There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

NOTE The report of the Committee without its annexes appears as Official Records of the General Assembly, Sixty-third Session, Supplement No. 46. The designations employed and the presentation of material in this publication do not imply the expression of any opinion whatsoever on the part of the Secretariat of the United Nations concerning the legal status of any country, territory, city or area, or of its authorities, or concerning the delimitation of its frontiers or boundaries. The country names used in this document are, in most cases, those that were in use at the time the data were collected or the text prepared. In other cases, however, the names have been updated, where this was possible and appropriate, to reflect political changes. Scientific Annexes Annex A. Medical radiation exposures Annex B. Exposures of the public and workers from various sources of radiation INTROdUCTION 1. In the course of the research and development for and the application of atomic energy and nuclear technologies, a number of radiation accidents have occurred. Some of these accidents have resulted in significant health effects and occasionally in fatal outcomes. The application of technologies that make use of radiation is increasingly widespread around the world. Millions of people have occupations related to the use of radiation, and hundreds of millions of individuals benefit from these uses. Facilities using intense radiation sources for energy production and for purposes such as radiotherapy, sterilization of products, preservation of foodstuffs and gamma radiography require special care in the design and operation of equipment to avoid radiation injury to workers or to the public. Experience has shown that such technology is generally used safely, but on occasion controls have been circumvented and serious radiation accidents have ensued. 2. Reviews of radiation exposures from accidents have been presented in previous UNSCEAR reports. The last report containing an exclusive chapter on exposures from accidents was the UNSCEAR 1993 Report [U6]. 3. This annex is aimed at providing a sound basis for conclusions regarding the number of significant radiation accidents that have occurred, the corresponding levels of radiation exposures and numbers of deaths and injuries, and the general trends for various practices. Its conclusions are to be seen in the context of the Committee's overall evaluations of the levels and effects of exposure to ionizing radiation. 4. The Committee's evaluations of public, occupational and medical diagnostic exposures are mostly concerned with chronic exposures of …

sources and effects of ionizing radiation

It is gratifying to see in overview the progress that quantitative planar imaging has made, from simple conjugate-view imaging, through conjugate-views combined with different correction techniques, and most recently towards more sophisticated approaches based on patient-specific transmission and CT images. Challenges remain, particularly in deriving the optimal and less constraining imaging protocol for applications in clinical routine and defining an objective approach for determining the calibration curves and correction factors needed for accurate quantification.

Methods for Planar Image Quantification

Gestational age (GA) is an illustrative indicator of fetal growth. The GA is estimated through biometric parameters, including the head circumference (HC) and Biparietal diameter (BPD). This paper proposes a deep learning-based method for automatic measurement of the BPD and HC based on the segmentation of the fetal head from ultrasound images. We utilized an efficient convolutional network architecture, named multi-feature pyramid Unet (MFP-Unet) previously proposed for left ventricle segmentation from echocardiography images. The proposed network tackles the main drawback of U-net, which ignores the contribution of all semantic strengths in the segmentation procedure. To train and evaluate MFP-Unet, a set of ultrasound images was used from the HC18 challenge dataset (fetal head circumference challenge). To evaluate the quantitative accuracy of anatomy segmentation, several metrics, including Dice similarity coefficient (DSC) and Hausdorff distance (HD) were employed. MFP-Unet achieved a DSC of 0.95 and HD of 4.5, which indicates the performance of the MFP-Unet algorithm in segmenting fetal ultrasound images for automatic measurement of biparietal diameter (BPD) and head circumference (HC).

Automatic Measurement of Fetal Head Biometry from Ultrasound Images Using Deep Neural Networks

https://link.springer.com/content/pdf/10.1007/s12350-017-1163-x.pdf

Artifact-free quantitative cardiovascular PET/MR imaging: An impossible dream?

The use of radiolabeled tracers in PET imaging raises concerns owing to potential risks from radiation exposure. Therefore, to reduce this potential risk in diagnostic PET imaging, efforts have been made to decrease the amount of radiotracer administered to the patient. However, decreasing the injected activity reduces the signal-to-noise Ratio (SNR) and deteriorates image quality, thus adversely impacting clinical diagnosis. Previously proposed techniques are complicated and slow, yet they yield satisfactory results at significantly low dose. In this work, we propose a deep learning algorithm to reconstruct full-dose (FD) from low-dose (LD) PET images using a fully convolutional encoder-decoder deep neural network model. The goal is to train a model to learn to reconstruct from images with only 5% of the counts to produce images corresponding to 100% of the dose. Brain PET/CT images of 140 patients acquired on the Siemens Biograph mCT with a standard injected activity of 18F-FDG (205 ± 10 MBq). Images were acquired for about 20 min. The sinograms of each scan were used to produce a low-dose sinogram by randomly selecting only 1/20th of the counts. To avoid over fitting, data augmentation was used. A modified 3D U-Net, was developed to predict standard-dose sinogram (PSS) from their corresponding LD sinogram. Detailed quantitative and qualitative comparison demonstrated the proposed method can generate artefact-free diagnostic quality images that preserve internal structures without noise amplification. The structural similarity index (SSIM) and peak signal to noise ratio (PSNR) were used as quantitative metrics for assessment. For instance, the PSNR and SSIM in selected slices were 37.30±0.71 and 0.97±0.02, respectively. The proposed algorithm operates in the projection space and is capable of producing diagnostic quality images with only 5% of the standard injected activity.

A novel convolutional neural network for predicting full dose from low dose PET scans

An x-ray computed tomography (CT) simulator based on the Monte Carlo N-particle radiation transport computer code (MCNP4C) was developed for simulation of both fanand cone-beam CT scanners. A user friendly interface running under Matlab 6.5.1 creates the scanner geometry at different views as MCNP4C input file. The full simulation of x-ray tube, phantom and detectors with single-slice, multi-slice and flat detector configurations was considered. The simulator was validated through comparison with experimental measurements of different non-uniform phantoms with varying size on both clinical and small-animal CT scanners. There is good agreement between the simulated and measured projections and reconstructed images. Thereafter, the effects of bowtie filter, phantom size and septa length on scatter distribution in fan-beam CT were studied in detail.

Habib Zaidi

Papers

Methods for Planar Image Quantification

Automatic Measurement of Fetal Head Biometry from Ultrasound Images Using Deep Neural Networks

Artifact-free quantitative cardiovascular PET/MR imaging: An impossible dream?

A novel convolutional neural network for predicting full dose from low dose PET scans

MCNP4C-based Monte Carlo simulator for fan- and cone-beam x-ray CT: development and experimental validation