Estimation of the Ultraviolet-C Doses from Mercury Lamps and Light-Emitting Diodes Required to Disinfect Surfaces.

Disinfection of surfaces by ultraviolet-C (UV-C) radiation is gaining importance in diverse applications. However, there is generally no accepted computational procedure to determine the minimum irradiation times and UV-C doses required for reliable and secure disinfection of surfaces. UV-C dose distributions must be comparable for devices presently on the market and future ones, as well as for the diverse surfaces of objects to be disinfected. A mathematical model is presented to estimate irradiance distributions. To this end, the relevant parameters are defined. These parameters are the optical properties of the UV-C light sources, such as wavelength and emitted optical power, as well as electrical features, like radiant efficiency and consumed power. Furthermore, the characteristics and geometry of the irradiated surfaces as well as the positions of the irradiated surfaces in relation to the UV-C light sources are considered. Because mercury (Hg) lamps are competitive with UV-C light-emitting diodes, a comparative analysis between these two light sources based on the simulation results is also discussed.

Is the fastest MRI a hologram?

Real-time MR imaging might exert a profound influence on neuroscience in the future by enabling the direct visualization of neuronal interactions. At this time, however, all practical embodiments of MRI require at least some degree of gradient encoding, and this in turn sets a lower limit of about 100 ms for volume acquisition. A novel formulation of MRI is proposed here which is given the acronym ULTRA (Unlimited Trains of Radio Acquisitions). In the preferred embodiment ULTRA is completely free of gradient reversals, which allows for signal acquisition from the entire object volume simultaneously. This permits a rate of signal acquisition that is increased hundreds of times compared with existing techniques, with full 3-D imaging in as little as one millisecond. The proposed detector now resembles a holographic recording.

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 Parkinson's 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.

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

We have developed an advanced MRI technique for detecting Parkinson's 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 T(1). 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 T(1) 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 (SN(C)). 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.

Inversion recovery MRI in idiopathic Parkinson disease is a very sensitive tool to assess neurodegeneration in the substantia nigra: preliminary investigation.

RATIONALE AND OBJECTIVES: Segmented inversion recovery (IR) ratio imaging (SIRRIM) has been established as a sensitive tool to assess neurodegeneration of the substantia nigra pars compacta (SN(C)) in patients with idiopathic Parkinson disease (IPD). The obtained results suggest the possibility of magnetic resonance imaging (MRI) as a biological marker for IPD. The strength and a parsimonious analysis of the technique are discussed to assess the potential of using MRI as a biological marker for IPD and improve the differential diagnosis of sporadic Parkinson disease. Our hypothesis states that the magnetic resonance SIRRIM technique allows direct visualization and quantitation of neural cell loss in the SN(C) and therefore could become a reliable biological marker for Parkinson disease. To achieve this goal, some key aspects of data acquisition and data analysis need to be addressed. The clinical impact of the SIRRIM technique could be considerable, considering that it might become a viable surrogate to other techniques. PATIENTS AND METHODS: Twelve patients with IPD and 12 age-matched control subjects were imaged by using the SIRRIM technique based on two IR imaging sequences that were designed to suppress white and gray matter to assess loss of neural cells in situ by means of a ratio image (white matter suppressed image to gray matter suppressed image). The radiological index was correlated with the Unified Parkinson Disease Rating Scale (UPDRS) for patients with IPD. RESULTS: All patients with IPD were identified correctly, and full dichotomization between healthy volunteers and patients was obtained with our database. Our SIRRIM technique shows that it can be used to rule out Parkinson disease from essential tremor and other forms of Parkinsonism, such as progressive supranuclear palsy and multisystem atrophy. In addition, it is sensitive enough to identify patients with early-stage IPD. CONCLUSION: The hypothesis of using SIRRIM as a biological marker to assess IPD is supported by excellent correlation with clinical UPDRS scoring and has proved useful for the evaluation and quantitation of neurodegeneration with our SIRRIM technique, showing, in addition, that the differential diagnosis of IPD can be improved. Technical aspects of acquisition and data processing that need to be addressed can be overcome. It ultimately confirms that our objectives can be achieved and allows us to expect assessment of the progressive development of neurodegeneration in longitudinal studies and the putative neuroprotective approaches taken during the evolution of the disease.

MRI correlates of pathology in parkinsonism: segmented inversion recovery ratio imaging (SIRRIM).

The two commonest, clinically well-defined, forms of parkinsonism are idiopathic Parkinson's disease (PD) and progressive supranuclear palsy (PSP). Both involve, inter alia, pathological changes in the substantia nigra pars compacta (SN(C)). In PD there is neuronal loss with associated Lewy body pathology and microglial activation. Three morphological features have been identified [Brain 114 (1991), 2283; Greenfield's Neuropathology 2 (1997), 289]. First, there is a gradient of pathological change such that the lateral segments are more affected than the medial. Second, there is thinning of the pigmented tissue, and third, there is a broadening of the overall structure in a dorsal-ventral direction, possibly caused by the migration of melanin-laden macrophages [Greenfield's Neuropathology 2 (1997), 289]. In contrast, PSP is characterized pathologically by intraneuronal neurofibrillary tangles. There are two morphological features in the SN(C) [Brain 114 (1991), 2283]. First, the gradient of pathological change is in the opposite direction to that of PD (i.e., the medial segments are more affected than the lateral). Second, there is atrophy. Any technique sensitive to neuropathology should be capable of detecting these features. We have previously reported on a new approach to detecting signal change in the substantia nigra in PD. This makes use of a ratio of images acquired by two distinct inversion recovery sequences [J. Neurol, Neurosurg. Psychiatry 67 (1999), 815; Am. J. Neuroradiol. 21 (2000) 697]. The prior work suggests that the technique is sensitive to, but not necessarily specific for, PD. We present here a preliminary report on an extension of the work. This is a semiautomated segmentation analysis that enables the substantia nigra to be displayed as an isolated structure. The technique is now given the acronym SIRRIM (segmented inversion recovery ratio imaging). In contrast to our earlier work, it allows for a more accurate assessment of the gross abnormalities. We report typical SIRRIM images of the SN(C). Images are shown for three subjects: a normal control, a patient with PD, and a "disease control" (a patient with PSP). In these examples all three morphological features of PD, as well as both morphological features of PSP, have radiological correlates. These preliminary findings suggest that SIRRIM may be specific for both diseases.

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.