Understanding Computed Tomography

Computed Tomography (CT) is a sophisticated imaging technique that allows doctors to see inside the human body with exceptional clarity. By using precisely collimated X-ray beams, gamma rays, or ultrasound, combined with highly sensitive detectors, CT scans create detailed images of various body parts.

The Principle of Computed Tomography

The fundamental principle of CT involves scanning a specific layer of the body using an X-ray beam. Detectors capture the X-rays that pass through the body, converting them into visible light. This light is then transformed into electrical signals through photoelectric conversion, which are subsequently digitized and processed by a computer. The result is a detailed image, known as a CT image, reconstructed from the processed data.

How CT Imaging Works

In CT imaging, the body is scanned in slices or layers. Each slice is processed individually to create a comprehensive image. The process involves several steps:

1.Scanning: A precisely collimated X-ray beam rotates around the body part being examined. Detectors capture the transmitted X-rays, which vary depending on tissue density.

2.Detection: The detectors convert the X-rays into visible light, which is then turned into electrical signals. These signals are digitized for computer processing.

3.Image Reconstruction: The computer processes the digitized signals to calculate the X-ray attenuation or absorption coefficients for each voxel (volume element). These coefficients are arranged into a digital matrix.

4.Image Display: The digital matrix is converted back into an image using a digital-to-analog converter. The final image consists of pixels, each representing a voxel's X-ray absorption coefficient, displayed in varying shades of gray.

Key Concepts in Computed Tomography

1.Voxel and Pixel

Voxel: A voxel is the smallest volume element in a CT scan, representing a specific volume in the scanned body part. It has three dimensions: length, width, and height.

Pixel: A pixel is the smallest unit of a CT image, corresponding to a voxel in the body. Pixels are arranged in a matrix to form the final image.

2.Scanning and Displaying Matrix

Scanning Matrix: This matrix consists of pixels arranged in a two-dimensional array, determining the quality of the reconstructed image. Common sizes are 512×512, 256×256, and 1024×1024.

Displaying Matrix: The matrix used to display the image after reconstruction. It is often equal to or larger than the scanning matrix to ensure image quality.

3.Raw Data

Raw data are the initial signals received by the detectors, converted into digital form but not yet reconstructed into an image. This data is essential for the subsequent image reconstruction process.

4.Reconstruction and Reformation

Reconstruction: The process of converting raw data into a usable cross-sectional image using specific algorithms.

Reformation: A technique that manipulates already reconstructed images to produce different views, such as multiplanar reformation or 3D images.

5.Scanning and Cycle Time

Scanning Time: The time required for the X-ray tube and detectors to rotate around the body and complete one scan. Modern CT machines offer scan times as short as 0.33 seconds.

Cycle Time: The total time from the start of scanning to the final image display. It includes scanning and reconstruction times but can be optimized by modern CT machines for efficiency.

Computed Tomography is a powerful diagnostic tool that revolutionizes medical imaging. Its ability to create detailed cross-sectional images of the body allows for accurate diagnosis and treatment of various conditions. With advancements in CT technology, scan times are becoming shorter, and image quality is continually improving, enhancing patient care and diagnostic accuracy.

References:

[1] Tikhonov regularization and prior information in electrical impedance tomography. Vauhkonen,M,Vadaz,D,Kaipio,J P. IEEE Transactions on Medical Imaging . 1998

[2] Radon transform theory for random fields and optimum image reconstruction from noisy projections. Jain,A.,Ansari,S. IEEE International Conference on ICASSP’84(Acoustics, Speech and Signal Processing) . 1984

[3] Image reconstruction from projections:the fundamentals of computerized tomography. Gabor T.Herman. . 1980

[4] Electrical capacitance tomography for flow imaging: system model for development of image reconstruction algorithms and design of primary sensor. Xie C G,Huang S M,Hoyle B S,et al. IEE Proceedings G . 1992

[5] Three-dimensional reconstruction from radiographs and electron micrographs: applica- tion of convolutions instead of fourier transforms. G.N. Ramachandran,A.V. Lakshminarayanan. Proceedings of the National Academy of Sciences of the United States of America . 1971

[6]Image Quality Improvement in Deep Learning Image Reconstruction of Head Computed Tomography Examination. Michal Pula, Emilia Kucharczyk, Agata Zdanowicz andMaciej Guzinski. Tomography 2023, 9(4), 1485-1493;https://doi.org/10.3390/tomography9040118