放疗新技术 4D IMRT

RESEARCH Open Access A 4D IMRT planning method using deformable image registration to improve normal tissue sparing with contemporary delivery techniques Xiaoqiang Li, Xiaochun Wang, Yupeng Li and Xiaodong Zhang* Abstract We propose a planning method to design true 4-dimensional (4D) intensity-modulated radiotherapy (IMRT) plans, called the t4Dplan method, in which the planning target volume (PTV) of the individual phases of the 4D computed tomography (CT) and the conventional PTV receive non-uniform doses but the cumulative dose to the PTV of each phase, computed using deformable image registration (DIR), are uniform. The non-uniform dose prescription for the conventional PTV was obtained by solving linear equations that required motion-convolved 4D dose to be uniform to the PTV for the end-exhalation phase (PTV50) and by constraining maximum inhomogeneity to 20%. A plug-in code to the treatment planning system was developed to perform the IMRT optimization based on this non-uniform PTV dose prescription. The 4D dose was obtained by summing the mapped doses from individual phases of the 4D CT using DIR. This 4D dose distribution was compared with that of the internal target volume (ITV) method. The robustness of the 4D plans over the course of radiotherapy was evaluated by computing the 4D dose distributions on repeat 4D CT datasets. Three patients with lung tumors were selected to demonstrate the advantages of the t4Dplan method compared with the commonly used ITV method. The 4D dose distribution using the t4Dplan method resulted in greater normal tissue sparing (such as lung, stomach, liver and heart) than did plans designed using the ITV method. The dose volume histograms of cumulative 4D doses to the PTV50, clinical target volume, lung, spinal cord, liver, and heart on the 4D repeat CTs for the two patients were similar to those for the 4D dose at the time of original planning. Keywords: 4D CT, IMRT, treatment planning, respiratory motion, deform 1. Introduction Implementations of four-dimensional (4D) radiotherapy based on 4D computed tomography (CT) datasets have been described by Rietzel et al [1] and Keall [2]. In 4D radiotherapy, the treatment plan is designed on each 4D CT image set (i.e., 4D treatment planning), and radia- tion is delivered throughout the patient’s breathing cycle (i.e., 4D treatment delivery), which ensures adequate coverage of the tumor target without increasing the treated volume. Because 4D treatment planning accounts for temporal changes in anatomy, 4D radio- therapy holds promise as the optimal method for treat- ing patients. However, 4D radiotherapy currently requires 4D treatment delivery, which necessitates sophisticated device(s) to synchronize the treatment delivery with the patient’s respiration. Most centers have the ability to acquire 4D CT images, but they do not have the ability to perform 4D radiation delivery. Instead, 4D CT images are primarily used to define the internal target volume (ITV), which is essentially the envelope needed to enclose the target as it moves throughout the breathing cycle. 4D CT [3-9] provides a more accurate tumor volume definition since it limits motion artifacts during CT acquisition, displays the ana- tomically correct shape and size of the tumor, and demonstrates respiration-induced motion of the tumor and organs at risk. Previous studies using 4D CT data- sets have mostly been focused on dosimetric verification to determine if dose distribution planned on one or part of the 4D CT datasets is adequate to estimate the cumu- lative dose from all 4D CT datasets [1,10]. Few studies * Correspondence: xizhang@mdanderson.org Department of Radiation Physics, The University of Texas, MD Anderson Cancer Center, Houston, Texas 77030, USA Li et al. Radiation Oncology 2011, 6:83 http://www.ro-journal.com/content/6/1/83 © 2011 Li et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. have investigated whether the information on anatomic motion provided by 4D CT can be used to design treat- ment plans that confer the advantages of 4D treatment delivery without requiring additional equipment. In this paper, we describe an effective and practical 4D treatment planning method, which we refer to true 4D planning (t4Dplan) method, for intensity-modulated radiotherapy (IMRT) using 4D CT datasets to maximize critical structure sparing. In traditional treatment plan- ning, the prescribed dose is planned to be distributed uniformly to the target while minimal dose is delivered to the surrounding normal structures on the planning CT under the assumption that the planning CT truly represents the patient anatomy that will be present dur- ing treatment. In our t4Dplan method, however, plan- ning deliberately creates non-uniform dose distribution in the target (i.e., it creates hot regions along the target’s direction of motion on the planning CT) to achieve a uniform dose distribution in the target and minimal dose to the surrounding normal structures on the final 4D dose distribution. The difference between the t4Dplan method and the traditional ITV method is illu- strated in figure 1. The t4Dplan method does not require 4D treatment delivery and is solely dependent on the 4D datasets acquired during the planning pro- cess. Compared to some other techniques such as respiratory gating [11], breath hold [12,13] and dynamic MLC tumor tracking [14-16], the t4Dplan method is easier to implement in the clinic because it uses the cur- rent treatment planning and delivery systems. 2. Materials and methods 2.1. t4Dplan The t4Dplan method, which uses 4D CT datasets, designs treatment plans as follows: 1. A reference CT dataset is selected from all the 4D CT datasets. Usually, an end-of-exhalation phase CT (i.e., the 50% phase [T50]) is selected as the refer- ence CT dataset [17] since patients spend more time at the end of exhalation [18]. 2. The target volume (TV) is outlined based on the reference CT. 3. The motion TV (MTV) is outlined on the refer- ence CT as the combined volume of the target at all phases of the 4D CT datasets (i.e., the MTV is an envelope enclosing the target as it moves throughout the breathing cycle). The t4Dplan method calculates a deliverable non-uni- form dose distribution (i.e., the apparent dose distri- bution [AppD]) to the MTV. The final 4D dose distribution is determined by recalculating the t4Dplan on each phase of the 4D CT dataset and creating a time-averaged cumulative dose distribution based on deformable image registration (DIR). For each voxel on the reference CT, the corresponding voxel on another phase of the CT dataset can be derived through DIR by transforming the source image (i.e., the reference CT) to the target image (i.e., another phase of the CT dataset), such that υ j i = T j,ref × υrefi , (1) where υ refi is the position vector for the ith voxel on the reference CT, Tj,ref represents the transform matrix from the reference CT to the jth phase of the CT data- set, and υ ji is the position vector for the corresponding voxel on the jth phase of the CT dataset for the ith voxel on the reference CT. In the current study, to derive the non-uniform dose, we first assumed that the dose on each phase of the 4D CT was approximately the same as the AppD on the reference CT. This approximation assumes the internal movement of anatomy will not impact the dose distri- bution and is a good approximation for photon dose calculation. It should be noted that this approximation is only used in the derivation of a non-uniform dose prescription. For the final designed plan, we used the exact 4D dose calculation without this approximation. The 4D dose for each voxel on the reference CT can be approximated as the time-averaged cumulative dose of the corresponding voxel on all phases in the CT dataset, such that D4D(υrefi ) = 1 K K∑ j=1 DAppD(υ ji), (2) where K represents the number of phases of the CT datasets, D4D(υrefi ) is the 4D dose for the ith voxel on the reference CT, and DAppD(υ ji) is the AppD for the corresponding voxel on the jth phase of the dataset. Assuming the MTV and TV on the reference CT have n and m (n >m) voxels, respectively, and the AppD values for the n voxels of the MTV are D1, D2, ..., Dn, the 4D dose distribution for the TV with m voxels can be determined using the following linear equations derived from equation (2): D4D(υref1 ) = 1 K (DAppD(υ11) +D AppD(υ21) + ...... +D AppD(υK1 )) = D0, 1 st voxel; D4D(υref2 ) = 1 K (DAppD(υ12) +D AppD(υ22) + ...... +D AppD(υK2 )) = D0, 2 nd voxel; D4D(υrefm ) = 1 K (DAppD(υ1m) +D AppD(υ2m) + ...... +D AppD(υKm)) = D0, mth voxel, (3) where DAppD(υ ji) = D1,D2, . . ., or Dn, are the unknown parameters, and D0 is the uniform dose pre- scribed to the TV (i.e., the final 4D dose distribution on the TV). Here, we have n unknown parameters (i.e., D1, Li et al. Radiation Oncology 2011, 6:83 http://www.ro-journal.com/content/6/1/83 Page 2 of 14 D2, ..., Dn) that need to be derived from m equations, with m