13 research outputs found
A microprocessor based speed and current level controller for a variable mutual reluctance machine
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1982.MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERINGIncludes bibliographical references.by William Robert Gandler.M.S
Bypassing the CAMAC data bus to read out FERA data at higher rates
Proceeding of: 1998 IEEE Nuclear Science Symposium and Medical Imaging Conference, Toronto, Ont., 08 - 14 Nov. 1998The CAMAC standard offers flexibility by providing power
and a data bus for various modules, but it is limited to a 1
Mword/sec bandwidth. LeCroy Research CAMAC modules
with an auxiliary data bus, FERA, provide a 10 Mwordsec
data transfer without CAMAC controller intervention. We
have used a National Instruments digital 1/0 board (PCI-DIO-
32HS) as a FERA bus-to-host bridge. The board provides
hardware handshaking, a 20 Mword/sec bandwidth, bus master
scatter-gather DMA, and can control up to 2 FERA busses
asynchronously. Multiple boards may reside on the same PCI
or Compact PCI bus. A 300 MHz Pentium I1 running
Windows NT 4.0 sustains >3.4 MB/sec throughput in 8255
emulation mode. These capabilities are being exploited in our
prototype small animal planar and PET imaging system where
32 ADC channels (16 bits each) and 3 scaler channels (32 bits
each) define an event.Publicad
Depth identification accuracy of a three layer phoswich PET detector module
We describe a PET detector module that provides three
levels of depth-of-interaction (DOI) information. The detector
is a 9 x 9 array of 2 mm x 2 mm x 12 mm deep phoswich
crystal elements, each consisting of 4 mm long LSO (entrance
layer), GSO (middle layer) and BGO (exit layer) crystals
joined optically together end-to-end. The BGO exit layer is
directly coupled to a miniature position-sensitive photomultiplier
tube (PSPMT). Delayed charge integration, a method
that exploits differences in the light decay times of these
scintillators, is used to determine the layer-of-interaction.
DO1 accuracy, measured by scanning a slit source of 5 1 1 keV
radiation along the length of the module was 86% for the LSO
layer, 80% for the GSO layer and 84% for the BGO layer.
Energy resolution at 511 keV was 19% for LSO, 21% for
GSO and 40% for BGO. Apparent gain differed between
layers in the ratios 2.7: 1.9: 1 .O (LS0:GSO:BGO). Crystal
separation was good between crystals in the LSO layer,
acceptable between crystals in the GSO layer and poor
between crystals in the BGO layer due, primarily, to the
pronounced spatial non-linearity of the PSPMT. The delayed
charge integration method, however, does appear suitable for
obtaining multi-level depth information when DO1 effects are
particularly significant, e.g. in very small ring diameter PET
scanners for small animal imaging.Was supported, in part, by a grant from CICYT (Spanish Government). S. S. was supported by a grant from the National Research Council.Publicad
Performance characteristics of a compact position-sensitive LSO detector module
We assembled a compact detector module comprised
of an array of small, individual crystals of lutetium oxyorthosilicate:
Ce (LSO) coupled directly to a miniature, metal-can,
position-sensitive photomultiplier tube (PSPMT).We exposed this
module to sources of 511-keV annihilation radiation and beams
of 30- and 140-keV photons and measured spatial linearity;
spatial variations in module gain, energy resolution, and event
positioning; coincidence timing; the accuracy and sensitivity of
identifying the crystal-of-first-interaction at 511 keV; and the
effects of intercrystal scatter and LSO background radioactivity.
The results suggest that this scintillator/phototube combination
should be highly effective in the coincidence mode and can be
used, with some limitations, to image relatively low-energy single
photon emitters.
Photons that are completely absorbed on their first interaction
at 511 keV are positioned by the module at the center of a
crystal. Intercrystal scatter events, even those that lead to total
absorption of the incident photon, are placed by the module in a
regular “connect-the-dot” pattern that joins crystal centers. As a
result, the accuracy of event positioning can be made to exceed
90%, though at significantly reduced sensitivity, by retaining
only events that occur within small regions-of-interest around
each crystal center and rejecting events that occur outside these
regions in the connect-the-dot pattern.The work was supported in part by a grant from CICYT (Spanish Government). The work of S. Siegel was supported by a grant from the National Research Council.Publicad
Initial results from a PET/planar small animal imaging system
A pair of stationary, opposed scintillation detectors in time
coincidence is being used to create planar projection or
tomographic images of small animals injected with positronemitting
radiotracers. The detectors are comprised of arrays
of individual crystals of bismuth germanate coupled to
position-sensitive photomultiplier tubes. The system uses
FERA (LeCroy Research Systems) charge-sensitive ADCs
and a low cost digital YO board as a E R A bus-to-host bridge.
In projection mode, the animal is placed within the 55 mm x
45 mm useful field-of-view of the detectors and images are
formed from coincidence lines that fall close to the normals of
both detectors. In tomographic mode, the animal is placed on
a rotation stage between the detectors and rotated around a
vertical axis to acquire all possible lines-of-response.
Tomographic images are then reconstructed from those lines
falling within a user-specified angle of each detector normal.
In mice, the system is capable of high-speed, whole-body
dynamic projection imaging, and whole body tomographic
imaging of slowly varying tracer distributions. An ECG gating capability is also available for evaluating cardiac
function. This system is currently being used to study tracer
transport in normal and genetically engineered mice.Publicad
Free Software Tools for Atlas-based Volumetric Neuroimage Analysis
We describe new and freely available software tools for measuring volumes in subregions of the brain. The method is fast, flexible, and employs well-studied techniques based on the Talairach-Tournoux atlas. The software tools are released as plug-ins for MIPAV, a freely available and user-friendly image analysis software package developed by the National Institutes of Health. Our software tools include a digital Talairach atlas that consists of labels for 148 different substructures of the brain at various scales
Patellar segmentation from 3D magnetic resonance images using guided recursive ray-tracing for edge pattern detection
The paper presents an automatic segmentation methodology for the patellar bone, based on 3D gradient recalled echo and gradient recalled echo with fat suppression magnetic resonance images. Constricted search space outlines are incorporated into recursive ray-tracing to segment the outer cortical bone. A statistical analysis based on the dependence of information in adjacent slices is used to limit the search in each image to between an outer and inner search region. A section based recursive ray-tracing mechanism is used to skip inner noise regions and detect the edge boundary. The proposed method achieves higher segmentation accuracy (0.23mm) than the current state-of-the-art methods with the average dice similarity coefficient of 96.0% (SD 1.3%) agreement between the auto-segmentation and ground truth surfaces.</p
Fully automated prostate whole gland and central gland segmentation on MRI using holistically nested networks with short connections
© 2019 Society of Photo-Optical Instrumentation Engineers (SPIE). Accurate and automated prostate whole gland and central gland segmentations on MR images are essential for aiding any prostate cancer diagnosis system. Our work presents a 2-D orthogonal deep learning method to automatically segment the whole prostate and central gland from T2-weighted axial-only MR images. The proposed method can generate high-density 3-D surfaces from low-resolution (z axis) MR images. In the past, most methods have focused on axial images alone, e.g., 2-D based segmentation of the prostate from each 2-D slice. Those methods suffer the problems of over-segmenting or under-segmenting the prostate at apex and base, which adds a major contribution for errors. The proposed method leverages the orthogonal context to effectively reduce the apex and base segmentation ambiguities. It also overcomes jittering or stair-step surface artifacts when constructing a 3-D surface from 2-D segmentation or direct 3-D segmentation approaches, such as 3-D U-Net. The experimental results demonstrate that the proposed method achieves 92.4 % ± 3 % Dice similarity coefficient (DSC) for prostate and DSC of 90.1 % ± 4.6 % for central gland without trimming any ending contours at apex and base. The experiments illustrate the feasibility and robustness of the 2-D-based holistically nested networks with short connections method for MR prostate and central gland segmentation. The proposed method achieves segmentation results on par with the current literature
Automatic magnetic resonance prostate segmentation by deep learning with holistically nested networks
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE). Accurate automatic segmentation of the prostate in magnetic resonance images (MRI) is a challenging task due to the high variability of prostate anatomic structure. Artifacts such as noise and similar signal intensity of tissues around the prostate boundary inhibit traditional segmentation methods from achieving high accuracy. We investigate both patch-based and holistic (image-to-image) deep-learning methods for segmentation of the prostate. First, we introduce a patch-based convolutional network that aims to refine the prostate contour which provides an initialization. Second, we propose a method for end-to-end prostate segmentation by integrating holistically nested edge detection with fully convolutional networks. Holistically nested networks (HNN) automatically learn a hierarchical representation that can improve prostate boundary detection. Quantitative evaluation is performed on the MRI scans of 250 patients in fivefold cross-validation. The proposed enhanced HNN model achieves a mean ± standard deviation. A Dice similarity coefficient (DSC) of 89.77%±3.29% and a mean Jaccard similarity coefficient (IoU) of 81.59%±5.18% are used to calculate without trimming any end slices. The proposed holistic model significantly (p\u3c0.001) outperforms a patch-based AlexNet model by 9% in DSC and 13% in IoU. Overall, the method achieves state-of-the-art performance as compared with other MRI prostate segmentation methods in the literature