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MR Correlated Spectroscopic Imaging for Diagnosing Breast Cancer

Breast Carcinoma Clinical Trial

Official title:
Non-Uniformly Sampled MR Correlated Spectroscopic Imaging in Breast Cancer and Nonlinear Reconstruction

Clinical Trial Summary

This trial studies how well an imaging technique called magnetic resonance (MR) spectroscopic imaging works in identifying breast cancer in women with benign or suspicious areas in the breast. Magnetic resonance imaging (MRI) is a diagnostic tool used to investigate the location of tumors in different organs. Since radiological pictures do not have sufficient information for tumor grades, invasive procedure such as biopsy is performed on patients with breast cancers for diagnosis. Breast tissue contains water, fat, and chemicals known as metabolites. MR spectroscopic imaging may help to characterize the various breast metabolite steady state levels and identify the differences between necrosis and tumor recurrence, which is difficult using radiological procedures such as MRI.

Clinical Trial Description

PRIMARY OBJECTIVES: I. Non-uniform undersampling schemes (NUS) will be combined with 5-dimensional (5D) echo-planar imaging based correlated spectroscopic imaging (EP-COSI) sequence. II. Group sparsity (GS)-based compressed-sensing (CS) reconstruction schemes will be developed for accelerated acquisition and optimized to reconstruct the NUS EP-COSI data with better reliability. III. Alterations in metabolite and lipid levels will be correlated with apparent diffusion coefficient (ADC) changes in breast cancer patients compared to healthy women which will improve the diagnostic accuracy. OUTLINE: Participants undergo diffusion weighted imaging (DWI)-MRI over 15 minutes and MR spectroscopic imaging over 45 minutes. After completion of study, participants are followed up for 6 months. ;

Study Design

Related Conditions & MeSH terms

Clinical Trial Details
NCT number NCT06090630
Study type Interventional
Source Jonsson Comprehensive Cancer Center
Contact
Status Completed
Phase Early Phase 1
Start date July 31, 2017
Completion date February 3, 2023
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