# Virtual growing child 5-dimensional functional models for treating respiratory anomalies

> **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2022 · $759,006

## Abstract

Thoracic Insufficiency Syndrome (TIS) is a group of serious disorders of the pediatric thorax resulting in an
inability of the thorax to support respiration or lung growth. TIS is associated with at least 28 pediatric
syndromes, with an estimated health care cost per patient that can easily exceed a million dollars. In TIS,
three-dimensional deformity of the thoracic components anatomically and functionally reduces the volume
available for ventilation. Pediatric specialists dealing with TIS currently face several serious challenges: (a) The
complex interplay among dynamic and growing thoracic structures and its influence on thoracic function and
growth are not understood at present. (b) The prime outcome measure for the corrective procedures has
remained the radiographic Cobb angle of the spine, a 60-year old metric with poor correlation with lung
dynamic function and limited true health assessment value. (c) A normative imaging database with functional
metrics describing dynamics and growth of the thoracic structures of the normal pediatric population does not
exist. Due to these hurdles, innovations in growth-modulating surgical techniques are difficult to achieve.
Supported by extensive preliminary results based on dynamic MRI (dMRI) of patients and normal subjects, the
overarching goal of this proposal is to develop novel dynamic functional metrics for TIS by establishing a
normative database of dMRI images and anatomic and functional models and metrics, and to translate these to
develop markers of TIS and of its corrective-surgery outcomes. The project has three aims. Aim 1: To develop
a new methodology called The Virtual Growing Child (VGC) consisting of 4 key components: a) To build a
normative database of dMRI images prospectively gathered from 200 normal children divided into 10 groups.
b) To build population anatomic models involving key thoraco-abdominal objects following an established
automatic anatomy recognition (AAR) technology and deep learning (DL) techniques. c) To develop and
validate joint AAR-DL algorithms to segment these objects in dMRI images of TIS patients. d) To build a
normative database of measurements derived from dMRI images describing normal thoracic architecture,
dynamic function, and growth. The database will also include a full battery of Pulmonary Function Testing data
and anthropometric measurements. Aim 2: To test retrospectively the utility of the VGC ensemble in deriving
markers of TIS and its surgical treatment effects on a cohort of 100 TIS patients. Aim 3: To retrospectively test
the utility of the VGC approach for planning surgery in 30 TIS patients by comparing VGC-guided surgical
planning to the current planning method. The post-operative key dMRI parameters of patients whose surgical
plan would have changed due to VGC data will be compared to those of patients whose plan did not change.
Expected outcomes: (i) A unique registry of thoracic dMRI of 200 normal pediatric subjects, segmented
objects, and...

## Key facts

- **NIH application ID:** 10308447
- **Project number:** 5R01HL150147-03
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Drew Torigian
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $759,006
- **Award type:** 5
- **Project period:** 2020-02-01 → 2023-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10308447

## Citation

> US National Institutes of Health, RePORTER application 10308447, Virtual growing child 5-dimensional functional models for treating respiratory anomalies (5R01HL150147-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10308447. Licensed CC0.

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