Biomedical subjects

James C Bouwer

Publications and source records attributed to James C Bouwer.

3 recordsLinked to original sources

PubMed2006

Transform-based backprojection for volume reconstruction of large format electron microscope tilt series.

Alignment of the individual images of a tilt series is a critical step in obtaining high-quality electron microscope reconstructions. We report on general methods for producing good alignments, and utilizing the alignment data in subsequent reconstruction steps. Our alignment techniques utilize bundle adjustment. Bundle adjustment is the simultaneous calculation of the position of distinguished markers in the object space and the transforms of these markers to their positions in the observed images, along the bundle of particle trajectories along which the object is projected to each EM image. Bundle adjustment techniques are general enough to encompass the computation of linear, projective or nonlinear transforms for backprojection, and can compensate for curvilinear trajectories through the object, sample warping, and optical aberration. We will also report on new reconstruction codes and describe our results using these codes.

Animals↗

PubMed2005

Active pixel sensor array as a detector for electron microscopy.

A new high-resolution recording device for transmission electron microscopy (TEM) is urgently needed. Neither film nor CCD cameras are systems that allow for efficient 3-D high-resolution particle reconstruction. We tested an active pixel sensor (APS) array as a replacement device at 200, 300, and 400 keV using a JEOL JEM-2000 FX II and a JEM-4000 EX electron microscope. For this experiment, we used an APS prototype with an area of 64 x 64 pixels of 20 microm x 20 microm pixel pitch. Single-electron events were measured by using very low beam intensity. The histogram of the incident electron energy deposited in the sensor shows a Landau distribution at low energies, as well as unexpected events at higher absorbed energies. After careful study, we concluded that backscattering in the silicon substrate and re-entering the sensitive epitaxial layer a second time with much lower speed caused the unexpected events. Exhaustive simulation experiments confirmed the existence of these back-scattered electrons. For the APS to be usable, the back-scattered electron events must be eliminated, perhaps by thinning the substrate to less than 30 microm. By using experimental data taken with an APS chip with a standard silicon substrate (300 microm) and adjusting the results to take into account the effect of a thinned silicon substrate (30 microm), we found an estimate of the signal-to-noise ratio for a back-thinned detector in the energy range of 200-400 keV was about 10:1 and an estimate for the spatial resolution was about 10 microm.

Cryoelectron Microscopy↗

PubMed2004

Automated most-probable loss tomography of thick selectively stained biological specimens with quantitative measurement of resolution improvement.

We describe the technique and application of energy filtering, automated most-probable loss (MPL) tomography to intermediate voltage electron microscopy (IVEM). We show that for thick, selectively stained biological specimens, this method produces a dramatic increase in resolution of the projections and the computed volumes versus standard unfiltered transmission electron microscopy (TEM) methods. This improvement in resolution is attributed to the reduction of chromatic aberration, which results from the large percentage of inelastic electron-scattering events for thick specimens. These improvements are particularly evident at the large tilt angles required to improve tomographic resolution in the z-direction. This method effectively increases the usable thickness of selectively stained samples that can be imaged at a given accelerating voltage by dramatically improving resolution versus unfiltered TEM and increasing signal-to-noise versus zero-loss imaging, thereby expanding the utility of the IVEM to deliver information from within specimens up to 3 microm thick.

Animals↗