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Objective: A common phase of early-stage oncological treatment is the surgical resection of cancerous tissue. The presence of cancer cells on the resection margin, referred to as positive margin, is correlated with the recurrence of cancer and may require re-operation, negatively impacting many facets of patient outcomes. There exists a significant gap in the surgeon’s ability to intraoperatively delineate between tissues. Mass spectrometry methods have shown considerable promise as intraoperative tissue profiling tools that can assist with the complete resection of cancer. To do so, the vastness of the information collected through these modalities must be digested, relying on robust and efficient extraction of insights through data analysis pipelines.Methods: We review clinical mass spectrometry literature and prioritize intraoperatively applied modalities. We also survey the data analysis methods employed in these studies. Results: Our review outlines the advantages and shortcomings of mass spectrometry imaging and point-based tissue probing methods. For each modality, we identify statistical, linear transformation and machine learning techniques that demonstrate high performance in classifying cancerous tissues across several organ systems. A limited number of studies presented results captured intraoperatively. Conclusion: Through continued research of data centric techniques, like mass spectrometry, and the development of robust analysis approaches, intraoperative margin assessment is becoming feasible. Significance: By establishing the relatively short history of mass spectrometry techniques applied to surgical studies, we hope to inform future applications and aid in the selection of suitable data analysis frameworks for the development of intraoperative margin detection technologies. [ABSTRACT FROM AUTHOR]

Objective: Accurate biopsy sampling of the suspected lesions is critical for the diagnosis and clinical management of prostate cancer. Transperineal in-bore MRI-guided prostate biopsy (tpMRgBx) is a targeted biopsy technique that was shown to be safe, efficient, and accurate. Our goal was to develop an open source software platform to support evaluation, refinement, and translation of this biopsy approach. Methods: We developed SliceTracker, a 3D Slicer extension to support tpMRgBx. We followed modular design of the implementation to enable customization of the interface and interchange of image segmentation and registration components to assess their effect on the processing time, precision, and accuracy of the biopsy needle placement. The platform and supporting documentation were developed to enable the use of software by an operator with minimal technical training to facilitate translation. Retrospective evaluation studied registration accuracy, effect of the prostate segmentation approach, and re-identification time of biopsy targets. Prospective evaluation focused on the total procedure time and biopsy targeting error (BTE). Results: Evaluation utilized data from 73 retrospective and ten prospective tpMRgBx cases. Mean landmark registration error for retrospective evaluation was 1.88 ± 2.63 mm, and was not sensitive to the approach used for prostate gland segmentation. Prospectively, we observed target re-identification time of 4.60 ± 2.40 min and BTE of 2.40 ± 0.98 mm. Conclusion: SliceTracker is modular and extensible open source platform for supporting image processing aspects of the tpMRgBx procedure. It has been successfully utilized to support clinical research procedures at our site. [ABSTRACT FROM AUTHOR]

Temporal enhanced ultrasound (TeUS), comprising the analysis of variations in backscattered signals from a tissue over a sequence of ultrasound frames, has been previously proposed as a new paradigm for tissue characterization. In this paper, we propose to use deep recurrent neural networks (RNN) to explicitly model the temporal information in TeUS. By investigating several RNN models, we demonstrate that long short-term memory (LSTM) networks achieve the highest accuracy in separating cancer from benign tissue in the prostate. We also present algorithms for in-depth analysis of LSTM networks. Ourin vivostudy includes data from 255 prostate biopsy cores of 157 patients. We achieve area under the curve, sensitivity, specificity, and accuracy of 0.96, 0.76, 0.98, and 0.93, respectively. Our result suggests that temporal modeling of TeUS using RNN can significantly improve cancer detection accuracy over previously presented works. [ABSTRACT FROM AUTHOR]

Objectives: Temporal enhanced ultrasound (TeUS) is a new ultrasound-based imaging technique that provides tissue-specific information. Recent studies have shown the potential of TeUS for improving tissue characterization in prostate cancer diagnosis. We study the temporal properties of TeUS—temporal order and length—and present a new framework to assess their impact on tissue information. Methods: We utilize a probabilistic modeling approach using hidden Markov models (HMMs) to capture the temporal signatures of malignant and benign tissues from TeUS signals of nine patients. We model signals of benign and malignant tissues (284 and 286 signals, respectively) in their original temporal order as well as under order permutations. We then compare the resulting models using the Kullback–Liebler divergence and assess their performance differences in characterization. Moreover, we train HMMs using TeUS signals of different durations and compare their model performance when differentiating tissue types. Results: Our findings demonstrate that models of order-preserved signals perform statistically significantly better (85% accuracy) in tissue characterization compared to models of order-altered signals (62% accuracy). The performance degrades as more changes in signal order are introduced. Additionally, models trained on shorter sequences perform as accurately as models of longer sequences. Conclusion: The work presented here strongly indicates that temporal order has substantial impact on TeUS performance; thus, it plays a significant role in conveying tissue-specific information. Furthermore, shorter TeUS signals can relay sufficient information to accurately distinguish between tissue types. Significance: Understanding the impact of TeUS properties facilitates the process of its adopting in diagnostic procedures and provides insights on improving its acquisition. [ABSTRACT FROM AUTHOR]

Objective: A fiber optic vibration sensor is developed and characterized with an ultrawide dynamic sensing range, from less than 1 Hz to clinical ultrasound frequencies near 6 MHz. The vibration sensor consists of a matched pair of fiber Bragg gratings coupled to a custom-built signal processing circuit. The wavelength of a laser diode is locked to one of the many cavity resonances using the Pound–Drever–Hall scheme. Methods: A calibrated piezoelectric vibration element was used to characterize the sensor's strain, temperature, and noise responses. To demonstrate its sensing capability, an ultrasound phantom with built-in low frequency vibration actuation was constructed. Results: The fiber optic senor was shown to simultaneously capture the low frequency vibration and the clinical ultrasound transmission waveforms with nanostrain sensitivity. Conclusion: This miniaturized and sensitive vibration sensor can provide comprehensive information regarding strain response and the resultant ultrasound waveforms. [ABSTRACT FROM PUBLISHER]

Temporal-enhanced ultrasound (TeUS) is a novel noninvasive imaging paradigm that captures information from a temporal sequence of backscattered US radio frequency data obtained from a fixed tissue location. This technology has been shown to be effective for classification of various in vivo and ex vivo tissue types including prostate cancer from benign tissue. Our previous studies have indicated two primary phenomena that influence TeUS: 1) changes in tissue temperature due to acoustic absorption and 2) micro vibrations of tissue due to physiological vibration. In this paper, first, a theoretical formulation for TeUS is presented. Next, a series of simulations are carried out to investigate micro vibration as a source of tissue characterizing information in TeUS. The simulations include finite element modeling of micro vibration in synthetic phantoms, followed by US image generation during TeUS imaging. The simulations are performed on two media, a sparse array of scatterers and a medium with pathology mimicking scatterers that match nuclei distribution extracted from a prostate digital pathology data set. Statistical analysis of the simulated TeUS data shows its ability to accurately classify tissue types. Our experiments suggest that TeUS can capture the microstructural differences, including scatterer density, in tissues as they react to micro vibrations. [ABSTRACT FROM PUBLISHER]

Robotic technologies can provide objective, reliable tools for assessing a broad range of sensory, motor and cognitive functions. However, as additional tasks are developed on these platforms, the time necessary to assess a patient increases. In this paper, we present a hierarchical task selection strategy for five tasks that form part of the battery of standard tests performed on the KINARM robotic system. The strategy is built using dependencies derived through three types of analyses: 1) non-linear hierarchical ordering theory is applied to determine the ordering of five tasks; 2) the parameters of all tasks are also ranked using non-linear hierarchical ordering theory; and 3) a modeling technique, fast orthogonal search, is applied to assess the predictive power of each robotic task for the estimation of other task parameters. The inferred hierarchical task selection strategy can lead to a reduction of up to 91% of the time required to assess a patient. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In surface-based registration for image-guided interventions, the presence of missing data can be a significant issue. This often arises with real-time imaging modalities such as ultrasound, where poor contrast can make tissue boundaries difficult to distinguish from surrounding tissue. Missing data poses two challenges: ambiguity in establishing correspondences; and extrapolation of the deformation field to those missing regions. To address these, we present a novel non-rigid registration method. For establishing correspondences, we use a probabilistic framework based on a Gaussian mixture model (GMM) that treats one surface as a potentially partial observation. To extrapolate and constrain the deformation field, we incorporate biomechanical prior knowledge in the form of a finite element model (FEM). We validate the algorithm, referred to as GMM-FEM, in the context of prostate interventions. Our method leads to a significant reduction in target registration error (TRE) compared to similar state-of-the-art registration algorithms in the case of missing data up to 30%, with a mean TRE of 2.6 mm. The method also performs well when full segmentations are available, leading to TREs that are comparable to or better than other surface-based techniques. We also analyze robustness of our approach, showing that GMM-FEM is a practical and reliable solution for surface-based registration. [ABSTRACT FROM AUTHOR]

This paper presents the results of a computer-aided intervention solution to demonstrate the application of RF time series for characterization of prostate cancer, in vivo. Methods: We pre-process RF time series features extracted from 14 patients using hierarchical clustering to remove possible outliers. Then, we demonstrate that the mean central frequency and wavelet features extracted from a group of patients can be used to build a nonlinear classifier which can be applied successfully to differentiate between cancerous and normal tissue regions of an unseen patient. Results: In a cross-validation strategy, we show an average area under receiver operating characteristic curve (AUC) of 0.93 and classification accuracy of 80%. To validate our results, we present a detailed ultrasound to histology registration framework. Conclusion: Ultrasound RF time series results in differentiation of cancerous and normal tissue with high AUC. [ABSTRACT FROM AUTHOR]

Objective: This paper presents the results of a new approach for selection of RF time series features based on joint independent component analysis for in vivo characterization of prostate cancer. Methods: We project three sets of RF time series features extracted from the spectrum, fractal dimension, and the wavelet transform of the ultrasound RF data on a space spanned by five joint independent components. Then, we demonstrate that the obtained mixing coefficients from a group of patients can be used to train a classifier, which can be applied to characterize cancerous regions of a test patient. Results: In a leave-one-patient-out cross validation, an area under receiver operating characteristic curve of 0.93 and classification accuracy of 84% are achieved. Conclusion: Ultrasound RF time series can be used to accurately characterize prostate cancer, in vivo without the need for exhaustive search in the feature space. Significance: We use joint independent component analysis for systematic fusion of multiple sets of RF time series features, within a machine learning framework, to characterize PCa in an in vivo study. [ABSTRACT FROM PUBLISHER]

To accurately estimate muscle forces using electromyogram (EMG) signals, precise EMG amplitude estimation, and a modeling scheme capable of coping with the nonlinearities and dynamics of the EMG-force relationship are needed. In this work, angle-based EMG amplitude calibration and parallel cascade identification (PCI) modeling are combined for EMG-based force estimation in dynamic contractions, including concentric and eccentric contractions of the biceps brachii and triceps brachii muscles. Angle-based calibration has been shown to improve surface EMG (SEMG) based force estimation during isometric contractions through minimization of the effects of joint angle related factors, and PCI modeling captures both the nonlinear and dynamic properties of the process. SEMG data recorded during constant force, constant velocity, and varying force, varying velocity flexion and extension trials are calibrated. The calibration values are obtained at specific elbow joint angles and interpolated to cover a continuous range of joint angles. The calibrated data are used in PCI models to estimate the force induced at the wrist. The experimental results show the effectiveness of the calibration scheme, combined with PCI modeling. For the constant force, constant velocity trials, minimum %RMSE of 8.3% is achieved for concentric contractions, 10.3% for eccentric contractions and 33.3% for fully dynamic contractions. Force estimation accuracy is superior in concentric contractions in comparison to eccentric contractions , which may be indicative of more nonlinearity in the eccentric SEMG-force relationship. [ABSTRACT FROM AUTHOR]

This paper presents the prototype for an augmented reality haptic simulation system with potential for spinal needle insertion training. The proposed system is composed of a torso mannequin, a MicronTracker2 optical tracking system, a PHANToM haptic device, and a graphical user interface to provide visual feedback. The system allows users to perform simulated needle insertions on a physical mannequin overlaid with an augmented reality cutaway of patient anatomy. A tissue model based on a finite-element model provides force during the insertion. The system allows for training without the need for the presence of a trained clinician or access to live patients or cadavers. A pilot user study demonstrates the potential and functionality of the system. [ABSTRACT FROM PUBLISHER]

This paper presents the results of a feasibility study to demonstrate the application of ultrasound RF time series imaging to accurately differentiate ablated and nonablated tissue. For 12 ex vivo and two in situ tissue samples, RF ultrasound signals are acquired prior to, and following, high-intensity ultrasound ablation. Spatial and temporal features of these signals are used to characterize ablated and nonablated tissue in a supervised-learning framework. In cross-validation evaluation, a subset of four features extracted from RF time series produce a classification accuracy of 84.5%, an area under ROC curve of 0.91 for ex vivo data, and an accuracy of 85% for in situ data. Ultrasound RF time series is a promising approach for characterizing ablated tissue. [ABSTRACT FROM PUBLISHER]

Ultrasound (US) radio-frequency (RF) time series is an effective tissue classification method that enables accurate cancer diagnosis, but the mechanisms underlying this method are not completely understood. This paper presents a model to describe the variations in tissue temperature and sound speed that take place during the RF time series scanning procedures and relate these variations to US backscattering. The model was used to derive four novel characterization features. These features were used to classify three animal tissues, and they obtained accuracies as high as 88.01%. The performance of the proposed features was compared with RF time series features proposed in a previous study. The results indicated that the US-induced variations in tissue temperature and sound speed, which were used to derive the proposed features, were important contributors to the tissue typing capabilities of the RF time series. Simulations carried out to estimate the heating induced during the scanning procedure employed in this study showed temperature rises lower than 2 ^\circ C. The model and results presented in this paper can be used to improve the RF time series. [ABSTRACT FROM PUBLISHER]

Purpose: Ultrasound (US) guidance in facet joint injections has been reported previously as an alternative to imaging modalities with ionizing radiation. However, this technique has not been adopted in the clinical routine, due to difficulties in the visualization of the target joint in US and simultaneous manipulation of the needle. Methods: We propose a technique to increase targeting accuracy and efficiency in facet joint injections. This is achieved by electromagnetically tracking the positions of the US transducer and the needle, and recording tracked US snapshots (TUSS). The needle is navigated using the acquired US snapshots. Results: In cadaveric lamb model, the success rate of facet joint injections by five orthopedic surgery residents significantly increased from 44.4% (p < 0.05) with freehand US guidance to 93.3% with TUSS guidance. Needle insertion time significantly decreased from 47.9 ± 34.2 s to 36.1 ± 28.7 s (mean ± SD). In a synthetic human spine model, a success rate of 96.7% was achieved with TUSS. The targeting accuracy of the presented system in a gel phantom was 1.03 ± 0.48 mm (mean ± SD). Conclusion: Needle guidance with TUSS improves the success rate and time efficiency in spinal facet joint injections. This technique readily translates also to other spinal needle placement applications. [ABSTRACT FROM PUBLISHER]

We propose a novel and accurate method based on ultrasound RF time series analysis and an extended version of support vector machine classification for generating probabilistic cancer maps that can augment ultrasound images of prostate and enhance the biopsy process. To form the RF time series, we record sequential ultrasound RF echoes backscattered from tissue while the imaging probe and the tissue are stationary in position. We show that RF time series acquired from agar-gelatin-based tissue mimicking phantoms, with difference only in the size of cellmimicking microscopic glass beads, are distinguishable with statistically reliable accuracies up to 80.5%. This fact indicates that the differences in tissue microstructures affect the ultrasound RF time series features. Based on this phenomenon, in an ex vivo study involving 35 prostate specimens, we show that the features extracted from RF time series are significantly more accurate and sensitive compared to two other established categories of ultrasound-based tissue typing methods. We report an area under receiver operating characteristic curve of 0.95 in tenfold cross validation and 0.82 in leave-one-patient-out cross validation for detection of prostate cancer. [ABSTRACT FROM AUTHOR]

Presents a study that described an iterative fuzzy algorithm which successfully segments centromeres from images of human chromosomes prepared using fluorescence in-situ hybridization technique. Overview of the data-base preparation and image acquisition procedures; Discussion of the centromere segmentation algorithm; Results of the algorithm; Conclusion.

Ultrasound image guided needle insertion is the method of choice for a wide variety of medical diagnostic and therapeutic procedures. When flexible needles are inserted in soft tissue, these needles generally follow a curved path. Segmenting the trajectory of the needles in ultrasound images will facilitate guiding them within the tissue. In this paper, a novel algorithm for curved needle segmentation in three-dimensional (3D) ultrasound images is presented. The algorithm is based on the projection of a filtered 3D image onto a two-dimensional (2D) image. Detection of the needle in the resulting 2D image determines a surface on which the needle is located. The needle is then segmented on the surface. The proposed technique is able to detect needles without any previous assumption about the needle shape, or any a priori knowledge about the needle insertion axis line.