Comparison of Patient Position and Midline Lumbar Neuraxial Access Via Statistical Model Registration to Ultrasound.

Patient positioning and needle puncture site are important for lumbar neuraxial anesthesia. We sought to identify optimal patient positioning and puncture sites with a novel ultrasound registration. We registered a statistical model to volumetric ultrasound data acquired from volunteers (n = 10) in three positions: (i) prone; (ii) seated with thoracic and lumbar flexion; and (iii) seated as in position ii, with a 10° dorsal tilt. We determined injection target size and penetration success by simulating lumbar injections on validated registered models. Injection window and target area sizes in seated positions were significantly larger than those in prone positions by 65% in L2-3 and 130% in L3-4; a 10° tilt had no significant effect on target sizes between seated positions. In agreement with computed tomography studies, simulated L2-3 and L3-4 injections had the highest success at the 50% and 75% midline puncture sites, respectively, measured from superior to inferior spinous process. We conclude that our registration to ultrasound technique is a potential tool for tolerable determination of puncture site success in vivo.

A deep learning approach for real time prostate segmentation in freehand ultrasound guided biopsy.

Targeted prostate biopsy, incorporating multi-parametric magnetic resonance imaging (mp-MRI) and its registration with ultrasound, is currently the state-of-the-art in prostate cancer diagnosis. The registration process in most targeted biopsy systems today relies heavily on accurate segmentation of ultrasound images. Automatic or semi-automatic segmentation is typically performed offline prior to the start of the biopsy procedure. In this paper, we present a deep neural network based real-time prostate segmentation technique during the biopsy procedure, hence paving the way for dynamic registration of mp-MRI and ultrasound data. In addition to using convolutional networks for extracting spatial features, the proposed approach employs recurrent networks to exploit the temporal information among a series of ultrasound images. One of the key contributions in the architecture is to use residual convolution in the recurrent networks to improve optimization. We also exploit recurrent connections within and across different layers of the deep networks to maximize the utilization of the temporal information. Furthermore, we perform dense and sparse sampling of the input ultrasound sequence to make the network robust to ultrasound artifacts. Our architecture is trained on 2,238 labeled transrectal ultrasound images, with an additional 637 and 1,017 unseen images used for validation and testing, respectively. We obtain a mean Dice similarity coefficient of 93%, a mean surface distance error of 1.10 mm and a mean Hausdorff distance error of 3.0 mm. A comparison of the reported results with those of a state-of-the-art technique indicates statistically significant improvement achieved by the proposed approach.

Enabling fast and high quality LED photoacoustic imaging: a recurrent neural networks based approach.

Photoacoustic (PA) techniques have shown promise in the imaging of tissue chromophores and exogenous contrast agents in various clinical applications. However, the key drawback of current PA technology is its dependence on a complex and hazardous laser system for the excitation of a tissue sample. Although light-emitting diodes (LED) have the potential to replace the laser, the image quality of an LED-based system is severely corrupted due to the low output power of LED elements. The current standard way to improve the quality is to increase the scanning time, which leads to a reduction in the imaging speed and makes the images prone to motion artifacts. To address the challenges of longer scanning time and poor image quality, in this work we present a deep neural networks based approach that exploits the temporal information in PA images using a recurrent neural network. We train our network using 32 phantom experiments; on the test set of 30 phantom experiments, we achieve a gain in the frame rate of 8 times with a mean peak-signal-to-noise-ratio of 35.4 dB compared to the standard technique.

Model-based registration of preprocedure MR and intraprocedure US of the lumbar spine.

PURPOSE: Epidural and spinal needle insertions, as well as facet joint denervation and injections are widely performed procedures on the lumbar spine for delivering anesthesia and analgesia. Ultrasound (US)-based approaches have gained popularity for accurate needle placement, as they use a non-ionizing, inexpensive and accessible modality for guiding these procedures. However, due to the inherent difficulties in interpreting spinal US, they yet to become the clinical standard-of-care. METHODS: A novel statistical shape [Formula: see text] pose [Formula: see text] scale (s [Formula: see text] p [Formula: see text] s) model of the lumbar spine is jointly registered to preoperative magnetic resonance (MR) and US images. An instance of the model is created for each modality. The shape and scale model parameters are jointly computed, while the pose parameters are estimated separately for each modality. RESULTS: The proposed method is successfully applied to nine pairs of preoperative clinical MR volumes and their corresponding US images. The results are assessed using the target registration error (TRE) metric in both MR and US domains. The s [Formula: see text] p [Formula: see text] s model in the proposed joint registration framework results in a mean TRE of 2.62 and 4.20 mm for MR and US images, respectively, on different landmarks. CONCLUSION: The joint framework benefits from the complementary features in both modalities, leading to significantly smaller TREs compared to a model-to-US registration approach. The s [Formula: see text] p [Formula: see text] s model also outperforms our previous shape [Formula: see text] pose model of the lumbar spine, as separating scale from pose allows to better capture pose and guarantees equally-sized vertebrae in both modalities. Furthermore, the simultaneous visualization of the patient-specific models on the MR and US domains makes it possible for clinicians to better evaluate the local registration accuracy.

Automatic Segmentation of Wrist Bones in CT Using a Statistical Wrist Shape + Pose Model.

Segmentation of the wrist bones in CT images has been frequently used in different clinical applications including arthritis evaluation, bone age assessment and image-guided interventions. The major challenges include non-uniformity and spongy textures of the bone tissue as well as narrow inter-bone spaces. In this work, we propose an automatic wrist bone segmentation technique for CT images based on a statistical model that captures the shape and pose variations of the wrist joint across 60 example wrists at nine different wrist positions. To establish the correspondences across the training shapes at neutral positions, the wrist bone surfaces are jointly aligned using a group-wise registration framework based on a Gaussian Mixture Model. Principal component analysis is then used to determine the major modes of shape variations. The variations in poses not only across the population but also across different wrist positions are incorporated in two pose models. An intra-subject pose model is developed by utilizing the similarity transforms at all wrist positions across the population. Further, an inter-subject pose model is used to model the pose variations across different wrist positions. For segmentation of the wrist bones in CT images, the developed model is registered to the edge point cloud extracted from the CT volume through an expectation maximization based probabilistic approach. Residual registration errors are corrected by application of a non-rigid registration technique. We validate the proposed segmentation method by registering the wrist model to a total of 66 unseen CT volumes of average voxel size of 0.38 mm. We report a mean surface distance error of 0.33 mm and a mean Jaccard index of 0.86.

Joint registration of ultrasound, CT and a shape+pose statistical model of the lumbar spine for guiding anesthesia.

PURPOSE: Facet joint injections and epidural needle insertions are widely used for spine anesthesia. Accurate needle placement is important for effective therapy delivery and avoiding complications arising from damage of soft tissue and nerves. Needle guidance is usually performed by fluoroscopy or palpation, resulting in radiation exposure and multiple needle re-insertions. Several ultrasound (US)-based approaches have been proposed but have not found wide acceptance in clinical routine. This is mainly due to difficulties in interpretation of the complex spinal anatomy in US, which leads to clinicians' lack of confidence in relying only on information derived from US for needle guidance. METHODS: We introduce a multimodal joint registration technique that takes advantage of easy-to-interpret preprocedure computed topography (CT) scans of the lumbar spine to concurrently register a shape+pose model to the intraprocedure 3D US. Common shape coefficients are assumed between two modalities, while pose coefficients are specific to each modality. RESULTS: The joint method was evaluated on patient data consisting of ten pairs of US and CT scans of the lumbar spine. It was successfully applied in all cases and yielded an RMS shape error of 2.1 mm compared to the CT ground truth. The joint registration technique was compared to a previously proposed method of statistical model to US registration Rasoulian et al. (Information processing in computer-assisted interventions. Springer, Berlin, pp 51-60, 2013). The joint framework improved registration accuracy to US in 7 out of 17 visible vertebrae, belonging to four patients. In the remaining cases, the two methods were equally accurate. CONCLUSION: The joint registration allows visualization and augmentation of important anatomy in both the US and CT domain and improves the registration accuracy in both modalities. Observing the patient-specific model in the CT domain allows the clinicians to assess the local registration accuracy qualitatively, which is likely to increase their confidence in using the US model for deriving needle guidance decisions.

Registration of a statistical model to intraoperative ultrasound for scaphoid screw fixation.

PURPOSE: Volar percutaneous scaphoid fracture fixation is conventionally performed under fluoroscopy-based guidance, where surgeons need to mentally determine a trajectory for the insertion of the screw and its depth based on a series of 2D projection images. In addition to challenges associated with mapping 2D information to a 3D space, the process involves exposure to ionizing radiation. Three-dimensional ultrasound has been suggested as an alternative imaging tool for this procedure; however, it has not yet been integrated into clinical routine since ultrasound only provides a limited view of the scaphoid and its surrounding anatomy. METHODS: We propose a registration of a statistical wrist shape + scale + pose model to a preoperative CT and intraoperative ultrasound to derive a patient-specific 3D model for guiding scaphoid fracture fixation. The registered model is then used to determine clinically important intervention parameters, including the screw length and the trajectory of screw insertion in the scaphoid bone. RESULTS: Feasibility experiments are performed using 13 cadaver wrists. In 10 out of 13 cases, the trajectory of screw suggested by the registered model meets all clinically important intervention parameters. Overall, an average 94 % of maximum allowable screw length is obtained based on the measurements from gold standard CT. Also, we obtained an average 92 % successful volar accessibility, which indicates that the trajectory is not obstructed by the surrounding trapezium bone. CONCLUSIONS: These promising results indicate that determining clinically important screw insertion parameters for scaphoid fracture fixation is feasible using 3D ultrasound imaging. This suggests the potential of this technology in replacing fluoroscopic guidance for this procedure in future applications.

Direct mean strain estimation for elastography using nearest-neighbor weighted least-squares approach in the frequency domain.

Ultrasound elastography is emerging with enormous potential as a medical imaging modality for effective discrimination of pathological changes in soft tissue. It maps the tissue elasticity or strain due to a mechanical deformation applied to it. The strain image most often calculated from the derivative of the local displacement field is highly noisy because of the de-correlation effect mainly due to unstable free-hand scanning and/or irregular tissue motion; consequently, improving the SNR of the strain image is still a challenging problem in this area. In this paper, a novel approach using the nearest-neighbor weighted least-squares is presented for direct estimation of the 'mean' axial strain for high quality strain imaging. Like other time/frequency domain reported schemes, the proposed method exploits the fact that the post-compression rf echo signal is a time-scaled and shifted replica of the pre-compression rf echo signal. However, the elegance of our technique is that it directly computes the mean strain without explicitly using any post filter and/or previous local displacement/strain estimates as is usually done in the conventional approaches. It is implemented in the short-time Fourier transform domain through a nearest-neighbor weighted least-squares-based Fourier spectrum equalization technique. As the local tissue strain is expected to maintain continuity with its neighbors, we show here that the mean strain at the interrogative window can be directly computed from the common stretching factor that minimizes a cost function derived from the exponentially weighted windowed pre- and post-compression rf echo segments in both the lateral and axial directions. The performance of our algorithm is verified for up to 8% applied strain using simulation and experimental phantom data and the results reveal that the SNR of the strain image can be significantly improved compared to other reported algorithms in the literature. The efficacy of the algorithm is also tested with in vivo breast data known to have malignant or benign masses from histology.

Classification of ring artifacts for their effective removal using type adaptive correction schemes.

High resolution tomographic images acquired with a digital X-ray detector are often degraded by the so called ring artifacts. In this paper, a detail analysis including the classification, detection and correction of these ring artifacts is presented. At first, a novel idea for classifying rings into two categories, namely type I and type II rings, is proposed based on their statistical characteristics. The defective detector elements and the dusty scintillator screens result in type I ring and the mis-calibrated detector elements lead to type II ring. Unlike conventional approaches, we emphasize here on the separate detection and correction schemes for each type of rings for their effective removal. For the detection of type I ring, the histogram of the responses of the detector elements is used and a modified fast image inpainting algorithm is adopted to correct the responses of the defective pixels. On the other hand, to detect the type II ring, first a simple filtering scheme is presented based on the fast Fourier transform (FFT) to smooth the sum curve derived form the type I ring corrected projection data. The difference between the sum curve and its smoothed version is then used to detect their positions. Then, to remove the constant bias suffered by the responses of the mis-calibrated detector elements with view angle, an estimated dc shift is subtracted from them. The performance of the proposed algorithm is evaluated using real micro-CT images and is compared with three recently reported algorithms. Simulation results demonstrate superior performance of the proposed technique as compared to the techniques reported in the literature.

Exploiting correlation of ECG with certain EMD functions for discrimination of ventricular fibrillation.

Ventricular fibrillation (VF) is a life-threatening cardiac arrhythmia. A high impulse current is required in this stage to save lives. In this paper, an empirical mode decomposition (EMD) based algorithm is presented to separate VF from other arrhythmias. The characteristics of the VF signal has high degree of similarity with the intrinsic mode functions (IMFs) of the EMD decomposition in comparison to other ECG pathologies. This high correlation between the VF signal and its certain IMFs is exploited to separate VF from other cardiac pathologies. Reliable databases are used to verify effectiveness of our algorithm and the results demonstrate superiority of our proposed technique compared to other well-known techniques of VF discrimination.

High-quality 3D correction of ring and radiant artifacts in flat panel detector-based cone beam volume CT imaging.

The use of an x-ray flat panel detector is increasingly becoming popular in 3D cone beam volume CT machines. Due to the deficient semiconductor array manufacturing process, the cone beam projection data are often corrupted by different types of abnormalities, which cause severe ring and radiant artifacts in a cone beam reconstruction image, and as a result, the diagnostic image quality is degraded. In this paper, a novel technique is presented for the correction of error in the 2D cone beam projections due to abnormalities often observed in 2D x-ray flat panel detectors. Template images are derived from the responses of the detector pixels using their statistical properties and then an effective non-causal derivative-based detection algorithm in 2D space is presented for the detection of defective and mis-calibrated detector elements separately. An image inpainting-based 3D correction scheme is proposed for the estimation of responses of defective detector elements, and the responses of the mis-calibrated detector elements are corrected using the normalization technique. For real-time implementation, a simplification of the proposed off-line method is also suggested. Finally, the proposed algorithms are tested using different real cone beam volume CT images and the experimental results demonstrate that the proposed methods can effectively remove ring and radiant artifacts from cone beam volume CT images compared to other reported techniques in the literature.