Stereotactic ablative radiotherapy (SABR) delivers a high dose of radiation to a small area and is frequently used to treat cancer patients with metastatic lesions in the lung. Selecting the appropriate prescription is a balance of delivering enough radiation to lesions to prevent recurrence, and minimizing the radiation delivered to organs at risk (OARs) to limit side effects. After a radiation oncologist (RO) selects a prescription, treatment planning software is used to create the dose distribution, and calculate radiation delivered to the lesions and OARs. If dose constraints are not met, a different prescription must be selected, and the process is repeated. Planning SABR treatments is resource intensive, and repeated iterations can lead to treatment delays. Recently, machine learning techniques have been used to create a dose distribution for a given SABR prescription. Thus far, these techniques only target single lesions and are not commonly implemented clinically. In this work, we create a conditional generative adversarial network (GAN) with a U-NET backbone to estimate the dose distribution of SABR treatments to 2-6 lesions in the lung. The GAN is conditioned on contours of the OARs and lesions, CT images, and an initial dose estimation. A novel loss function is used during training. Through the mean squared error and dose metrics used by ROs, the output of the GAN demonstrates good agreement with the ground truth dose. The model will allow ROs to efficiently compare prescriptions options, reduce departmental workload by the multidisciplinary team, and circumvent treatment delivery delays for patients.
Brachytherapy (BT) is a form of radiation therapy which typically relies on the insertion of needles to deliver radiation and is commonly used to treat a variety of cancer sites including prostate cancer. Accurate needle tip identification is key for safe and effective BT, as errors can result in radiation delivery that deviates from the planned dose. Typically, standard brightness (B)-mode ultrasound (US) imaging is used, however, artifacts can limit the visibility of needles. We propose a novel wireless mechanical oscillator for needle perturbation in a power Doppler (PD)-based needle tip identification method to overcome these limitations and improve needle tip identification accuracy in BT. We evaluated our method using a tissue equivalent phantom with the clinical US system and needles. In this proof-of-concept study, we assessed the performance of our PD US method using tip error computed based on a reference needle. Signed mean ± SD tip error was -0.03 ± 0.49 mm, while absolute mean error was 0.33 ± 0.33 mm, demonstrating our PD US method provided accurate needle tip identification for ideal visualization in phantom. We also demonstrated that our PD US method is capable of visualizing needles that cannot be seen using B-mode US due to shadowing, providing promise for the clinical utility of our method. Our wireless mechanical oscillator for PD-based needle tip identification is simple to use and easy to implement, requiring no modifications to clinical equipment. A prospective clinical trial has been approved to assess our method in the clinic.
Prostate cancer has the second highest noncutaneous cancer incidence in men worldwide. A common treatment technique for intermediate and high-risk localized prostate cancer is ultrasound (US)-guided high-dose-rate brachytherapy. This minimally invasive procedure uses a radioactive source passed through multiple needles to deliver radiation to the prostate and relies on accurate identification of needle tips to ensure patient safety and delivery of the prescribed doses. Image artifacts from nearby needles and the surrounding tissue often limit the accuracy of needle tip identification when using standard US imaging. To overcome these limitations and improve the accuracy of intraoperative needle tip identification, we propose the use of power Doppler (pD) US imaging while a mechanical perturbation is applied to the needle of interest. A mock procedure employing the standard clinical workflow was completed in a tissue-mimicking agar phantom. Inserted needles were imaged using standard US, followed by pD imaging of the same needles while a custom-made mechanical oscillator was used to perturb the needle. Physical measurements of the needle end lengths were used to estimate insertion depth errors (IDEs). 13 unobstructed needles and 10 shadowed needles were imaged using standard and pD US, resulting in mean IDEs ± standard deviation of 2.2 ± 0.9 mm and 1.3 ± 0.9 mm, respectively, for unobstructed needles, and 2.1 ± 1.6 mm and 1.6 ± 1.2 mm for shadowed needles. Mean IDEs were reduced in all cases when pD imaging was used, suggesting our method may be useful in improving HDR-BT treatment accuracy and patient safety.
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