From formation to interpretation, this article analyzes medical image processing for you

Fig. 1. Structural classification of topic types in medical image processing.

The image formation process consists of data acquisition and image reconstruction steps to solve mathematical inverse problems. The goal of image computing is to improve the interpretability of the reconstructed images and to extract clinically relevant information from them. Finally, image management deals with the compression, archiving, retrieval, and transmission of acquired images and derived information.

data collection

The first necessary step in image formation is the acquisition of raw imaging data. This data contains raw information about the captured physical quantities describing each internal organ of the body. This information becomes the main subject of all subsequent image processing steps.

Different types of imaging modalities may exploit different physical principles and thus involve the detection of different physical quantities. For example, in digital radiography (DR) or computed tomography (CT), it is the energy of the incident photons; in positron emission tomography (PET), it is the photon energy and its detection time; in magnetic resonance imaging (MRI), it is the parameters of the radiofrequency signal emitted by the excited atoms; and in ultrasound, it is the echo parameters.

However, regardless of the type of imaging modality, the data acquisition process can be broken down into the detection of physical quantities, their conversion into electrical signals, preconditioning of the acquired signals, and digitization of the physical quantities. A general block diagram representing all of these steps that apply to most medical imaging modalities is shown in Figure 2.

Figure 2. General block diagram of the data acquisition process.

Image reconstruction

Image reconstruction is the mathematical process of forming an image from the acquired raw data. For multidimensional imaging, the process also includes the combination of multiple data sets captured at different angles or at different time steps. This part of medical image processing solves the inversion problem, which is a fundamental theme in this field. There are two main types of algorithms used to solve this type of problem - analytical and iterative.

Typical examples of analytical methods include filtered back projection (FBP), which is widely used in tomography; Fourier transform (FT), which is particularly important in MRI; and delay-and-sum (DAS) beamforming, which is an indispensable technique in ultrasound. These algorithms are elegant and efficient in terms of the required processing power and computation time.

However, they are based on idealized models and thus have some significant limitations, including their inability to handle complex factors such as the statistical properties of measurement noise and the physics of the imaging system.

Iterative algorithms overcome these limitations, greatly improving the insensitivity to noise and the ability to reconstruct the best image using incomplete raw data. Iterative methods usually use systematic and statistical noise models to calculate projections based on an initial target model using assumed coefficients. The difference between the calculated projections and the raw data defines new coefficients used to update the object model. This process is repeated using multiple iterative steps until a cost function mapping the estimated value to the true value is minimized, thereby integrating the reconstruction process into the final image.

There are many iterative methods, including Maximum Likelihood Expectation Maximization (MLEM), Maximum A Posteriori (MAP), Algebraic Reconstruction (ARC) techniques, and many others that are currently widely used in medical imaging modalities.

Image computing involves computational and mathematical methods that operate on reconstructed imaging data to extract clinically relevant information. These methods are used for enhancement, analysis, and visualization of imaging results.

Enhancement

Image enhancement optimizes the transform representation of an image to improve the interpretability of the information it contains. Its methods can be divided into spatial domain and frequency domain techniques.

Spatial domain techniques act directly on image pixels and are particularly useful for contrast optimization. These techniques typically rely on logarithmic, histogram, and power law transforms. Frequency domain methods employ frequency transforms and are best suited for smoothing and sharpening images by applying different types of filters.

Utilizing all of these techniques can reduce noise and inhomogeneities, optimize contrast, enhance edges, eliminate artifacts, and improve other related characteristics that are critical for subsequent image analysis and their accurate interpretation.

analyze

Image analysis is the core process in image computing, and the various methods it uses can be divided into three major categories: image segmentation, image registration, and image quantization.

The image segmentation process divides the image into meaningful outlines of different anatomical structures. Image registration ensures that multiple images are properly aligned, which is particularly important for analyzing temporal changes or combining images acquired using different modalities. The quantification process determines the properties of the identified structures, such as volume, diameter, composition, and other relevant anatomical or physiological information. All of these processes directly affect the quality of the imaging data and the accuracy of the medical results.

Visualization

The visualization process presents the image data as an intuitive representation of anatomical and physiological imaging information in a specific form over defined dimensions. By directly interacting with the data, visualization can be performed at the initial and intermediate stages of the imaging analysis (e.g., to assist in the segmentation and registration process), and at the final stage to display the optimized results.

The final part of medical image processing involves the management of the acquired information, including various techniques for image data storage, retrieval, and transmission. Several standards and technologies have been developed to address various aspects of image management. For example, the medical imaging technology Picture Archiving and Communication System (PACS) provides economical storage and access to images from multiple modalities, while the Digital Imaging and Communications in Medicine (DICOM) standard is used to store and transmit medical images. Special techniques for image compression and streaming accomplish these tasks efficiently.

Medical imaging is a relatively conservative field, and the transition from research to clinical application can often take more than a decade. However, its complex nature and multifaceted challenges in all aspects of its constituent scientific disciplines have steadily driven the continuous development of new methods. These developments represent the main trends that can be identified in the core field of medical image processing today.

The field of image acquisition benefits from innovative hardware technologies developed to improve the quality of raw data and enrich its information content. Integrated front-end solutions enable faster scan times, finer resolutions and advanced architectures such as ultrasound/mammography, CT/PET or PET/MRI combination systems.

Fast and efficient iterative algorithms are increasingly being used for image reconstruction, replacing analytical methods. They can significantly improve image quality in PET, reduce X-ray dose in CT, and perform compressed detection in MRI. Data-driven signal models are replacing manually defined models, providing better solutions to inversion problems based on limited or noisy data. The main research areas that represent trends and challenges in image reconstruction include system physical modeling and development of signal models, optimization algorithms, and image quality assessment methods.

As imaging hardware captures more and more data and algorithms become more complex, there is an urgent need for more efficient computing techniques. This huge challenge can be addressed with more powerful graphics processors and multiprocessing technology, providing new opportunities for transitioning from research to applications.

The key trends and challenges associated with this shift in image computing and image management cover many topics, some of which are shown in Figure 3.

Figure 3. Examples of major trending topics in medical image computing today.

New technologies related to all of these topics continue to develop, narrowing the gap between research and clinical application, promoting the integration of the field of medical image processing into the physician's workflow, and ensuring more accurate and reliable imaging results.

ADI offers a variety of solutions to meet the most demanding medical imaging requirements for data acquisition electronics design, including dynamic range, resolution, accuracy, linearity, and noise. Below are a few examples of such solutions developed to ensure the highest initial quality of raw imaging data.

The highly integrated analog front end ADAS1256 with 256 channels is designed for DR applications. The multi-channel data acquisition systems ADAS1135 and ADAS1134 with excellent linearity can maximize the image quality of CT applications. The multi-channel ADCs AD9228, AD9637, AD9219, and AD9212 are optimized with excellent dynamic performance and low power consumption to meet PET requirements. The pipeline ADC AD9656 provides excellent dynamic performance and low power consumption for MRI. The integrated receiver front end AD9671 is designed for low-cost, low-power medical ultrasound applications that require a small package size.

Medical image processing is a very complex and interdisciplinary field that covers a wide range of scientific disciplines, from mathematics and computer science to physics and medicine. This article attempts to present a simplified but well-structured framework of core areas that represents the field and its main themes, trends, and challenges. The data acquisition process is one of the first and most important areas that defines the initial quality level of the raw data used in all subsequent stages of the medical image processing framework.

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