Winged helix transcription factor BF-1 is essential for the development of the cerebral hemispheres

AbstractWe generated mice with a null mutation of the forebrain-restricted transcription factor BF-1 to examine its function in brain development. Heterozygous animals have an apparently normal phenotype. Homozygous null BF-1 mutants die at birth and have a dramatic reduction in the size of the cerebral hemispheres. The development of the ventral telencephalon is more severely affected than that of the dorsal telencephalon. Telencephalic neuroepithelial cells are specified in the BF-1 mutant, but their proliferation is reduced. Dorsal telencephalic neuroepithelial cells also differentiate prematurely, leading to early depletion of the progenitor population. These results suggest that BF-1 controls the morphogenesis of the telencephalon by regulating the rate of neuroepithelial cell proliferation and the timing of neuronal differentiation

VVBP-Tensor in the FBP Algorithm: Its Properties and Application in Low-Dose CT Reconstruction

For decades, commercial X-ray computed tomography (CT) scanners have been using the filtered backprojection (FBP) algorithm for image reconstruction. However, the desire for lower radiation doses has pushed the FBP algorithm to its limit. Previous studies have made significant efforts to improve the results of FBP through preprocessing the sinogram, modifying the ramp filter, or postprocessing the reconstructed images. In this paper, we focus on analyzing and processing the stacked view-by-view backprojections (named VVBP-Tensor) in the FBP algorithm. A key challenge for our analysis lies in the radial structures in each backprojection slice. To overcome this difficulty, a sorting operation was introduced to the VVBP-Tensor in its ${z}$ direction (the direction of the projection views). The results show that, after sorting, the tensor contains structures that are similar to those of the object, and structures in different slices of the tensor are correlated. We then analyzed the properties of the VVBP-Tensor, including structural self-similarity, tensor sparsity, and noise statistics. Considering these properties, we have developed an algorithm using the tensor singular value decomposition (named VVBP-tSVD) to denoise the VVBP-Tensor for low-mAs CT imaging. Experiments were conducted using a physical phantom and clinical patient data with different mAs levels. The results demonstrate that the VVBP-tSVD is superior to all competing methods under different reconstruction schemes, including sinogram preprocessing, image postprocessing, and iterative reconstruction. We conclude that the VVBP-Tensor is a suitable processing target for improving the quality of FBP reconstruction, and the proposed VVBP-tSVD is an effective algorithm for noise reduction in low-mAs CT imaging. This preliminary work might provide a heuristic perspective for reviewing and rethinking the FBP algorithm

Representative sequences and the corresponding NGFs.

<p>Representative sequences and the corresponding NGFs.</p

New metric for optimizing Continuous Loop Averaging Deconvolution (CLAD) sequences under the 1/f noise model - Fig 3

<p><i>G</i>_dec<b>and</b><i>C</i>_dec<b>values in the frequency range of 10–350 Hz for the total 630 sequences sorted by increased</b><i>G</i>_dec<b>(A). The first 410 sequences with reasonable NGFs (< 10) are selected and plotted in a scatter graph in (B), where the Pearson correlation coefficient between</b><i>G</i>_dec<b>and</b><i>C</i>_dec<b>is R = 0.47. </b></p

Adjusted NGFs and ANGs for the optimized sequences with respect to the stimulus number from 4 to 13 in a sweep.

<p>Both <i>G</i>_dec (solid) and <i>C</i>_dec (dashed) values are plotted for sequences optimized based on objective function of <i>G</i>_dec (A) and <i>C</i>_dec (B), respectively. The corresponding ANGs for these sequences based on <i>G</i>_dec and <i>C</i>_dec are presented in (C) and (D), respectively.</p

Diagram of GA searching space for three variables (<i>x</i>₁, <i>x</i>₂, and <i>x</i>₃) which represent a stimulus sweep that contains three SOAs.

<p>Due to the constraint of JR, the searching space is confined within the cubic box.</p

Pre- (thin traces) and post-deconvolution (bold traces) responses from five subjects (Sub 1–5) and the grand averages over them (Avg) for the three representative sequences (Seq-1, 6 and 14, in columns A, B and C, respectively).

<p>Pre- (thin traces) and post-deconvolution (bold traces) responses from five subjects (Sub 1–5) and the grand averages over them (Avg) for the three representative sequences (Seq-1, 6 and 14, in columns A, B and C, respectively).</p

Auditory responses and corresponding spectra for pre- and post- deconvolution for a typical subject (Sub1) using the selected sequences (Seq1, Seq6 and Seq14).

<p>Thick and thin traces indicate the estimated responses and noises, respectively. The filled "∇" markers indicate the primary peaks in the spectral plots (C) causing the oscillations seen in the 2nd column (B).</p