MIMIC-IV-Echo: Echocardiogram Matched Subset

@article{PhysioNet-mimic-iv-echo-1.0.1,
  author = {Gow, Brian and Pollard, Tom and Greenbaum, Nathaniel and Moody, Benjamin and Han, Ahram and Waks, Jonathan W and Johnson, Alistair and Herbst, Elizabeth and Eslami, Parastou and Chaudhari, Ashish and Carbonati, Tanner and Berkowitz, Seth and Mark, Roger and Horng, Steven},
  title = {{MIMIC-IV-Echo: Echocardiogram Matched Subset}},
  journal = {{PhysioNet}},
  year = {2026},
  month = aug,
  note = {Version 1.0.1},
  doi = {10.13026/307c-mr50},
  url = {https://doi.org/10.13026/307c-mr50}
}

Abstract

The MIMIC-IV-Echo module contains structured echocardiographic measurements and DICOM files from echocardiography exams.

Structured measurements are available from 206,488 echocardiogram studies (including 179,928 transthoracic, 16,389 stress, and 10,171 transesophageal echocardiograms). These are from 91,372 unique patients who are in the MIMIC-IV Clinical Database and who had echocardiograms conducted between 2008–2022. The dataset encompasses a broad range of cardiologist-recorded echocardiographic measurements, including chamber sizes and volumes, systolic and diastolic function, valvular morphology and function, aortic root and great vessel dimensions, and Doppler-derived hemodynamics. Clinical interpretations and additional findings are contained in cardiologist free-text reports, which will be made available under the MIMIC-IV-Note module.

The DICOM dataset contains 524,137 files across 7,228 studies from 4,572 distinct patients. This subset contains transthoracic echocardiograms for patients who appear in the MIMIC-IV Clinical Database and were admitted between 2017–2019. A given study consists of numerous sequences of images, with each sequence representing a particular view of the patient's heart. 

Records in MIMIC-IV-Echo are matched to the MIMIC-IV Clinical Database, making it possible to link to information across the MIMIC-IV modules. We have also provided information for linking the DICOM files to the structured measurements and notes where available. This linkage supports multimodal research combining echocardiographic imaging and quantitative measurements with longitudinal clinical data.

Background

An echocardiogram uses high-frequency sound waves (ultrasound) to take pictures of the heart [1], revealing information about the heart's structure and how it is functioning. Echocardiography is used to diagnose, monitor, and assess treatment results in patients who have or are suspected to have heart problems. 

An echocardiogram study typically contains multiple views and sometimes uses multiple ultrasound techniques. A change in the position and angle of the ultrasound probe relative to the heart produces a different view [2]. Different views reveal information about different areas of the heart. All views of the DICOM dataset provided here are taken with the probe at the patient's chest (i.e., transthoracic echocardiography, or TTE). However, the structured measurement table also includes data from transesophageal echocardiography (TEE) and stress echocardiography. TEE provides high-resolution images of posterior cardiac structures via an esophageal probe, while stress echocardiography evaluates myocardial contractile response to exercise or pharmacologic stress. Common types of echocardiography include 2-D, Doppler, and 3-D [2]. In a 2-D echocardiogram, real-time cross-sectional images of the heart are produced. Doppler echocardiography is an extension of 2-D echocardiography with information on blood flow velocities and directions. 3-D echocardiography produces three-dimensional images of the heart. 

While echocardiograms are a valuable tool for the management of heart problems, they typically only comprise a small part of understanding the overall condition of a patient at the hospital. Echocardiograms are most informative when combined with a broader set of data such as patient demographics, diagnoses, medications, laboratory tests, and electrocardiograms. This broader set of information is shared as part of the MIMIC-IV Clinical Database [3].

Methods

Echocardiograms and reports containing structured measurements are collected at the Beth Israel Deaconess Medical Center (BIDMC). Three types of datasets are associated with echocardiograms – structured measurement reports from the echocardiography machine, cardiologist reports, and echocardiogram DICOM files. The structured measurements and DICOM files are presented here. 

We provide unique identifiers, such as subject_id, that allow studies to be connected to other information in the MIMIC-IV Clinical Database. All of the information is deidentified to satisfy the US Health Insurance Portability and Accountability Act of 1996 (HIPAA) Safe Harbor requirements.

Electronic Health Record

When the echocardiograms are recorded, the machine is populated with the patient's demographic details and their medical record number (MRN). The MRN was used to match records to the corresponding patient in the MIMIC-IV Clinical Database. Dates were shifted to obscure the actual date, but relative date shifts are retained for a given patient. 

Timestamps for events in the MIMIC-IV Clinical Database, such as drug administration, are aligned with the timestamps in MIMIC-IV-Echo. However, some of the echocardiograms provided here were collected outside of Emergency Department (ED) or Intensive Care Unit (ICU) visits at the hospital. Since the MIMIC-IV Clinical Database is composed solely of ED and ICU data, the echocardiogram timestamp can occur before or after a visit in the MIMIC-IV Clinical Database.

Structured Echocardiographic Measurements

During echocardiography, images are acquired and analyzed through a combination of clinician input and machine algorithms. The level of automation varies by measurement type: some require direct clinician-performed measurements (e.g., wall thickness), while others involve clinician-guided tracing with algorithmic calculation (e.g., left ventricular ejection fraction (LVEF) from traced ventricular borders). All measurements are reviewed and, if necessary, manually corrected by the clinician before being finalized. We refer to these algorithm-assisted, clinician-verified quantitative parameters as echocardiographic structured measurements.

All echocardiographic structured measurement records available in the BIDMC echocardiography systems between 2008 and 2022 were extracted. We filtered echocardiography records to those belonging to patients present in the MIMIC-IV Clinical Database by matching their MRNs. The matched MRNs were then converted to the deidentified subject_id using the same deterministic MRN-to-subject identifier mapping applied across all MIMIC-IV modules. No additional filters were applied based on location or encounter type, so the dataset includes echocardiograms performed in the ICU and ED as well as studies obtained outside these settings. In rare cases (~0.01%), duplicate structured measurement records existed for a single echocardiography study. These were consolidated by keeping the record containing the most complete set of measurements.

After linkage, the structured measurement reports underwent standardized deidentification. All identifiers and timestamps were shifted or replaced according to the MIMIC-IV deidentification scheme. In this process, the original measurement timestamp was shifted to produce the deidentified measurement_datetime, and each study was assigned a unique measurement_id. Measurements containing residual PHI were removed or modified, and the final deidentified reports were validated using a rule-based procedure to ensure that no PHI remained.

Echocardiogram DICOM Files

Each echocardiogram consists of a sequence of images representing specific views of the heart, along with metadata. The echocardiogram DICOM files originate from studies collected between 2017–2019 on General Electric Vivid E90, Vivid E95, and Vivid S7 machines. Storage and processing constraints limited extraction of the full eligible population, so only a subset of studies from this period is included. To avoid introducing clinical selection bias, patients in MIMIC-IV were evaluated in a randomized order, and all eligible echocardiograms for each selected patient were retained to preserve longitudinal follow-up. The released subset therefore reflects these technical constraints rather than study-level clinical criteria. Additional DICOM studies may be included in future updates.

Text embedded in the images was identified using Optical Character Recognition. PHI was identified in 69 DICOM files across 6 patients, and in each case the entire study was omitted from the dataset.

We also removed all PHI from the DICOM metadata:

• IDs: Source identifiers used by the hospital were shifted to a random value. In particular, mrn was translated to subject_id, consistent with MIMIC-IV. A unique study_id was generated for each recording.
• UIDs: Unique identifiers (UIDs) provide the capability to identify a wide variety of items, typically providing a guarantee of uniqueness across countries, sites, vendors, equipment, and formats. Some UIDs, such as the Study Instance UID, contain sensitive information. In such cases, we deterministically shifted the UID by using a UID root assigned to our laboratory (1.2.840.113554.6.1.) followed by a three-digit value specific to the variable and a randomized value.
• Dates: Raw dates were either shifted (consistent with all date shifts in MIMIC-IV for a given subject) or removed (in the case of Patient Birth Date).
• Names: Person names were either deterministically replaced with a randomized string or removed (in the case of Referring Physician Name).
• Private tags: Information recorded in private tags was removed.

Cardiologist Reports

Cardiologist interpretations of the echocardiograms will be made available at a later date in the MIMIC-IV Note module [4]. These reports were deidentified using a hybrid rule-based and machine learning approach [5-8], similar to that used for other MIMIC reports. Each instance of PHI was replaced by three underscores.

Data Description

Structured Echocardiographic Measurements

Structured echocardiographic measurements from 206,488 studies — including 179,928 transthoracic, 16,389 stress, and 10,171 transesophageal echocardiograms — are provided in the structured_measurement.csv file. These data are derived from 91,372 unique patients in the MIMIC-IV Clinical Database who underwent echocardiography during the study period. A subset (769 studies from 437 patients) was reserved as a hidden test set for future use. This hidden subset was defined at the patient level following the MIMIC-IV train/test split, in which the training set corresponds to the publicly released MIMIC-IV data. Accordingly, all structured measurements for test-set patients were withheld, while all measurements for training-set (publicly available) patients were retained.

The structured measurement file includes the following variables:

• subject_id: An identifier for the subject that is consistent across the MIMIC-IV database.
• measurement_id: An identifier linking each record to the corresponding echocardiographic study.
• measurement_datetime: The deidentified date and time when the diagnostic echocardiogram was performed.
• test_type: Specifies the modality of the echocardiographic study, categorized as transthoracic echocardiogram (TTE), transesophageal echocardiogram (TEE), or stress echocardiogram.
• measurement: An identifier for the specific echocardiographic measurement.
• measurement_description: A descriptive label for the measurement.
• result: The recorded measurement value.
• unit: The unit of measurement for the result.

The file is structured in long format, with each row representing a single measurement from a study. Approximately 180–230 unique measurement variables are recorded per study, depending on the echocardiographic test type. Notably, during the study period, a transition in the echocardiography laboratory system introduced variability in the naming and availability of measurement variables. Even within the same modality (e.g., TTE), different variable sets may be present depending on the source system — for example, LVEF-related variables included lvef and lvef_upper in one system, while the other captured biplane_lvef and lvef_3d. Users should note that the absence of a variable may therefore reflect the source system rather than a clinical missing value. Separately, where measurement values were recorded but empty, these were retained as-is to preserve the original data structure.

Echocardiogram DICOM Files

A total of 524,137 echocardiogram DICOM files across 7,228 transthoracic echocardiogram studies from 4,572 patients are included in the MIMIC-IV-Echo module. A total of 358 echocardiogram studies from 232 patients were withheld at the patient level for use as a hidden test set. Patients in this module are linked to the MIMIC-IV Clinical Database on subject_id.

In addition, the source data do not include an identifier that directly links a structured measurement to a given DICOM. To help address this issue, we have provided a derived table (detailed below) that indicates which reports occur within two days of a given study. Approximately 99% of the echocardiogram DICOM files are temporally linked to a structured measurement record in the derived table. 

The echocardiograms are stored as DICOM (.dcm) files. DICOM or Digital Imaging and Communications in Medicine defines standards for the storage of medical images and related information [9]. Each DICOM file contains a sequence of images for a particular view of the heart.

Echocardiograms are grouped into subdirectories based on subject_id. Each DICOM study path follows the pattern files/pNN/pXXXXXXXX/sZZZZZZZZ/ZZZZZZZZ_VVVV, where:

• NN is the first two characters of the subject_id,
• XXXXXXXX is the subject_id,
• ZZZZZZZZ is the study_id, and
• VVVV is the view number.

An example of the file structure is as follows:

files
├── p10
|   └── p10690270
|       ├── s95240362
|       │   ├── 95240362_0004.dcm
|       │   . 
|       │   └── 95240362_0093.dcm
|       └── s90045402
|           ├── 90045402_0001.dcm
|           .
|           └── 90045402_0088.dcm
└── p19
    └── p19425623
        └── s90267113
            ├── 90267113_0001.dcm
            .
            └── 90267113_0088.dcm

The example shows a subject under the p10 directory and another under the p19 directory. Subject p10690270 has two studies. The first study, s95240362, has 90 DICOM files with view numbers between 4 and 93. The second study, s90045402, has 83 DICOM files with view numbers between 1 and 88. The subject under the p19 directory, p19425623, has only one study s90267113. We find 83 DICOM files under this study with view numbers between 1 and 88.

A number of open-source programs are available for viewing DICOMs such as Miele-LXIV(Mac), MicroDicom(Windows), and ImageMagick(Linux, Mac, Windows). We also provide example code for loading DICOMs into Python in the Usage Notes section below.

Summary Tables

The echo-record-list.csv provides the path to each DICOM file (dicom_filepath) along with the acquisition_datetime of the DICOM file (date and time that the acquisition started for a given view), the associated study_id and the subject's MIMIC-IV subject_id.

The echo-study-list.csv provides a temporal link between the DICOM study_id and its associated structured measurement, as well as the cardiologist's report, where available. When structured measurements are available within two days of the study_datetime of the DICOM study, the corresponding measurement_id and measurement_datetime are provided, enabling temporal linkage to quantitative parameters in the structured_measurement.csv file. Similarly, when a cardiologist's report is available within two days of the DICOM study, the corresponding note_id, note_seq, and note_charttime are included, allowing temporal linkage to the narrative text in the MIMIC-IV Note module. 

These summary tables are also provided on BigQuery [10].

Usage Notes

This module enables multimodal research linking echocardiographic imaging and quantitative measurements to longitudinal clinical data in the MIMIC-IV Clinical Database.

Loading a DICOM in Python

The following code demonstrates how to use the Pydicom library to load a DICOM into Python, read its metadata and plot an image. 

import matplotlib.pyplot as plt
import pydicom
from pydicom.pixel_data_handlers import convert_color_space

file_path = '/files/p10/p10690270/s95240362/95240362_0004.dcm'

# read in the DICOM with the pydicom module
dicom_data = pydicom.dcmread(file_path)

# print the DICOM metadata
for element in dicom_data:
    print(element)

# note the value for Photometric Interpretation that was printed, it should show:
# (0028, 0004) Photometric Interpretation          CS: 'YBR_FULL_422'
# we need to convert from YBR_FULL_422 to RGB to display the image properly
images_rgb = convert_color_space(dicom_data.pixel_array, "YBR_FULL_422", "RGB", per_frame=True)
# plot the first frame/image
plt.imshow(images_rgb[0])
plt.show()

Linking to MIMIC-IV

In the example below, we show how a patient in MIMIC-IV-Echo with subject_id = 10690270 can be linked to admission information in the MIMIC-IV Clinical Database. Running the following SQL command in Google BigQuery returns the dates of available echocardiograms for the patient:

SELECT DISTINCT study_id, acquisition_datetime
FROM `physionet-data.mimiciv_echo.echo_record_list`
WHERE subject_id = 10690270

Executing the following queries returns the admission and discharge times for the patient in the MIMIC-IV Clinical Database:

SELECT admittime, dischtime
FROM `physionet-data.mimiciv_3_1_hosp.admissions`
WHERE subject_id = 10690270

SELECT intime, outtime
FROM `physionet-data.mimiciv_ed.edstays`
WHERE subject_id = 10690270

The results show two hospital admissions and one emergency department stay for the patient. The echocardiogram with study_id = 95240362 is associated with the hospital admission in 2179, while the 2176 visit has no associated echocardiogram. The echocardiogram with study_id = 90045402 has no corresponding visit in the MIMIC-IV Clinical Database, illustrating that echocardiogram timestamps may occur before, after, or independently of a recorded ED or ICU encounter.

Release Notes

This release corrects issues identified during post-release validation of the MIMIC-IV-Echo DICOM dataset. Fifteen affected studies have been removed due to an identifier collision issue, and nine DICOM files that were inadvertently included outside the intended release scope have also been removed. No schema changes were made in this update.

Ethics

The project was approved by the Institutional Review Boards of Beth Israel Deaconess Medical Center (Boston, MA) and the Massachusetts Institute of Technology (Cambridge, MA). Requirement for individual patient consent was waived because the project did not impact clinical care and all protected health information was deidentified.

Acknowledgements

SH, RM, BG, and TP are funded by the Massachusetts Life Sciences Center, Nov. 30, 2020. NG is supported by National Institutes of Health National Library of Medicine Biomedical Informatics and Data Science Research Training Program under grant number T15LM007092-30. BG, TP, AJ, BM, CF, DM, and RM are supported by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under NIH grant number R01EB030362.

Conflicts of Interest

The authors have no conflicts of interest to declare.

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