Digital Fluoroscopy PPT created by: Jed Miles, BSRS, RT(R), CRT-CA Radiologic Science for Technologist, 9th Ed. By: Stewart Bushong, Scd, FAAPM, FACR Principals of Radiographic Imaging, 5th Ed. By: Richard Carlton,M.S ., RT(R)(CV), FAEIRS & Arlene Adler, M.Ed., RT(R), FAEIRS Digital Radiography an Introduction By: Euclid Seeram, RT(R), BSc, MSc, FCAMRT PPT created by: Jed Miles, BSRS, RT(R), CRT-CA

Objectives Describe various attributes of the digital image Describe the parts of a digital imaging system Explain critical elements used to create a digital fluoroscopy image Discuss limitations inherent in currently available digital radiography systems Explain why digital radiography systems have greater latitude than conventional fluoroscopy systems

Objective - cont Discuss image display options of digital fluoroscopy systems

History of Digital Fluoroscopy

Pioneering Days 1896: Thomas Edison developed the first direct viewing fluoroscope using glass coated with calcium tungstate Resulted in a faint image only viewable with scotopic (night) vision Once hazards of direct viewing of fluoro image became known, a design change placed glass plate in a lead lined box Viewing the optical image took place via lenses and mirrors 1948: Image Intensification Tubes were developed Resulted in being able to view the image using photopic (day) vision

Pioneering Days 1970’s: Medical physics groups pioneered digital image output with conventional image intensified fluoro Required a computer being placed between the television camera tube and the television monitor 1980’s: Digital Subtraction Angiography (DSA) systems gained acceptance by medical community 1983: CCD Camera began replacing TV camera tubes 2000’s: Dynamic flat panel detectors replaced image intensification tube in fluoroscopy

The Digital Image A Brief Review

Digital Image Characteristics Digital Image consists of pixels that represent the original image arranged in a matrix format Pixels Size determines the spatial resolution Bit depth determines scale of contrast Matrix Created by pixels arranged into rows and columns Matrix size determined by numbers of pixels

The Pixel The Pixel is the smallest element in a digital image Pixel size determines the spatial resolution of image Smaller pixels increases visibility of smaller structures Increases recorded detail Pixel size is related to matrix size and size of image intensifier surface area being utilized Smaller pixel size will result in a larger matrix size Larger matrix size will result in increased spatial resolution Pixel bit depth Determines the numbers of shades of gray to be displayed Increased bit depth results in more shades of gray

Pixel Size Size matters: A digital fluoro system typically uses a 1024 x 1024 image matrix Sometimes referred to as a 1000-line system Digital fluoro spatial resolution is determined by both the image matrix and by the input size of the image intensifier being utilized Larger matrix size results = increased spatial resolution Smaller input size utilization = increased spatial resolution

Pixel Size: Let’s do the Math…! Formula: Pixel size = Image Intensifier size / matrix Q: What is the pixel size of a 1000-line DF system operating in a 6-inch mag mode? Conversion factors: 6 inches x 25.4 mm/inch = 152.4 mm 1000-line system = 1024 x 1024 matrix A: 152.4 mm / 1024 = 0.149 mm

Pixel Bit Depth Pixel bit depth aka bits per pixel (bpp) Determines the number of gray shades or brightness levels available for image display by each pixel The numerical value of each pixel determines its brightness level

Pixel Bit Depth Number of gray shades or brightness levels is determined by the bit depth (2n) Where “n” is the number of bits (brightness levels) available for each pixel 12 bit depth (212) will produce an image consisting of each pixel within the matrix being able to produce 4096 shades of gray or brightness levels 2 shades 64 shades 256 shades 1 Bit (21) 6 Bit (26) 8 Bit (28)

The Matrix Image Matrix Consists of pixels arranged into rows and columns Size of pixels and pixel density determine spatial resolution 4 X 4 8 X 8 16 X 16 32 x 32 64 X 64 512 X 512

Spatial Resolution Spatial resolution Describes the ability to resolve two objects in close proximity Smaller pixel size will result increased spatial resolution* Increased spatial resolution enables increased visibility of smaller objects and detail *Spatial resolution is not related the amount of exposure Pixel size determines spatial resolution Exposure is related to the ‘visibility’ of spatial resolution and detail Object: Line pair phantom Image: Line pair phantom

Signal to Noise Ratio (SNR) Describes the amount of image forming signal to inherent noise in any electronic system Other descriptors are used Signal difference to noise ratio Subject contrast also known as contrast-to-noise ratio (CNR) The image above has a sufficiently high SNR to clearly separate the image information from background noise A low SNR would produce an image where the "signal" and noise are more comparable and thus harder to discern from one another http://www.cambridgeincolour.com/tutorials/noise.htm

Digital File Size Matrix (pixel density) Pixel Bit depth Digital data requires hard drive file space for display, archive and retrieval Digital Image file size is affected by Matrix (pixel density) Larger matrix size will increase data file size 512 x 512 = 262,144 pixels, 1024 x 1024 = 1,048,576 pixels Doubling the matrix size will quadruple the numbers of pixels Pixel Bit depth Increasing pixel bit depth results in larger data file size

Digital File Size Image format Image file size is correlated to the compression algorithm used to compress the original pixel data. Images can be compressed in various ways Compression uses an algorithm that stores an exact representation or an approximation of the original image in a smaller number of bytes that can be expanded back to its uncompressed form with a corresponding decompression algorithm. Considering different compressions available for use, it is common for two images of the same number of pixels and bit depth to have a very different compressed file size Lossless image compression results in larger file sizes with no loss of resolution Lossy image compression results in smaller file sizes with resulting loss of resolution This loss of resolution can range from a small to large amount

Lossless Versus Lossy Compression Algorithm