enhancement filter. Third, a new reconstructed vascular image is established according to the fusion of vascular grayscale and shape features using Dempster-Shafer evidence theory; subsequently, the corresponding segmentation structures are obtained. Finally, according to the noise distribution characteristic of the data, segmentation ratio coefficient, which increased linearly from top to bottom, is proposed to control the segmentation result, thereby preventing over-segmentation.

RESULTS

Experiment results show that, through the proposed method, vascular structures can be detected not only when both grayscale and shape features are strong, but also when either of them is strong. Compared with traditional grayscale feature- and shape feature-based methods, it is better in the evaluation of testing in segmentation accuracy, and over-segmentation and under-segmentation ratios.

CONCLUSIONS

The proposed grayscale and shape features combined vascular segmentation is not only effective but also accurate. It may be used for diagnosis of vascular diseases and planning of neurosurgery.

A vessel segmentation method for multi-modality angiographic images based on multi-scale filtering and statistical models

Background

Accurate segmentation of blood vessels plays an important role in the computer-aided diagnosis and interventional treatment of vascular diseases. The statistical method is an important component of effective vessel segmentation; however, several limitations discourage the segmentation effect, i.e., dependence of the image modality, uneven contrast media, bias field, and overlapping intensity distribution of the object and background. In addition, the mixture models of the statistical methods are constructed relaying on the characteristics of the image histograms. Thus, it is a challenging issue for the traditional methods to be available in vessel segmentation from multi-modality angiographic images.

Methods

To overcome these limitations, a flexible segmentation method with a fixed mixture model has been proposed for various angiography modalities. Our method mainly consists of three parts. Firstly, multi-scale filtering algorithm was used on the original images to enhance vessels and suppress noises. As a result, the filtered data achieved a new statistical characteristic. Secondly, a mixture model formed by three probabilistic distributions (two Exponential distributions and one Gaussian distribution) was built to fit the histogram curve of the filtered data, where the expectation maximization (EM) algorithm was used for parameters estimation. Finally, three-dimensional (3D) Markov random field (MRF) were employed to improve the accuracy of pixel-wise classification and posterior probability estimation. To quantitatively evaluate the performance of the proposed method, two phantoms simulating blood vessels with different tubular structures and noises have been devised. Meanwhile, four clinical angiographic data sets from different human organs have been used to qualitatively validate the method. To further test the performance, comparison tests between the proposed method and the traditional ones have been conducted on two different brain magnetic resonance angiography (MRA) data sets.

Results

The results of the phantoms were satisfying, e.g., the noise was greatly suppressed, the percentages of the misclassified voxels, i.e., the segmentation error ratios, were no more than 0.3%, and the Dice similarity coefficients (DSCs) were above 94%. According to the opinions of clinical vascular specialists, the vessels in various data sets were extracted with high accuracy since complete vessel trees were extracted while lesser non-vessels and background were falsely classified as vessel. In the comparison experiments, the proposed method showed its superiority in accuracy and robustness for extracting vascular structures from multi-modality angiographic images with complicated background noises.

Conclusions

The experimental results demonstrated that our proposed method was available for various angiographic data. The main reason was that the constructed mixture probability model could unitarily classify vessel object from the multi-scale filtered data of various angiography images. The advantages of the proposed method lie in the following aspects: firstly, it can extract the vessels with poor angiography quality, since the multi-scale filtering algorithm can improve the vessel intensity in the circumstance such as uneven contrast media and bias field; secondly, it performed well for extracting the vessels in multi-modality angiographic images despite various signal-noises; and thirdly, it was implemented with better accuracy, and robustness than the traditional methods. Generally, these traits declare that the proposed method would have significant clinical application.

Automatic SWI Venography Segmentation Using Conditional Random Fields

Susceptibility-weighted imaging (SWI) venography can produce detailed venous contrast and complement arterial dominated MR angiography (MRA) techniques. However, these dense reversed-contrast SWI venograms pose new segmentation challenges. We present an automatic method for whole-brain venous blood segmentation in SWI using Conditional Random Fields (CRF). The CRF model combines different first and second order potentials. First-order association potentials are modeled as the composite of an appearance potential, a Hessian-based shape potential and a non-linear location potential. Second-order interaction potentials are modeled using an auto-logistic (smoothing) potential and a data-dependent (edge) potential. Minimal post-processing is used for excluding voxels outside the brain parenchyma and visualizing the surface vessels. The CRF model is trained and validated using 30 SWI venograms acquired within a population of deep brain stimulation (DBS) patients (age range [Formula: see text] years). Results demonstrate robust and consistent segmentation in deep and sub-cortical regions (median kappa = 0.84 and 0.82), as well as in challenging mid-sagittal and surface regions (median kappa = 0.81 and 0.83) regions. Overall, this CRF model produces high-quality segmentation of SWI venous vasculature that finds applications in DBS for minimizing hemorrhagic risks and other surgical and non-surgical applications.

Quantitative amyloid imaging using image-derived arterial input function

Amyloid PET imaging is an indispensable tool widely used in the investigation, diagnosis and monitoring of Alzheimer’s disease (AD). Currently, a reference region based approach is used as the mainstream quantification technique for amyloid imaging. This approach assumes the reference region is amyloid free and has the same tracer influx and washout kinetics as the regions of interest. However, this assumption may not always be valid. The goal of this work is to evaluate an amyloid imaging quantification technique that uses arterial region of interest as the reference to avoid potential bias caused by specific binding in the reference region. 21 participants, age 58 and up, underwent Pittsburgh compound B (PiB) PET imaging and MR imaging including a time-of-flight (TOF) MR angiography (MRA) scan and a structural scan. FreeSurfer based regional analysis was performed to quantify PiB PET data. Arterial input function was estimated based on coregistered TOF MRA using a modeling based technique. Regional distribution volume (V_T) was calculated using Logan graphical analysis with estimated arterial input function. Kinetic modeling was also performed using the estimated arterial input function as a way to evaluate PiB binding (DVR_kinetic) without a reference region. As a comparison, Logan graphical analysis was also performed with cerebellar cortex as reference to obtain DVR_REF. Excellent agreement was observed between the two distribution volume ratio measurements (r>0.89, ICC>0.80). The estimated cerebellum V_T was in line with literature reported values and the variability of cerebellum V_T in the control group was comparable to reported variability using arterial sampling data. This study suggests that image-based arterial input function is a viable approach to quantify amyloid imaging data, without the need of arterial sampling or a reference region. This technique can be a valuable tool for amyloid imaging, particularly in population where reference normalization may not be accurate.

Imaging the effects of oxygen saturation changes in voluntary apnea and hyperventilation on susceptibility-weighted imaging

BACKGROUND AND PURPOSE

Cerebrovascular oxygenation changes during respiratory challenges have clinically important implications for brain function, including cerebral autoregulation and the rate of brain metabolism. SWI is sensitive to venous oxygenation level by exploitation of the magnetic susceptibility of deoxygenated blood. We assessed cerebral venous blood oxygenation changes during simple voluntary breath-holding (apnea) and hyperventilation by use of SWI at 3T.

MATERIALS AND METHODS

We performed SWI scans (3T; acquisition time of 1 minute, 28 seconds; centered on the anterior commissure and the posterior commissure) on 10 healthy male volunteers during baseline breathing as well as during simple voluntary hyperventilation and apnea challenges. The hyperventilation and apnea tasks were separated by a 5-minute resting period. SWI venograms were generated, and the signal changes on SWI before and after the respiratory stress tasks were compared by means of a paired Student t test.

RESULTS

Changes in venous vasculature visibility caused by the respiratory challenges were directly visualized on the SWI venograms. The venogram segmentation results showed that voluntary apnea decreased the mean venous blood voxel number by 1.6% (P < .0001), and hyperventilation increased the mean venous blood voxel number by 2.7% (P < .0001). These results can be explained by blood CO2 changes secondary to the respiratory challenges, which can alter cerebrovascular tone and cerebral blood flow and ultimately affect venous oxygen levels.

CONCLUSIONS

These results highlight the sensitivity of SWI to simple and noninvasive respiratory challenges and its potential utility in assessing cerebral hemodynamics and vasomotor responses.

Dynamic contrast-enhanced MRI-based early detection of acute renal transplant rejection

A novel framework for the classification of acute rejection versus nonrejection status of renal transplants from 2-D dynamic contrast-enhanced magnetic resonance imaging is proposed. The framework consists of four steps. First, kidney objects are segmented from adjacent structures with a level set deformable boundary guided by a stochastic speed function that accounts for a fourth-order Markov-Gibbs random field model of the kidney/background shape and appearance. Second, a Laplace-based nonrigid registration approach is used to account for local deformations caused by physiological effects. Namely, the target kidney object is deformed over closed, equispaced contours (iso-contours) to closely match the reference object. Next, the cortex is segmented as it is the functional kidney unit that is most affected by rejection. To characterize rejection, perfusion is estimated from contrast agent kinetics using empirical indexes, namely, the transient phase indexes (peak signal intensity, time-to-peak, and initial up-slope), and a steady-phase index defined as the average signal change during the slowly varying tissue phase of agent transit. We used a kn-nearest neighbor classifier to distinguish between acute rejection and nonrejection. Performance of our method was evaluated using the receiver operating characteristics (ROC). Experimental results in 50 subjects, using a combinatoric kn-classifier, correctly classified 92% of training subjects, 100% of the test subjects, and yielded an area under the ROC curve that approached the ideal value. Our proposed framework thus holds promise as a reliable noninvasive diagnostic tool.

Vascular masking for improved unfolding in 2D SENSE-accelerated 3D contrast-enhanced MR angiography

PURPOSE

To describe and evaluate the method we refer to as "vascular masking" for improving signal-to-noise ratio (SNR) retention in sensitivity encoding (SENSE)-accelerated contrast-enhanced magnetic resonance angiography (CE-MRA).

MATERIALS AND METHODS

Vascular masking is a technique that restricts the SENSE unfolding of an accelerated subtraction angiogram to the voxels within the field of view known to have enhancing signal. This is a more restricted voxel set than that identified with conventional masking, which excludes only voxels in the air around the object. Thus, improved retention of SNR is expected. Evaluation was done in phantom and in vivo studies by comparing SNR and the g-factor in results reconstructed using vascular versus conventional masking. A radiological evaluation was also performed comparing conventional and vascular masking in R = 8 accelerated CE-MRA studies of the thighs (n = 21) and calves (n = 13).

RESULTS

Images reconstructed with vascular masking showed a significant reduction in g-factor and improved retention of SNR versus those reconstructed with conventional masking. In the radiological evaluation, vascular masking consistently provided reduced background noise, improved luminal signal smoothness, and better small vessel conspicuity.

CONCLUSION

Vascular masking provides improved SNR retention and improved depiction of the vasculature in accelerated, subtraction 3D CE-MRA of the thighs and calves.

Quantification of the human cerebrovasculature: a 7Tesla and 320-row CT in vivo study

OBJECTIVE

Human cerebrovasculature has not been quantified in volume, length, and vascular-brain relationships. We investigated this using imaging.

METHODS

From 0.5-mm 7T and 320-row CT acquisitions, 6 arterial and 4 venous systems were reconstructed, measured, and analyzed.

RESULTS

The ratio of the volume of arterial to venous system is approximately 1:3. The ratio of the volume of dural sinuses to vasculature is 1:2. The ratio of the posterior (PCA) to anterior (ACA) to middle cerebral artery (MCA) is 1:2:4 in volume and length. Ratios of left to right vessels are 1:1 for arteries and veins. Ratios of branching frequency for the ACA, MCA, and PCA are 1:1:1. The branching frequency ratio for superficial to deep veins is 1:2. The MCA occupies 1/2 of arterial length and 1/4 of vascular length. The ratio of the length of superficial to deep veins is 1:1 and each is equal to 1/4 of the vascular length. The ratio of cerebrovasculature to brain volume is 2.5%.

CONCLUSIONS

Despite its enormous complexity, cerebrovasculature is characterized by 4 approximate proportions, 1:1, 1:2, 1:3, 1:4, and their combinations, 1:1:1 and 1:2:4.

Segmentation of intracranial vessels and aneurysms in phase contrast magnetic resonance angiography using multirange filters and local variances

Segmentation of intensity varying and low-contrast structures is an extremely challenging and rewarding task. In computer-aided diagnosis of intracranial aneurysms, segmenting the high-intensity major vessels along with the attached low-contrast aneurysms is essential to the recognition of this lethal vascular disease. It is particularly helpful in performing early and noninvasive diagnosis of intracranial aneurysms using phase contrast magnetic resonance angiographic (PC-MRA) images. The major challenges of developing a PC-MRA-based segmentation method are the significantly varying voxel intensity inside vessels with different flow velocities and the signal loss in the aneurysmal regions where turbulent flows occur. This paper proposes a novel intensity-based algorithm to segment intracranial vessels and the attached aneurysms. The proposed method can handle intensity varying vasculatures and also the low-contrast aneurysmal regions affected by turbulent flows. It is grounded on the use of multirange filters and local variances to extract intensity-based image features for identifying contrast varying vasculatures. The extremely low-intensity region affected by turbulent flows is detected according to the topology of the structure detected by multirange filters and local variances. The proposed method is evaluated using a phantom image volume with an aneurysm and four clinical cases. It achieves 0.80 dice score in the phantom case. In addition, different components of the proposed method-the multirange filters, local variances, and topology-based detection-are evaluated in the comparison between the proposed method and its lower complexity variants. Owing to the analogy between these variants and existing vascular segmentation methods, this comparison also exemplifies the advantage of the proposed method over the existing approaches. It analyzes the weaknesses of these existing approaches and justifies the use of every component involved in the proposed method. It is shown that the proposed method is capable of segmenting blood vessels and the attached aneurysms on PC-MRA images.

Noninvasive estimation of the arterial input function in positron emission tomography imaging of cerebral blood flow

Positron emission tomography (PET) with (15)O-labeled water can provide reliable measurement of cerebral blood flow (CBF). Quantification of CBF requires knowledge of the arterial input function (AIF), which is usually provided by arterial blood sampling. However, arterial sampling is invasive. Moreover, the blood generally is sampled at the wrist, which does not perfectly represent the AIF of the brain, because of the effects of delay and dispersion. We developed and validated a new noninvasive method to obtain the AIF directly by PET imaging of the internal carotid artery in a region of interest (ROI) defined by coregistered high-resolution magnetic resonance angiography. An ROI centered at the petrous portion of the internal carotid artery was defined, and the AIF was estimated simultaneously with whole brain blood flow. The image-derived AIF (IDAIF) method was validated against conventional arterial sampling. The IDAIF generated highly reproducible CBF estimations, generally in good agreement with the conventional technique.

Precise segmentation of 3-D magnetic resonance angiography

Accurate automatic extraction of a 3-D cerebrovascular system from images obtained by time-of-flight (TOF) or phase contrast (PC) magnetic resonance angiography (MRA) is a challenging segmentation problem due to the small size objects of interest (blood vessels) in each 2-D MRA slice and complex surrounding anatomical structures (e.g., fat, bones, or gray and white brain matter). We show that due to the multimodal nature of MRA data, blood vessels can be accurately separated from the background in each slice using a voxel-wise classification based on precisely identified probability models of voxel intensities. To identify the models, an empirical marginal probability distribution of intensities is closely approximated with a linear combination of discrete Gaussians (LCDG) with alternate signs, using our previous EM-based techniques for precise linear combination of Gaussian-approximation adapted to deal with the LCDGs. The high accuracy of the proposed approach is experimentally validated on 85 real MRA datasets (50 TOF and 35 PC) as well as on synthetic MRA data for special 3-D geometrical phantoms of known shapes.

Magnetic Resonance Image Analysis for Brain CAD Systems with Machine Learning

This chapter describes the image analysis for brain Computer-Aided Diagnosis (CAD) systems with machine learning techniques, which could assist radiologists in the detection of such brain diseases as asymptomatic unruptured aneurysms, Alzheimer’s Disease (AD), vascular dementia, and Multiple Sclerosis (MS) by magnetic resonance imaging. Image analysis in CAD systems consists of image enhancement, initial detection, and image feature extraction, including segmentation. In addition, the authors review the classification of true and false positives using machine learning techniques, as well as the evaluation methods and development cycle for CAD systems.

A fast and fully automatic method for cerebrovascular segmentation on time-of-flight (TOF) MRA image

The precise three-dimensional (3-D) segmentation of cerebral vessels from magnetic resonance angiography (MRA) images is essential for the detection of cerebrovascular diseases (e.g., occlusion, aneurysm). The complex 3-D structure of cerebral vessels and the low contrast of thin vessels in MRA images make precise segmentation difficult. We present a fast, fully automatic segmentation algorithm based on statistical model analysis and improved curve evolution for extracting the 3-D cerebral vessels from a time-of-flight (TOF) MRA dataset. Cerebral vessels and other tissue (brain tissue, CSF, and bone) in TOF MRA dataset are modeled by Gaussian distribution and combination of Rayleigh with several Gaussian distributions separately. The region distribution combined with gradient information is used in edge-strength of curve evolution as one novel mode. This edge-strength function is able to determine the boundary of thin vessels with low contrast around brain tissue accurately and robustly. Moreover, a fast level set method is developed to implement the curve evolution to assure high efficiency of the cerebrovascular segmentation. Quantitative comparisons with 10 sets of manual segmentation results showed that the average volume sensitivity, the average branch sensitivity, and average mean absolute distance error are 93.6%, 95.98%, and 0.333 mm, respectively. By applying the algorithm to 200 clinical datasets from three hospitals, it is demonstrated that the proposed algorithm can provide good quality segmentation capable of extracting a vessel with a one-voxel diameter in less than 2 min. Its accuracy and speed make this novel algorithm more suitable for a clinical computer-aided diagnosis system.

Automated multiscale vessel analysis for the quantification of MR angiography of peripheral arteriogenesis

PURPOSE

To automatically analyze the time course of collateralization in a rat hindlimb ischemia model based on signal intensity distribution (SID).

MATERIALS AND METHODS

Time-of-flight magnetic resonance angiograms (TOF-MRA) were acquired in eight rats at 2, 7, and 21 days after unilateral femoral artery ligation. Analysis was performed on maximum intensity projections filtered with multiscale vessel enhancement filter. Differences in SID between ligated limb and a reference region were monitored over time and compared to manual collateral artery identification.

RESULTS

The differences in SID correlated well with the number of collateral arteries found with manual quantification. The time courses of ultrasmall (diameter ≪0.5 mm) and small (diameter ≈0.5 mm) collateral artery development could be differentiated, revealing that maturation of the collaterals and enlargement of their feeding arteries occurred mainly after the first week postligation.

CONCLUSION

SID analysis performed on axial maximum intensity projections is easy to implement, fast, and objective and provides more insight in the time course of arteriogenesis than manual identification.

Comparison of magnetic resonance angiography scans on 1.5, 3, and 7 Tesla units: a quantitative study of 3-dimensional cerebrovasculature

BACKGROUND

Although multiple studies demonstrate benefits of high field imaging of cerebrovasculature, a detailed quantitative analysis of complete cerebrovascular system is unavailable. To compare quality of MR angiography (MRA) acquisitions at various field strengths, we used 3-dimensional (3D) geometric cerebrovascular models extracted from 1.5 T/3 T/7 T scans.

METHODS

The 3D cerebrovascular models were compared in volume, length, and number of branches. A relationship between the vascular length and volume was statistically derived. Acquisition performance was benchmarked against the maximum volume at infinitive length.

RESULTS

The numbers of vessels discernible on 1.5 T/3 T/7 T are 138/363/907. 3T shows 3.3(1.9) and 7 T 1.2(9.1) times more arteries (veins) than 1.5 T. The vascular lengths and volumes at 1.5 T/3 T/7 T are 3.7/12.5/22.7 m and 15.8/26.6/28.0 cm(3). For arteries: 3T-1.5 T gain is very high in length, high in volume; 7 T-3T gain is medium in length, small in volume. For veins: 3 T-1.5 T gain is moderate in length, high in volume; 7 T-3T gain is very high in length, moderate in volume. 1.5 T shows merely half of vascular volume. At 3 T 6%, while at 7 T only 1% of vascular volume is missing.

CONCLUSION

Our approach differs from standard approaches based on visual assessment and signal (contrast)-to-noise ratio. It also measures absolute acquisition performance, provides a unique length-volume relationship, and predicts length/volume for intermediate teslages.

Tubular surface segmentation for extracting anatomical structures from medical imagery

This work provides a model for tubular structures, and devises an algorithm to automatically extract tubular anatomical structures from medical imagery. Our model fits many anatomical structures in medical imagery, in particular, various fiber bundles in the brain (imaged through diffusion-weighted magnetic resonance (DW-MRI)) such as the cingulum bundle, and blood vessel trees in computed tomography angiograms (CTAs). Extraction of the cingulum bundle is of interest because of possible ties to schizophrenia, and extracting blood vessels is helpful in the diagnosis of cardiovascular diseases. The tubular model we propose has advantages over many existing approaches in literature: fewer degrees-of-freedom over a general deformable surface hence energies defined on such tubes are less sensitive to undesirable local minima, and the tube (in 3-D) can be naturally represented by a 4-D curve (a radius function and centerline), which leads to computationally less costly algorithms and has the advantage that the centerline of the tube is obtained without additional effort. Our model also generalizes to tubular trees, and the extraction algorithm that we design automatically detects and evolves branches of the tree. We demonstrate the performance of our algorithm on 20 datasets of DW-MRI data and 32 datasets of CTA, and quantify the results of our algorithm when expert segmentations are available.

Vascular editor: from angiographic images to 3D vascular models

Modern imaging techniques are able to generate high-resolution multimodal angiographic scans. The analysis of vasculature using numerous 2D tomographic images is time consuming and tedious, while 3D modeling and visualization enable presentation of the vasculature in a more convenient and intuitive way. This calls for development of interactive tools facilitating processing of angiographic scans and enabling creation, editing, and manipulation of 3D vascular models. Our objective is to develop a vascular editor (VE) which provides a suitable environment for experts to create and manipulate 3D vascular models correlated with surrounding anatomy. The architecture, functionality, and user interface of the VE are presented. The VE includes numerous interactive tools for building a vascular model from multimodal angiographic scans, editing, labeling, and manipulation of the resulting 3D model. It also provides comprehensive tools for vessel visualization, correlation of 2D and 3D representations, and tracing of small vessels of subpixel size. Education, research, and clinical applications of the VE are discussed, including the atlas of cerebral vasculature. To our best knowledge, there are no other systems offering similar functionality as the VE does.

Vascular Tree Reconstruction by Minimizing A Physiological Functional Cost

The reconstruction of complete vascular trees from medical images has many important applications. Although vessel detection has been extensively investigated, little work has been done on how connect the results to reconstruct the full trees. In this paper, we propose a novel theoretical framework for automatic vessel connection, where the automation is achieved by leveraging constraints from the physiological properties of the vascular trees. In particular, a physiological functional cost for the whole vascular tree is derived and an efficient algorithm is developed to minimize it. The method is generic and can be applied to different vessel detection/segmentation results, e.g. the classic rigid detection method as adopted in this paper. We demonstrate the effectiveness of this method on both 2D and 3D data.

Diminished visibility of cerebral venous vasculature in multiple sclerosis by susceptibility-weighted imaging at 3.0 Tesla

Multiple sclerosis (MS) is a disease of the central nervous system characterized by widespread demyelination, axonal loss and gliosis, and neurodegeneration; susceptibility-weighted imaging (SWI), through the use of phase information to enhance local susceptibility or T2* contrast, is a relatively new and simple MRI application that can directly image cerebral veins by exploiting venous blood oxygenation. Here, we use high-field SWI at 3.0 Tesla to image 15 patients with clinically definite relapsing-remitting MS and to assess cerebral venous oxygen level changes. We demonstrate significantly reduced visibility of periventricular white matter venous vasculature in patients as compared to control subjects, supporting the concept of a widespread hypometabolic MS disease process. SWI may afford a noninvasive and relatively simple method to assess venous oxygen saturation so as to closely monitor disease severity, progression, and response to therapy.

An improved spatial tracking algorithm applied to coronary veins into Cardiac Multi-Slice Computed Tomography volume

This paper describes an enhanced vessel tracking algorithm. The method specifity relies on the coronary venous tree extraction through Cardiac Multi-Slice Computed Tomography (MSCT). Indeed, contrast inhomogeneities are a major issue in the data sets that necessitate a robust tracking procedure. The method is based on an existing moment-based algorithm designed for coronary arteries into MSCT volume. In order to extract the whole path of interest, improvements concerning progression strategy are proposed. Furthermore, the original procedure is combined with an automatic recentring method based on ray casting. This enhanced method has been tested on three data sets. According to the first results, the method appears robust to curvatures, contrast inhomogeneities and low contrast blood veins.

A novel 3D joint Markov-Gibbs model for extracting blood vessels from PC-MRA images

New techniques for more accurate segmentation of a 3D cerebrovascular system from phase contrast (PC) magnetic resonance angiography (MRA) data are proposed. In this paper, we describe PC-MRA images and desired maps of regions by a joint Markov-Gibbs random field model (MGRF) of independent image signals and interdependent region labels but focus on most accurate model identification. To better specify region borders, each empirical distribution of signals is precisely approximated by a Linear Combination of Discrete Gaussians (LCDG) with positive and negative components. We modified the conventional Expectation-Maximization (EM) algorithm to deal with the LCDG. The initial segmentation based on the LCDG-models is then iteratively refined using a MGRF model with analytically estimated potentials. Experiments with both the phantoms and real data sets confirm high accuracy of the proposed approach.

A variational method for geometric regularization of vascular segmentation in medical images

In this paper, a level-set-based geometric regularization method is proposed which has the ability to estimate the local orientation of the evolving front and utilize it as shape induced information for anisotropic propagation. We show that preserving anisotropic fronts can improve elongations of the extracted structures, while minimizing the risk of leakage. To that end, for an evolving front using its shape-offset level-set representation, a novel energy functional is defined. It is shown that constrained optimization of this functional results in an anisotropic expansion flow which is usefull for vessel segmentation. We have validated our method using synthetic data sets, 2-D retinal angiogram images and magnetic resonance angiography volumetric data sets. A comparison has been made with two state-of-the-art vessel segmentation methods. Quantitative results, as well as qualitative comparisons of segmentations, indicate that our regularization method is a promising tool to improve the efficiency of both techniques.

Automatic segmentation of blood vessels from dynamic MRI datasets

In this paper we present an approach for blood vessel segmentation from dynamic contrast-enhanced MRI datasets of the hand joints acquired from patients with active rheumatoid arthritis. Exclusion of the blood vessels is needed for accurate visualisation of the activation events and objective evaluation of the degree of inflammation. The segmentation technique is based on statistical modelling motivated by the physiological properties of the individual tissues, such as speed of uptake and concentration of the contrast agent; it incorporates Markov random field probabilistic framework and principal component analysis. The algorithm was tested on 60 temporal slices and has shown promising results.

Adaptive segmentation of cerebrovascular tree in time-of-flight magnetic resonance angiography

Accurate segmentation of the human vasculature is an important prerequisite for a number of clinical procedures, such as diagnosis, image-guided neurosurgery and pre-surgical planning. In this paper, an improved statistical approach to extracting whole cerebrovascular tree in time-of-flight magnetic resonance angiography is proposed. Firstly, in order to get a more accurate segmentation result, a localized observation model is proposed instead of defining the observation model over the entire dataset. Secondly, for the binary segmentation, an improved Iterative Conditional Model (ICM) algorithm is presented to accelerate the segmentation process. The experimental results showed that the proposed algorithm can obtain more satisfactory segmentation results and save more processing time than conventional approaches, simultaneously.

Vessels as 4-D curves: global minimal 4-D paths to extract 3-D tubular surfaces and centerlines

In this paper, we propose an innovative approach to the segmentation of tubular structures. This approach combines all of the benefits of minimal path techniques such as global minimizers, fast computation, and powerful incorporation of user input, while also having the capability to represent and detect vessel surfaces directly which so far has been a feature restricted to active contour and surface techniques. The key is to represent the trajectory of a tubular structure not as a 3-D curve but to go up a dimension and represent the entire structure as a 4-D curve. Then we are able to fully exploit minimal path techniques to obtain global minimizing trajectories between two user supplied endpoints in order to reconstruct tubular structures from noisy or low contrast 3-D data without the sensitivity to local minima inherent in most active surface techniques. In contrast to standard purely spatial 3-D minimal path techniques, however, we are able to represent a full tubular surface rather than just a curve which runs through its interior. Our representation also yields a natural notion of a tube's "central curve." We demonstrate and validate the utility of this approach on magnetic resonance (MR) angiography and computed tomography (CT) images of coronary arteries.

Coil sensitivity estimation for optimal SNR reconstruction and intensity inhomogeneity correction in phased array MR imaging

Magnetic resonance (MR) images can be acquired by multiple receiver coil systems to improve signal-to-noise ratio (SNR) and to decrease acquisition time. The optimal SNR images can be reconstructed from the coil data when the coil sensitivities are known. In typical MR imaging studies, the information about coil sensitivity profiles is not available. In such cases the sum-of-squares (SoS) reconstruction algorithm is usually applied. The intensity of the SoS reconstructed image is modulated by a spatially variable function due to the non-uniformity of coil sensitivities. Additionally, the SoS images also have sub-optimal SNR and bias in image intensity. All these effects might introduce errors when quantitative analysis and/or tissue segmentation are performed on the SoS reconstructed images. In this paper, we present an iterative algorithm for coil sensitivity estimation and demonstrate its applicability for optimal SNR reconstruction and intensity inhomogeneity correction in phased array MR imaging.