Log In Sign Up

Tomographic reconstruction to detect evolving structures

by   Preeti Gopal, et al.

The need for tomographic reconstruction from sparse measurements arises when the measurement process is potentially harmful, needs to be rapid, or is uneconomical. In such cases, information from previous longitudinal scans of the same object helps to reconstruct the current object while requiring significantly fewer updating measurements. Our work is based on longitudinal data acquisition scenarios where we wish to study new changes that evolve within an object over time, such as in repeated scanning for disease monitoring, or in tomography-guided surgical procedures. While this is easily feasible when measurements are acquired from a large number of projection views, it is challenging when the number of views is limited. If the goal is to track the changes while simultaneously reducing sub-sampling artefacts, we propose (1) acquiring measurements from a small number of views and using a global unweighted prior-based reconstruction. If the goal is to observe details of new changes, we propose (2) acquiring measurements from a moderate number of views and using a more involved reconstruction routine. We show that in the latter case, a weighted technique is necessary in order to prevent the prior from adversely affecting the reconstruction of new structures that are absent in any of the earlier scans. The reconstruction of new regions is safeguarded from the bias of the prior by computing regional weights that moderate the local influence of the priors. We are thus able to effectively reconstruct both the old and the new structures in the test. In addition to testing on simulated data, we have validated the efficacy of our method on real tomographic data. The results demonstrate the use of both unweighted and weighted priors in different scenarios.


page 3

page 7

page 21

page 22

page 23

page 24

page 31

page 33


Learning from past scans: Tomographic reconstruction to detect new structures

The need for tomographic reconstruction from sparse measurements arises ...

Compressed sensing for longitudinal MRI: An adaptive-weighted approach

Purpose: Repeated brain MRI scans are performed in many clinical scenari...

Low radiation tomographic reconstruction with and without template information

Low-dose tomography is highly preferred in medical procedures for its re...

Active Object Reconstruction Using a Guided View Planner

Inspired by the recent advance of image-based object reconstruction usin...

Common Pets in 3D: Dynamic New-View Synthesis of Real-Life Deformable Categories

Obtaining photorealistic reconstructions of objects from sparse views is...

Tomographic Reconstruction using Global Statistical Prior

Recent research in tomographic reconstruction is motivated by the need t...

Bayesian Experimental Design for Computed Tomography with the Linearised Deep Image Prior

We investigate adaptive design based on a single sparse pilot scan for g...