Digestive Disease Interventions 2022; 06(01): 003-012
DOI: 10.1055/s-0041-1741520
Review Article

State of the Art: Contrast Enhanced 4D Ultrasound to Monitor or Assess Locoregional Therapies

1   Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania
,
Susan Shamimi-Noori
2   Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania
,
Colette M. Shaw
1   Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania
,
John R. Eisenbrey
1   Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania
› Author Affiliations
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Abstract

Locoregional therapies (LRTs) are an essential management tool in the treatment of primary liver cancers or metastatic liver disease. LRTs include curative and palliative modalities. Monitoring treatment response of LRTs is crucial for maximizing benefit and improving clinical outcomes. Clinical use of contrast-enhanced ultrasound (CEUS) was introduced more than two decades ago. Its portability, cost effectiveness, lack of contraindications and safety make it an ideal tool for treatment monitoring in numerous situations. Two-dimensional dynamic CEUS has been proved to be equivalent to the current imaging standard in the guidance of LRTs, assessment of their adequacy, and detection of early tumor recurrence. Recent technical advances in ultrasound transducers and image processing have made 3D CEUS scanning widely available on most commercial ultrasound systems. 3D scanning offers a broad multiplanar view of anatomic structures, overcoming many limitations of two-dimensional scanning. Furthermore, many ultrasound systems provide real-time dynamic 3D CEUS, also known as 4D CEUS. Volumetric CEUS has shown to perform better than 2D CEUS in the assessment and monitoring of some LRTs. CEUS presents a valid alternative to the current imaging standards with reduced cost and decreased risk of complications. Future efforts will be directed toward refining the utility of 4D CEUS through approaches such as multi-parametric quantitative analysis and machine learning algorithms.



Publication History

Received: 25 April 2021

Accepted: 09 November 2021

Article published online:
11 January 2022

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