Introduction to Medical Imaging

Medical imaging is the acquisition and processing of images of the human body for clinical applications. You can use medical image processing to improve the quality of medical images, for diagnosis of medical conditions, for surgical planning, or for research. Medical imaging enables the detailed, yet non-invasive, study of human anatomy. The success of medical imaging requires collaboration between medical professionals such as radiologists, pathologists, or clinicians, and technology professionals skilled in image processing. The major types of medical images you can process for clinical applications are radiology images, such as MRI scans, CT scans, X-ray scans, ultrasound scans, or PET/SPECT scans, or pathological microscopy images, such as biopsies and blood smears.

You can analyze radiology images for a variety of applications.

• Diagnostic Systems: For example, to detect tumors from brain MRI scans, to detect COVID-19 from CT scans, to detect pneumonia from chest X-ray scans, or to detect tumors from breast ultrasound.

• Biomedical Engineering: For example, to model bones or to design prostheses.

• Functional Analysis: For example, to analyze brain function from functional MRI.

• Pharmaceutical Research: For example, to measure drug efficacy and clearance time.

• Device Design: For example, to build new MRI, CT, ultrasound devices.

You can use Medical Imaging Toolbox™ to analyze radiology images for such applications. Although the functions in Medical Imaging Toolbox are modality-agnostic, the major medical imaging modalities that you can use the toolbox for include MRI, CT, X-ray, ultrasound, and PET/SPECT.

Common Medical Imaging Modalities

Magnetic Resonance Imaging (MRI)

The human body consists mostly of water, and therefore contains many hydrogen nuclei. MRI scans acquire an image by disturbing the magnetic equilibrium of the hydrogen nuclei in the body. The scanner then measures the time taken by the hydrogen nuclei to regain equilibrium, which varies based on the composition of the organ being imaged. MRI scans are particularly useful for imaging soft tissues such as the brain, spinal cord, nerves, muscles, ligaments, and tendons, as soft tissues have more water content than bone. Unlike other radiology modalities, MRI scans do not use any ionizing radiation. However, medical professionals must ensure that the patient undergoing the scan does not have any metal on or in their body that might be attracted to the magnetic field. There are several forms of MRI, based on the nature of particles and the type of magnetization property measured, including T1-weighted MRI, T2-weighted MRI, diffusion MRI, and functional MRI. The different types of MRI provide different insights into the human body.

Mathematically, an MRI scanner generates an image in the Fourier domain also known as k-space. Each scan typically consists of a collection of 2-D slices imaged in k-space. The scanner transforms the k-space image to the spatial domain, enabling you to observe the imaged anatomy. The final output of the MRI scanner is a 3-D volume in the spatial domain with spatial localization details. You can use a medicalVolume object to store the voxel data and spatial referencing information for the MRI volume. MRI images are prone to degradations in the form of acquisition noise, undersampling artifacts, and patient motion artifacts.

Computed Tomography (CT)

CT scans use X-ray radiation to image human anatomy. The magnitude of attenuation of the radiation depends on the organ being imaged. Because bones effectively block X-rays, CT scans image them particularly well. You can image tissues in the human body, using contrast agents, which help attenuate the X-rays. As a result, CT scans are versatile and can be used for imaging of the head and neck, as well as organs such as heart, lungs, abdomen, and pelvis. Additionally, CT scans are fast and cost-effective for patients.

Mathematically, a CT scanner reconstructs the image from a series of projections obtained using the Radon transform, typically represented as a sinogram. The Radon transform produces a collection of projections of radiation through the body along different angles. There are a variety of techniques to reconstruct an image from the projection data, including the inverse Radon transformation and other iterative methods, enabling you to observe the imaged anatomy. The final output of the CT scanner is a 3-D volume in the spatial domain with spatial localization details. You can use a medicalVolume object to store the voxel data and spatial referencing information for the CT volume. CT images are prone to degradations in the form of low contrast and artifacts due to miscalibration of the X-ray detectors.

X-Ray Imaging

X-ray imaging is a direct digitized recording of the attenuation of the X-ray radiation on a 2-D sensor array or a radiographic film. It is fast and cost-effective compared to other scans. Thus, it is a good option for preliminary diagnosis. Mathematically, an X-ray image is a simple 2-D image captured by an X-ray detector. You can use a medicalImage object to store the pixel data and metadata for an X-ray image.

Ultrasound (US)

Ultrasound imaging involves emitting of ultrasound waves and measuring the strength of the reflected echo waves. The strength of the reflected echo waves depends on the organ being imaged. Because air can block ultrasound waves, it is not suitable for imaging bones, or tissues that contain air such as lungs. You can use ultrasound imaging to, for example, monitor the development of a fetus during pregnancy, or for imaging the heart, breast, and abdomen. You can use Doppler ultrasound for functional imaging of the blood flow in the blood vessels.

Mathematically, an ultrasound image is derived from the reflected ultrasound waves. The final output of the ultrasound scan is a sequence of 2-D images in the spatial domain. You can use a medicalImage object to store the pixel data and metadata for an ultrasound image sequence. Note that the emitted and reflected ultrasound waves can cause interference, which can show up as speckle in the ultrasound image.

Nuclear Medicine Imaging

Nuclear medicine imaging involves introducing radioactive tracers, also known as radiotracers or radiopharmaceuticals, that contain radioactive isotopes into the body of the patient. The movement of the radiotracers in the body of the patient provides insights about the organs being imaged. Different types of nuclear medicine imaging employ different radiotracers and are used for different purposes. Mathematically, nuclear medicine imaging is performed as a tomography. The decay of the radiotracers emits radiation, and the scanner measures the attenuation of the radiation in the tomography. The final output of the scanner is a 3-D volume in the spatial domain with spatial localization details. You can use a medicalVolume object to store the voxel data and spatial referencing information for a 3-D volume.

Positron emission tomography (PET) and single-photon emission computed tomography (SPECT) use different types of radiotracers for imaging. The decay of the radiotracers used in PET emits positrons. PET is used primarily for diagnosis and tracking of cancer. The decay of the radiotracers used in SPECT emits gamma rays. SPECT is used primarily for diagnosis and tracking of heart disease.

Typical Workflow for Medical Image Analysis

Import and Spatial Referencing

A typical medical imaging workflow begins with importing the medical images into the workspace. Medical images are available in file formats such as NIfTI, DICOM, NRRD, Analyze7.5, and Interfile. Medical Imaging Toolbox provides several functions you can use to import medical images into the workspace and export them back to medical image formats after processing. For more information, see Read, Process, and Write 3-D Medical Images.

Display, Volume Rendering, and Surfaces

Once you have imported a medical image into the workspace, you can view and inspect the image to plan your workflow. Medical Imaging Toolbox provides tools for detailed viewing of the medical images in different orientations, for volume rendering to visualize intensity volumes in 3-D, and for generating surfaces for efficient display or 3-D printing or modeling applications. For more information, see Visualize 3-D Medical Image Data Using Medical Image Labeler.

Preprocessing and Augmentation

An imported medical image can contain noise that you must reduce. Multiple medical images that you must process together can be misaligned and require registration. Also, if the medical imaging data set is too small for your intended application, you might need to augment it. Medical Imaging Toolbox provides functions for preprocessing and augmentation. For more information, see Medical Image Preprocessing.

Ground Truth Labeling and Segmentation

For object detection in deep learning applications, you might need to perform segmentation and labeling of the preprocessed medical image training data set. Medical Imaging Toolbox provides the Medical Image Labeler app for segmentation and ground truth labeling. For more information, see Get Started with Medical Image Labeler.