High-dose MVCT image guidance for stereotactic body radiation therapy

Hypofractionation and SABR: 25 years of evolution in medical physics and a glimpse of the future

As we were invited to write an article for celebrating the 50^th Anniversary of Medical Physics journal, on something historically significant, commemorative, and exciting happening in the past decades, the first idea came to our mind is the fascinating radiotherapy paradigm shift from conventional fractionation to hypofractionation and stereotactic ablative radiotherapy (SABR). It is historically and clinically significant since as we all know this RT treatment revolution not only reduces treatment duration for patients, but also improves tumor control and cancer treatment outcomes. It is also commemorative and exciting for us medical physicists since the technology development in medical physics has been the main driver for the success of this treatment regimen which requires high precision and accuracy throughout the entire treatment planning and delivery. This article provides an overview of the technological development and clinical trials evolvement in the past 25 years for hypofractionation and SABR, with an outlook to the future improvement.

Training of deep cross-modality conversion models with a small dataset, and their application in megavoltage CT to kilovoltage CT conversion

PURPOSE

In recent years, deep-learning-based image processing has emerged as a valuable tool for medical imaging owing to its high performance. However, the quality of deep-learning-based methods heavily relies on the amount of training data; the high cost of acquiring a large dataset is a limitation to their utilization in medical fields. Herein, based on deep learning, we developed a computed tomography (CT) modality conversion method requiring only a few unsupervised images.

METHODS

The proposed method is based on CycleGAN with several extensions tailored for CT images, which aims at preserving the structure in the processed images and reducing the amount of training data. This method was applied to realize the conversion of megavoltage computed tomography (MVCT) to kilovoltage computed tomography (kVCT) images. Training was conducted using several datasets acquired from patients with head and neck cancer. The size of the datasets ranged from 16 slices (two patients) to 2745 slices (137 patients) for MVCT and 2824 slices (98 patients) for kVCT.

RESULTS

The required size of the training data was found to be as small as a few hundred slices. By statistical and visual evaluations, the quality improvement and structure preservation of the MVCT images converted by the proposed model were investigated. As a clinical benefit, it was observed by medical doctors that the converted images enhanced the precision of contouring.

CONCLUSIONS

We developed an MVCT to kVCT conversion model based on deep learning, which can be trained using only a few hundred unpaired images. The stability of the model against changes in data size was demonstrated. This study promotes the reliable use of deep learning in clinical medicine by partially answering commonly asked questions, such as "Is our data sufficient?" and "How much data should we acquire?" This article is protected by copyright. All rights reserved.

Management of metallic implants in radiotherapy

The number of patients with metallic implant and treated with radiotherapy is constantly increasing. These hardware are responsible for the deterioration in the quality of the CT images used at each stage of the radiation therapy, during delineation, dosimetry and dose delivery. We present the update of the recommendations of the French society of oncological radiotherapy on the pros and cons of the different methods, existing and under evaluation, which limit the impact of metallic implants on the quality and safety of radiation treatments.

Optimisation of CT scan parameters to increase the accuracy of gross tumour volume identification in brain radiotherapy

Aim:

This study aimed to optimise computed tomography (CT) simulation scan parameters to increase the accuracy for gross tumour volume identification in brain radiotherapy. For this purpose, high-contrast scan protocols were assessed.

Materials and methods:

A CT accreditation phantom (ACR Gammex 464) was used to optimise brain CT scan parameters on a Toshiba Alexion 16-row multislice CT scanner. Dose, tube voltage, tube current–time and CT dose index (CTDI) were varied to create five image quality enhancement (IQE) protocols. They were assessed in terms of contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR) and noise level and compared with a standard clinical protocol. Finally, the ability of the selected protocols to identify low-contrast objects was examined based on a subjective method.

Results:

Among the five IQE protocols, the one with the highest tube current–time product (250 mA) and lowest tube voltage (100 kVp) showed higher CNR, while another with a tube current–time product of 150 mA and a tube voltage of 135 kVp had improved SNR and lower noise level compared to the standard protocol. In contouring low-contrast objects, the protocol with the highest milliampere and lowest peak kilovoltage exhibited the lowest error rate (1%) compared to the standard protocol (25%).

Findings:

CT image quality should be optimised using the high-dose parameters created in this study to provide better soft tissue contrast. This could lead to an accurate identification of gross tumour volume recognition in the planning of radiotherapy treatment.

Improved contrast and noise of megavoltage computed tomography (MVCT) through cycle-consistent generative machine learning

PURPOSE

Megavoltage computed tomography (MVCT) has been implemented on many radiation therapy treatment machines as a tomographic imaging modality that allows for three-dimensional visualization and localization of patient anatomy. Yet MVCT images exhibit lower contrast and greater noise than its kilovoltage CT (kVCT) counterpart. In this work, we sought to improve these disadvantages of MVCT images through an image-to-image-based machine learning transformation of MVCT and kVCT images. We demonstrated that by learning the style of kVCT images, MVCT images can be converted into high-quality synthetic kVCT (skVCT) images with higher contrast and lower noise, when compared to the original MVCT.

METHODS

Kilovoltage CT and MVCT images of 120 head and neck (H&N) cancer patients treated on an Accuray TomoHD system were retrospectively analyzed in this study. A cycle-consistent generative adversarial network (CycleGAN) machine learning, a variant of the generative adversarial network (GAN), was used to learn Hounsfield Unit (HU) transformations from MVCT to kVCT images, creating skVCT images. A formal mathematical proof is given describing the interplay between function sensitivity and input noise and how it applies to the error variance of a high-capacity function trained with noisy input data. Finally, we show how skVCT shares distributional similarity to kVCT for various macro-structures found in the body.

RESULTS

Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were improved in skVCT images relative to the original MVCT images and were consistent with kVCT images. Specifically, skVCT CNR for muscle-fat, bone-fat, and bone-muscle improved to 14.8 ± 0.4, 122.7 ± 22.6, and 107.9 ± 22.4 compared with 1.6 ± 0.3, 7.6 ± 1.9, and 6.0 ± 1.7, respectively, in the original MVCT images and was more consistent with kVCT CNR values of 15.2 ± 0.8, 124.9 ± 27.0, and 109.7 ± 26.5, respectively. Noise was significantly reduced in skVCT images with SNR values improving by roughly an order of magnitude and consistent with kVCT SNR values. Axial slice mean (S-ME) and mean absolute error (S-MAE) agreement between kVCT and MVCT/skVCT improved, on average, from -16.0 and 109.1 HU to 8.4 and 76.9 HU, respectively.

CONCLUSIONS

A kVCT-like qualitative aid was generated from input MVCT data through a CycleGAN instance. This qualitative aid, skVCT, was robust toward embedded metallic material, dramatically improves HU alignment from MVCT, and appears perceptually similar to kVCT with SNR and CNR values equivalent to that of kVCT images.

Phase I dose escalation study of helical intensity-modulated radiotherapy-based stereotactic body radiotherapy for hepatocellular carcinoma

Background

Phase I trial was conducted to determine feasibility and toxicity of helical intensity-modulated radiotherapy (IMRT)-based stereotactic body radiotherapy (SBRT) for hepatocellular carcinoma (HCC).

Results

Eighteen patients (22 lesions) were enrolled. With no DLT at 52 Gy (13 Gy/fraction), protocol was amended for further escalation to 60 Gy (15 Gy/fraction). Radiologic complete response rate was 88.9%. Two outfield intrahepatic, 2 distant, 4 concurrent local and outfield, and 1 concurrent local, outfield and distant failures (no local failure at dose levels 3–4) occurred. The worst toxicity was grade 3 hematologic in five patients, with no gastrointestinal toxicity > grade 1. At median follow-up of 28 months for living patients, 2-year local control, progression-free (PFS), and overall survival rates were 71.3%, 49.4% and 69.3%, respectively. Multi-segmental recurrences prior to SBRT was independent prognostic factor for PFS (p = 0.033).

Materials and Methods

Eligible patients had Child-Pugh's class A or B, unresectable HCC, ≤ 3 lesions, and cumulative tumor diameter ≤ 6 cm. Starting at 36 Gy in four fractions, dose was escalated with 2 Gy/fraction per dose-level. CTCAE v 3.0 ≥ grade 3 gastrointestinal toxicity and radiation induced liver disease defined dose-limiting toxicity (DLT).

Conclusions

Helical IMRT-based SBRT was tolerable and showed encouraging results. Confirmatory phase II trial is underway.

[Consequences of prosthesis on quality of the radiation therapy]

Dose prescription, delineation and dose calculation are clearly complicated when a patient have been operated on with insertion of prosthesis. Knowledge of the physical and material characteristics is needed to decrease incertitude of calculations. Recommendations for each step of treatments are proposed in this article allowing to optimization of the treatment safety.

Radiation-induced CT number changes in GTV and parotid glands during the course of radiation therapy for nasopharyngeal cancer

OBJECTIVE

To investigate the changes in CT number (CTN) in gross tumour volume (GTV) and organs at risk (OARs) during the course of radiation therapy (RT) for nasopharyngeal cancer (NPC).

METHODS

Daily megavoltage CT (MVCT) data collected from 30 patients with NPC treated with a prescription dose of 70 Gy in 30-33 fractions using helical tomotherapy were retrospectively analyzed. The contours of GTV and OARs on daily MVCTs were obtained by populating the planning contours from planning CT to daily MVCTs with manual editing, if necessary. The changes of GTV and OAR volumes and the histograms of CTN in the GTV and OARs during the course of RT delivery were analyzed.

RESULTS

Volumes of GTV and parotid glands were reduced during the course of radiation treatment, with an average shrinkage rate of 0.23% per day (range, 0.02-0.8%) and 1.2% per day (range, 0.2-2.3%), respectively. The mean CTN changes in GTV and ipsilateral and contralateral parotid glands were reduced by 52 ± 35 HU, 18 ± 20 HU and 17 ± 22 HU, respectively. For GTV, the CTN and GTV volume decreases were found to be correlated with each other (p < 0.0001). No noticeable CTN change was found in the spinal cord and non-specified tissue irradiated with low doses.

CONCLUSION

The CTN changes in GTV and parotids are measurable during the delivery of fractionated radiotherapy for NPC, were associated with the doses received (the number of fractions delivered) and were patient specific.

ADVANCES IN KNOWLEDGE

The CTN change during radiotherapy is dose dependent and is measurable for NPC.

Megavoltage CT imaging quality improvement on TomoTherapy via tensor framelet

PURPOSE

This work is to investigate the feasibility of improving megavoltage imaging quality for TomoTherapy using a novel reconstruction technique based on tensor framelet, with either full-view or partial-view data.

METHODS

The reconstruction problem is formulated as a least-square L1-type optimization problem, with the tensor framelet for the image regularization, which is a generalization of L1, total variation, and wavelet. The high-order derivatives of the image are simultaneously regularized in L1 norm at multilevel along the x, y, and z directions. This convex formulation is efficiently solved using the Split Bregman method. In addition, a GPU-based parallel algorithm was developed to accelerate image reconstruction. The new method was compared with the filtered backprojection and the total variation based method in both phantom and patient studies with full or partial projection views.

RESULTS

The tensor framelet based method improved the image quality from the filtered backprojection and the total variation based method. The new method was robust when only 25% of the projection views were used. It required ∼2 min for the GPU-based solver to reconstruct a 40-slice 1 mm-resolution 350×350 3D image with 200 projection views per slice and 528 detection pixels per view.

CONCLUSIONS

The authors have developed a GPU-based tensor framelet reconstruction method with improved image quality for the megavoltage CT imaging on TomoTherapy with full or undersampled projection views. In particular, the phantom and patient studies suggest that the imaging quality enhancement via tensor framelet method is prominent for the low-dose imaging on TomoTherapy with up to a 75% projection view reduction.

Quantitative characterization of tomotherapy MVCT dosimetry

Megavoltage computed tomography (MVCT) is used as image guidance for patient setup in almost every tomotherapy treatment. Frequent use of ionizing radiation for image guidance has raised concern of imaging dose. The purpose of this work is to quantify and characterize tomotherapy MVCT dosimetry. Our dose calculation was based on a commissioned dose engine, and the calculation result was compared with film measurement. We studied dose profiles, center dose, maximal dose, surface dose, and mean dose on homogeneous cylindrical water phantoms of various diameters for various scanning parameters, including 3 different jaw openings (of nominal value J4, J1, and J0.1) and couch speeds (fine, normal, and coarse). The comparison between calculation and film measurement showed good agreement. In particular, the thread pattern on the film of the helical delivery matched very well with calculation. For the J1 jaw and coarse imaging mode, the maximum difference between calculation and measurement was about 6% of the center dose. Calculation on various sizes of synthesized phantoms showed that the center dose decreases almost linearly as the phantom diameter increases, and that the fine mode (couch speed of 4mm/rotation) received twice the dose of the normal mode (couch speed of 8mm/rotation) and 3 times that of the coarse mode (couch speed of 12mm/rotation) as expected. The maximal dose ranged from 100% to ∼200% of the center dose, with increasing ratios for larger phantoms, smaller jaws, and faster couch speed. For all jaw settings and couch speeds, the mean dose and average surface dose vary from 95% to 125% of the center dose with increasing ratios for larger phantoms. We present a quantitative dosimetric characterization of the tomotherapy MVCT in terms of scanning parameters, phantom size, center dose, maximal dose, surface dose, and mean dose. The results can provide an overall picture of dose distribution and a reference data set that enables estimation of CT dose index for the tomotherapy MVCT.