No abstract

Background To evaluate the performance of a Deep Learning Image Reconstruction (DLIR) algorithm in pediatric head CT for improving image quality and lesion detection with 0.625 mm thin-slice images. Methods Low-dose axial head CT scans of 50 children with 120 kV, 0.8 s rotation and age-dependent 150–220 mA tube current were selected. Images were reconstructed at 5 mm and 0.625 mm slice thickness using Filtered back projection (FBP), Adaptive statistical iterative reconstruction-v at 50% strength (50%ASIR-V) (as reference standard), 100%ASIR-V and DLIR-high (DL-H). The CT attenuation and standard deviation values of the gray and white matters in the basal ganglia were measured. The clarity of sulci/cisterns, boundary between white and gray matters, and overall image quality was subjectively evaluated. The number of lesions in each reconstruction group was counted. Results The 5 mm FBP, 50%ASIR-V, 100%ASIR-V and DL-H images had a subjective score of 2.25 ± 0.44, 3.05 ± 0.23, 2.87 ± 0.39 and 3.64 ± 0.49 in a 5-point scale, respectively with DL-H having the lowest image noise of white matter at 2.00 ± 0.34 HU; For the 0.625 mm images, only DL-H images met the diagnostic requirement. The 0.625 mm DL-H images had similar image noise (3.11 ± 0.58 HU) of the white matter and overall image quality score (3.04 ± 0.33) as the 5 mm 50% ASIR-V images (3.16 ± 0.60 HU and 3.05 ± 0.23). Sixty-five lesions were recognized in 5 mm 50%ASIR-V images and 69 were detected in 0.625 mm DL-H images. Conclusion DL-H improves the head CT image quality for children compared with ASIR-V images. The 0.625 mm DL-H images improve lesion detection and produce similar image noise as the 5 mm 50%ASIR-V images, indicating a potential 85% dose reduction if current image quality and slice thickness are desired.

Investigating the neurobiological mechanism of suicidal ideation (SI) in major depressive disorder (MDD) may be beneficial to prevent the suicidal behavior. Mounting evidence showed that habenula contributed to the etiology of MDD. The habenula is a key brain region that links the forebrain to midbrain, crucial for the processing of reward and aversion. The aim of the present study was to identify whether first-episode, drug-naive MDD patients with SI displayed altered habenula neural circuitry. Forty-three and 38 drug-naïve patients with first-episode MDD with or without SI (SI+/– group) and 35 healthy control subjects (HC) underwent resting-state functional magnetic resonance imaging. The whole-brain habenula static (sFC) and dynamic functional connectivity (dFC) were calculated to identify regions showing significant difference among these three groups followed by region of interest to region of interest post hoc analysis. For sFC, compared with SI– and HC groups, SI+ group showed decreased sFC from habenula to the precuneus and the inferior frontal gyrus. Patients with MDD displayed increased sFC from habenula to the putamen but decreased sFC to the precentral gyrus. For dFC, SI+ group showed increased dFC from habenula to the superior temporal gyrus, the precuneus, but decreased dFC to the lingual gyrus, the postcentral gyrus, when comparing with SI– and HC groups. Patients with MDD, regardless of SI, displayed decreased dFC from the habenula to the angular gyrus. These findings provide evidence that SI in first-episode, drug-naïve patients with MDD may be related to an abnormality in habenula neural circuitry, which may provide the theoretical basis of novel treatments.

This paper presents a method for correcting beam hardening (BH) in cardiac CT perfusion imaging. The proposed algorithm works with reconstructed images instead of projection data. It applies thresholds to separate low (soft tissue) and high (bone and contrast) attenuating material in a CT image. The BH error in each projection is estimated by a polynomial function of the forward projection of the segmented image. The error image is reconstructed by back-projection of the estimated errors. A BH-corrected image is then obtained by subtracting a scaled error image from the original image. Phantoms were designed to simulate the BH artifacts encountered in cardiac CT perfusion studies of humans and animals that are most commonly used in cardiac research. These phantoms were used to investigate whether BH artifacts can be reduced with our approach and to determine the optimal settings, which depend upon the anatomy of the scanned subject, of the correction algorithm for patient and animal studies. The correction algorithm was also applied to correct BH in a clinical study to further demonstrate the effectiveness of our technique.

A filtered backprojection (FBP) algorithm is derived based on Feldkamp's FBP algorithm for a cone beam geometry that has a displaced center of rotation. In cone beam single photon emission computed tomography (CB-SPECT) the center of rotation displacement can degrade the reconstructed images. The center of rotation displacement of interest is mechanical shift, which is the displacement of the midplane of the cone beam collimator off the rotation center. Mechanical shift is characterized by two orthogonal components: the shift of the midplane of the cone beam collimator along the direction of the axis of rotation, and the distance between the midline of the cone beam collimator and the axis of rotation. This new algorithm corrects mechanical shift directly by incorporating mechanical shift into the algorithm. This new algorithm is evaluated using both Monte Carlo simulated data and experimentally acquired data. The results demonstrate that this algorithm is able to correct for blurring and the "doughnut" type artifacts caused by system mechanical shift and improve the image resolution.