Learning Multi-Catheter Reconstructions for Interstitial Breast Brachytherapy

Type: MA thesis

Status: finished

Date: November 15, 2021 - May 16, 2022

Supervisors: Florian Kordon, Andreas Maier, Prof. Dr. rer. nat. Christoph Bert (Strahlenklinik des Universitätsklinikums Erlangen), Dr.-Ing. Holger Kunze (Siemens Healthcare GmbH)

Thesis Description

Female breast cancer accounts for 355.000 new cases among all types of cancer in EU-27 countries in 2020. In Germany alone, approximately 69,000 new cases are diagnosed each year [1]. During the past four decades, breast conserving surgery (BCS) after lumpectomy in combination with radiotherapy (RT) has been most widely accepted as this treatment technique reduces both a patient’s emotional as well as psychological traumata due to superior aesthetic outcome [2]. The standard technique of giving RT after BCS is whole breast irradiation (WBI) where a patient’s entire breast is irradiated up to a total dose of 40 to 50 Gray (Gy). BCS with adjuvant WBI yields evident equivalence in terms of local tumor control compared to mastectomy where the entire breast is amputated. However, approximately 50 % of early breast cancer patients still undergo mastectomy in order to omit either RT at all or 5 to 7 weeks of treatment time [3]. In contrast to external breast irradiation, accelerated partial breast irradiation (APBI) is an emerging standalone post-operative alternative treatment option in brachytherapy [4]. One valid strategy of applying APBI is multi-catheter interstitial brachytherapy (iBT). Thereby, up to 30 highly flexible plastic catheters are implanted into a patient’s breast in order to precisely and locally damage the tumor by guiding a radioactive source through the tissue. In BCT, the radioactive dose is delivered by utilizing a high dose rate (HDR) technique where the prescribed dose is administered with a rate of 12 Gy/h by single source within minutes [5]. This is performed by an afterloading system connected to the catheters via transfer tubes [4, 6]. Sole APBI is not only intended to drastically reduce treatment times to only 4 to 5 days but also to decrease the amount of radiation exposure of adjacent organs at risk (OAR) such as the lung, the skin and, in particular, the heart [7]. After implantation, catheter traces are manually reconstructed based on an acquired computed tomography (CT) image for treatment planning and determining the implant geometry. Then, in the acquired CT of the patient’s breast, physicians precisely define the target volume depending on a tumor’s size and location [6]. While treatment planning, implanted plastic catheters are manually reconstructed slice by slice which takes approximately 45% of the whole treatment time [8]. Along each catheter trajectory dwell positions (DPs) connecting the points in the slices as well as dwell times (DTs) are defined. DPs determine positions where the radioactive source stops for a certain DT, thus irradiation surrounding tumor tissue. Active DPs and DTs are defined at the location of the target volume to optimally deliver prescribed radioactive dose [9]. As treatment plan dosimetry and DP positioning are directly related, accurate and fast catheter trace reconstructions are crucial [4].

However, the manual reconstruction of up to 30 catheter tubes is a time-consuming process. Kallis et al. state that manual reconstructions on average take up to 139 ± 47 seconds(s) per catheter. They also observed an interobserver variability of 0.6 ± 0.35 millimeter (mm) in terms of mean Euclidean distance between two experienced medical physicists and the autoreconstruction approach proposed by [8], thus, yielding reproducible and reliable reconstructions [6]. Similar findings were proven by Milickovic et al. in 2001 [10]. The insufficient amount of ground truth catheter trace positions as well as blurry CT imaging quality make it hard to reliably and accurately reconstruct DPs. Hence, this suggests further research to conducting automated reconstruction approaches [10].

In the last 20 years, mainly two different catheter auto-reconstruction approaches were proposed. Both techniques aim to minimize the error of implant geometries, thus, improve optimal dose coverage as well as drastically reduce reconstruction times. Milickovic et al. developed an automated catheter reconstruction algorithm based on analyzing post-implant CT data [8, 10]. However as stated by Kallis et al., CT based treatment planning in multi-catheter iBT highly depends on image quality. Due to patient movements, artifacts, as well as acquisition noise, automatically extracted DPs have to be corrected by manual intervention which increases reconstruction times [6]. As introduced by Zhou et al. in 2013, electromagnetic tracking (EMT) became a promising alternative compared to CT based auto-reconstruction [11]. Further analysis has proven that EMT is applicable to iBT as this technique of localizing dwell positions in iBT offers sparse, precise, and sufficiently accurate dose calculations [12]. Reducing uncertainties including measurement noise is investigated by postprocessing of sensor data by particle filters. In their work, a mean error of 2.3 mm between clinically approved plan and reconstructed DPs has been reported [13]. Although tracking multi-catheter positions in iBT based on EMT offers imaging artifact independent and fast results, the performance of EMT systems depends heavily on system configurations, e.g. the distance between CT table and patient bed. The error drastically increases from approximately 1 to 4 mm when decreasing the table/bed distance [12].

In recent years, deep learning (DL) has shown to be a powerful technique tackling a variety of computer vision tasks such as medical image analysis. DL based approaches offer highly competitive results in terms of accuracy and efficiency [14, 15]. Deep neural network (DNN) model architectures are able to represent high dimensional non-linear spaces, thus are well suited for the task of automatically reconstructing multi-catheter traces in iBT. Built upon an elegant way of designing DNN architectures – so called Fully Convolutional Networks (FCN) [16] – the UNet architecture has proven to be well suited for image based segmentation tasks as this specific model structure’s output has the same shape as the input [17]. C¸i¸cek et al. developed an extended version of the UNet where all 2D operations are replaces with corresponding 3D ones. This topological modification enables volumetric semantic segmentations [18]. In this Master’s thesis a deep learning based multi-catheter reconstruction method for iBT is presented, investigated, and evaluated using real world breast cancer data from the radiation clinic in Erlangen, Germany. To the best of our knowledge this is the first approach where we introduce artificial intelligence based multi-catheter reconstruction algorithm in breast brachytherapy.


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