Research article Special Issues

Combinatorial optimisation in radiotherapy treatment planning

  • The goal of radiotherapy is to cover a target area with a desired radiation dose while keeping the exposition of non-target areas as low as possible in order to reduce radiation side effects. In the case of Intensity Modulated Proton Therapy (IMPT), the dose distribution is typically designed via a treatment planning optimisation process based on classical  optimisation algorithms on some objective functions.We investigate the planning optimisation problem under the point of view of the Theory of Complexity in general and, in particular, of the Combinatorial Optimisation Theory. We firstly give a formal definition of a simplified version of the problem that is in the complexity class NPO.We prove that above version is computationally hard, i.e. it belongs to the class NPO$\setminus$PTAS if $\mathbb{NP}\neq \mathbb{P}$.We show how Combinatorial Optimisation Theory can give valuable tools, both conceptual and practical, in treatment plan definition, opening the way for new deterministic algorithms with bounded time complexity which have to support the technological evolution up to adaptive plans exploiting near real time solutions.

    Citation: Emma Altobelli, Maurizio Amichetti, Alessio Langiu, Francesca Marzi, Filippo Mignosi, Pietro Pisciotta, Giuseppe Placidi, Fabrizio Rossi, Giorgio Russo, Marco Schwarz, Stefano Smriglio, Sabina Vennarini. Combinatorial optimisation in radiotherapy treatment planning[J]. AIMS Medical Science, 2018, 5(3): 204-223. doi: 10.3934/medsci.2018.3.204

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  • The goal of radiotherapy is to cover a target area with a desired radiation dose while keeping the exposition of non-target areas as low as possible in order to reduce radiation side effects. In the case of Intensity Modulated Proton Therapy (IMPT), the dose distribution is typically designed via a treatment planning optimisation process based on classical  optimisation algorithms on some objective functions.We investigate the planning optimisation problem under the point of view of the Theory of Complexity in general and, in particular, of the Combinatorial Optimisation Theory. We firstly give a formal definition of a simplified version of the problem that is in the complexity class NPO.We prove that above version is computationally hard, i.e. it belongs to the class NPO$\setminus$PTAS if $\mathbb{NP}\neq \mathbb{P}$.We show how Combinatorial Optimisation Theory can give valuable tools, both conceptual and practical, in treatment plan definition, opening the way for new deterministic algorithms with bounded time complexity which have to support the technological evolution up to adaptive plans exploiting near real time solutions.



    Fixed point theory is one of the most powerful and fundamental tools of modern mathematics and may be considered a core subject of nonlinear analysis. The theory has developed rapidly since Banach's contraction principle [1] was introduced. There are many theorems that have the same conclusion as the contraction principle but with different sufficient conditions. For example, Kannan [2], Chatterjea [3], Geraghty [4], and Ćirić [5]. Next, we recall the concept of Kannan mapping.

    Let $ (X, d) $ be a metric space, $ T:X\to X $ is said to be a Kannan mapping if there exists a constant $ \lambda\in [0, \frac{1}{2}) $ such that

    $ d(x,y)\leq\lambda(d(x,Tx)+d(y,Ty)), $

    for all $ x, y\in X $. Kannan proved that every Kannan mapping in a complete metric space has a unique fixed point [2]. In our view, Kannan's fixed point theorem is very important because Subrahmanyam [6] proved that a metric space $ X $ is complete if and only if every Kannan mapping has a fixed point. Thereafter, Suzuki [8,9,10] further generalized this conclusion. In recent years, Lu [11] introduced the best area of Kannan system with degree $ s $ in $ b $-metric spaces with constant $ s $. Futhermore, Berinde and Pacurar [12] presented the concept of enriched Kannan mappings. Mohapatra et al. [13] defined the new concepts of mutual Kannan contractivity and mutual contractivity that generalized the Kannan mapping and contraction. In [14], Debnath generalized Kannan's fixed point Theorem and used it to solve a particular type of integral equation. For more conclusions on Geraghty type contractions, see [4,16,18,19,25]. About multi-valued mappings, see [15,26,27,28,29,30].

    On the other hand, in 2018, Górnicki [7] proved some extensions of Kannan's fixed point theorem in the framework of metric space. In 2021, Doan [17] extended a result of [7] and proved some generalizations of Kannan-type fixed point theorems for singlevalued and multivalued mappings defined on a complete strong $ b $-metric space. On this basis, Doan raised two open questions. Our main purpose of this paper is to give positive answers to those two questions and establish a new type of Riech's fixed point theorem to improve results of Doan.

    Kirk and Shahzad [20] introduced the notion of strong $ b $-metric space. Some deep results about strong $ b $-metric spaces are obtained in [21,22,23,24].

    Definition 1.1. [20] Let $ X $ be a nonempty set, $ K\geq1 $, $ D:X\times X\to [0, \infty) $ be a mapping. If for all $ x, y, z\in X $,

    (1) $ D(x, y) = 0 \Leftrightarrow x = y $;

    (2) $ D(x, y) = D(y, x) $;

    (3) $ D(x, y)\leq KD(x, z)+D(z, y) $.

    Then $ D $ is called a strong $ b $-metric on $ X $ and $ (X, D, K) $ is called a strong $ b $-metric space.

    Remark 1.2. Let $ (X, D, K) $ be a strong $ b $-metric space. From Definition 1.1, we can derive the inequality,

    $ D(x,y)\leq D(x,z)+KD(z,y),\; \text{for all}\; x,y,z\in X. $

    In fact, for all $ x, y, z\in X $, we have

    $ D(x,y) = D(y,x)\leq KD(y,z)+D(z,x) = D(x,z)+KD(z,y). $

    Therefore, for every strong $ b $-metric $ D $ with constant $ K $, it implies that

    $ D(x,y)\leq\min\{{\; KD(x,z)+D(z,y)},{D(x,z)+KD(z,y)}\; \}, $

    refer to [21].

    It is obvious that if $ (X, D) $ is a metric space, then it is a strong $ b $-metric space.

    Definition 1.3. [20] Let $ (X, D, K) $ be a strong $ b $-metric space, $ \{x_n\} $ be a sequence in $ X $ and $ x\in X $. Then

    (1) $ \{x_n\} $ is said to converge to $ x $ if $ \lim\limits_{n\to\infty}D(x_n, x) = 0 $;

    (2) $ \{x_n\} $ is called Cauchy if $ \lim\limits_{n, m\to\infty}D(x_n, x_m) = 0 $;

    (3) $ (X, D, K) $ is said to be complete if every Cauchy sequence converges.

    Throughout this paper, we denote $ \mathbb{N^{*}} $ as the set of all positive integers. Let $ (X, D) $ be a metric space. We denote by $ CB(X) $ the collection of all nonempty bounded closed subsets of $ (X, D) $. Let $ T:X\to CB(X) $ be a multi-valued mapping, we say that $ x $ is a fixed point of $ T $ if $ x\in Tx $. Let $ H:CB(X)\times CB(X)\to [0, \infty) $ be the Hausdorff metric on $ CB(X) $ defined by

    $ H(A,B): = \max\{\sup\limits_{x\in B}d(x,A),\sup\limits_{x\in A}d(x,B)\}, $

    where $ A, B\in CB(X) $ and $ d(x, A): = \inf\limits_{y\in A}D(x, y) $.

    In order to characterize the open problems posed by Doan [17]. We will use the following class of functions

    $ \Psi_{q} = \{\psi:(0,\infty)\to [0,q)\; |\; \psi(t_n)\to q \; \text{implies} \; t_n\to 0\}, $

    where $ q\in(0, \frac{1}{2}) $. We call $ \Psi_{q} $ the class of Geraghty functions. We next introduce the two questions raised by Doan.

    Theorem 1.4. [17, Theorem 2.4] Let $ (X, D, K) $ be a complete strong $ b $-metric space, $ T:X\to X $ be a mapping, $ q\in(0, \frac{1}{2}) $. If there exists $ \psi\in\Psi_{q} $ satisfying for all $ x, y\in X $ with $ x\neq y $,

    $ \frac{1}{K+1}D(x,Tx)\leq D(x,y), $

    implies

    $ D(Tx,Ty)\leq\psi(D(x,y))(D(x,Tx)+D(y,Ty)). $

    Then, $ T $ has a unique fixed point $ x^{*}\in X $.

    Question 1.5. Does there exist $ q = \frac{1}{2} $ such that the above theorem holds?

    For brevity, we denote $ \Psi_{\frac{1}{2}}: = \{\psi:(0, \infty)\to [0, \frac{1}{2})\; |\; \psi(t_n)\to \frac{1}{2} \; \text{implies} \; t_n\to 0\} $.

    Theorem 1.6. [17, Theorem 3.3] Let $ (X, D, K) $ be a complete strong $ b $-metric space and $ T:X\to CB(X) $ be a multi-valued mapping. Suppose there exists $ s\in(0, k) $ with $ 0 < k < \frac{1}{2} $ satisfying

    $ \frac{1}{K+1}d(x,Tx)\leq D(x,y)\; \mathit{\text{implies}}\; H(Tx,Ty)\leq s(d(x,Tx)+d(y,Ty)), $

    for each $ x, y\in X $. Then $ T $ has a fixed point.

    Question 1.7. Does there exist $ k = \frac{1}{2} $ such that mapping $ T $ in Theorem 1.6 has a fixed point free?

    In this section, we answer question 1, and first we give the following lemma.

    Lemma 2.1. Let $ (X, D, K) $ be a strong $ b $-metric space, $ T:X\to X $ be a mapping. If there exists $ q\in (0, \frac{1}{2}] $ and $ \psi\in\Psi_{q} $ satisfying for all $ x, y\in X $ with $ x\neq y $,

    $ \frac{1}{K+1}D(x,Tx)\leq D(x,y), $

    implies

    $ D(Tx,Ty)\leq\psi(D(x,y))(D(x,Tx)+D(y,Ty)). $

    Then,

    (1) $ D(Tx, T^2x)\leq D(x, Tx) $, for each $ x\in X $;

    (2) for all $ x, y\in X $, either $ \frac{1}{K+1}D(x, Tx)\leq D(x, y) $ or $ \frac{1}{K+1}D(Tx, T^2x)\leq D(Tx, y) $.

    Proof. (1) Let $ x\in X $ be an arbitrary point. Without loss of generality, we can suppose that $ x\neq Tx $. From $ \frac{1}{K+1}D(x, Tx)\leq D(x, Tx) $, we have

    $ D(Tx,T(Tx))ψ(D(x,Tx))(D(x,Tx)+D(Tx,T(Tx)))<12(D(x,Tx)+D(Tx,T(Tx))),
    $

    which implies that

    $ D(Tx,T2x)D(x,Tx),xX.
    $
    (2.1)

    (2) By contradiction, assume that there exists $ x', y'\in X $ such that $ D(x', y') < \frac{1}{K+1}D(x', Tx') $ and $ D(Tx', y') < \frac{1}{K+1}D(Tx', T^2x') $. Using the triangle inequality and (2.1), we have

    $ D(x,Tx)D(x,y)+KD(y,Tx)<1K+1D(x,Tx)+KK+1D(Tx,T2x)1K+1D(x,Tx)+KK+1D(x,Tx)=D(x,Tx),
    $

    which contradicts the fact that $ D(x', Tx') > 0 $ (because $ D(x', Tx') > (K+1)D(x', y')\geq0 $). Thus, we proved (2).

    Theorem 2.2. Let $ (X, D, K) $ be a complete strong $ b $-metric space, $ T:X\to X $ be a mapping. If there exists $ \psi\in\Psi_{\frac{1}{2}} $ satisfying for all $ x, y\in X $ with $ x\neq y $,

    $ \frac{1}{K+1}D(x,Tx)\leq D(x,y), $

    implies

    $ D(Tx,Ty)\leq\psi(D(x,y))(D(x,Tx)+D(y,Ty)). $

    Then, $ T $ has a unique fixed point $ x^{*}\in X $.

    Proof. Let $ x $ be an arbitrary point in $ X $. Let $ x_n = T^{n}x $, $ n\in\mathbb{N}^{*} $. If for some $ n_0\in\mathbb{N}^{*} $, $ x_{n_0} = x_{n_0+1} $, then $ x_{n_0} $ will be a fixed point of $ T $. So, we can suppose that $ x_n\neq x_{n+1} $ for all $ n\in\mathbb{N}^{*} $. From Lemma 2.1, for all $ n\in\mathbb{N}^{*} $, we have

    $ D(x_{n+1},x_{n+2}) = D(Tx_{n},T^{2}x_{n})\leq D(x_{n},Tx_{n}) = D(x_{n},x_{n+1}). $

    Therefore, $ \{D(x_{n}, x_{n+1})\}_{n = 1}^{\infty} $ is a decreasing sequence of nonnegative real numbers, which implies that it has a limit. Let $ \lim\limits_{n\to\infty}D(x_{n}, x_{n+1}) = t\geq0 $. In order to prove that $ t = 0 $, suppose that $ t > 0 $. In such a case, since $ 0 < \frac{1}{K+1}D(x_{n}, x_{n+1})\leq D(x_{n}, x_{n+1}) $, for all $ n\in\mathbb{N}^{*} $, we have

    $ D(x_{n+1},x_{n+2})\leq\psi(D(x_{n},x_{n+1}))(D(x_{n},x_{n+1})+D(x_{n+1},x_{n+2})). $

    Then

    $ \frac{D(x_{n+1},x_{n+2})}{D(x_{n},x_{n+1})+D(x_{n+1},x_{n+2})}\leq \psi(D(x_{n},x_{n+1})) < \frac{1}{2}. $

    Passing to the limit as $ n\to\infty $, we get $ \lim\limits_{n\to\infty}\psi(D(x_{n}, x_{n+1})) = \frac{1}{2} $, which implies that $ \lim\limits_{n\to\infty}D(x_{n}, x_{n+1}) = 0 $, which is a contradiction. Therefore, $ t = 0 $ and $ \lim\limits_{n\to\infty}D(x_{n}, x_{n+1}) = 0 $.

    Given $ \varepsilon > 0 $, there exists $ N\in\mathbb{N}^{*} $ such that

    $ D(xn1,xn)<εK+1,n>N.
    $

    From Lemma 2.1, for all $ n, m\in \mathbb{N}^{*} $ with $ m > n > N $, either $ \frac{1}{K+1}D(x_{n-1}, Tx_{n-1})\leq D(x_{n-1}, x_{m-1}) $ or $ \frac{1}{K+1}D(Tx_{n-1}, T^2x_{n-1})\leq D(Tx_{n-1}, x_{m-1}) $. We consider two cases.

    Case 1. If $ \frac{1}{K+1}D(x_{n-1}, Tx_{n-1})\leq D(x_{n-1}, x_{m-1}) $. In this case, notice that $ D(x_{n-1}, Tx_{n-1}) = D(x_{n-1}, x_{n}) > 0 $, we have

    $ D(xn,xm)=D(Txn1,Txm1)ψ(D(xn1,xm1))(D(xn1,xn)+D(xm1,xm))<12(D(xn1,xn)+D(xm1,xm))max{D(xn1,xn),D(xm1,xm)}<εK+1<ε.
    $

    Case 2. If $ \frac{1}{K+1}D(Tx_{n-1}, T^2x_{n-1})\leq D(Tx_{n-1}, x_{m-1}) $. In this case, notice that $ D(Tx_{n-1}, T^2x_{n-1}) = D(x_{n}, x_{n+1}) > 0 $, we have

    $ D(xn,xm)KD(xn,xn+1)+D(Txn,Txm1)KD(xn,xn+1)+ψ(D(xn,xm1))(D(xn,xn+1)+D(xm1,xm))<KD(xn,xn+1)+max{D(xn,xn+1),D(xm1,xm)}<KεK+1+εK+1=ε.
    $

    Thus, combining all the cases we have

    $ D(x_n,x_m) < \varepsilon. $

    Therefore, $ \{x_n\} $ is a Cauchy sequence in $ (X, D, K) $. As it is complete, there exists $ x^*\in X $ such that $ \lim\limits_{n\to\infty}x_n = x^{*} $.

    Since $ \lim\limits_{n\to\infty}x_n = x^{*} $ and $ \lim\limits_{n\to\infty}D(x_{n}, x_{n+1}) = 0 $, for all $ \varepsilon' > 0 $, there exists $ N'\in\mathbb{N}^{*} $ such that

    $ D(x,Txn)<ε4KandD(xn,xn+1)<ε2,n>N.
    $
    (2.2)

    Obviously, the sequence $ \{x_n\} $ has an infinite number of terms not equal to $ x^* $. By Lemma 2.1, for all $ x_{n} $, where $ x_{n}\neq x^* $ and $ n > N' $, either $ \frac{1}{K+1}D(x_{n}, Tx_{n})\leq D(x_{n}, x^*) $ or $ \frac{1}{K+1}D(Tx_{n}, T^2x_{n})\leq D(Tx_{n}, x^*) $. Clearly, there exists $ x_{n_0} $, where $ x_{n_0}\neq x^* $ and $ n_0 > N' $, such that $ \frac{1}{K+1}D(x_{n_0}, Tx_{n_0})\leq D(x_{n_0}, x^*) $. Then

    $ D(x,Tx)KD(x,Txn0)+D(Txn0,Tx)KD(x,Txn0)+ψ(D(xn0,x))(D(xn0,xn0+1)+D(x,Tx))<KD(x,Txn0)+12(D(xn0,xn0+1)+D(x,Tx)).
    $

    From (2.2), we have

    $ D(x^*,Tx^*)\leq 2KD(x^*,Tx_{n_0})+D(x_{n_0},x_{n_0+1}) < 2K\cdot\frac{\varepsilon'}{4K}+\frac{\varepsilon'}{2} = \varepsilon'. $

    Then, $ D(x^*, Tx^*) = 0 $, $ x^* $ is a fixed point of $ T $.

    Now, suppose that $ y^* $ is another fixed point of $ T $ such that $ y^*\neq x^* $. Since $ \frac{1}{K+1}D(x^*, Tx^*)\leq D(x^*, y^*) $, we have

    $ D(x^*,y^*) = D(Tx^*,Ty^*)\leq\psi(D(x^*,y^*))(D(x^*,Tx^*)+D(y^*,Ty^*)) = 0, $

    which is a contradiction. Therefore, $ T $ has a unique fixed point $ x^* $ and $ \lim\limits_{n\to\infty}T^nx = x^* $ for all $ x\in X $.

    Remark 2.3. Theorem 1.4 is a corollary of Theorem 2.2.

    Proof. Let $ (X, D, K) $ be a complete strong $ b $-metric space, $ q\in(0, \frac{1}{2}) $, $ T:X\to X $ be a mapping, which satisfying the condition of Theorem 1.4 with $ \psi\in\Psi_{q} $. It is not difficult to observe that the function $ \varphi:(0, \infty)\to[0, q) $ defined by

    $ \varphi(t) = \frac{\psi(t)}{2q},\quad t\in(0,\infty), $

    belongs to $ \Psi_{\frac{1}{2}} $. For all $ x, y\in X $ with $ x\neq y $, if $ \frac{1}{K+1}D(x, Tx)\leq D(x, y) $, then

    $ D(Tx,Ty)ψ(D(x,y))(D(x,Tx)+D(y,Ty))ψ(D(x,y))2q(D(x,Tx)+D(y,Ty))=φ(D(x,y))(D(x,Tx)+D(y,Ty)).
    $

    According to Theorem 2.2, $ T $ has a unique fixed point.

    Corollary 2.4. [17, Theorem 2.1] Let $ (X, D, K) $ be a complete strong $ b $-metric space, $ T:X\to X $ be a mapping. If there exists $ \psi\in\Psi_{\frac{1}{2}} $ satisfying for all $ x, y\in X $,

    $ D(Tx,Ty)\leq\psi(D(x,y))(D(x,Tx)+D(y,Ty)). $

    Then, $ T $ has a unique fixed point $ x^{*}\in X $.

    In order to answer question 2, we first need a couple of lemmas.

    Lemma 2.5. [17] Let $ (X, D, K) $ be a strong $ b $-metric space and $ A, B\in CB(X) $. If $ H(A, B) > 0 $ then for all $ h > 1 $ and $ a\in A $, there exists $ b\in B $ such that

    $ D(a,b) < h\cdot H(A,B). $

    Lemma 2.6. [24] Let $ (X, D, K) $ be a strong $ b $-metric space and let $ \{x_n\} $ be a sequence in $ X $. Assume that there exists $ \lambda\in[0, 1) $ satisfying

    $ D(x_{n+1},x_{n+2})\leq\lambda D(x_n,x_{n+1}), $

    for any $ n\in\mathbb{N}^* $. Then $ \{x_n\} $ is Cauchy.

    Lemma 2.7. [26] Let $ (X, D, K) $ be a strong $ b $-metric space, then for all $ a\in X $ and $ A, B\in CB(X) $

    $ d(a,A)\leq Kd(a,B)+H(A,B). $

    Proof. Let $ a\in X $, $ A, B\in CB(X) $. Using the triangular inequality, for all $ y\in B $, we have

    $ d(a,A)=infxAD(a,x)infxA(KD(a,y)+D(y,x))=KD(a,y)+infxAD(y,x)=KD(a,y)+d(y,A)KD(a,y)+H(A,B).
    $

    Hence, we have

    $ d(a,A)infyBKD(a,y)+H(A,B)=Kd(a,B)+H(A,B).
    $

    The proof is complete.

    Theorem 2.8. Let $ (X, D, K) $ be a complete strong $ b $-metric space and $ T:X\to CB(X) $ be a multi-valued mapping. Suppose there exists $ s\in(0, \frac{1}{2}) $ satisfying

    $ \frac{1}{K+1}d(x,Tx)\leq D(x,y)\; \mathit{\text{implies}}\; H(Tx,Ty)\leq s(d(x,Tx)+d(y,Ty)), $

    for each $ x, y\in X $. Then $ T $ has at least one fixed point.

    Proof. First, we construct a sequence $ \{x_n\}\subseteq X $ such that for each $ n\in\mathbb{N}^* $, $ x_{n}\in Tx_{n-1} $ and

    $ D(xn,xn+1)<hH(Txn1,Txn),
    $
    (2.3)

    where $ h = \frac{1}{4s}+\frac{1}{2} > 1 $. Let $ x_0\in X $ and $ x_1\in Tx_0 $. If $ H(Tx_0, Tx_1) = 0 $, which implies that $ Tx_0 = Tx_1 $, then $ x_1\in Tx_0 = Tx_1 $ and $ x_1 $ is a fixed point of $ T $. So, let us suppose that $ H(Tx_0, Tx_1) > 0 $. From Lemma 2.5, for $ h = \frac{1}{4s}+\frac{1}{2} > 1 $ and $ x_1\in Tx_0 $, there exists $ x_2\in Tx_1 $ such that

    $ D(x_1,x_2) < hH(Tx_0,Tx_1). $

    Similarly, let us suppose that $ H(Tx_1, Tx_2) > 0 $, by Lemma 2.5, there exists $ x_3\in Tx_2 $ such that

    $ D(x_2,x_3) < hH(Tx_1,Tx_2). $

    Suppose that $ H(Tx_{n-1}, Tx_n) > 0 $, for each $ n\in\mathbb{N}^*. $ Using Lemma 2.5 and proceeding inductively, we can obtain a sequence $ \{x_n\} $ such that $ x_{n}\in Tx_{n-1} $ and (2.3) holds for each $ n\in\mathbb{N}^* $.

    Since $ x_{n}\in Tx_{n-1} $ for all $ n\in\mathbb{N}^* $, then $ \frac{1}{K+1}d(x_{n-1}, Tx_{n-1})\leq D(x_{n-1}, x_n) $. Hence, we have

    $ H(Txn1,Txn)s(d(xn1,Txn1)+d(xn,Txn))s(D(xn1,xn)+D(xn,xn+1)).
    $
    (2.4)

    From (2.3) and (2.4), we get

    $ D(x_n,x_{n+1}) < hs(D(x_{n-1},x_n)+D(x_n,x_{n+1})). $

    Therefore, for all $ n\in\mathbb{N}^* $, we have

    $ D(xn,xn+1)<λD(xn1,xn),
    $
    (2.5)

    where $ \lambda = \frac{hs}{1-hs} = \frac{1+2s}{3-2s}\in(\frac{1}{3}, 1) $. According to Lemma 2.6, $ \{x_n\} $ is Cauchy. Since $ (X, D, K) $ complete, there exists $ x^*\in X $ such that $ \lim\limits_{n\to\infty}x_n = x^* $.

    We claim that for all $ n\in\mathbb{N}^* $, either $ \frac{1}{K+1}d(x_n, Tx_n)\leq D(x_n, x^*) $, or $ \frac{1}{K+1}d(x_{n+1}, Tx_{n+1})\leq D(x_{n+1}, x^*) $. In order to prove our claim, we argue by contradiction. If there exists $ n_0\in\mathbb{N}^* $ such that $ D(x_{n_0}, x^*) < \frac{1}{K+1}d(x_{n_0}, Tx_{n_0}) $ and $ D(x_{n_0+1}, x^*) < \frac{1}{K+1}d(x_{n_0+1}, Tx_{n_0+1}) $. By (2.5), we have

    $ D(xn0,xn0+1)KD(xn0,x)+D(x,xn0+1)<KK+1d(xn0,Txn0)+1K+1d(xn0+1,Txn0+1)KK+1D(xn0,xn0+1)+1K+1D(xn0+1,xn0+2)KK+1D(xn0,xn0+1)+λK+1D(xn0,xn0+1)<D(xn0,xn0+1).
    $

    On the other hand, since $ H(Tx_{n_0}, Tx_{n_0+1}) > 0 $, then $ Tx_{n_0}\neq Tx_{n_0+1} $. Hence, $ D(x_{n_0}, x_{n_0+1}) > 0 $. This contradiction guarantees that our claim holds.

    Without loss of the generality, we may assume that $ \frac{1}{K+1}d(x_n, Tx_n)\leq D(x_n, x^*) $ holds for infinity positive integers $ n $. Then, there exists $ \{x_{n_i}\}_{i = 1}^{\infty}\subseteq \{x_n\} $ such that

    $ \frac{1}{K+1}d(x_{n_i},Tx_{n_i})\leq D(x_{n_i},x^*),\quad i\in\mathbb{N}^*. $

    By Lemma 2.7, for each $ i\in\mathbb{N}^* $, we have

    $ d(x,Tx)Kd(x,Txni)+H(Txni,Tx)Kd(x,Txni)+s(d(xni,Txni)+d(x,Tx)).
    $

    Then, from (2.5), we get

    $ d(x,Tx)K1sd(x,Txni)+s1sd(xni,Txni)2KD(x,xni+1)+D(xni,xni+1)<2KD(x,xni+1)+λD(xni1,xni)<2KD(x,xni+1)+λniD(x0,x1),
    $

    where $ \lambda\in(\frac{1}{3}, 1) $. Letting $ i\to\infty $ in the above inequality, we obtain $ d(x^*, Tx^*) = 0 $. Then $ x^* $ is a fixed point of $ T $.

    Remark 2.9. Notice that the Hausdorff semidistance is utilized in the fixed point theorems for multi-valued mappings, for example [31,32,33]. It is obvious that the Hausdorff semidistance $ e(A, B) $ and the Hausdorff distance $ H(A, B) $ are distinct. However, we can demonstrate that Lemma 2.5, Lemma 2.7, and Theorem 2.8 hold, if replacing "$ H(A, B) $" with "$ e(A, B) $", "$ e(B, A) $", and "$ e(A, B) $", respectively.

    Remark 2.10. It is evident to see that Theorem 1.6 can be obtained from Theorem 2.8.

    Corollary 2.11. [15] Let $ (X, d) $ be a complete metric space, $ 0\leq s < \frac{1}{2} $. Suppose $ T:X\to CB(X) $ is a continuous multi-valued mapping satisfying

    $ H(Tx,Ty)\leq s(d(x,Tx)+d(y,Ty)),\quad\mathit{\text{for all}}\; x,y\in X, $

    then $ T $ has at least one fixed point.

    We give an example of a multi-valued mapping $ T $ that satisfies the conditions of Theorem 2.8. It is worth noting that all points in $ X $ are fixed points of $ T $.

    Example 2.12. Let $ X = \mathbb{N}^* $, $ D:X\times X\to[0, \infty) $ defined by $ D(x, y) = |x-y| $, for all $ x, y\in X $. It is easy to verify that $ (X, D, 1) $ is a complete strong $ b $-metric space. Let $ T:X\to CB(X) $ defined by

    $ Tx\equiv X,\quad \mathit{\text{for all}}\; x\in X. $

    Then it is clear that $ d(x, Tx) = 0 $ and $ H(Tx, Ty) = 0 $ for each $ x, y\in X $. By Theorem 2.8, $ T $ has at least one fixed point. Furthermore, it is easy to see that any point in $ X $ is an fixed point of $ T $.

    Lemma 3.1. Let $ (X, D, K) $ be a strong $ b $-metric space, $ T:X\to X $ be a mapping. If there exists $ \varphi\in\Psi_{\frac{1}{3}} $ satisfying for all $ x, y\in X $ with $ x\neq y $,

    $ \frac{1}{K+1}D(x,Tx)\leq D(x,y), $

    implies

    $ D(Tx,Ty)\leq\varphi(D(x,y))(D(x,Tx)+D(y,Ty)+D(x,y)). $

    Then,

    (1) $ D(Tx, T^2x)\leq D(x, Tx) $, for each $ x\in X $;

    (2) for all $ x, y\in X $, either $ \frac{1}{K+1}D(x, Tx)\leq D(x, y) $ or $ \frac{1}{K+1}D(Tx, T^2x)\leq D(Tx, y) $.

    Proof. For any $ x\in X $, without loss of generality, we may consider $ x\neq Tx $. By $ \frac{1}{K+1}D(x, Tx)\leq D(x, Tx) $, we have

    $ D(Tx,T(Tx))φ(D(x,Tx))(D(x,Tx)+D(Tx,T(Tx))+D(x,Tx))<23D(x,Tx)+13D(Tx,T(Tx)).
    $

    Thus, $ D(Tx, T^2x)\leq D(x, Tx) $ for all $ x\in X $. The proof of the second part of this Lemma follows in a similar manner as Lemma 2.1 and so is omitted.

    Theorem 3.2. Let $ (X, D, K) $ be a complete strong $ b $-metric space, $ T:X\to X $ be a mapping. If there exists $ \varphi\in\Psi_{\frac{1}{3}} $ satisfying for all $ x, y\in X $ with $ x\neq y $,

    $ \frac{1}{K+1}D(x,Tx)\leq D(x,y), $

    implies

    $ D(Tx,Ty)\leq\varphi(D(x,y))(D(x,Tx)+D(y,Ty)+D(x,y)). $

    Then, $ T $ has a unique fixed point $ x^{*}\in X $.

    Proof. Let $ x\in X $ be an arbitrary point and $ \{x_n\} $ be a sequence defined by $ x_n = T^nx $ for all $ n\in\mathbb{N}^* $, suppose that every $ D(x_n, x_{n+1}) > 0 $. By Lemma 3.1,

    $ D(x_{n+1},x_{n+2}) = D(Tx_{n},T^{2}x_{n})\leq D(x_{n},Tx_{n}) = D(x_{n},x_{n+1}), \quad n\in\mathbb{N^*}. $

    Then, $ \{D(x_{n}, x_{n+1})\}_{n = 1}^{\infty} $ is monotonically decreasing with a lower bound. Hence, $ \{D(x_{n}, x_{n+1})\} $ converges. For each $ n\in\mathbb{N}^* $, since $ D(x_n, x_{n+1}) > 0 $ and $ \frac{1}{K+1}D(x_n, Tx_n)\leq D(x_n, x_{n+1}) $, we get

    $ D(Tx_{n},Tx_{n+1})\leq\varphi(D(x_{n},x_{n+1}))(2D(x_{n},x_{n+1})+D(x_{n+1},x_{n+2})). $

    Then

    $ \frac{D(x_{n+1},x_{n+2})}{2D(x_{n},x_{n+1})+D(x_{n+1},x_{n+2})}\leq\varphi(D(x_{n},x_{n+1})) < \frac{1}{3}. $

    Suppose that $ \lim\limits_{n\to\infty}D(x_{n}, x_{n+1}) > 0 $. Letting $ n\to\infty $, we obtain $ \varphi(D(x_{n}, x_{n+1}))\to\frac{1}{3} $, which implies $ D(x_{n}, x_{n+1})\to0 $. This contradiction guarantees that $ \lim\limits_{n\to\infty}D(x_{n}, x_{n+1}) = 0 $.

    According to Lemma 3.1, for each $ p, q\in\mathbb{N}^* $, either $ 0 < \frac{1}{K+1}D(x_p, Tx_p)\leq D(x_p, x_q) $ or $ 0 < \frac{1}{K+1}D(Tx_p, T^2x_p)\leq D(Tx_p, x_q) $. Let $ M(p, q) = (K+\frac{K+1}{3})D(x_p, x_{p+1})+\frac{1}{3}D(x_q, x_{q+1})+\frac{1}{3}D(x_p, x_{q}) $, where $ p, q\in\mathbb{N}^* $. We claim that

    $ D(Txp,Txq)M(p,q),p,qN.
    $
    (3.1)

    Now there are the following two cases.

    Case 1. If $ 0 < \frac{1}{K+1}D(x_p, Tx_p)\leq D(x_p, x_q) $. In this case, we have

    $ D(Txp,Txq)φ(D(xp,xq))(D(xp,xp+1)+D(xq,xq+1)+D(xp,xq))<13(D(xp,xp+1)+D(xq,xq+1)+D(xp,xq))M(p,q).
    $

    Case 2. If $ 0 < \frac{1}{K+1}D(Tx_p, T^2x_p)\leq D(Tx_p, x_q) $. In this case, by Lemma 3.1, we have

    $ D(Txp,Txq)KD(Txp,T2xp)+D(T2xp,Txq)KD(Txp,T2xp)+φ(D(Txp,xq))(D(Txp,T2xp)+D(xq,Txq)+D(Txp,xq))(K+13)D(Txp,T2xp)+13D(xq,Txq)+K3D(Txp,xp)+13D(xp,xq)(K+1+K3)D(xp,Txp)+13D(xq,Txq)+13D(xp,xq)=M(p,q).
    $

    Therefore, we obtain (3.1).

    Next, we demonstrate that $ \{x_n\} $ is a Cauchy sequence reasoning by contradiction. If not, it is easy to show that there exists $ \varepsilon_0 > 0 $ and two subsequence $ \{x_{n_k}\} $ and $ \{x_{m_k}\} $ of $ \{x_n\} $ such that for each $ k\in\mathbb{N}^* $, we have

    $ D(xnk,xmk)ε0andD(xnk,xmk1)<ε0.
    $
    (3.2)

    From $ \lim\limits_{n\to\infty}D(x_n, x_{n+1}) = 0 $, there exists $ N\in\mathbb{N}^* $ such that $ D(x_n, x_{n+1}) < \frac{\varepsilon_0}{7K+2} $ for each $ n\geq N $. For all $ k > N $, since $ \min{\{n_k, m_k, m_k-1\}}\geq K-1\geq N $, then

    $ \max{\{D(x_{n_k},x_{n_k+1}),D(x_{m_k},x_{m_k+1}),D(x_{m_k-1},x_{m_k})\}} < \frac{\varepsilon_0}{7K+2}. $

    By (3.1) and (3.2), we have

    $ D(Txnk,Txmk)D(xnk+1,xmk)+KD(xmk+xmk+1)M(nk,mk1)+KD(xmk+xmk+1)=(K+K+13)D(xnk,xnk+1)+13D(xmk1,xmk)+KD(xmk+xmk+1)+13D(xnk,xmk1)(2K+K+23)max{D(xnk,xnk+1),D(xmk1,xmk),D(xmk+xmk+1)}+13D(xnk,xmk1)<(2K+K+23)ε07K+2+ε03=2ε03.
    $

    Hence, we obtain

    $ D(xnk,xmk)KD(xnk,xnk+1)+D(xnk+1+xmk)KD(xnk,xnk+1)+KD(xmk+xmk+1)+D(xmk+1+xnk+1)2Kmax{D(xnk,xnk+1),D(xmk+xmk+1)}+2ε03<2Kε07K+2+2ε03<ε03+2ε03=ε0,
    $

    which contradicts (3.2). This contradiction shows that $ \{x_n\} $ is Cauchy. As $ (X, D, K) $ is complete, there exists $ x^*\in X $ such that $ \lim\limits_{n\to\infty}x_n = x^* $.

    According to Lemma 3.1, for each $ n\in\mathbb{N}^* $, either $ \frac{1}{K+1}D(x_{n}, Tx_{n})\leq D(x_{n}, x^*) $ or $ \frac{1}{K+1}D(Tx_{n}, T^2x_{n})\leq D(Tx_{n}, x^*) $. Similarly, let us consider two cases.

    Case 1. If $ \frac{1}{K+1}D(x_{n}, Tx_{n})\leq D(x_{n}, x^*) $, since $ D(x_{n}, Tx_{n}) = D(x_{n}, x_{n+1}) > 0 $, we have

    $ D(x,Tx)KD(x,Txn)+D(Txn,Tx)KD(x,Txn)+φ(D(xn,x))(D(xn,xn+1)+D(x,Tx)+D(xn,x))KD(x,Txn)+13(D(xn,xn+1)+D(x,Tx)+D(xn,x)).
    $

    Then

    $ D(x^*,Tx^*)\leq \frac{3}{2}KD(x^*,x_{n+1})+\frac{1}{2}(D(x_{n},x_{n+1})+D(x_n,x^*)). $

    Case 2. If $ \frac{1}{K+1}D(Tx_{n}, T^2x_{n})\leq D(Tx_{n}, x^*) $, by $ D(Tx_{n}, T^2x_{n}) = D(x_{n+1}, x_{n+2}) > 0 $, we get

    $ D(x,Tx)KD(x,T2xn)+D(T2xn,Tx)KD(x,T2xn)+13(D(Txn,T2xn)+D(x,Tx)+D(Txn,x)).
    $

    Then

    $ D(x^*,Tx^*)\leq \frac{3}{2}KD(x^*,x_{n+2})+\frac{1}{2}(D(x_{n+1},x_{n+2})+D(x_{n+1},x^*)). $

    Therefore, for all $ n\in\mathbb{N}^* $, we have

    $ D(x,Tx)max{32KD(x,xn+1)+12(D(xn,xn+1)+D(xn,x)),32KD(x,xn+2)+12(D(xn+1,xn+2)+D(xn+1,x))}.
    $

    Letting $ n\to\infty $ in the above inequality, we obtain $ D(x^*, Tx^*) = 0 $ and $ x^* $ is a fixed point of $ T $.

    Suppose that $ y^* $ is another fixed point of $ T $ and $ D(y^*, x^*) > 0 $. Since $ D(x^*, Tx^*) = 0 $, it follows that $ \frac{1}{K+1}D(x^*, Tx^*)\leq D(x^*, y^*) $. Then

    $ D(x^*,y^*) = D(Tx^*,Ty^*)\leq\varphi(D(x^*,y^*))(D(x^*,Tx^*)+D(y^*,Ty^*)+D(x^*,y^*)) < \frac{1}{3}D(x^*,y^*), $

    which is a contradiction with the fact that $ D(x^*, y^*) > 0 $. As a consequence, $ T $ has a unique fixed point $ x^* $ and $ \lim\limits_{n\to\infty}T^nx = x^* $ for all $ x\in X $.

    Corollary 3.3. [17, Theorem 2.7] Let $ (X, D, K) $ be a complete strong $ b $-metric space, $ T:X\to X $ be a mapping. If there exists $ \varphi\in\Psi_{\frac{1}{3}} $ satisfying for all $ x, y\in X $ with $ x\neq y $,

    $ D(Tx,Ty)\leq\varphi(D(x,y))(D(x,Tx)+D(y,Ty)+D(x,y)). $

    Then, $ T $ has a unique fixed point $ x^{*}\in X $ and for any $ x\in X $ the sequence of iterates $ \{T^nx\} $ converges to $ x^* $.

    We focus on a new type of Kannan's fixed point theorem in the setting of strong $ b $-metric spaces. Using some useful lemmas, we derive three fixed point theorems. The first two theorems give positive answers to Questions 1.5 and 1.7, respectively. The third theorem is a new type of Reich's fixed point theorem and also a generalization of Doan's result (Theorem 2.7 in [17]).

    The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.

    The authors are thankful to the referees for their valuable comments and suggestions to improve this paper.

    Research supported by the National Natural Science Foundation of China (12061050, 11561049) and the Natural Science Foundation of Inner Mongolia (2020MS01004).

    The authors declare that there is no conflict of interest.

    [1] Wilson RR (1946) Radiological use of fast protons. Radiology 47: 487–491. doi: 10.1148/47.5.487
    [2] Chuong MD, Mehta MP, Langen K, et al. (2014) Is proton beam therapy better than standard radiation therapy? The available evidence points to benefits of proton beam therapy, Clinical advances in hematology and oncology 12:861–864. http://europepmc.org/abstract/MED/ 25674846
    [3] Ezzell GA, Galvin JM, Low D, et al. (2003) Guidance document on delivery, treatment planning, and clinical implementation of IMRT: Report of the IMRT subcommittee of the AAPM radiation therapy committee. Medical Physics 30: 2089–2115. https://doi.org/10.1118/1.1591194
    [4] Hartmann J, Wölfelschneider J, Stache C, et al. (2016) Novel technique for high-precision stereotactic irradiation of mouse brains. Strahlentherapie und Onkologie 192: 806–814. https: //doi.org/10.1007/s00066-016-1014-8
    [5] Lomax A (1999) Intensity modulation methods for proton radiotherapy. Physics in Medicine and Biology 44: 185. https://doi.org/10.1259/bjr.20150195
    [6] Pieplenbosch S (2015) Potential Benefits of Proton Therapy in Clinic, Master's thesis.
    [7] van de Schoot AJAJ, de Boer P, Crama KF, et al. (2016) Dosimetric advantages of proton therapy compared with photon therapy using an adaptive strategy in cervical cancer. Acta Oncologica 55: 892–899. https://doi.org/10.3109/0284186X.2016.1139179
    [8] Zelefsky MJ, Fuks Z, Happersett L, et al. (2000) Clinical experience with intensity modulated radiation therapy (IMRT) in prostate cancer. Radiotherapy and Oncology 55: 241–249. https: //doi.org/10.1016/S0167-8140(99)00100-0
    [9] Börgers C (1999) The Radiation Therapy Planning Problem: 1–16, Springer New York, New York, NY, https://doi.org/10.1007/978-1-4612-1550-9_1
    [10] De Ruysscher D, Sterpin E, Haustermans K, et al. (2015) Tumour movement in proton therapy: Solutions and remaining questions: A review. Cancers 7: 1143–1153. https://doi.org/10. 3390/cancers7030829
    [11] Engelsman M, Schwarz M, Dong L (2013) Physics controversies in Proton Therapy. Seminars in Radiation Oncology 23: 88–96. https://doi.org/10.1016/j.semradonc.2012.11.003
    [12] Grutters JP, Kessels AG, Pijls-Johannesma M, et al. (2010) Comparison of the effectiveness of radiotherapy with photons, protons and carbon-ions for non-small cell lung cancer: A meta-analysis. Radiotherapy and Oncology 95: 32–40. https://doi.org/10.1016/j.radonc.2009.08.003
    [13] Kabarriti R, Mark D, Fox J, et al. (2015) Proton therapy for the treatment of pediatric head and neck cancers: A review. International Journal of Pediatric Otorhinolaryngology 79: 1995–2002. https://doi.org/10.1016/j.ijporl.2015.10.042
    [14] Lee KA, O'Sullivan C, Daly P, et al. (2017) Proton therapy in paediatric oncology: an Irish perspective. Irish Journal of Medical Science 186: 577–582. https://doi.org/10.1007/ s11845-016-1520-9
    [15] Mohan R, Grosshans D (2017) Proton therapy - present and future. Advanced Drug Delivery Reviews 109: 26–44.
    [16] Salama JK, Willett CG (2014) Is proton beam therapy better than standard radiation therapy? A paucity of practicality puts photons ahead of protons. Clinical advances in hematology and oncology 12: 861, 865–6, 869. http://europepmc.org/abstract/MED/25674847
    [17] Schulz-Ertner D, Tsujii H (2007) Particle radiation therapy using proton and heavier ion beams. Journal of Clinical Oncology 25: 953–964. https://doi.org/10.1200/JCO.2006.09.7816
    [18] Fellin F, Azzeroni R, Maggio A, et al. (2013) Helical tomotherapy and intensity modulated proton therapy in the treatment of dominant intraprostatic lesion: A treament planning comparison. Radiotherapy and Oncology 107: 207–212. https://doi.org/10.1016/j.radonc.2013.02. 016
    [19] Fredriksson A (2013) Robust optimization of radiation therapy accounting for geometric uncertainty, PhD thesis.
    [20] Giap H, Roda D, Giap F (2015) Can proton beam therapy be clinically relevant for the management of lung cancer? Translational Cancer Research 4. https://doi.org/10.3978/j.issn. 2218-676X.2015.08.15
    [21] Guta B (2003) Subgradient Optimization Methods in Integer Programming with an Application to a Radiation Therapy Problem, PhD thesis. http://nbn-resolving.de/urn/resolver.pl? urn:nbn:de:bsz:386-kluedo-16224
    [22] McGowan SE, Burnet NG, Lomax AJ (2013) Treatment planning optimisation in proton therapy. The British Journal of Radiology 86: 20120288–20120288. https://doi.org/10.1259/bjr. 20120288
    [23] Schwarz M (2011) Treatment planning in proton therapy. The European Physical Journal Plus 126: 67. https://doi.org/10.1140/epjp/i2011-11067-y
    [24] Schwarz M, Cattaneo GM, Marrazzo L (2017) Geometrical and dosimetrical uncertainties in hypofractionated radiotherapy of the lung: A review. Physica Medica 36: 126–139. https://doi.org/10.1016/j.ejmp.2017.02.011
    [25] Schwarz M, Molinelli S (2016) What can particle therapy add to the treatment of prostate cancer?. Physica Medica 32: 485–491. https://doi.org/10.1016/j.ejmp.2016.03.017
    [26] Alber M, Meedt G, N¨usslin F, et al. (2002) On the degeneracy of the IMRT optimization problem. Med Physics 29: 2584–2589. https://doi.org/10.1118/1.1500402
    [27] Edimo P, Clermont C, Kwato M, et al. (2009) Evaluation of a commercial VMC++ Monte Carlo based treatment planning system for electron beams using EGSnrc/BEAMnrc simulations and measurements. Physica Medica: European Journal of Medical Physics 25: 111–121. https://doi.org/10.1016/j.ejmp.2008.07.001
    [28] Li H, Liu W, Park P, et al. (2014) Evaluation of the systematic error in using 3d dose calculation in scanning beam proton therapy for lung cancer. Journal of Applied Clinical Medical Physics 15: 47–56. https://doi.org/10.1120/jacmp.v15i5.4810 29. Paganetti H (2012) Range uncertainties in proton therapy and the role of Monte Carlo simulations. Physics in Medicine and Biology 57: R99–R117 https://doi.org/10.1088/0031-9155/57/ 11/R99
    [29] 30. Spirou SV, Chui CS (1998) A gradient inverse planning algorithm with dose-volume constraints. Medical Physics 25: 321–333. https://doi.org/10.1118/1.598202
    [30] 31. Amichetti M (2016) The actual interest in radiotherapy for the utilization of proton beam, highlighting physics basis, technology and common clinical indications. J Tumor 4: 378–385.
    [31] 32. Brombal L, Barbosa D, Belcari N, et al. (2017) Proton therapy treatment monitoring with inbeam pet: investigating space and time activity distributions. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. https://doi.org/10.1016/j.nima.2017.05.002
    [32] 33. Kiely JPB, White BM (2016) Robust proton pencil beam scanning treatment planning for rectal cancer radiation therapy. International Journal of Radiation Oncology*Biology*Physics 95: 208– 215. https://doi.org/10.1016/j.ijrobp.2016.02.037
    [33] 34. Moignier A, Gelover E, Wang D, et al. (2016) Theoretical benefits of dynamic collimation in pencil beam scanning proton therapy for brain tumors: Dosimetric and radiobiological metrics. International Journal of Radiation Oncology*Biology*Physics 95: 171–180. https://doi.org/ 10.1016/j.ijrobp.2015.08.030
    [34] 35. Scalco E, Schwarz M, Sutto M, et al. (2016) Evaluation of different ct lung anatomies for proton therapy with pencil beam scanning delivery, using a validated non-rigid image registration method. Acta Oncologica 55: 647–651. https://doi.org/10.3109/0284186X.2015.1105383 36. Schwarz M, Algranati C, Widesott L, et al. (2016) Clinical Pencil Beam Scanning: Present and Future Practices, Springer India, New Delhi, 95–110. https://doi.org/10.1007/ 978-81-322-2622-2_7
    [35] 37. Cao W, Lim GJ, Lee A, et al. (2012) Uncertainty incorporated beam angle optimization for IMPT treatment planning. Medical Physics 39: 5248–5256. https://doi.org/10.1118/1.4737870
    [36] 38. Fredriksson A, Forsgren A, Härdemark B (2011) Minimax optimization for handling range and setup uncertainties in proton therapy. Medical Physics 38: 1672–1684. https://doi.org/10. 1118/1.3556559
    [37] 39. Li H, Zhu XR, Zhang X (2015) Reducing dose uncertainty for spot-scanning proton beam therapy of moving tumors by optimizing the spot delivery sequence. International Journal of Radiation Oncology*Biology*Physics 93: 547–556. https://doi.org/10.1016/j.ijrobp.2015.06. 019
    [38] 40. Liu W, Frank SJ, Li X, et al. (2013) PTV-based IMPT optimization incorporating planning risk volumes vs robust optimization. Medical Physics 40: 021709. https://doi.org/10.1118/1. 4774363
    [39] 41. Liu W, Li Y, Li X, et al. (2012) Influence of robust optimization in intensity-modulated proton therapy with different dose delivery techniques. Medical Physics 3089–3101. https://doi. org/10.1118/1.4711909
    [40] 42. Liu W, Zhang X, Li Y, et al. (2012) Robust optimization of intensity modulated proton therapy. Medical Physics 39: 1079–1091. https://doi.org/10.1118/1.3679340
    [41] 43. Alber M, Reemtsen R (2007) Intensity modulated radiotherapy treatment planning by use of a barrier-penalty multiplier method. Optimization Methods Software 22: 391–. https://doi. org/10.1080/10556780600604940
    [42] 44. Dionisi F, Ben-Josef E (2014) The use of proton therapy in the treatment of gastrointestinal cancers: Liver. Cancer Journal 20: 371–377.
    [43] 45. Kessler ML, Mcshan DL, Epelman MA, et al. (2005) Costlets: A generalized approach to cost functions for automated optimization of IMRT treatment plans. Optimization and Engineering 6: 421–448. https://doi.org/10.1007/s11081-005-2066-2
    [44] 46. Schwarz M, Pierelli A, Fiorino C, et al. (2011) Helical tomotherapy and intensity modulated proton therapy in the treatment of early stage prostate cancer: A treatment planning comparison. Radiotherapy and Oncology 98: 74–80. https://doi.org/10.1016/j.radonc.2010.10.027
    [45] 47. Witte MG, van der Geer J, Schneider C, et al. IMRT optimization including random and systematic geometric errors based on the expectation of tcp and ntcp. Medical Physics,34: 3544–3555. https://doi.org/10.1118/1.2760027
    [46] 48. Bokrantz R, Fredriksson A (2017) Necessary and su cient conditions for pareto e ciency in robust multiobjective optimization. European J Operational Res. https://doi.org/10.1016/ j.ejor.2017.04.012
    [47] 49. Janssen F, Landry G, Lopes PC, et al. (2014) Factors influencing the accuracy of beam range estimation in proton therapy using prompt gamma emission. Physics in Medicine and Biology 59: 4427. https://doi.org/10.1088/0031-9155/59/15/4427
    [48] 50. Cheung JP (2014) Image-Guided Proton Therapy for Online Dose-Evaluation and Adaptive Planning, PhD thesis. http://digitalcommons.library.tmc.edu/utgsbs_dissertations/439
    [49] 51. Dionisi F, Avery S, Lukens JN, et al. (2014) Proton therapy in adjuvant treatment of gastric cancer: Planning comparison with advanced x-ray therapy and feasibility report. Acta Oncologica 53: 1312–1320. https://doi.org/10.3109/0284186X.2014.912351
    [50] 52. Grant JD, Chang JY (2014) Proton-based stereotactic ablative radiotherapy in early-stage nonsmall-cell lung cancer. BioMed Research International
    [51] 53. Krämer M, Jäkel O, Haberer T, et al. (2000) Treatment planning for heavy-ion radiotherapy: physical beam model and dose optimization. Physics in Medicine and Biology 45: 3299. https: //doi.org/10.1088/0031-9155/45/11/313 54. Riboldi M, Baroni G (2015) Challenges and opportunities in image guided particle therapy, in: 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 5227–5230.
    [52] 55. Widesott L, Amichetti M, Schwarz M (2008) Proton therapy in lung cancer: Clinical outcomes and technical issues. A systematic review. Radiotherapy and Oncology 86: 154–164. https: //doi.org/10.10 6/j.radonc.2008.01.003
    [53] 56. Widesott L, Lomax AJ, Schwarz M (2012) Is there a single spot size and grid for intensity modulated proton therapy? Simulation of head and neck, prostate and mesothelioma cases. Medical Physics 39: 1298–1308. https://doi.org/10.1118/1.3683640
    [54] 57. Boland N, Hamacher HW, Lenzen F (2004) Minimizing beam-on time in cancer radiation treatment using multileaf collimators, Networks 43: 226–240, https://doi.org/10.1002/net. 20007 58. Cantone MC, Ciocca M, Dionisi F, et al. (2013) Application of failure mode and effects analysis to treatment planning in scanned proton beam radiotherapy. Radiation Oncology 8: 127. https: //doi.org/10.1186/1748-717X-8-127
    [55] 59. Hoffmann L, Alber M, Jensen MF, et al. (2017) Adaptation is mandatory for intensity modulated proton therapy of advanced lung cancer to ensure target coverage. Radiotherapy and Oncology 122: 400–405. https://doi.org/10.1016/j.radonc.2016.12.018
    [56] 60. Jäkel O, Hartmann GH, Karger CP, et al. (2000) Quality assurance for a treatment planning system in scanned ion beam therapy. Medical Physics 27: 1588–1600. https://doi.org/10.1118/1.599025
    [57] 61. Lewis MW (2009) On the use of guided design search for discovering significant decision variables in the fixed-charge capacitated multicommodity network design problem. Networks 53: 6–18. https:/ doi.org/10.1002/net.20255 62. van de Schoot AJAJ, Visser J, van Kesteren Z, et al. (2016) Beam configuration selection for robust intensity-modulated proton therapy in cervical cancer using pareto front comparison. Physics in Medicine and Biology 61: 1780. https://doi.org/10.1088/0031-9155/61/4/1780
    [58] 63. Baatar D, HamacherHW, Ehrgott M, et al. (2005) Decomposition of integer matrices and multileaf collimator sequencing. Discrete Applied Mathematics 152: 6–34, https://doi.org/10.1016/j.dam.2005.04.008
    [59] 64. Ausiello G, Marchetti-Spaccamela A, Crescenzi P, et al. (1999) Complexity and Approximation, Springer Berlin Heidelberg, https://doi.org/10.1007/978-3-642-58412-1
    [60] 65. Bovet DP, Crescenzi P (1994) Introduction to the Theory of Complexity, Prentice Hall International (UK) Ltd., Hertfordshire, UK, UK.
    [61] 66. Garey MR, Johnson DS (1979) Computers and Intractability: A Guide to the Theory of NPCompleteness. W. H. Freeman.
    [62] 67. Felzenszwalb P, Huttenlocher D (2004) Effcient graph-based image segmentation. International Journal of Computer Vision 59: 167–181. https://doi.org/10.1023/B:VISI.0000022288. 19776.77
    [63] 68. A compendium of NP optimization problems, 2005. Available from: https://www.nada.kth. se/~viggo/problemlist/compendium.html
    [64] 69. Alimonti P, Kann V (1997) Hardness of approximating problems on cubic graphs, in Proceedings of the Third Italian Conference on Algorithms and Complexity CIAC '97, Springer-Verlag, London, UK, UK, 1997, 288–298. https://doi.org/10.1007/3-540-62592-5_80
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