scholarly journals Fractal Analysis of Trabecular Architecture : With Special Reference to Slice Thickness and Pixel Size of the Image

2005 ◽  
Vol 61 (6) ◽  
pp. 819-825
Author(s):  
TATSUSHI TOMOMITSU ◽  
HIROAKI MIMURA ◽  
KENYA MURASE ◽  
TSUTOMU TAMADA ◽  
TERUKI SONE ◽  
...  
Keyword(s):  
Special Reference ◽  
Fractal Analysis ◽  
Slice Thickness ◽  
Trabecular Architecture ◽  
Pixel Size

scholarly journals Optimisation of T2 and T2* sequences in MRI for better quantification of iron on transfused dependent sickle cell patients

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Azza Ahmed ◽  
Amani Baldo ◽  
A. Sulieman ◽  
Hind Mirghani ◽  
Fouad A. Abolaban ◽  
...  
Keyword(s):  
Sickle Cell ◽  
Acquisition Time ◽  
Thickness Variation ◽  
Slice Thickness ◽  
Pixel Size ◽  
Significant Difference ◽  
Repetition Time

AbstractThis work aimed to investigate the effect of different shim techniques, voxel sizes, and repetition time (TR) on using theT2 and T2* sequences to determine their optimum settings to investigate the quantification of iron in transfused dependent sickle cell patients. The effect of each of these parameters was investigated on phantoms of different Gadolinium (Gd) concentrations, on 10 volunteers and 25 patients using a1 5T MRI Philips scanner. No significant difference between the three shim techniques was noticed in either T2 or T2* sequence measurements. Pixel sizes of 1 × 1 and 2 × 2 mm provided optimum results for T2 measurements. At 1 × 1 mm pixel size the T2* measurements experienced less error in measurements than the size of 2.5 × 2.5 mm used in the literature. Even though the slice thickness variation did not provide any changes in T2 measurements, the 12 mm provided optimum T2* measurements. TR variation did not yield significant changes on either T2 or T2* measurements. These results indicate that both T2 and T2* sequences can be further improved by providing more reliable measurements and reducing acquisition time.

Determination of optimum pixel size and slice thickness for tractography and ulnar nerve diffusion tensor imaging at the cubital tunnel using 3T MRI

2020 ◽  
pp. 028418512095196
Author(s):  
Sun-Young Park ◽  
Sung Hye Koh ◽  
In Jae Lee ◽  
Kwanseop Lee ◽  
Yul Lee
Keyword(s):  
Diffusion Tensor Imaging ◽  
Ulnar Nerve ◽  
Diffusion Tensor ◽  
Nerve Fibers ◽  
Cubital Tunnel ◽  
Slice Thickness ◽  
Fiber Tractography ◽  
Pixel Size ◽  
Cross Sectional ◽  
Visual Score

Background Small peripheral nerve tractography is challenging because of the trade-off among resolution, image acquisition time, and signal-to-noise ratio. Purpose To optimize pixel size and slice thickness parameters for fiber tractography and diffusion tensor imaging (DTI) of the ulnar nerve at the cubital tunnel using 3T magnetic resonance imaging (MRI). Material and Methods Fifteen healthy volunteers (mean age 30 ± 6.8 years) were recruited prospectively. Axial T2-weighted and DTI scans were acquired, covering the cubital tunnel, using different pixel sizes and slice thicknesses. Three-dimensional (3D) nerve tractography was evaluated for the median number and length of the reconstructed fiber tracts and visual score from 0 to 5. Two-dimensional (2D) cross-sectional DTI was evaluated for fractional anisotropy (FA) values throughout the length of the ulnar nerve. Results A pixel size of 1.3 mm2 revealed the highest number of reconstructed nerve fibers compared to that of 1.1 mm2 ( P = 0.048), with a good visual score. A slice thickness of 4 mm had the highest number of reconstructed nerve fibers and visual score compared with other thicknesses (all P < 0.05). In 2D cross-sectional images, the median FA values were in the range of 0.40–0.63 at the proximal, central, and distal portions of the cubital tunnel. Inter-observer agreement for all parameters was good to excellent. Conclusion For fiber tractography and DTI of the ulnar nerve at the cubital tunnel, optimal image quality was obtained using a 1.3-mm2 pixel size and 4-mm slice thickness under MR parameters of this study at 3T.

scholarly journals Impact of slice thickness, pixel size, and CT dose on the performance of automatic contouring algorithms

2021 ◽  
Author(s):  
Kai Huang ◽  
Dong Joo Rhee ◽  
Rachel Ger ◽  
Rick Layman ◽  
Jinzhong Yang ◽  
...  
Keyword(s):  
Slice Thickness ◽  
Pixel Size ◽  
Ct Dose ◽  
Automatic Contouring

scholarly journals Robustness of magnetic resonance radiomic features to pixel size resampling and interpolation in patients with cervical cancer

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Shin-Hyung Park ◽  
Hyejin Lim ◽  
Bong Kyung Bae ◽  
Myong Hun Hahm ◽  
Gun Oh Chong ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Magnetic Resonance ◽  
Intraclass Correlation ◽  
Correlation Coefficients ◽  
Texture Features ◽  
Slice Thickness ◽  
Pixel Size ◽  
First Order ◽  
Intraclass Correlation Coefficients ◽  
Standardization Process

Abstract Background Radiomics is a promising field in oncology imaging. However, the implementation of radiomics clinically has been limited because its robustness remains unclear. Previous CT and PET studies suggested that radiomic features were sensitive to variations in pixel size and slice thickness of the images. The purpose of this study was to assess robustness of magnetic resonance (MR) radiomic features to pixel size resampling and interpolation in patients with cervical cancer. Methods This retrospective study included 254 patients with a pathological diagnosis of cervical cancer stages IB to IVA who received definitive chemoradiation at our institution between January 2006 and June 2020. Pretreatment MR scans were analyzed. Each region of cervical cancer was segmented on the axial gadolinium-enhanced T1- and T2-weighted images; 107 radiomic features were extracted. MR scans were interpolated and resampled using various slice thicknesses and pixel spaces. Intraclass correlation coefficients (ICCs) were calculated between the original images and images that underwent pixel size resampling (OP), interpolation (OI), or pixel size resampling and interpolation (OP+I) as well as among processed image sets with various pixel spaces (P), various slice thicknesses (I), and both (P + I). Results After feature standardization, ≥86.0% of features showed good robustness when compared between the original and processed images (OP, OI, and OP+I) and ≥ 88.8% of features showed good robustness when processed images were compared (P, I, and P + I). Although most first-order, shape, and texture features showed good robustness, GLSZM small-area emphasis-related features and NGTDM strength were sensitive to variations in pixel size and slice thickness. Conclusion Most MR radiomic features in patients with cervical cancer were robust after pixel size resampling and interpolation following the feature standardization process. The understanding regarding the robustness of individual features after pixel size resampling and interpolation could help future radiomics research.

scholarly journals PROCESSING AND DISPLAY OF DIGITIZED MEDICAL IMAGES : SPECIAL REFERENCE TO THE MATRIX COUNT AND PIXEL SIZE AT DIGITIZATION

1987 ◽  
Vol 43 (12) ◽  
pp. 1728-1733
Author(s):  
YASUHIKO TOHYAMA ◽  
NAOKI AKAGI ◽  
YOSHIHIKO KUBO ◽  
NOBORU YAMAMOTO ◽  
TAKASHI HATAKEYAMA ◽  
...  
Keyword(s):  
Special Reference ◽  
Medical Images ◽  
Pixel Size ◽  
The Matrix

Visualisation Technique for Trabecular Architecture of Bone Using Ultrasound Signals

2004 ◽  
Vol 1-2 ◽  
pp. 99-106
Author(s):  
Fumio Nogata
Keyword(s):  
Bone Marrow ◽  
Attenuation Factor ◽  
Bovine Bone ◽  
Trabecular Architecture ◽  
Pixel Size ◽  
Image Size ◽  
Heel Bone ◽  
The Difference ◽  
A New Technique ◽  
Visualisation Technique

We have proposed a new technique for visualising trabecular architecture of spongy bone using ultrasound A-mode signals from a transducer of medical ultrasound system. The technique can be established by finding a method to distinguish between bone and bone marrow. Firstly, the A-mode signal intensity was fitted by the form, A=A0exp(-ax), where A, A0, a, and x represent echo intensity, initial value of echo, attenuation factor, and distance, respectively. Then the curve was moved slightly downward or upward by multiplying with the coefficient k (0.9~1.2), and it was used as an index line to distinguish between bone and bone marrow. To clarify the validity of the proposed technique, we examined bovine bone specimen and a spongy-shaped specimen made by ceramics. The pixel size for creating architecture was 0.2 (width) × 0.15 (depth) mm for bone marrow and 0.2 × 0.3 mm for bone substance, and the pixel size differs due to the difference in wave speeds. The visualisation technique was capable to create an image size of ~10 mm depths from the surface of cortical bone. We also tried to visualise the heel bone.

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