Image-based registration methods for quantification and compensation of prostate motion during trans-rectal ultrasound (TRUS)-guided biopsy

Prostate biopsy is the clinical standard for cancer diagnosis and is typically performed under two-dimensional (2D) transrectal ultrasound (TRUS) for needle guidance. Unfortunately, most early stage prostate cancers are not visible on ultrasound and the procedure suffers from high false negative rates due to the lack of visible targets. Fusion of pre-biopsy MRI to 3D TRUS for targeted biopsy could improve cancer detection rates and volume of tumor sampled. In MRI-TRUS fusion biopsy systems, patient or prostate motion during the procedure causes misalignments in the MR targets mapped to the live 2D TRUS images, limiting the targeting accuracy of the biopsy system. In order to sample smallest clinically significant tumours of 0.5 cm3with 95% confidence, the root mean square (RMS) error of the biopsy system needs to be The target misalignments due to intermittent prostate motion during the procedure can be compensated by registering the live 2D TRUS images acquired during the biopsy procedure to the pre-acquired baseline 3D TRUS image. The registration must be performed both accurately and quickly in order to be useful during the clinical procedure. We developed an intensity-based 2D-3D rigid registration algorithm and validated it by calculating the target registration error (TRE) using manually identified fiducials within the prostate. We discuss two different approaches that can be used to improve the robustness of this registration to meet the clinical requirements. Firstly, we evaluated the impact of intra-procedural 3D TRUS imaging on motion compensation accuracy since the limited anatomical context available in live 2D TRUS images could limit the robustness of the 2D-3D registration. The results indicated that TRE improved when intra-procedural 3D TRUS images were used in registration, with larger improvements in the base and apex regions as compared with the mid-gland region. Secondly, we developed and evaluated a registration algorithm whose optimization is based on learned prostate motion characteristics. Compared to our initial approach, the updated optimization improved the robustness during 2D-3D registration by reducing the number of registrations with a TRE \u3e 5 mm from 9.2% to 1.2% with an overall RMS TRE of 2.3 mm. The methods developed in this work were intended to improve the needle targeting accuracy of 3D TRUS-guided biopsy systems. The successful integration of the techniques into current 3D TRUS-guided systems could improve the overall cancer detection rate during the biopsy and help to achieve earlier diagnosis and fewer repeat biopsy procedures in prostate cancer diagnosis

Robustness and Accuracy of Feature-Based Single Image 2-D–3-D Registration Without Correspondences for Image-Guided Intervention

published_or_final_versio

Quantitative performance characterization of three-dimensional noncontact fluorescence molecular tomography

© 2016 The Authors.Fluorescent proteins and dyes are routine tools for biological research to describe the behavior of genes, proteins, and cells, as well as more complex physiological dynamics such as vessel permeability and pharmacokinetics. The use of these probes in whole body in vivo imaging would allow extending the range and scope of current biomedical applications and would be of great interest. In order to comply with a wide variety of application demands, in vivo imaging platform requirements span from wide spectral coverage to precise quantification capabilities. Fluorescence molecular tomography (FMT) detects and reconstructs in three dimensions the distribution of a fluorophore in vivo. Noncontact FMT allows fast scanning of an excitation source and noninvasive measurement of emitted fluorescent light using a virtual array detector operating in free space. Here, a rigorous process is defined that fully characterizes the performance of a custom-built horizontal noncontact FMT setup. Dynamic range, sensitivity, and quantitative accuracy across the visible spectrum were evaluated using fluorophores with emissions between 520 and 660 nm. These results demonstrate that high-performance quantitative three-dimensional visible light FMT allowed the detection of challenging mesenteric lymph nodes in vivo and the comparison of spectrally distinct fluorescent reporters in cell culture

Compensating for model uncertainty in the control of cooperative field robots

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2002.Includes bibliographical references (p. 113-123).Current control and planning algorithms are largely unsuitable for mobile robots in unstructured field environment due to uncertainties in the environment, task, robot models and sensors. A key problem is that it is often difficult to directly measure key information required for the control of interacting cooperative mobile robots. The objective of this research is to develop algorithms that can compensate for these uncertainties and limitations. The proposed approach is to develop physics-based information gathering models that fuse available sensor data with predictive models that can be used in lieu of missing sensory information. First, the dynamic parameters of the physical models of mobile field robots may not be well known. A new information-based performance metric for on-line dynamic parameter identification of a multi-body system is presented. The metric is used in an algorithm to optimally regulate the external excitation required by the dynamic system identification process. Next, an algorithm based on iterative sensor planning and sensor redundancy is presented to enable field robots to efficiently build 3D models of their environment. The algorithm uses the measured scene information to find new camera poses based on information content. Next, an algorithm is presented to enable field robots to efficiently position their cameras with respect to the task/target. The algorithm uses the environment model, the task/target model, the measured scene information and camera models to find optimum camera poses for vision guided tasks. Finally, the above algorithms are combined to compensate for uncertainties in the environment, task, robot models and sensors. This is applied to a cooperative robot assembly task in an unstructured environment.(cont.) Simulations and experimental results are presented that demonstrate the effectiveness of the above algorithms on a cooperative robot test-bed.by Vivek Anand Sujan.Ph.D

Study of spacecraft direct readout meteorological systems

Characteristics are defined of the next generation direct readout meteorological satellite system with particular application to Tiros N. Both space and ground systems are included. The recommended space system is composed of four geosynchronous satellites and two low altitude satellites in sun-synchronous orbit. The goesynchronous satellites transmit to direct readout ground stations via a shared S-band link, relayed FOFAX satellite cloud cover pictures (visible and infrared) and weather charts (WEFAX). Basic sensor data is transmitted to regional Data Utilization Stations via the same S-band link. Basic sensor data consists of 0.5 n.m. sub-point resolution data in the 0.55 - 0.7 micron spectral region, and 4.0 n.m. resolution data in the 10.5 - 12.6 micron spectral region. The two low altitude satellites in sun-synchronous orbit provide data to direct readout ground stations via a 137 MHz link, a 400 Mhz link, and an S-band link

Pulmonary Image Segmentation and Registration Algorithms: Towards Regional Evaluation of Obstructive Lung Disease

Pulmonary imaging, including pulmonary magnetic resonance imaging (MRI) and computed tomography (CT), provides a way to sensitively and regionally measure spatially heterogeneous lung structural-functional abnormalities. These unique imaging biomarkers offer the potential for better understanding pulmonary disease mechanisms, monitoring disease progression and response to therapy, and developing novel treatments for improved patient care. To generate these regional lung structure-function measurements and enable broad clinical applications of quantitative pulmonary MRI and CT biomarkers, as a first step, accurate, reproducible and rapid lung segmentation and registration methods are required. In this regard, we first developed a 1H MRI lung segmentation algorithm that employs complementary hyperpolarized 3He MRI functional information for improved lung segmentation. The 1H-3He MRI joint segmentation algorithm was formulated as a coupled continuous min-cut model and solved through convex relaxation, for which a dual coupled continuous max-flow model was proposed and a max-flow-based efficient numerical solver was developed. Experimental results on a clinical dataset of 25 chronic obstructive pulmonary disease (COPD) patients ranging in disease severity demonstrated that the algorithm provided rapid lung segmentation with high accuracy, reproducibility and diminished user interaction. We then developed a general 1H MRI left-right lung segmentation approach by exploring the left-to-right lung volume proportion prior. The challenging volume proportion-constrained multi-region segmentation problem was approximated through convex relaxation and equivalently represented by a max-flow model with bounded flow conservation conditions. This gave rise to a multiplier-based high performance numerical implementation based on convex optimization theories. In 20 patients with mild- to-moderate and severe asthma, the approach demonstrated high agreement with manual segmentation, excellent reproducibility and computational efficiency. Finally, we developed a CT-3He MRI deformable registration approach that coupled the complementary CT-1H MRI registration. The joint registration problem was solved by exploring optical-flow techniques, primal-dual analyses and convex optimization theories. In a diverse group of patients with asthma and COPD, the registration approach demonstrated lower target registration error than single registration and provided fast regional lung structure-function measurements that were strongly correlated with a reference method. Collectively, these lung segmentation and registration algorithms demonstrated accuracy, reproducibility and workflow efficiency that all may be clinically-acceptable. All of this is consistent with the need for broad and large-scale clinical applications of pulmonary MRI and CT

Automatic Extrinsic Calibration of Vision and Lidar by Maximizing Mutual Information

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/112212/1/rob21542.pd

Metoder och krav för noggrann lokalisering av sensorer i on-scalp magnetoencefalografi

Magnetoencephalography (MEG) is a noninvasive functional neuroimaging method which is used both in neuroscientific research and clinical medicine. Current state-of-the-art MEG systems require cryogenic cooling as well as thermal insulation between the sensors and the head of subjects, leading to lower sensitivity due to the relatively large spatial separation. Recently, a new type of sensor has been developed that does not require cryogenic temperatures to operate and can thus be placed much closer to the scalp of subjects. In such an on-scalp MEG system, the sensors of the array could be freely moveable in relation to each other as to conform to the head shape and size of individual subjects. To properly estimate the location and extent of neural sources within the brain, one needs to accurately know the position of all sensors in relation to the head. In on-scalp MEG systems this seemingly mundane issue becomes important, as all sensors must be localised individually. Large errors in the sensor positions may result in considerable errors in source estimates. In this thesis, different sensor localisation methods to be used in co-registration of MEG data with structural magnetic resonance images were examined, and the performance requirements for such methods were determined through the use of simulations. We found that the maximum acceptable root-mean-square sensor position error is $\sim3$ mm, which is achievable for most localisation methods examined. Thus the choice of method depends less on the localisation accuracy and more on other parameters such as ease of use, cost and commercial availability.Magnetoencefalografi (MEG) är en noninvasiv metod för undersökning av hjärnfunktion. MEG används både inom neurovetenskaplig forskning och klinisk medicin. Nuvarande MEG-system kräver kryogen nedkylning och värmeisolering mellan sensorerna och försökspersonens huvud, vilket leder till nedsatt känslighet på grund av det relativt stora avståndet mellan sensorerna och hjärnan. Nyligen har en ny typ av sensorer utvecklats som inte kräver kryogen nedkylning, och kan därmed placeras mycket närmare huvudet. I ett så kallat "on-scalp" MEG-system kunde sensorerna vara fritt flyttbara i förhållande till varandra för att på bästa sätt passa försökspersonens huvudform och -storlek. För att kunna avgöra varifrån inuti hjärnan MEG-signaler härstammar bör man veta sensorernas exakta position i förhållande till huvudet. I ett on-scalp MEG-system blir detta synligtvis triviala problem viktigt, i och med att alla sensorer måste lokaliseras enskilt. Ifall det uppstår fel i deras positioner kan detta orsaka märkbara fel i var hjärnaktiviteten som givit upphov till MEG-signalen avgörs vara. I detta diplomarbete har olika metoder för att lokalisera sensorerna undersökts, och noggrannhetskraven för dessa metoder har fastställts genom flera olika typers simuleringar. Utgående från dessa fastställdes det maximala tolererbara kvadratiska medelvärdesfelet i sensorernas position till $\sim3$ mm. Denna noggrannhetsnivå är uppnåelig för de flesta av de undersökta lokaliseringsmetoderna. Därmed bör valet av lokaliseringsmetod grunda sig på andra variabler såsom användarvänlighet, bekostnad och kommersiell tillgänglighet