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| Name | Class |
|---|---|
| Duke University | OTHER |
| The Hong Kong Polytechnic University | OTHER |
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The purpose of this study is to develop new ways to make medical images of the lungs and liver of adults using a technique called four-dimensional magnetic resonance imaging (4D-MRI). This technique produces three-dimensional movies of the inside of the chest and abdomen while the patient is breathing. (The fourth dimension is time!)
This new way of medical imaging is being developed to help cancer patients undergoing radiation therapy. Radiation therapy is used to treat cancerous tumors. For radiation therapy to be effective, the precise size, shape, and location of the tumor within the body must be known. A particular difficulty for radiation treatment of lung and liver cancer is that the tumor moves during treatment because the patient is breathing. Therefore, tumor motion must also be incorporated into the treatment plan. This study aims to improve radiation treatment planning through better targeting and dose estimation based on 4D-MRI. Before this new imaging method can be used for radiation treatment planning, it must be tested in living, breathing volunteers.
Radiotherapy for cancer has been a forerunner of personalized medicine, developing individualized treatments based on patient-specific anatomical information. Despite many advances in radiotherapy over the past decade, which have effectively enhanced local or loco-regional tumor control for many patients, there remains substantial room for improvement. The challenges for radiotherapy to further widen the therapeutic window in the era of precision medicine are mainly two-fold: (a) further improve radiation dose conformity to the defined target volume, and (b) adapt novel biological strategies for personalized treatment. Four-dimensional (4D) imaging and deformable image registration (DIR) are key tools in modern radiotherapy, playing critical roles in many recent advances, including 4D radiotherapy, adaptive radiotherapy, and treatment assessment. However, current 4D imaging and DIR technologies are facing significant challenges as the requirement for precision increases.
The current standard of 4D imaging in radiotherapy is 4D-CT. However, it has two major limitations preventing it from precision radiotherapy applications: (a) low soft-tissue contrast. 4D-CT is therefore not ideal for abdominal applications; (b) motion artifacts caused by irregular breathing. 4D-CT motion artifacts have been shown to cause errors in various radiotherapy applications, including motion measurement, target volume delineation, dose calculation, DIR, and lung ventilation calculation. 4D-MRI is an emerging 4D imaging technology for radiotherapy. It has superior soft-tissue contrast to 4D-CT and is therefore superb for abdominal imaging. Despite many recent advances in 4D-MRI, current 4D-MRI implementations have inadequate image quality for precision radiotherapy application due to at least one of the following deficiencies: low temporal and/or spatial resolutions, long image acquisition time, and suboptimal contrast in the lungs. Resulting 4D-MRI images lack sufficient anatomical details for clinical applications, which can adversely affect the performance of DIR. Current DIR techniques focus on morphological similarity but not on the physiological plausibility of the deformation. Studies have shown that an increased morphological similarity of the aligned data does not always imply increased registration accuracy. Therefore, more sophisticated approaches are desirable.
The investigators will take a systematic approach to address the aforementioned limitations of 4D imaging and deformable image registration (DIR) based on the development and cross-fertilization of two major techniques: ultra-quality 4D-MRI and physiological-based hybrid DIR. There are two parts of this research, comprising three main objectives:
Part 1. Technical development in healthy subjects: The investigators will extend their existing pulse sequence strategy for ultra-quality 3D MRI to enable ultra-quality 4D-MRI. Compared to 4D-CT and current 4D-MRI techniques, the proposed ultra-quality 4D-MRI technique offers the following advantages: (a) high spatial resolution (1.5 mm isotropic) with rich image features (e.g. vessel trees) in the whole torso; (b) high temporal pseudo-resolution (>20 phases/cycle); and (c) (nearly) free of motion artifacts.
• Objective 1: Develop an MRI pulse sequence and image reconstruction pipeline that generates images meeting these three design goals.
Part 2. Evaluation of 4D-MRI in a patient study: 4D-MRI will be compared with existing DIR and 4D-CT methods. There will be two classes of comparisons, each formulated as a separate objective:
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Healthy | Healthy volunteers from the local community |
| |
| Liver cancer | Patients undergoing radiotherapy for liver cancer |
| |
| Lung cancer | Patients undergoing radiotherapy for lung cancer |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Magnetic Resonance Imaging | Diagnostic Test | Four-dimensional MRI of torso |
|
| Measure | Description | Time Frame |
|---|---|---|
| Image quality metrics in healthy volunteers | General 4D-MRI image quality will be assessed based on signal-to-noise ratio, number of distinct images per breathing cycle, total necessary imaging time, and image quality index. | single imaging session, lasting up to 2 hours |
| Image quality metrics in cancer patients | We hypothesize that our ultra-quality 4D-MRI methodology will outperform 4D-CT for motion management of radiotherapy in the lungs and the liver. We will test this hypothesis by comparing image quality based on tumor volume consistency, number of trackable landmarks, motion measurement accuracy, and image quality index. | single imaging session, lasting up to 2 hours |
| DVF errors in healthy volunteers and cancer patients | We hypothesize that our motion modeling method based on 4D-MRI will outperform current DIR algorithms for respiratory motion estimation. We will test this hypothesis by comparing our method to five existing DIR algorithms, based on the magnitude error (Em) and the angular error (Ea) of the calculated deformation vector field (DVF). | single imaging session, lasting up to 2 hours |
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Inclusion Criteria:The inclusion criteria for lung and liver cancer patients are:
The inclusion criteria for healthy volunteers are:
Exclusion Criteria:
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Up to 30 lung cancer patients, 30 liver cancer patients, and 40 healthy human volunteers will receive MRI examinations at the University of Virginia to directly measure respiratory motion in the thorax and abdomen. All subject populations will include roughly equal male and female representations. The subject populations will have minority and ethnic representation typical of the local population area that includes the University of Virginia. No specific racial or ethnic group will be excluded. All subjects must have the ability to understand the requirements of the study and must provide informed consent with a personal signature. No persons from vulnerable populations will be included in this study.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Roselove N Nunoo-Asare, MA | Contact | 4342436074 | RNN3B@VIRGINIA.EDU |
| Name | Affiliation | Role |
|---|---|---|
| G. Wilson Miller, PhD | Univsersity of Virginia | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| University of Virginia | Recruiting | Charlottesville | Virginia | 22908 | United States |
Data acquired from cancer patients using the optimized 4D-MRI protocol will be used to construct digital phantoms for use by the radiotherapy research community to improve radiotherapy planning methods.
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Starting in approximately 2023, with no anticipated end date
undecided
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| ID | Term |
|---|---|
| D008113 | Liver Neoplasms |
| D008175 | Lung Neoplasms |
| ID | Term |
|---|---|
| D004067 | Digestive System Neoplasms |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D004066 | Digestive System Diseases |
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| ID | Term |
|---|---|
| D008279 | Magnetic Resonance Imaging |
| ID | Term |
|---|---|
| D014054 | Tomography |
| D003952 | Diagnostic Imaging |
| D019937 | Diagnostic Techniques and Procedures |
| D003933 | Diagnosis |
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| D008107 |
| Liver Diseases |
| D012142 | Respiratory Tract Neoplasms |
| D013899 | Thoracic Neoplasms |
| D008171 | Lung Diseases |
| D012140 | Respiratory Tract Diseases |