D2BGAN: Dual Discriminator Bayesian Generative Adversarial Network for Deformable MR-Ultrasound Registration Applied to Brain Shift Compensation.

During neurosurgical procedures, the neuro-navigation system's accuracy is affected by the brain shift phenomenon. One popular strategy is to compensate for brain shift using intraoperative ultrasound (iUS) registration with pre-operative magnetic resonance (MR) scans. This requires a satisfactory multimodal image registration method, which is challenging due to the low image quality of ultrasound and the unpredictable nature of brain deformation during surgery. In this paper, we propose an automatic unsupervised end-to-end MR-iUS registration approach named the Dual Discriminator Bayesian Generative Adversarial Network (D2BGAN). The proposed network consists of two discriminators and a generator optimized by a Bayesian loss function to improve the functionality of the generator, and we add a mutual information loss function to the discriminator for similarity measurements. Extensive validation was performed on the RESECT and BITE datasets, where the mean target registration error (mTRE) of MR-iUS registration using D2BGAN was determined to be 0.75 ± 0.3 mm. The D2BGAN illustrated a clear advantage by achieving an 85% improvement in the mTRE over the initial error. Moreover, the results confirmed that the proposed Bayesian loss function, rather than the typical loss function, improved the accuracy of MR-iUS registration by 23%. The improvement in registration accuracy was further enhanced by the preservation of the intensity and anatomical information of the input images.

Photoacoustic-MR Image Registration Based on a Co-Sparse Analysis Model to Compensate for Brain Shift.

Brain shift is an important obstacle to the application of image guidance during neurosurgical interventions. There has been a growing interest in intra-operative imaging to update the image-guided surgery systems. However, due to the innate limitations of the current imaging modalities, accurate brain shift compensation continues to be a challenging task. In this study, the application of intra-operative photoacoustic imaging and registration of the intra-operative photoacoustic with pre-operative MR images are proposed to compensate for brain deformation. Finding a satisfactory registration method is challenging due to the unpredictable nature of brain deformation. In this study, the co-sparse analysis model is proposed for photoacoustic-MR image registration, which can capture the interdependency of the two modalities. The proposed algorithm works based on the minimization of mapping transform via a pair of analysis operators that are learned by the alternating direction method of multipliers. The method was evaluated using an experimental phantom and ex vivo data obtained from a mouse brain. The results of the phantom data show about 63% improvement in target registration error in comparison with the commonly used normalized mutual information method. The results proved that intra-operative photoacoustic images could become a promising tool when the brain shift invalidates pre-operative MRI.

Bronchoscope motion tracking using centerline-guided Gaussian mixture model in navigated bronchoscopy.

Electromagnetic-based navigation bronchoscopy requires accurate and robust estimation of the bronchoscope position inside the bronchial tree. However, respiratory motion, coughing, patient movement, and airway deformation inflicted by bronchoscope significantly hinder the accuracy of intraoperative bronchoscopic localization. In this study, a real-time and automatic registration procedure was proposed to superimpose the current location of the bronchoscope to corresponding locations on a centerline extracted from bronchial computed tomography (CT) images. A centerline-guided Gaussian mixture model (CG-GMM) was introduced to register a bronchoscope's position concerning extracted centerlines. A GMM was fitted to bronchoscope positions where the orientation likelihood was chosen to assign the membership probabilities of the mixture model, which led to preserving the global and local structures. The problem was formulated and solved under the expectation maximization framework, where the feature correspondence and spatial transformation are estimated iteratively. Validation was performed on a dynamic phantom with four different respiratory motions and four human real bronchoscopy (RB) datasets. Results of the experiments conducted on the bronchial phantom showed that the average positional tracking error using the proposed approach was equal to 1.98 [Formula: see text] 0.98 mm that was reduced in comparison with independent electromagnetic tracking (EMT), iterative closest point (ICP), and coherent point drift (CPD) methods by 64%, 58%, and 53%, respectively. In the patient assessment part of the study, the average positional tracking error was 4.73 [Formula: see text] 4.76 mm and compared to ICP, and CPD methods showed 31.4% improvement of successfully tracked frames. Our approach introduces a novel method for real-time respiratory motion compensation that provides reliable guidance during bronchoscopic interventions and, thus could increase the diagnostic yield of transbronchial biopsy.

Photoacoustic image improvement based on a combination of sparse coding and filtering.

SIGNIFICANCE: Photoacoustic imaging (PAI) has been greatly developed in a broad range of diagnostic applications. The efficiency of light to sound conversion in PAI is limited by the ubiquitous noise arising from the tissue background, leading to a low signal-to-noise ratio (SNR), and thus a poor quality of images. Frame averaging has been widely used to reduce the noise; however, it compromises the temporal resolution of PAI. AIM: We propose an approach for photoacoustic (PA) signal denoising based on a combination of low-pass filtering and sparse coding (LPFSC). APPROACH: LPFSC method is based on the fact that PA signal can be modeled as the sum of low frequency and sparse components, which allows for the reduction of noise levels using a hybrid alternating direction method of multipliers in an optimization process. RESULTS: LPFSC method was evaluated using in-silico and experimental phantoms. The results show a 26% improvement in the peak SNR of PA signal compared to the averaging method for in-silico data. On average, LPFSC method offers a 63% improvement in the image contrast-to-noise ratio and a 33% improvement in the structural similarity index compared to the averaging method for objects located at three different depths, ranging from 10 to 20 mm, in a porcine tissue phantom. CONCLUSIONS: The proposed method is an effective tool for PA signal denoising, whereas it ultimately improves the quality of reconstructed images, especially at higher depths, without limiting the image acquisition speed.

Automatic detection of symmetry plane for computer-aided surgical simulation in craniomaxillofacial surgery.

Symmetry plane calculation is used in fracture reduction or reconstruction in the midface. Estimating a reliable symmetry plane without advanced anatomic knowledge is the most critical challenge. In this work, we developed a new automated method to find the mid-plane in CT images of an intact skull and a skull with a unilateral midface fracture. By use of a 3D point-cloud of a skull, we demonstrate that the proposed algorithm could find a mid-plane that meets clinical criteria. There is no need for advanced anatomical knowledge through the use of this algorithm. The algorithm used principal component analysis to find the initial plane. Then the rotation matrix, derived from an iterative closest point (ICP) registration method, is used to update the normal vector of the plane and find the optimum symmetry plane. A mathematical index, Hausdorff distance (HD), is used to evaluate the similarity of one mid-plane side in comparison to the contralateral side. HD decreased by 66% in the intact skull and 65% in a fractured skull and converged in just six iterations. High convergence speed, low computational load, and high accuracy suggest the use of the algorithm in the planning procedure. This easy-to-use algorithm with its advantages, as mentioned above, could be used as an operator in craniomaxillofacial software.

Dictionary learning technique enhances signal in LED-based photoacoustic imaging.

There has been growing interest in low-cost light sources such as light-emitting diodes (LEDs) as an excitation source in photoacoustic imaging. However, LED-based photoacoustic imaging is limited by low signal due to low energy per pulse-the signal is easily buried in noise leading to low quality images. Here, we describe a signal de-noising approach for LED-based photoacoustic signals based on dictionary learning with an alternating direction method of multipliers. This signal enhancement method is then followed by a simple reconstruction approach delay and sum. This approach leads to sparse representation of the main components of the signal. The main improvements of this approach are a 38% higher contrast ratio and a 43% higher axial resolution versus the averaging method but with only 4% of the frames and consequently 49.5% less computational time. This makes it an appropriate option for real-time LED-based photoacoustic imaging.

High-quality photoacoustic image reconstruction based on deep convolutional neural network: towards intra-operative photoacoustic imaging.

The use of intra-operative imaging system as an intervention solution to provide more accurate localization of complicated structures has become a necessity during the neurosurgery. However, due to the limitations of conventional imaging systems, high-quality real-time intra-operative imaging remains as a challenging problem. Meanwhile, photoacoustic imaging has appeared so promising to provide images of crucial structures such as blood vessels and microvasculature of tumors. To achieve high-quality photoacoustic images of vessels regarding the artifacts caused by the incomplete data, we proposed an approach based on the combination of time-reversal (TR) and deep learning methods. The proposed method applies a TR method in the first layer of the network which is followed by the convolutional neural network with weights adjusted to a set of simulated training data for the other layers to estimate artifact-free photoacoustic images. It was evaluated using a generated synthetic database of vessels. The mean of signal to noise ratio (SNR), peak SNR, structural similarity index, and edge preservation index for the test data were reached 14.6 dB, 35.3 dB, 0.97 and 0.90, respectively. As our results proved, by using the lower number of detectors and consequently the lower data acquisition time, our approach outperforms the TR algorithm in all criteria in a computational time compatible with clinical use.

Compensation of dynamic electromagnetic field distortion using simultaneous localization and mapping method with application in endobronchial ultrasound-transbronchial needle aspiration (EBUS-TBNA) guidance.

BACKGROUND: Electromagnetic (EM)-based navigation methods without line-of-sight restrictions may improve lymph node sampling precision in transbronchial needle aspiration (TBNA) procedure. However, EM tracking susceptibility to metallic objects severely declines its precision. METHOD: We proposed to track the location of a tool in a dynamic bronchial phantom and compensate field distortion in a real-time procedure. Extended Kalman filter simultaneous localization and mapping (EKF-SLAM) algorithm employ the bronchial motion and observations of a redundant sensor. The proposed approach was applied to the phantom with four different amplitudes of breathing motion in the presence of two types of field-distorting objects. RESULTS: The proposed approach improved the accuracy of EM tracking on average from 18.94 ±1.17 mm to 4.59 ±0.29 mm and from 14.2 ±0.69 mm to 4.31 ±0.18mm in the presence of steel and aluminum, respectively. CONCLUSIONS: With EM tracking position error reduction based on the EKF-SLAM technique, the approach is appeared promising for a navigated ultrasound TBNA procedure.

Application of multi-wavelength technique for photoacoustic imaging to delineate tumor margins during maximum-safe resection of glioma: A preliminary simulation study.

Accurate margin delineation and safe maximal resection of glioma is one of the most challenging problems of neurosurgery, due to its close resemblance to normal brain parenchyma. However, different intraoperative visualization methods have been used for real-time intraoperative investigation of the borders of the resection cavity, each having advantages and limitations. This preliminary study was designed to simulate multi-wavelength photoacoustic imaging for brain tumor margin delineation for maximum safe resection of glioma. Since the photoacoustic signal is directly related to the amount of optical energy absorption by the endogenous tissue chromophores such as hemoglobin; it may be able to illustrate the critical structures such as tumor vessels during surgery. The simulation of the optical and acoustic part was done by using Monte-Carlo and k-wave toolbox, respectively. As our simulation results proved, at different wavelengths and depths, the amount of optical absorption for the blood layer is significantly different from others such as normal and tumoral tissues. Furthermore, experimental validation of our approach confirms that, by using multi-wavelengths proportional to the depth of the tumor margin during surgery, tumor margin can be differented using photoacoustic imaging at various depths. Photoacoustic imaging may be considered as a promising imaging modality which combines the spectral contrast of optical imaging as well as the spatial resolution of ultrasound imaging, and may be able to delineate the vascular-rich glioma margins at different depths of the resection cavity during surgery.

Mitral valve prolapse detection using landmark extraction from echocardiography sequences.

The mitral valve is one of the four valves of the heart, whose function is to keep the blood flow in the physiological direction when the heart contracts. There is no satisfactory method allowing an automated assessment for Mitral Valve Prolapse (MVP) detection. In this paper an algorithm is proposed for detecting MVPs automatically from an echocardiography sequence. Our algorithm has two steps; first landmarks are extracted from the echocardiography sequence. Then landmarks are tracked in the whole frames of a sequence. In order to detect MVP and isolate it from a normal mitral motion, we extracted some features (such as maximum deviation of valve angle and spectral power ratio) from the motion pattern of a mitral valve and we gave these features to a SVM classifier. The results show that the mitral motion trajectory may have good discriminative features for detecting MVP (87% specificity and 84% sensitivity).

On the performance of improved ICP algorithms for registration of intra-ultrasound with pre-MR images; a phantom study.

Ultrasound imaging as a simple and being real time has been found very applicable for intra-operative updates of pre-operative MRI data in image guided neurosurgery system. The main challenge here is the presence of speckle noise which influences the accuracy of registration of US-MR images, intra-operatively. In this paper the performance of two improved versions of the well known Iterative Closest Point (ICP) algorithms to deal with noise and outliers are considered and compared with conventional ICP method. To perform this study in a condition close to real clinical setting, a PVA-C brain phantom is made. As the results show improved versions of ICP are found more robust and precise than ICP algorithms in the presence of noise and outliers. Then the effect of various de-noising methods including diffusion filters on the accuracy of point-based registration is evaluated. The role of a proper diffusion filter for de-noising of US images has also improved the performance of the ICP algorithm and its variants about 35% and 20%, respectively.

Segmentation of brain tumors in MRI images using multi-scale gradient vector flow.

The gradient vector flow (GVF) algorithm has been used extensively as an efficient method for medical image segmentation. This algorithm suffers from poor robustness against noise as well as lack of convergence in small scale details and concavities. As a cure to this problem, in this paper the idea of multi scale is applied to the traditional GVF algorithm for segmentation of brain tumors in MRI images. Using this idea, the active contour is evolved with respect to scaled edge maps in a multi scale manner. The edge detection performance of the modified GVF algorithm is further enhanced by applying a threshold-based edge detector to improve the edge map. The Bspline snake is selected for representation of the active contour, due to its ability to capture corners and its local control. The results showed an improvement of 30% in the accuracy of tumor segmentation against traditional GVF and 10 % as compared to Bspline GVF in the presence of noise, besides the repeatability of the algorithm in contrast to traditional GVF. The clinical evaluation also proved the accuracy and sensitivity of the proposed method as 92.8% and 95.4%, respectively.

An efficient point based registration of intra-operative ultrasound images with MR images for computation of brain shift; a phantom study.

Intra-operative brain deformation (brain shift) limits the accuracy of image-guided neuro-surgery systems. Ultrasound imaging as a simple, fast and being real time has become an alternative to MR imaging which is an expensive system for brain shift calculation. The main challenges due to speckle noise and artifacts in US images, is to perform an accurate and fast registration of Us images with pre-operative MR images. In this paper an efficient point based registration method based on the alignment of probability density functions called Coherent Point Drift (CPD) is implemented and compared to the conventional ICP method. To perform this, a brain phantom that allows simulating the brain deformation is made. As the results of our phantom study confirm the CPD method clearly outperforms the ICP algorithm for brain shift calculation. Also the result proves that using intra-operative US has led to recover almost 80% of displacement in the region of interest.