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dicomtk: Comprehensive ‘DICOM’ Toolkit for Medical Imaging in R

dicomtk is a complete, modern rebuild of the R dcmtk wrapper, synchronized with OFFIS DCMTK 3.7.1 (PS 3.6-2026d) and powered by a bundled C++17 / Rcpp engine requiring zero external installation.

Installation

Install pre-compiled binaries or source directly from R-Universe:

install.packages(
  "dicomtk",
  repos = c(
    "https://lauyeehow1986-hub.r-universe.dev",
    "https://cloud.r-project.org"
  )
)

Or install the development version from GitHub:

# install.packages("remotes")
remotes::install_github("lauyeehow1986-hub/dicomtk")

Key Features

• Zero External Dependencies: Bundles the full 5,354-tag DCMTK 3.7.1 data dictionary, headers (inst/include/dcmtk/), and native C++17 DICOM engine directly inside the package.
• Tidyverse Compliant: All tabular functions (read_dicom(), dicom_tags(), dicom_find(), read_dicom_sr(), dicom_report()) return tibble objects (tbl_df) and compose naturally with |> and dplyr/tidyr.
• Full DCMTK Module Coverage:
  • Core (dcmdata): read_dicom(), read_dicom_header(), write_dicom(), dicom_tags(), dicom_modify(), dicom_tag_value(), dicom_tag_lookup(), dicom_anonymize(), dcm2xml(), dcm2json(), dcm2pdf().
  • Image Processing & Codecs (dcmimgle, dcmimage, dcmjpeg, dcmjpls): read_dicom_pixels(), read_dicom_series(), dicom_window(), dicom_compress(), dicom_decompress(), dicom_to_png(), dicom_to_nifti().
  • Visualization: plot_dicom() and ggdicom() (ggplot2 with clinical CT/MR window presets), dicom_viewer() (interactive Shiny app), and render_volume() (3-plane MPR, MIP, and interactive rgl 3D rendering).
  • PACS Networking (dcmnet, dcmqrdb, dcmwlm): dicom_connect(), dicom_echo(), dicom_find(), dicom_get(), dicom_send(), dicom_receive().
  • Structured Reporting & Specialized IODs (dcmsr, dcmrt, dcmseg, dcmpmap, dcmtract, dcmect, dcmsign): read_dicom_sr(), create_dicom_sr(), read_dicom_rt(), read_dicom_seg(), read_dicom_pmap(), read_dicom_ect(), dicom_verify_signature().
• Local & Cloud LLM Integration:
  • Native support for local Ollama (http://localhost:11434), OpenAI (gpt-4o, etc.), Anthropic Claude (claude-sonnet-4-20250514, etc.), and any OpenAI-compatible endpoint (vLLM, LM Studio, OpenRouter, Azure).
  • Automatic HIPAA Safe Harbor de-identification before sending metadata or images to remote APIs.
• 100% Backward Compatible: Preserves legacy dcmtk functions (dcmdump(), dcmhd(), wide_hdr(), burned_annot(), dcmodify(), dcm_anon(), dcm_subtract_date(), dcmj2pnm(), dcmdjpeg(), dcmdrle(), dcmcrle()).

Quick Start

library(dicomtk)
library(dplyr)

dcm_file <- system.file("extdata", "example.dcm", package = "dicomtk")

# 1. Read DICOM header as a tidy tibble
read_dicom(dcm_file) |>
  filter(group == "0028") |>
  select(tag, vr, keyword, value)

# 2. Visualize with ggplot2 and clinical CT/MR window presets
plot_dicom(dcm_file, window = "auto", palette = "gray")

# 3. Launch interactive Shiny DICOM viewer
if (interactive()) {
  dicom_viewer(dcm_file)
}

# 4. Connect to Local Ollama or OpenAI / Claude for AI-Assisted Analysis
llm <- dicom_llm("ollama", model = "llava:13b")
dicom_summarize(dcm_file, llm = llm)
dicom_describe_image(dcm_file, llm = llm)
dicom_report(dcm_file, llm = llm)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.