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Lifei Zhang

Publications and source records attributed to Lifei Zhang.

5 recordsLinked to original sources

Genome-wide association study reveals candidate genes associated with body weight and wool traits in Ordos fine-wool sheep.

BACKGROUND: The Ordos fine-wool sheep is a high-quality fine-wool breed in China, renowned for its excellent wool quality, meat production, and adaptability to the arid and semi-arid regions of Inner Mongolia. Body weight and wool traits are important economic characteristics in sheep breeding. This study aimed to identify genetic loci associated with body weight (BW), wool length (WL), and wool fineness (WF) in Ordos fine-wool sheep. METHODS: A genome-wide association study (GWAS) was conducted in 388 Ordos fine-wool sheep genotyped using the GenoBaits® Ovine 40K SNP panel. Single nucleotide polymorphisms (SNPs) associated with BW, WL, and WF were identified, and candidate genes located near the SNPs reaching the suggestive threshold were subjected to functional annotation and enrichment analysis. RESULTS: A total of 22 SNPs were identified as potentially associated with BW, WL, and WF traits, corresponding to 27 annotated genes. Functional annotation highlighted six potential candidate genes, including LAMA2, ARHGAP18, IGFBP2, IGFBP5, CA10, and AXIN1, which may play important roles in regulating body weight and wool growth in sheep. CONCLUSIONS: The identified genes provide valuable candidate loci for BW, WL, and WF traits in Ordos fine-wool sheep. The results of this study provide preliminary references for further exploration of the genetic mechanisms of wool traits in Ordos fine-wool sheep and the development of molecular breeding markers.

GWAS↗

Reducing metal artifacts in cone-beam CT images by preprocessing projection data.

PURPOSE: Computed tomography (CT) streak artifacts caused by metallic implants remain a challenge for the automatic processing of image data. The impact of metal artifacts in the soft-tissue region is magnified in cone-beam CT (CBCT), because the soft-tissue contrast is usually lower in CBCT images. The goal of this study was to develop an effective offline processing technique to minimize the effect. METHODS AND MATERIALS: The geometry calibration cue of the CBCT system was used to track the position of the metal object in projection views. The three-dimensional (3D) representation of the object can be established from only two user-selected viewing angles. The position of the shadowed region in other views can be tracked by projecting the 3D coordinates of the object. Automatic image segmentation was used followed by a Laplacian diffusion method to replace the pixels inside the metal object with the boundary pixels. The modified projection data were then used to reconstruct a new CBCT image. The procedure was tested in phantoms, prostate cancer patients with implanted gold markers and metal prosthesis, and a head-and-neck patient with dental amalgam in the teeth. RESULTS: Both phantom and patient studies demonstrated that the procedure was able to minimize the metal artifacts. Soft-tissue visibility was improved near or away from the metal object. The processing time was 1-2 s per projection. CONCLUSION: We have implemented an effective metal artifact-suppressing algorithm to improve the quality of CBCT images.

Algorithms↗

Dosimetric comparison of four target alignment methods for prostate cancer radiotherapy.

PURPOSE: The aim of this study was to compare the dosimetric consequences of 4 treatment delivery techniques for prostate cancer patients treated with intensity-modulated radiotherapy (IMRT). METHODS AND MATERIALS: During an 8-week course of radiotherapy, 10 patients underwent computed tomography (CT) scans 3 times per week (243 total) before daily treatment with a CT-linear accelerator. Treatment delivery was simulated by realigning a fixed-margin treatment plan on each CT scan and calculating doses. The alignment methods were those based on the following: skin marks, bony registration, ultrasonography (US), and in-room CT. For the last two methods, prostate was the alignment target. The dosimetric effects of these alignment methods on the prostate, seminal vesicles, rectum, and bladder were compared. The average daily minimum dose to 0.1 cm3 was used as the metric for target coverage. RESULTS: Skin and bone alignments provided acceptable prostate coverage for only 70% of patients, US alignment for 90%, and CT alignment for 100%. CT-based alignment of the prostate provided seminal vesicle (SV) coverage of > or = 69 Gy for all patients; US and bone alignments provided SV coverage of > or = 60 Gy. This SV coverage may be acceptable for early-stage cancer (equivalent SV dose = 55.8 Gy at 1.8 Gy per fraction), but unacceptable for late-stage cancer (SV dose = 75.6 Gy). At 75.6 Gy, the acceptable rate for SV coverage was 40% for skin and bone alignments, 70% for US, and 80% for CT. CONCLUSIONS: Direct target alignment methods (US and CT) provided better target coverage. CT-guided alignment provided the best and most consistent dosimetric coverage. A larger planning target volume margin is needed for SV coverage when the alignment target is the prostate.

Algorithms↗

Multiple regions-of-interest analysis of setup uncertainties for head-and-neck cancer radiotherapy.

PURPOSE: To analyze three-dimensional setup uncertainties for multiple regions of interest (ROIs) in head-and-neck region. METHODS AND MATERIALS: In-room computed tomography (CT) scans were acquired using a CT-on-rails system for 14 patients. Three separate bony ROIs were defined: C2 and C6 vertebral bodies and the palatine process of the maxilla. Translational shifts of 3 ROIs were calculated relative to the marked isocenter on the immobilization mask. RESULTS: The shifts for all 3 ROIs were highly correlated. However, noticeable differences on the order of 2-6 mm existed between any 2 ROIs, indicating the flexibility and/or rotational effect in the head-and-neck region. The palatine process of the maxilla had the smallest right-left shifts because of the tight lateral fit in the face mask, but the largest superior-inferior movement because of in-plane rotation and variations in jaw positions. The neck region (C6) had the largest right-left shifts. The positioning mouthpiece was found effective in reducing variations in the superior-inferior direction. There was no statistically significant improvement for using the S-board (8 out of 14 patients) vs. the short face mask. CONCLUSIONS: We found variability in setup corrections for different regions of head-and-neck anatomy. These relative positional variations should be considered when making setup corrections or designing treatment margins.

Algorithms↗

A deformable image registration method to handle distended rectums in prostate cancer radiotherapy.

In image-guided adaptive radiotherapy, it is important to have the capability to automatically and accurately delineate the rectal wall, which is a major dose-limiting organ in prostate cancer radiotherapy. As image registration is a process to find the spatial correspondence between two images, a major challenge in intensity-based deformable image registration is to deal with the situation where no correspondence exists for some objects between the two images to be registered. One example is the variation of rectal contents due to the presence and absence of bowel gas. The intensity-based deformable image registration methods alone cannot create the correct spatial transformation if there is no correspondence between the source and target images. In this study we implemented an automatic image intensity modification procedure to create artificial gas pockets in the planning computed tomography (CT) images. A diffusion-based deformable image registration algorithm was developed to use an adaptive smoothing algorithm to better handle large organ deformations. The process was tested in 15 prostate cancer cases and 30 daily CT images containing the largest distended rectums. The manually delineated rectums agreed well with the autodelineated rectums when using the image-intensity modification procedure.

Algorithms↗