5 Imaging Fractures

       S.M. Puchalski1 and G.J. Minshall2

       1 Puchalski Equine Inc., Petaluma, CA, USA

       2Newmarket Equine Hospital, Newmarket, UK

Introduction

      Fracture is defined by Dorland's Medical Dictionary as ‘a break or discontinuity in bone’. In almost all cases, diagnostic imaging, in its various forms, is necessary to identify, classify and monitor fractures. The goals, independent of modality, are to accurately depict, characterize and quantify bone defects. Ideally, the imaging test would also identify all other injuries including associated soft tissue and vascular damage. Accurate and comprehensive evaluation is important to direct a rational course of action, predict clinical dangers, avoid potential complications and to provide an accurate prognosis.

      In its simplest form, the diagnosis of a fracture is binary so with perfect sensitivity and specificity of the diagnostic imaging test, a fracture either is or is not present. The reality of clinical medicine is that numerous and complicated factors are involved in the identification and interpretation of imaging or roentgen findings that lead to a true and fully characterized diagnosis. These include, but may not be limited to, factors associated with the biological system (pathophysiology of fracture genesis, patient health etc.), the utilized imaging modality (‐ies) and the observer. Most diagnostic imaging tests are a representation of the anatomy with some modalities providing a representation of the physiology. All tests require that the observer accurately identifies the pertinent imaging signs and interprets them correctly.

      The means by which discontinuity in bone is identified depends on the imaging modality used. Radiography, ultrasound, computed tomography (CT) and magnetic resonance imaging (MRI) are morphological or anatomical studies, and nuclear scintigraphy is a functional study. All are commonly used in equine practice and employ different physics to create images. While overlap of principles exists, each has unique characteristics that must be understood to use the technique accurately and to its fullest capacity. Understanding the information provided by individual modalities together with anamnesis and results of the clinical examination are critical in order to choose, utilize and accurately interpret the best diagnostic test(s) for each patient. Similarly, knowledge and understanding of the expected biological behaviour of bone are necessary in order to use diagnostic imaging to monitor fracture healing.

      Image Quality

      Image quality is a broadly understood concept, and assurance of diagnostic quality is critical to accurate use of medical imaging. There are several measures of image quality that are common to all techniques. Understanding these and their interactions aids in the recognition of a high‐quality image and variation from this.

Contrast is the greyscale value difference between adjacent regions on the image. On the final image, this is determined by a number of factors including the inherent subject contrast, detector contrast and displayed contrast. Subject contrast is determined by the tissues and the type of energy (radiation, sound wave and signal intensity) recorded. Detector contrast refers to the way that an input signal is converted to an output or a recorded signal. In most digital radiography (DR) systems, the characteristic curve or relationship between the energy of the X‐rays hitting the detector and the emitted or recorded image is close to linear. A linear characteristic curve without image processing would appear very ‘flat’ or washed out. Almost all digital imaging systems therefore process the output so that contrast is increased and the displayed image has a non‐linear output. Displayed contrast simply refers to the ability of the end user to manipulate the greyscale so that the image can have more or less contrast as desired. To put this into context, in order to identify a fracture on radiographs, subject contrast would be the variation in tissue density between the fracture gap and the fracture margins, the detector contrast would be determined by the settings of the radiographic system and its processing, and the displayed contrast would be chosen by the observer at the viewing station. Together these have a substantial influence on the ability to detect a fracture.

      Resolution (spatial resolution) is the ability of an imaging system to depict two objects as separate as these get smaller and closer together, i.e. how small an object can be seen on a given modality [1]. Higher spatial resolution is the ability to see objects that are smaller and closer together. The historical method of measuring radiographic and CT spatial resolution was by using test phantoms that actually measured the ability to separate line pairs per millimetre. Many factors influence spatial resolution for all modalities. Most importantly, in digital imaging systems is the pixel size. The size of the pixel is determined by the number of pixels across the field of view. Thus, a larger field of view with the same pixel matrix will result in lower resolution. Objects smaller than the pixel size cannot be resolved as separate structures. Blurring in the image will also detract from spatial resolution, thus geometric magnification and motion (patient or imaging apparatus) should be avoided. In cross‐sectional imaging, in plane resolution is directly related to pixel size, but the z‐axis (slice thickness) determines the voxel size. In cross‐sectional modalities, the z‐axis is an important consideration in the identification of fractures. If a linear structure or plane such as a fracture is oblique to the acquisition plane, the margins of the line/plane will be blurred by a factor related to the slice thickness (z‐axis/voxel size) and the angle of obliquity through the image. Spatial resolution is particularly important in the diagnosis of incomplete or non‐displaced fractures. For many modalities, the disruption of mineral substance in these cases will be at the limits of spatial resolution.

      Image noise is an important contributor to degradation of image quality or degradation of the utility of a given image. Noise caused by various systematic or random variables contributes extraneous optical density (echogenicity, signal intensity, etc.). Digital imaging systems (as compared to film screen systems) have systematic noise from the electronics and the structure of the detector. Anatomic structures that are not of interest to the viewer are also a form of noise, e.g. radiographs with bowel superimposed over the lumbar vertebral bodies. Quantum noise is important in digital diagnostic imaging. In most instances, images made with X‐rays and gamma rays use the lowest number of rays (quanta) possible to obtain a diagnostic image. When the dose is limited, the ratio of useful to non‐useful information (noise) shifts in favour of the latter. Thus, increasing the dose of radiation can shift the ratio towards useful information. In MRI, this is achieved by recording intensity multiple times (number of excitations).

      Bit depth determines the number of possible shades of grey that can be applied to the imaging systems output. Most medical imaging devices range from 10 to 14 bit depth thus having the capability of recording 1024, 4096, or 16 384 shades of grey. This is beyond the limits of most digital displays and human resolution. The conversion of the image from a 10 bit depth image (1024) shades of grey to something more useable occurs by the application of a lookup table that determines the displayed greyscale values relative to the recorded greyscale value.

Contrast‐to‐noise ratio (CNR) and signal‐to‐noise‐ratio