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With an aim to increase the capture range and accelerate the performance of state-of-the-art inter-subject and subject-to-template 3-D rigid registration, we propose deep learning-based methods that are trained to find the 3-D position of arbitrarily-oriented subjects or anatomy in a canonical space based on slices or volumes of medical images. For this, we propose regression convolutional neural networks (CNNs) that learn to predict the angle-axis representation of 3-D rotations and translations using image features. We use and compare mean square error and geodesic loss to train regression CNNs for 3-D pose estimation used in two different scenarios: slice-to-volume registration and volume-to-volume registration. As an exemplary application, we applied the proposed methods to register arbitrarily oriented reconstructed images of fetuses scanned in-utero at a wide gestational age range to a standard atlas space. Our results show that in such registration applications that are amendable to learning, the proposed deep learning methods with geodesic loss minimization achieved 3-D pose estimation with a wide capture range in real-time (<100ms). We also tested the generalization capability of the trained CNNs on an expanded age range and on images of newborn subjects with similar and different MR image contrasts. We trained our models on T2-weighted fetal brain MRI scans and used them to predict the 3-D pose of newborn brains based on T1-weighted MRI scans. We showed that the trained models generalized well for the new domain when we performed image contrast transfer through a conditional generative adversarial network. This indicates that the domain of application of the trained deep regression CNNs can be further expanded to image modalities and contrasts other than those used in training. A combination of our proposed methods with accelerated optimization-based registration algorithms can dramatically enhance the performance of automatic imaging devices and image processing methods of the future. [ABSTRACT FROM AUTHOR]

Prostate cancer (PCa) recurrences are often predicted by assessing the status of surgical margins (SM)– positive surgical margins (PSM) increase the chances of biochemical recurrence by 2-4 times which may lead to PCa recurrence. To this end, an electrical impedance acquisition system with a microendoscopic probe was employed in an ex-vivo study of human prostates. This system measures the tissue bioimpedance over a range of frequencies (1 kHz to 1MHz), and computes a number of Composite Impedance Metrics (CIM). A classifier trained using CIM data can be used to classify tissue as benign or cancerous. The system was used to collect the impedance spectra from 14 excised prostates, which were obtained from men undergoing radical prostatectomy, for a total of 23 cancerous and 53 benign measurements. The data revealed statistically significant (p < 0.05) differences in the impedance properties of the benign and tumorous tissues, and among the measurements taken on the apical, base, and lateral surface of the prostate. Further, in the leave-one-patient-out cross validation, a maximum predictive accuracy of 90.79% was achieved by combining high frequency CIM phase data to train a support vector machine classifier with a radial basis function kernel. The observations are consistent with the physiology and morphology of benign and malignant prostate tissue. CIMs were found to be an effective tool in distinguishing benign from cancerous tissues. [ABSTRACT FROM PUBLISHER]

Radially configured microendoscopic electrical impedance probes intended for intraoperative surgical margin assessment during robot-assisted laparoscopic prostatectomy (RALP) were examined through simulation, bench-top experimentation, and ex vivo tissue studies. Three probe designs with 8, 9, and 17 electrodes, respectively, were analyzed through finite element method based simulations. One mm diameter spherical inclusions (\sigma inclusion =1~\rm S/\rm m) are positioned at various locations within a hemispherical background (\sigma background =0.1~\rm S/\rm m) of radius 5 mm. An 8-electrode configuration is not able to localize the inclusion at these positions while 9 and 17-electrode configurations are able to accurately reconstruct the inclusion at maximum depth of 1 mm and 3 mm, respectively. All three probe designs were constructed and evaluated using saline phantoms and ex vivo porcine and human prostate tissues. The 17-electrode probe performed best in saline phantom studies, accurately reconstructing high contrast, 1-mm-diameter metal cylindrical inclusions in a saline bath (\sigma saline =0.1~\rm S/\rm m) with a position and area error of 0.46 mm and 0.84 mm^2, respectively. Additionally, the 17-electrode probe was able to adequately distinguish cancerous from benign tissues in three ex vivo human prostates. Simulations, bench-top saline experiments, and ex vivo tissue sampling suggest that for intraoperative surgical margin assessment during RALP, the 17-electrode probe (as compared to an 8 and 9 electrode probe) will be necessary to provide sufficient accuracy and sensitivity. [ABSTRACT FROM AUTHOR]

Electrical impedance tomography (EIT) is used to image the electrical property distribution of a tissue under test. An EIT system comprises complex hardware and software modules, which are typically designed for a specific application. Upgrading these modules is a time-consuming process, and requires rigorous testing to ensure proper functioning of new modules with the existing ones. To this end, we developed a modular and reconfigurable data acquisition (DAQ) system using National Instruments' (NI) hardware and software modules, which offer inherent compatibility over generations of hardware and software revisions. The system can be configured to use up to 32-channels. This EIT system can be used to interchangeably apply current or voltage signal, and measure the tissue response in a semi-parallel fashion. A novel signal averaging algorithm, and 512-point fast Fourier transform (FFT) computation block was implemented on the FPGA. FFT output bins were classified as signal or noise. Signal bins constitute a tissue's response to a pure or mixed tone signal. Signal bins' data can be used for traditional applications, as well as synchronous frequency-difference imaging. Noise bins were used to compute noise power on the FPGA. Noise power represents a metric of signal quality, and can be used to ensure proper tissue-electrode contact. Allocation of these computationally expensive tasks to the FPGA reduced the required bandwidth between PC, and the FPGA for high frame rate EIT. In 16-channel configuration, with a signal-averaging factor of 8, the DAQ frame rate at 100 kHz exceeded 110 frames s ^-1, and signal-to-noise ratio exceeded 90 dB across the spectrum. Reciprocity error was found to be < 1\% for frequencies up to 1 MHz. Static imaging experiments were performed on a high-conductivity inclusion placed in a saline filled tank; the inclusion was clearly localized in the reconstructions obtained for both absolute current and voltage mode data. [ABSTRACT FROM PUBLISHER]