Left Ventricle T1 Mapping Quantification

Purpose

Report quantitative left ventricle myocardial T1 relaxation time, before and after IV contrast. Use these values to compute extracellular volume.

Tag(s)

 

Panel

 Cardiac

TOUCH-AI ID

 TAI-04180018

Originator

Peter Filev

Panel Chair

Carlo De Cecco

Panel Reviewers

Cardiac Panel

License

Creative Commons 4.0

Status Public Commenting

Clinical Implementation


Value Proposition

Cardiac magnetic resonance has become a critical, noninvasive diagnostic tool for evaluating cardiac morphology, tissue, and function. The technique of T1 mapping has been shown to be effective in providing a quantitative assessment of the myocardial tissue and, in particular, offers increased sensitivity for detecting diffuse or infiltrative myocardial disease processes. Extracellular volume (ECV) has been shown to be a marker for myocardial tissue remodeling, which is quantifiable and can be computed using T1 mapping measurements before and after contrast. An algorithm can be developed that automatically reports the numerical T1 values obtained from the T1 map sequences, alerts the user if these measurements are outside of normal range, and also computes the ECV. The algorithm will compute an ECV map with a segmented left ventricle (LV) myocardium that is color scaled based on signal intensity.

Narrative(s)

A 45-year-old male with no past medical history is admitted from the ER with new onset heart failure. Acute myocarditis is suspected, and a cardiac MRI is obtained as part of the workup to confirm this diagnosis. The proposed algorithm demonstrates diffusely elevated precontrast T1 map values as well as diffusely elevated ECV values. Using these findings along with other findings (nonischemic late gadolinium enhancement pattern and decreased LV function), the radiologist/cardiologist is able to confirm a diagnosis of acute myocarditis.

Workflow Description

Cardiac MRI protocol with T1 mapping pre- and postcontrast is performed. A precontrast native T1 map image is generated as well as a postcontrast T1 map image. The radiologist is prompted to provide the patient’s hematocrit level. An ECV map image is generated. The LV myocardium is segmented in these images, which are color labeled based on their signal intensities. The images are in the short axis plane at the mid-heart level. For each of these images (T1 maps and ECV map), the algorithm draws 4 regions of interest (ROIs) of approximately 25 mm2, which are in the anterior wall, septum, inferior wall, and lateral wall. The average values and standard deviations of signal intensities are displayed, as well as the normal (expected) ranges (which may vary on scanner and magnetic field strength).

The algorithm prompts the clinician on what diagnosis is clinically suspected. The algorithm alerts the physician if the computed values (T1 map values and ECV values) as measured in the ROI meet the appropriate threshold for that diagnosis.

Considerations for Dataset Development


Procedures(s): Cardiac MRI

View(s): Cardiac MRI with T1 mapping protocol

 

Sex at Birth: {Male, Female}

 

Hematocrit Lab Value: Varied 

Technical Specifications


Inputs

DICOM Study

Procedure

Cardiac MRI

Views

Cardiac MRI with T1 mapping protocol

Data Type

DICOM

Modality

MR

Body Region

Chest

Anatomic Focus

Heart

Primary Outputs

Native T1

RadElement ID

RDE215

Definition

Native average T1 tissue as (average value from 4 ROIs)

Data Type

Numeric

Value Set

 

Units

ms

 

Extracellular Volume

RadElement ID

RDE216

Definition

Average ECV (average signal value from 4 ROIs)

Data Type

Numeric

Value Set

[0,100]

Units

Percentage

Secondary Outputs

Presence of Suspected Disease

RadElement ID

RDE217

Definition

Alert if calculated native T1 map value and ECV meet threshold for suspected cardiovascular disease

Data Type

Categorical

Value Set

0-Unknown

1-Suspected disease

2-No disease

Units

N/A

Public Commenting


Use cases are meant to be a primary vehicle for distributing clinical information to the developer community. They pinpoint precise scenarios within radiology workflows where potential automation could add noticeable value and establish standards for interpreting and passing corresponding common data elements. Implementing effective standards requires the perspective from all stakeholders. So to that end, we encourage your feedback on use cases.

To submit comments, please email DSIUseCases@acr.org with the use case title(s) and relevant comments by January 1, 2019. If more convenient, you may also download this use case and comment directly on the PDF. Just attach the PDF copy on the email.