Abstract
In this paper, we propose a diffeomorphic multi-modality image registration model based on the Rényi’s statistical dependence measure by taking mesh folding elimination into consideration. The existence of solution for the proposed model is proved, and a multi-grid algorithm is presented for multi-modality image registration. And the convergence of minimizing sequence is proved. Moreover, numerical tests are performed to demonstrate that the proposed algorithm works for both mono-modality image registration and multi-modality image registration without mesh folding.
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Acknowledgements
This work was supported by National Key Research and Development Program of China (no. 2020YFA0714200) and National Natural Science Foundation of China (nos. 11931012, 11901443 and 12171379).
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This work was supported by National Key Research and Development Program of China (no. 2020YFA0714200) and National Natural Science Foundation of China (nos. 11931012, 11901443 and 12171379)
Appendices
Appendix A: The convergence of alternating direction method in (53)–(55)
To prove the convergence of sequences \(({\overline{{\mathbf {x}}} ^k},{\overline{{\mathbf {y}}} ^k},{{{\mathbf {u}}}^k})\) obtained from subproblems (53)–(55), we assume the set of discontinuous points of \(F( \cdot )\) and \({\nabla _F}\) at \({\mathbf {x}} \in \Omega \) are two zero measure sets, and F is piecewise two-times differentiable on \(\Omega \). Then \( \Delta \buildrel \Delta \over = \{ {{\mathbf {x}}}|\nabla F( \cdot )\) or \(\nabla F( \cdot )\) is discontinuous at \({\mathbf {x}}\}\) is a zero measure set. Motivated by Chen and Ye (2010), we introduce two new functions \({{\varvec{\alpha }}} \in {{\mathbb {R}}^p},{{\varvec{\beta }}} \in {{\mathbb {R}}^l}\) to transform (53)–(55) into following problems:
where \({\theta _1} > 0\) is small enough, and \({E_1}({{\varvec{\alpha }}},{\mathbf {{\overline{x}}}},{{\varvec{\beta }}},{\mathbf {{\overline{y}}}},{{\mathbf {u}}}) = S({{\varvec{\alpha }}},{{\varvec{\beta }}},{{\mathbf {u}}}) + \delta {R_0}({{\mathbf {u}}}) + \Theta R({{\mathbf {u}}}) + \frac{1}{{2\theta _1 }}{({\mathbf {{\overline{x}}}} - {{\varvec{\alpha }}})^2} + \frac{1}{{2\theta _1 }}{({\mathbf {{\overline{y}}}} - {{\varvec{\beta }}})^2}\).
Based on these notations, we transform (A1) into the following five subproblems:
Concerning the convergence of (A2)–(A6), we have the following theorem.
Theorem 3
Suppose \(\mathrm {ess}\) \({\sup _{{\mathbf {x}} \in \Omega }}|{F({\mathbf {x}} )} |\le {\overline{M}}\) and \(\mathrm {ess}\) \(\sup _{{\mathbf {x}} \in \Omega } |T({\mathbf {x}})|\le {\overline{M}}\) for some \({\overline{M}}<+\infty \), \(F({\mathbf {x}})\not \equiv c\), \(T({\mathbf {x}})\not \equiv c\) (c is a constant) for any \({\mathbf {x}} \in \Omega \), \(0< {\theta _1} < {\frac{1}{{4\lambda |\Omega |{{\left\| {\nabla ^2 \mathrm{CC}(P( \cdot ),Q( \cdot ))} \right\| }_\infty }}}} \), \(\delta > 2b\lambda |\Omega |{\left\| {{\nabla ^2}_{{\mathbf {u}}}\mathrm{CC}(P( \cdot ),Q( \cdot ))} \right\| _\infty }\); then the sequence \(({{{\varvec{\alpha }}}^k},{\overline{{\mathbf {x}}} ^k},{{{\varvec{\beta }}}^k},{\overline{{\mathbf {y}}} ^k},{{{\mathbf {u}}}^k})\) converges to the solution of (A1) as \(k \rightarrow \infty \).
Proof
Step 1: we claim that there exists \(({{\varvec{\alpha }}},\overline{{\mathbf {x}}} ,{{\varvec{\beta }}},\overline{{\mathbf {y}}} ,{{\mathbf {u}}}) \in {{\mathbb {R}}^p} \times {{\mathbb {R}}^p} \times {{\mathbb {R}}^l} \times {{\mathbb {R}}^l} \times {[H_0^\alpha (\Omega )]^2}\) such that
The Euler–Lagrange equation of (A2) is
On the other hand,
where \({I_1}=\lambda |\Omega |{[\mathrm{CC}(P({{{\varvec{\alpha }}}^{k + 1}},{{{\mathbf {u}}}^k}),Q({{{\varvec{\beta }}}^k})) - \mathrm{CC}(P({{{\varvec{\alpha }}}^k},{{{\mathbf {u}}}^k}),Q({{{\varvec{\beta }}}^k}))]^2} + 2\lambda |\Omega |[1 - \mathrm{CC}(P({{{\varvec{\alpha }}}^{k + 1}},{{{\mathbf {u}}}^k}),Q({{{\varvec{\beta }}}^k}))] \cdot [\mathrm{CC}(P({{{\varvec{\alpha }}}^{k + 1}},{{{\mathbf {u}}}^k}),Q({{{\varvec{\beta }}}^k})) - \mathrm{CC}(P({{{\varvec{\alpha }}}^k},{{{\mathbf {u}}}^k}),Q({{{\varvec{\beta }}}^k}))]\), \({I_2}=\frac{1}{{2{\theta _1}}}{({{{\varvec{\alpha }}}^{k + 1}} - {{{\varvec{\alpha }}}^k})^2} + \frac{1}{{{\theta _1}}}({{{\varvec{\alpha }}}^{k + 1}} - {{{\varvec{\alpha }}}^k})({\overline{{\mathbf {x}}} ^k} - {{{\varvec{\alpha }}}^{k + 1}})\).
Next, we will prove that \(\mathrm{CC}(P( \cdot ),Q( \cdot ))\) is two-times differentiable with respect to \({{\varvec{\alpha }}}\).
Because \(P = \sum \nolimits _{1 \le s \le p} {{{{\varvec{\alpha }}}_s}K(F({{\mathbf {x}}} + {{\mathbf {u}}}({{\mathbf {x}}})),{{{\mathbf {m}}}_s})} \) is infinitely differentiable with respect to \({{\varvec{\alpha }}}\), \(E(P) = \frac{1}{{|\Omega |}}\int _\Omega {Pd{{\mathbf {x}}}} \) is infinitely differentiable with \({{\varvec{\alpha }}}\). Then \(\mathrm{Cov}(P,Q) = \frac{1}{{|\Omega |}}\int _\Omega {(P - E(P))(Q - E(Q))d{{\mathbf {x}}}} \) and \(\mathrm{Var}(P) = \frac{1}{{|\Omega |}}\int _\Omega {{{(P - E(P))}^2}d{{\mathbf {x}}}} \) are also infinitely differentiable with respect to \({{\varvec{\alpha }}}\). Furthermore, for any \({\mathbf {x}} \in \Omega \), \(F({{\mathbf {x}}})\not \equiv c\) and f is a Borel measurable function with finite positive variance, then it can be obtained from the proof by contradiction similar to Theorem 1 that \(\mathrm{Var}(P)\) is strictly greater than zero. Similarly, \(\mathrm{Var}(Q)\) is also strictly greater than zero. Therefore, \(\mathrm{CC}(P( \cdot ),Q( \cdot ))\) is infinitely differentiable with \({\varvec{\alpha }}\).
By Taylor’s formula,
where \({\varvec{\vartheta }}\) is around \({\varvec{\alpha }}\).
This implies,
where \([1 - \mathrm{CC}(P({{{\varvec{\alpha }}}^{k + 1}},{{{\mathbf {u}}}^k}),Q({{{\varvec{\beta }}}^k}))] \in [0,1], {{\overline{M}} _1} = 2\lambda |\Omega |{\left\| {{\nabla ^2 _{{\varvec{\alpha }}}}\mathrm{CC}(P((\cdot ),Q(\cdot ))} \right\| _\infty }\).
It follows from (A8), (A9), (A11) that
where \(b_1=\frac{1}{{2{\theta _1}}} - {{\overline{M}} _1}>0\) because of the fact that \(0< {\theta _1} < \frac{1}{{4\lambda |\Omega |{{\left\| {{{\nabla }^2 _{{\varvec{\alpha }}}}\mathrm{CC}(P( \cdot ),Q( \cdot ))} \right\| }_\infty }}} \).
In addition,
For fixed \({{{\varvec{\alpha }}}^k},{{{\varvec{\beta }}}^k},{\overline{{\mathbf {y}}} ^k},{{{\mathbf {u}}}^k}\), when \({\overline{{\mathbf {x}}} ^{k+1}}={{{\varvec{\alpha }}}^{k + 1}}\), \(E_1\) reaches the minimum. Then
Similarly, we can obtain that
and
where \({{\overline{M}} _2} = 2\lambda |\Omega |{\left\| {{\nabla ^2}_{{\varvec{\beta }}}\mathrm{CC}(P(\cdot ),Q(\cdot ))} \right\| _\infty }\), \({b_2} \buildrel \Delta \over = \frac{1}{{2{\theta _1}}} - {{\overline{M}} _2} >0\) because of the fact that \(0< {\theta _1} < {\frac{1}{{4\lambda |\Omega |{{\left\| {{{\nabla ^2}_{{\varvec{\beta }}}} \mathrm{CC}(P( \cdot ),Q( \cdot ))} \right\| }_\infty }}}} \).
Furthermore, \({\mathbf {u}}\) satisfies the following formula:
where \(\int _\Omega {{\nabla ^\alpha }{{\mathbf {u}}} \cdot } {\nabla ^\alpha }{\varvec{\eta }}d{{\mathbf {x}}} = \int _\Omega {\sum \nolimits _{i,j = 1}^2 {\frac{{{\partial ^\alpha }{u_i}}}{{\partial x_j^\alpha }}} } \frac{{{\partial ^\alpha }{\eta _i}}}{{\partial x_j^\alpha }}d{{\mathbf {x}}}\).
By letting \({\varvec{\eta }}({{\mathbf {x}}}) = {{{\mathbf {u}}}^k} - {{{\mathbf {u}}}^{k + 1}}\), we obtain that
Then, we have
Since F is piecewise two-times differentiable, one can use the similar way of proving the differentiability of \(\mathrm{CC}(P(\cdot ),Q(\cdot ))\) with respect to \({\varvec{\alpha }}\) to get that \(\mathrm{CC}(P(\cdot ),Q(\cdot ))\) is also two-times differentiable with respect to \({\mathbf {u}}\).
By Taylors formula, there holds
where \(H(\sigma ,\tau )\) is Hessian matrix of \(F(\cdot )\) around \({\mathbf {u}}^{k+1}\). Therefore,
where \({\nabla _{{\mathbf {u}}}}\mathrm{CC}(P({{{\varvec{\alpha }}}^{k + 1}},{{{\mathbf {u}}}^{k + 1}}),Q({{{\varvec{\beta }}}^{k + 1}})) = \frac{1}{{|\Omega |}} H({{{\varvec{\alpha }}}^{k + 1}},{{{\varvec{\beta }}}^{k + 1}},{{{\mathbf {u}}}^{k + 1}}), {{\overline{M}} _3} = 2\lambda |\Omega |{\left\| {\nabla _{{\mathbf {u}}}^2\mathrm{CC}(P( \cdot ),Q( \cdot ))} \right\| _\infty }\).
It follows from (A18), (A19), (A21) that
where b is a constant and \(b_3>0\) because of the fact that \(\delta > 2b\lambda |\Omega |{\left\| {\nabla _{{\mathbf {u}}}^2CC\left( P\left( \cdot \right) ,Q\left( \cdot \right) \right) } \right\| _\infty }\).
It follows from (A12), (A14), (A15), (A16) and (A22) that
Let \(e_k \buildrel \Delta \over = {E_1}({{{\varvec{\alpha }}}^k},{\overline{{\mathbf {x}}} ^k},{{{\varvec{\beta }}}^k},{\overline{{\mathbf {y}}} ^k},{{{\mathbf {u}}}^k}) \ge 0\), then \(\{ {e_k}\} \) is a decreasing sequence with lower bound. Therefore, \(\mathop {\lim }\nolimits _{k \rightarrow + \infty } ({e_k} - {e_{k+1}})= \mathop {\lim }\nolimits _{k \rightarrow + \infty } {E_1}({{{\varvec{\alpha }}}^k},{\overline{{\mathbf {x}}} ^k},{{{\varvec{\beta }}}^k},{\overline{{\mathbf {y}}} ^k},{{{\mathbf {u}}}^k}) - {E_1}({{{\varvec{\alpha }}}^{k + 1}},{\overline{{\mathbf {x}}} ^{k + 1}},{{{\varvec{\beta }}}^{k + 1}},{\overline{{\mathbf {y}}} ^{k + 1}},{{{\mathbf {u}}}^{k + 1}})= 0\). That is, there exists an e, such that \(\mathop {\lim }\nolimits _{k \rightarrow + \infty } {e_k} = e\). This implies that
Since ess \({\sup _{\Omega \backslash \Delta }}|F({{\mathbf {x}}})|\le {\overline{M}} < + \infty \) and f is bounded and continuous, this implies that
and for any \(k \in {\mathbb {N}}\), we have
Then, for any \(k \in {\mathbb {N}}\), we have
where \({{\varvec{m}}} = {({m_s})_{1 \times p}}\). Therefore,
Similarly, we can obtain the uniform boundedness of \(\overline{{\mathbf {x}}}^k\), \(\overline{{\mathbf {y}}}^k\) and \({{\varvec{\beta }}}^k\). Therefore, there exists \(({{\varvec{\alpha }}},\overline{{\mathbf {x}}} ,{{\varvec{\beta }}},\overline{{\mathbf {y}}} ,{{\mathbf {u}}}) \in {{\mathbb {R}}^p} \times {{\mathbb {R}}^p} \times {{\mathbb {R}}^l} \times {{\mathbb {R}}^l} \times {[H_0^\alpha (\Omega )]^2}\) such that
because of the fact that \({{{\mathbb {R}}^p}}\), \({{{\mathbb {R}}^l}}\), \(H_0^\alpha (\Omega )\) are Banach spaces.
This concludes claim (A7).
Step 2: In this step, we will proof that \(({{\varvec{\alpha }}},\overline{{\mathbf {x}}} ,{{\varvec{\beta }}},\overline{{\mathbf {y}}} ,{{\mathbf {u}}})\) is a solution of (A1).
Because ess \({\sup _{\Omega \backslash \Delta }}|F({{\mathbf {x}}})|\le {\overline{M}} \), \(\Delta \) is a zero measure set and f is continuous and bounded, by the definition of P in model (40), P is bounded in \(\Omega \). Furthermore, since \(P( \cdot ) = \sum \nolimits _{1 \le s \le p} {{\alpha _s}} K(F({{\mathbf {x}}} + {{\mathbf {u}}}({{\mathbf {x}}})),{m_s})\) is continuous with respect to \({\varvec{\alpha }}\), then similar to the continuity proof process of \({\mathbf {u}}\) in Theorem 1, we can get the continuity of \({\varvec{\alpha }}\). That is, \(\mathrm{CC}(P({{\mathbf {u}}},{{{\varvec{\alpha }}}^k}),Q({{\varvec{\beta }}})) {\mathop {\longrightarrow }\limits ^{k}} \mathrm{CC}(P({{\mathbf {u}}},{{{\varvec{\alpha }}}}),Q({{\varvec{\beta }}})), \mathrm{Var}(P({{\mathbf {u}}},{{{\varvec{\alpha }}}^k})) {\mathop {\longrightarrow }\limits ^{k}} \mathrm{Var}(P({{\mathbf {u}}},{{\varvec{\alpha }}})), \quad \mathrm{Cov}(P({{\mathbf {u}}},{{{\varvec{\alpha }}}^k}), \cdot ) {\mathop {\longrightarrow }\limits ^{k}} \mathrm{Cov}(P({{\mathbf {u}}},{{{\varvec{\alpha }}}}), \cdot )\).
Then, based on the conclusion
we can obtain that
Similarly, we can obtain that \({\varvec{\beta }}\) and \({\mathbf {u}}\) satisfy the Euler–Lagrange equation of (A4) and (A6), respectively. Furthermore, since \(\overline{{\mathbf {x}}}\) and \(\overline{{\mathbf {y}}}\) are the minimizer of (A3) and (A5), respectively, \(({{\varvec{\alpha }}},\overline{{\mathbf {x}}} ,{{\varvec{\beta }}},\overline{{\mathbf {y}}} ,{{\mathbf {u}}})\) is a solution of (A1).
Finally, based on the conclusion of step 1 and step 2, we can obtain that the convergence of sequences \(({\overline{{\mathbf {x}}} ^k},{\overline{{\mathbf {y}}} ^k},{{{\mathbf {u}}}^k})\) obtained from subproblems (53)–(55). \(\square \)
Remark 5
\({\varvec{\alpha }}\) and \({\varvec{\beta }}\) are introduced to prove the convergence of sequences \(({\overline{{\mathbf {x}}} ^k},{\overline{{\mathbf {y}}} ^k},{{{\mathbf {u}}}^k})\) obtained from subproblems (53)–(55), which is not used in numerical implementation because of the fact that \(\overline{{\mathbf {x}}} = {{\varvec{\alpha }}}\) and \(\overline{{\mathbf {y}}} = {{\varvec{\beta }}}\) are the minimizer of (A3) and (A5), respectively.
Appendix B: The discretization of \(R_0({\mathbf {u}})\)
For \({{\mathbf {x}}} = ({x_1},{x_2}) \in \Omega ,i = 1,2\), \(\alpha >2\), and function \({g_1}:\Omega \rightarrow {\mathbb {R}}\), define
where \({\nabla ^\alpha }{{\mathbf {u}}} = {(\frac{{{\partial ^\alpha }{u_i}}}{{\partial x_j^\alpha }})_{2 \times 2}},\Phi (s) = \int _0^{ + \infty } {{t^{s - 1}}{e^{ - t}}} \mathrm{d}t,[ \cdot ]\) is a round-down function, \(g_1^{(1)}({{\mathbf {x}}},t) = {g_1}(t,{x_2}),g_1^{(2)}({{\mathbf {x}}},t) = {g_1}({x_1},t)\), and \(|{\nabla ^\alpha }{{\mathbf {u}}}|= \sqrt{\sum \nolimits _{i,j = 1}^2 {{{\left( \frac{{{\partial ^\alpha }{u_i}}}{{\partial x_j^\alpha }}\right) }^2}} }\).
Using Grunwald approximation (Hou et al. 2017; Tian et al. 2015), \(\frac{{{\partial ^\alpha }{g_1}({{\mathbf {x}}})}}{{\partial x_i^\alpha }},\frac{{{\partial ^{{\alpha _*}}}{g_1}({{\mathbf {x}}})}}{{\partial x_i^{{\alpha _*}}}}\) (\(i=1,2\)) are discretized by
where \(\delta _{1 - }^\alpha {g_1}_{i,j} = \frac{1}{{h_1^\alpha }}\sum \nolimits _{l = 1}^{i + 1} {\rho _l^{(\alpha )}} {g_1}_{i - l + 1,j},\delta _{1 + }^\alpha {g_1}_{i,j} = \frac{1}{{h_1^\alpha }}\sum \nolimits _{l = 1}^{{N_1} - i + 2} {\rho _l^{(\alpha )}} {g_1}_{i + l - 1,j}, \delta _{2 - }^\alpha {g_1}_{i,j} = \frac{1}{{h_2^\alpha }}\sum \nolimits _{m = 1}^{j + 1} {\rho _m^{(\alpha )}} {g_1}_{i,j - m + 1}, \delta _{2 + }^\alpha {g_1}_{i,j} = \frac{1}{{h_2^\alpha }}\sum \nolimits _{m = 1}^{{N_2} - j + 2} {\rho _m^{(\alpha )}} {g_1}_{i,j + m - 1}, \rho _0^{(\alpha )} = 1,\rho _l^{(\alpha )} = (1 - \frac{{1 + \alpha }}{l})\rho _{l - 1}^{(\alpha )}\).
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Ding, Z., Han, H. & Wang, H. Two-dimensional diffeomorphic model for multi-modality image registration. Comp. Appl. Math. 42, 17 (2023). https://doi.org/10.1007/s40314-022-02145-1
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DOI: https://doi.org/10.1007/s40314-022-02145-1
Keywords
- Diffeomorphic image registration
- Multi-modality
- Rényi’s statistical dependence measure
- Multi-grid
- Mesh folding