Reviews

Alistair Young

Sunday 6 July 2008

This manuscript proposes a method for the analysis of heart wall motion using an electromechanical model. The goal is to recover the motion of the heart, including the recovery of through-plane motion and rotational motion, which are poorly constrained by the image data, due to the aperture problem. A deformable model framework is presented which uses the model as an “internal energy” providing physiologically realistic motions constrained by the given data. This framework is shown to be equivalent to a data assimilation approach derived from system identification theory (reference 7). This is an interesting paper, well written on the whole, and clearly presented.

General comments:

The application of state estimation methods to cardiac motion recovery is not new and the authors should reveiw the work by Shi et al.
1. The application is limited to motion recovery. Another application could be the recovery of biophysical information such as the model parameters, but this is not explored here. Model parameters are fixed and not optimized to the motion (unlike reference 7 in which physiological parameters of the model itself are also estimated). Rather, the method utilizes a model in a similar way to previous methods which incorporate a priori knowledge as a smoothing term (model energy) in the objective function. If so, the motivation for a physiological electromechanical energy term should be made clearer in the Introduction, as opposed to simpler terms used in the past. It should also be made clearer in the Methods that the model parameters are not optimized.
2. In the absence of data, the model will deform in a manner totally prescribed by the model energy. With data, there is a tradeoff between the model energy and the data term leading to a solution outside the space of physiological solutions. It should be made clear in the Discussion that this formulation is not physiological, for example boundary forces are not applied in this fashion in the real heart.
3. It should be noted in the conclusions that the assumptions of the model are not realistic, given that we know the heart is a nonlinear material undergoing large strains. This does not detract from the clinical utility of the method, so long as the simplistic nature of the assumptions are noted.

 

Specific Comments:

 

The explanation of the acquisition and problems with the real data (RV segmentation etc) should occur in the Methods section (around 3.2), rather than in the Results section 5.2. This would explain the RV-LV volume mismatch near Fig 3, where it is demonstrated. Give details of what images were acquired, pulse sequence parameters etc.
1. There are a few mistakes in the English: eg. in the Introduction “the heart electromechanical activity” should be “the heart’s electromechanical activity”, “has received a growing attention” should be “has received growing attention”, “personalised electromechanical model of the heart” should be “personalised electromechanical models of the heart”, “In such framework” should be “In such a framework”, “mesh is fit on the” should be “mesh is fit to the”, “assimilation techniques consisting in adjusting” should be “assimilation techniques consisting of adjusting”, “since they involves full covariance” should be “since they involve full covariance”.
2. Section 3.2: What passive material parameters are taken from what literature?

   

 

Heye Zhang

Sunday 6 July 2008

Originality 3
Methodological originality 1
Biologic originality 3
Completeness of discussion 4
Appropriate references 4
Organisation 4
Clarity 4
Is the technical treatment plausible and free from technical errors?
Have you checked the equations Yes
Are you aware of prior publication or presentation of this work No
Is the paper too long No
Recommendation:
(A) Accept
(B) Accept subject to minor revisions
(C) Accept with major revisions
(D) Reject
My recommendation: Accept with major revisions
Should this paper be presented as poster or as podium presentation (this recommendation does not reflect
upon the relative quality of the paper)?
poster
Comments to the manuscript:
I have seen very similar work in term of electromechanical modelling and data assimilation from authors
and Dr. Pengcheng Shi's group before. The main issue that I concern is difference between this paper and
Dr. Pengcheng Shi previuos work.
Authors claims that their data assimilation, which actually is the state space motion estimation, is inspired
by reference [7]. But Dr. Pengcheng Shi's group have published a series of papers of the state space motion
estimation framework:
Stochastic finite element framework for simultaneous estimation of cardiac kinematic functions and
material parameters in Medical Image Analysis 2003.
Huafeng Liu, Pengcheng Shi: Simultaneous Estimation of Left Ventricular Motion and Material Properties
with Maximum a Posteriori Strategy. CVPR 2003.
Ken C.L. Wong, Pengcheng Shi: Finite Deformation Guided Nonlinear Filtering for Multiframe Cardiac
Motion Analysis. MICCAI 2004
Authors of reference [7] may not know medical image community so well, but author of this paper should
realize the simality of reference [7] and Dr. Pengcheng Shi's work. It is hard not to ask the simality between
reference [7] and [12]. Obviously I can see Dr. Pengcheng Shi has introduced the state space into medical
image community first. It is not hard to see that data assimilation in this paper is very similar to Dr.
Pengcheng Shi's the state space motion estimation framework, but authors only mention one of their works.
So authors should clearify their originality in this paper with a comparison of this work and Dr. Pengcheng
Shi's works, otherwise I recommend a direct reject. Furthermore, in the part of data assimilation, authors
failed to quantitively describe their data assimilation method, such as what is the noise rate.
The conclusion in reference in [17] is correct, but authors should notice that their groups have developed
several efforts to decrease the size of covariance matrices.
More details should be added. Such as equation (1). what is the value of APD, sigma_c and sigma_0.
What is HP? How to determine Td and Tr from authors' electrical simulation? Though authors describe
what is Td and Tr qualitively, readers still can not determine Td and Tr quantitively. These inefficiencies
dmage the quality of paper a lot. Authors should definitely revise them.