Michael Hutchinson

Parkinson's disease: a novel MRI method for determining structural changes in the substantia nigra

OBJECTIVES To use MRI in a novel way to image and quantify the changes occurring in the substantia nigra in Parkinsons disease. METHODS Six patients with idiopathic Parkinsons disease were compared with six age matched control subjects. The subjects were imaged using a combination of pulse sequences hypothesised to be sensitive to cell loss. RESULTS The images showed patterns of change in patients with Parkinsons disease. Highly significant differences between the patients and control population were found (p<0.001). CONCLUSIONS This methodology suggests the possibility of detecting presymptomatic disease in those judged to be at risk, and also in confirming the diagnosis in patients with early disease. Furthermore, the technique seems to hold promise as a means for staging the disease, and possibly differentiating other forms of parkinsonism.

A review of source term estimation methods for atmospheric dispersion events using static or mobile sensors

Understanding atmospheric transport and dispersal events has an important role in a range of scenarios. Of particular importance is aiding in emergency response after an intentional or accidental chemical, biological or radiological (CBR) release. In the event of a CBR release, it is desirable to know the current and future spatial extent of the contaminant as well as its location in order to aid decision makers in emergency response. Many dispersion phenomena may be opaque or clear, thus monitoring them using visual methods will be difficult or impossible. In these scenarios, relevant concentration sensors are required to detect the substance where they can form a static network on the ground or be placed upon mobile platforms. This paper presents a review of techniques used to gain information about atmospheric dispersion events using static or mobile sensors. The review is concluded with a discussion on the current limitations of the state of the art and recommendations for future research.

Detection of Parkinson's disease by MRI: Spin-lattice distribution imaging†

We have developed an advanced MRI technique for detecting Parkinsons Disease (PD) which depends on an image constructed as a ratio of images from two inversion recovery sequences (one generating a white matter suppressed image, the other a gray matter suppressed image). This technique was designed to be exceptionally sensitive to the spin‐lattice relaxation time T1. It was refined with the introduction of segmentation analysis and given the acronym SIRRIM (Segmented Inversion Recovery Ratio Imaging). Our objectives are, first, to reinvestigate the sensitivity of MRI with new subjects and second, to investigate whether a new form of analysis, using the gray level distribution of signal in the image, may prove more sensitive than SIRRIM. For each subject, a ratio image was constructed (WMS/GMS) and the substantia nigra segmented out to be displayed as an isolated structure. From the segmented image a measure of disease severity, the Radiological Index (RI), was calculated for each subject. Since the pixel value in the ratio image is a strong function of the local T1 relaxation time, the distribution of pixel values gives the distribution of spin‐lattice relaxation times. A refinement in the analysis is introduced, the Spin‐Lattice Distribution Index (SI), which is an automated measure of MRI signal in the Substantia Nigra pars compacta (SNC). Both RI and SI were calculated for each of 24 subjects, 12 patients and 12 controls. The SI may further improve the separation of patient and control groups, and may therefore be more sensitive than the RI. Unlike the RI it is completely automatic and circumvents two of the limitations of the RI. The work is consistent with the proposition that MRI, when properly configured, is a highly sensitive marker for PD.

Computer assessment of neurodegeneration in Parkinson disease using data fusion techniques with MR images

RATIONALE AND OBJECTIVES Recently developed MR imaging techniques using inversion recovery are a sensitive tool to identify and quantify morphologic changes in the substantia nigra due to neurodegeneration. Using a semi-automated computer segmentation technique to isolate the substantia nigra pars compacta (SN(c)), we propose a colored image fusion technique to visually assess the sites of damage in the SN(c) and integrate the information obtained from two implemented inversion-recovery sequences. PATIENTS AND METHODS Six patients and six age-matched control subjects were scanned using a combination of two MR imaging inversion-recovery (IR) pulse sequences. A subgroup of them was used to develop our technique. Images were blended together into a final (RGBA) image, where A stands for the alpha channel describing transparency. RESULTS Abnormalities in the SN(c) can be accurately assessed in location, shape, and variations of signal intensities within the segmented SN(c) by varying the transparency (alpha) channel of the color fusion image. Several previous findings such as the lateral-medial gradient of signal change and a ventral-dorsal broadening of the pars compacta are accompanied by an overall mild-to-severe heterogeneity of neurodegeneration patterns. CONCLUSION Color fusion techniques revealed subtle changes in the neurodegeneration of the substantia nigra in Parkinson disease, which can be helpful for an objective and hence effective visual assessment of disease progression.

Quantitation of T2 lesion load in patients with multiple sclerosis: A novel semiautomated segmentation technique*

RATIONALE AND OBJECTIVES The authors designed a segmentation technique that requires only minimal operator input at the initial and final supervision stages of segmentation and has computer-driven segmentation as the primary determinant of lesion boundaries. The technique was applied to compute total T2-hyperintense lesion volumes in patients with multiple sclerosis (MS). A semi-automated segmentation technique is presented and shown to have a test-retest reliability of <5%. MATERIALS AND METHODS The method used a single segmented section with MS lesions. A probabilistic neural net performed segmentation into four tissue classes after supervised training. This reference section was deconstructed into the entire set of possible 4 x 4-pixel subregions, which was used to segment all-brain sections in steps of 4 x 4-pixel, adjacent image blocks. Intra- and interimage variabilities were tested by using 3-mm-thick, T2-weighted, dual-echo, spin-echo MR images from five patients, each of whom was imaged twice on the same day. Five different reference sections and three temporally separated. training sessions involving the same reference section were used to test the segmentation technique. RESULTS The coefficient of variation ranged from 0.013 to 0.068 (mean +/- standard deviation, 0.037 +/- 0.039) for results from five different reference sections for each brain and from 0.007 to 0.037 (mean, 0.027 +/- 0.021) for brains segmented with the same reference section on three temporally separated occasions. Test-retest (intra-imaging) reliability did not exceed 5% (except for a small lesion load of 1 cm3 in one patient). Interimaging differences were approximately 10%. CONCLUSION The segmentation technique yielded intra-imaging variabilities (2%-3%, except for very small MS lesion loads) that compare favorably with previously published results. New repositioning techniques that minimize imaging-repeat imaging variability could make this approach attractive for resolving MS lesion detection problems.

Spin-lattice distribution MRI maps nigral pathology in progressive supranuclear palsy (PSP) during life: a pilot study.

An MRI biomarker for Parkinsonism has long been sought, but almost all attempts at conventional field strengths have proved unsatisfactory, since patients and controls are not separated. The exception is Spin-Lattice Distribution MRI (SLD-MRI), a technique which detects changes in the substantia nigra (SN) due to changes in the spin-lattice relaxation time, T1. This easily separates patients with Parkinsons disease (PD) from control subjects at 1.5 Tesla, suggesting that it may be sensitive to presymptomatic disease. SLD-MRI demonstrates a topography of signal change within the SN which is the same as the known topography of pathological change, where the lateral portions of the nucleus are more affected than the medial. In a further step towards its validation, we apply SLD-MRI to a disease control, Progressive Supranuclear Palsy (PSP), the most common of the atypical forms of Parkinsonism. In PSP the topography of pathological change in the SN is reversed. We therefore hypothesized that PSP would show a topography of SLD-MRI signal change in the SN that is the reverse of PD (i.e. the medial portion is more affected than the lateral). All 7 patients showed such a topography of MR signal, and all patients were separated from control subjects. Although this is a step toward validation of SLD-MRI with respect to sensitivity and disease specificity, nevertheless we stress that this is a pilot project only. Validation will only be possible when comparing larger cohorts of PSP, PD and control subjects.

Reliability and Validity of Subjective Measures of Aerobic Intensity in Adults With Spinal Cord Injury: A Systematic Review

To systematically synthesize and appraise research regarding test‐retest reliability or criterion validity of subjective measures for assessing aerobic exercise intensity in adults with spinal cord injury (SCI).

Assessment of peak oxygen uptake during handcycling: test-retest reliability and comparison of a ramp-incremented and perceptually-regulated exercise test

Purpose To examine the reliability of a perceptually-regulated maximal exercise test (PRETmax) to measure peak oxygen uptake (V˙O2peak) during handcycle exercise and to compare peak responses to those derived from a ramp-incremented protocol (RAMP). Methods Twenty recreationally active individuals (14 male, 6 female) completed four trials across a 2-week period, using a randomised, counterbalanced design. Participants completed two RAMP protocols (20 W·min-1) in week 1, followed by two PRETmax in week 2, or vice versa. The PRETmax comprised five, 2-min stages clamped at Ratings of Perceived Exertion (RPE) 11, 13, 15, 17 and 20. Participants changed power output (PO) as often as required to maintain target RPE. Gas exchange variables (oxygen uptake, carbon dioxide production, minute ventilation), heart rate (HR) and PO were collected throughout. Differentiated RPE were collected at the end of each stage throughout trials. Results For relative V˙O2peak, coefficient of variation (CV) was equal to 4.1% and 4.8%, with ICC(3,1) of 0.92 and 0.85 for repeated measures from PRETmax and RAMP, respectively. Measurement error was 0.15 L·min-1 and 2.11 ml·kg-1·min-1 in PRETmax and 0.16 L·min-1 and 2.29 ml·kg-1·min-1 during RAMP for determining absolute and relative V˙O2peak, respectively. The difference in V˙O2peak between PRETmax and RAMP was tending towards statistical significance (26.2 ± 5.1 versus 24.3 ± 4.0 ml·kg-1·min-1, P = 0.055). The 95% LoA were -1.9 ± 4.1 (-9.9 to 6.2) ml·kg-1·min-1. Conclusion The PRETmax can be used as a reliable test to measure V˙O2peak during handcycle exercise in recreationally active participants. Whilst PRETmax tended towards significantly greater V˙O2peak values than RAMP, the difference is smaller than measurement error of determining V˙O2peak from PRETmax and RAMP.

A Generic ROS Based System for Rapid Development and Testing of Algorithms for Autonomous Ground and Aerial Vehicles

This chapter presents a Robot Operating System (ROS) framework for development and testing of autonomous control functions. The developed system offers the user significantly reduced development times over prior methods. Previously, development of a new function from theory to flight test required a range of different test systems which offered minimal integration; this would have required great effort and expense. A generic system has been developed that can operate a large range of robotic systems. By design, a developed controller can be taken from numerical simulation, through Software/Hardware in the loop simulation to flight test, with no adjustment of code required. The flexibility and power of ROS was combined with the Robotic Systems toolbox from MATLAB/Simulink, Linux embedded systems and a commercially available autopilot. This affords the user a low cost, simple, highly flexible and reconfigurable system. Furthermore, by separating experimental controllers from the autopilot at the hardware level, flight safety is maintained as manual override is available at all times, regardless of faults in any experimental systems. This chapter details the system and demonstrates the functionality with two case studies.

Entrotaxis as a strategy for autonomous search and source reconstruction in turbulent conditions

This paper proposes a strategy for performing an efficient autonomous search to find an emitting source of sporadic cues of noisy information. We focus on the search for a source of unknown strength, releasing particles into the atmosphere where turbulence can cause irregular gradients and intermittent patches of sensory cues. Bayesian inference, implemented via the sequential Monte Carlo method, is used to update posterior probability distributions of the source location and strength in response to sensor measurements. Posterior sampling is then used to approximate a reward function, leading to the manoeuvre to where the entropy of the predictive distribution is the greatest. As it is developed based on the maximum entropy sampling principle, the proposed framework is termed as Entrotaxis. We compare the performance and search behaviour of Entrotaxis with the popular Infotaxis algorithm, for searching in sparse and turbulent conditions where typical gradient-based approaches become inefficient or fail. The algorithms are assessed via Monte Carlo simulations with simulated data and an experimental dataset. Whilst outperforming the Infotaxis algorithm in most of our simulated scenarios, by achieving a faster mean search time, the proposed strategy is also more computationally efficient during the decision making process.