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Massachusetts Institute of Technology

A Power Efficient Analog Front End for Continuous Ultrasound Imaging of the Bladder

Abstract

dc:description.abstract

Continuous bladder monitoring is important for the monitoring of bedridden patients. One method to continuously monitor the bladder is to capture ultrasound images and use machine learning processing to measure the bladder volume from these images. Circuits for implementing these functions can be integrated onto a wearable device, and each of these functions can be integrated onto a single chip. In this thesis, we analyze ultrasound imaging in the context of the bladder to come up with algorithms and hardware to perform continuous bladder monitoring. We first assemble a discrete setup which can form ultrasound images. Using this setup, we describe a new algorithm for generating an ultrasound image by to power gate the hardware during the imaging process to save additional power when capturing the image. We combine these concepts into a single Analog Front End (AFE) chip that can capture images in a power efficient manner.

Degree

thesis:*
Name thesis:degree_name
Master
Department dc:contributor.department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2024

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Manohara, Mohith
Advisor dc:contributor.advisor
  • Chandrakasan, Anantha P.

Rights

dc:rights
Statement dc:rights
  • In Copyright - Educational Use Permitted
  • Copyright retained by author(s)

Identifiers

dc:identifier.*
Handle dc:identifier.uri
https://hdl.handle.net/1721.1/158517
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/158517

Chain of custody

source
Harvested from
MIT
Base URL
dspace.mit.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
related terms
citation

Manohara, Mohith. A Power Efficient Analog Front End for Continuous Ultrasound Imaging of the Bladder. Massachusetts Institute of Technology, 2024. https://hdl.handle.net/1721.1/158517