The tutorials use a fair amount of DICOM vocabulary, and explaining each term where it first appears
would make them unreadable. This section answers the question directly instead: what a term means,
when it is used, and what to watch out for. Each entry ends with a link to the page that shows how to
work with it in Weasis.
Window and level select which range of stored pixel values is mapped to the shades of gray on screen.
Subsections of Glossary
What Are DICOM RT Objects?
What Are DICOM RT Objects
Radiotherapy uses a family of DICOM objects that describe a treatment rather than an image.
An RT Structure Set (RTSTRUCT) holds the contours drawn on the planning images: the target
volumes and the organs at risk, each as a named set of outlines with a frame of reference tying it to
the CT it was drawn on. An RT Plan describes how the treatment is to be delivered — beams,
segments, fractions. An RT Dose carries the computed dose as a three-dimensional grid, displayed
as isodose lines over the anatomy or as a color wash.
The dose-volume histogram (DVH) summarizes that grid against the contours: for each structure,
how much of its volume receives at least a given dose. It is the standard way to check that a plan
covers the target while sparing what surrounds it, and it can be stored in the RT Dose object or
recomputed from the dose grid and the structures.
Read together, these objects let a viewer show contours, isodoses and the DVH against the planning
images — which is a different task from reading a diagnostic study, and why viewers treat them
separately.
A DICOM Segmentation, usually written SEG, is a DICOM object that stores which voxels
belong to what: one or more labeled segments — an organ, a lesion, a target volume — each
referencing the frames of the source series they were derived from. The segmentation travels as its
own file, so the original images are never modified.
Every segment carries coded metadata: an anatomical category and type, and often the algorithm that
produced it, which is how a viewer can tell an automatically generated segment from a manually drawn
one.
SEG objects come in two flavors. A binary segmentation marks each voxel as in or out. A
fractional one stores a value per voxel, expressing either the fraction of the voxel occupied by
the structure or a probability — which is what many AI models produce, and what lets a viewer show a
soft, thresholded overlay rather than a hard edge.
Because a segmentation references its source frames, it is displayed over the series it belongs to.
Its geometry is defined independently of the source resolution, so a viewer may have to resample it
to draw the overlay.
A DICOM Structured Report (SR) stores the content of a report as a tree of coded items
instead of a block of prose. Each node pairs a concept with a value — a measurement and its unit, a
finding and its code, a piece of text, an image reference — and the nesting expresses which
observation belongs to which finding.
The point is that the content stays usable by software. A measurement recorded in an SR can be
extracted, compared across examinations and fed into a report or a database without anyone parsing
sentences.
Two kinds of node make SR useful in a viewer. Image references tie an observation to the exact
image it was made on, and spatial coordinates (SCOORD) tie it to a position within that image, so
a viewer can take you to the measurement rather than just telling you it exists.
SR is what carries the measurements produced by CAD and AI tools, ultrasound machines and dose
monitoring systems. What a reader sees is a structured document, not a rendered page: how it is laid
out is the viewer’s decision, not the file’s.
DICOMweb is the part of the DICOM standard that defines web services over HTTP, as an
alternative to the classic DIMSE network protocol. Instead of a dedicated port and association
negotiation, a client makes ordinary HTTP requests — which is what makes a browser, a portal or a
firewalled viewer able to talk to an archive at all.
Three services carry most of the traffic. QIDO-RS searches for studies, series or instances and
answers with metadata, typically JSON. WADO-RS retrieves — the instances themselves, their
metadata, rendered frames or bulk pixel data. STOW-RS stores, sending instances to the archive.
A fourth, UPS-RS, manages worklist items.
Practically, DICOMweb means an archive is reachable with a URL, standard HTTP status codes and an
Authorization header — so a token can travel in a header instead of being pasted into a query
string, and access can be brokered by the same infrastructure as the rest of a hospital’s web
traffic.
It does not replace DIMSE everywhere: C-FIND, C-GET, C-MOVE and C-STORE remain how most archives and
modalities talk to each other inside the network.
Maximum intensity projection (MIP) casts a ray through a slab of the volume for every pixel of
the output image and keeps the highest value it meets. Bright structures therefore survive and
everything dimmer disappears.
That makes it the natural way to look at anything bright and thin spread over many slices:
contrast-filled vessels, calcifications, a lung nodule that is hard to spot slice by slice.
The trade-off is depth. Because only the maximum along each ray is kept, a MIP says nothing about
which of two bright structures lies in front of the other, and a single very bright voxel — metal, a
clip, contrast in an adjacent vessel — can hide what is behind it. This is why a thin slab is
usually more informative than projecting the whole volume: it limits how much anatomy is flattened
into one image.
A MIP is a projection, not a rendering: it has no lighting and no surfaces, and it is not a
substitute for reading the source slices.
Multiplanar reconstruction (MPR) takes a stack of parallel images — typically axial CT or MR
slices — treats it as a volume, and re-slices that volume in another plane. From one axial
acquisition you get coronal and sagittal views, and any oblique plane in between, without scanning
the patient again.
It is useful whenever anatomy does not follow the plane of acquisition: a vessel running
head-to-foot, a fracture line crossing slices, a structure easier to follow along its own axis than
across it.
Two things decide how good the result looks. Slice spacing — a volume reconstructed from thick,
widely spaced slices is blurry or stepped in the other planes, because the data simply is not there.
And geometry — the slices must be parallel, regularly spaced and share a frame of reference;
irregular spacing, non-parallel slices or a tilted gantry distort the reconstruction unless the
viewer corrects for it.
A reconstructed plane is a computed image, not an acquired one. Measurements taken on it are only as
trustworthy as the geometry underneath.
The standardized uptake value (SUV) turns the raw activity concentration a PET scanner measures
into a number that can be compared between patients and between examinations. It divides the
measured concentration by the injected dose and normalizes for body size — by weight for
SUVbw, the most common form, and by lean body mass or body surface area for the variants.
A region of interest usually reports several figures: SUVmax, the single hottest voxel, robust
to how the region was drawn but sensitive to noise; SUVmean, the average over the region,
steadier but dependent on where the boundary was placed; and SUVpeak as a compromise.
SUV is only as good as the data behind it. It depends on the injected activity, the injection time
and the decay correction being recorded correctly in the DICOM attributes, on the uptake interval
between injection and acquisition, and on the reconstruction the scanner applied. Values from
different scanners, protocols or uptake times are not directly comparable, which is why a change in
SUV on a follow-up study is read with the acquisition parameters in hand rather than on its own.
A DICOM image stores far more distinct values than a display can show or an eye can separate — CT,
for instance, covers thousands of Hounsfield units. Windowing chooses which part of that range
becomes visible: the window width is how wide a band of values is mapped to the available shades
of gray, and the window center (the level) is where that band sits.
Narrow the window and small differences become visible while everything outside the band turns pure
black or white; widen it and more of the range fits on screen with less contrast between nearby
values. This is why the same CT is read at one setting for lung and another for bone or soft tissue:
the pixel data does not change, only the mapping.
The values are not arbitrary. A study often carries VOI LUT information — either a window
width and center chosen at acquisition, or a full lookup table — and viewers add presets per
modality so a reader can jump between the settings that matter for a body region.
Because windowing is display only, it never alters the stored pixels; what it does alter is what a
screenshot or an exported picture will show.