Accurate heterogeneous dose calculation for lung cancer patients without high-resolution CT densities

Evaluating synthetic computed tomography images for adaptive radiotherapy decision making in head and neck cancer

Background and purpose

Adaptive radiotherapy (ART) decision-making benefits from dosimetric information to supplement image inspection when assessing the significance of anatomical changes. This study evaluated a dosimetry-based clinical decision workflow for ART utilizing deformable registration of the original planning computed tomography (CT) image to the daily Cone Beam CT (CBCT) to replace the need for a replan CT for dose estimation.

Materials and methods

We used 12 retrospective Head & Neck patient cases having a ground truth - a replan CT (rCT) in response to anatomical changes apparent in the daily CBCT - to evaluate the accuracy of dosimetric assessment conducted on synthetic CTs (sCT) generated by deforming the original planning CT Hounsfield Units to the daily CBCT anatomy.The original plan was applied to the sCT and dosimetric accuracy of the sCT was assessed by analyzing plan objectives for targets and organs-at-risk compared to calculations on the ground-truth rCT. Three commercial DIR algorithms were compared.

Results

For the best-performing algorithms, the majority of dose metrics calculated on the sCTs differed by less than 4 Gy (5.7% of 70 Gy prescription dose). An uncertainty of ±2.5 Gy (3.6% of 70 Gy prescription) is recommended as a conservative tolerance when evaluating dose metrics on sCTs for head and neck.

Conclusions

Synthetic CTs present a valuable addition to the adaptive radiotherapy workflow, and synthetic CT dose estimates can be effectively used in addition to the current practice of visually inspecting the overlay of the planning CT and CBCT to assess the significance of anatomical change.

Synthetic computed tomography generation for abdominal adaptive radiotherapy using low-field magnetic resonance imaging

Background and Purpose

Magnetic Resonance guided Radiotherapy (MRgRT) still needs the acquisition of Computed Tomography (CT) images and co-registration between CT and Magnetic Resonance Imaging (MRI). The generation of synthetic CT (sCT) images from the MR data can overcome this limitation. In this study we aim to propose a Deep Learning (DL) based approach for sCT image generation for abdominal Radiotherapy using low field MR images.

Materials and methods

CT and MR images were collected from 76 patients treated on abdominal sites. U-Net and conditional Generative Adversarial Network (cGAN) architectures were used to generate sCT images. Additionally, sCT images composed of only six bulk densities were generated with the aim of having a Simplified sCT.Radiotherapy plans calculated using the generated images were compared to the original plan in terms of gamma pass rate and Dose Volume Histogram (DVH) parameters.

Results

sCT images were generated in 2 s and 2.5 s with U-Net and cGAN architectures respectively.Gamma pass rates for 2%/2mm and 3%/3mm criteria were 91% and 95% respectively. Dose differences within 1% for DVH parameters on the target volume and organs at risk were obtained.

Conclusion

U-Net and cGAN architectures are able to generate abdominal sCT images fast and accurately from low field MRI.

Image similarity evaluation of the bulk-density-assigned synthetic CT derived from MRI of intracranial regions for radiation treatment

Objective

Various methods for radiation-dose calculation have been investigated over previous decades, focusing on the use of magnetic resonance imaging (MRI) only. The bulk-density-assignment method based on manual segmentation has exhibited promising results compared to dose-calculation with computed tomography (CT). However, this method cannot be easily implemented in clinical practice due to its time-consuming nature. Therefore, we investigated an automatic anatomy segmentation method with the intention of providing the proper methodology to evaluate synthetic CT images for a radiation-dose calculation based on MR images.

Methods

CT images of 20 brain cancer patients were selected, and their MR images including T1-weighted, T2-weighted, and PETRA were retrospectively collected. Eight anatomies of the patients, such as the body, air, eyeball, lens, cavity, ventricle, brainstem, and bone, were segmented for bulk-density-assigned CT image (_BCT) generation. In addition, water-equivalent CT images (_WCT) with only two anatomies—body and air—were generated for a comparison with _BCT. Histogram comparison and gamma analysis were performed by comparison with the original CT images, after the evaluation of automatic segmentation performance with the dice similarity coefficient (DSC), false negative dice (FND) coefficient, and false positive dice (FPD) coefficient.

Results

The highest DSC value was 99.34 for air segmentation, and the lowest DSC value was 73.50 for bone segmentation. For lens segmentation, relatively high FND and FPD values were measured. The cavity and bone were measured as over-segmented anatomies having higher FPD values than FND. The measured histogram comparison results of _BCT were better than those of _WCT in all cases. In gamma analysis, the averaged improvement of _BCT compared to _WCT was measured. All the measured results of _BCT were better than those of _WCT. Therefore, the results of this study show that the introduced methods, such as histogram comparison and gamma analysis, are valid for the evaluation of the synthetic CT generation from MR images.

Conclusions

The image similarity results showed that _BCT has superior results compared to _WCT for all measurements performed in this study. Consequently, more accurate radiation treatment for the intracranial regions can be expected when the proper image similarity evaluation introduced in this study is performed.

CNR considerations for rapid real-time MRI tumor tracking in radiotherapy hybrid devices: Effects of B0 field strength

PURPOSE

This work examines the subject of contrast-to-noise ratio (CNR), specifically between tumor and tissue background, and its dependence on the MRI field strength, B0. This examination is motivated by the recent interest and developments in MRI/radiotherapy hybrids where real-time imaging can be used to guide treatment beams. The ability to distinguish a tumor from background tissue is of primary importance in this field, and this work seeks to elucidate the complex relationship between the CNR and B0 that is too often assumed to be purely linear.

METHODS

Experimentally based models of B0-dependant relaxation for various tumor and normal tissues from the literature were used in conjunction with signal equations for MR sequences suitable for rapid real-time imaging to develop field-dependent predictions for CNR. These CNR models were developed for liver, lung, breast, glioma, and kidney tumors for spoiled gradient-echo, balanced steady-state free precession (bSSFP), and single-shot half-Fourier fast spin echo sequences.

RESULTS

Due to the pattern in which the relaxation properties of tissues are found to vary over B0 field (specifically the T1 time), there was always an improved CNR at lower fields compared to linear dependency. Further, in some tumor sites, the CNR at lower fields was found to be comparable to, or sometimes higher than those at higher fields (i.e., bSSFP CNR for glioma, kidney, and liver tumors).

CONCLUSIONS

In terms of CNR, lower B0 fields have been shown to perform as well or better than higher fields for some tumor sites due to superior T1 contrast. In other sites this effect was less pronounced, reversing the CNR advantage. This complex relationship between CNR and B0 reveals both low and high magnetic fields as viable options for tumor tracking in MRI/radiotherapy hybrids.

A portal dosimetry dose prediction method based on collapsed cone algorithm using the clinical beam model

PURPOSE

Amorphous silicon electronical portal imaging devices (EPIDs) are widely used for dosimetric measurements in Radiation Therapy. The purpose of this work was to determine if a portal dose prediction method can be utilized for dose map calculations based on the linear accelerator model within a commercial treatment planning system (Pinnacle³ v8.0 m).

METHODS

The method was developed for a 6 MV photon beam on the Varian Clinac 21-EX, at a nominal dose rate of 400 MU/min. The Varian aS1000 EPID was unmounted from the linear accelerator and scanned to acquire CT images of the EPID. The CT images were imported into Pinnacle³ and were used as a quality assurance phantom to calculate dose on the EPID setup at a source to detector distance of 105 cm. The best match of the dose distributions was obtained considering the image plane located at 106 cm from the source to detector plane. The EPID was calibrated according to the manufacturer procedure and corrections were made for output factors. Arm-backscattering effect, based on profile correction curves, has been introduced. Five low-modulated and three high-modulated clinical planned treatments were predicted and measured with the method presented here and with MatriXX (IBA Dosimetry, Schwarzenbruck, Germany).

RESULTS

A portal dose prediction method based on Pinnacle³ was developed without modifying the commissioned parameters of the model in use in the clinic. CT images of the EPID were acquired and used as a quality assurance phantom. The CT images indicated a mean density of 1.16 g/cm³ for the sensitive area of the EPID. Output factor measured with the EPID were lower for small fields and larger for larger fields (beyond 10 × 10 cm² ). Arm-backscatter correction showed a better agreement at the target side of the EPID. Analysis of Gamma index comparison (3%, 3 mm) indicated a minimum of 97.4% pass rate for low modulated and 98.3% for high modulated treatments. Pass rates were similar for MatriXX measurements.

CONCLUSIONS

The method developed here can be easily implemented into clinic, as neither additional modeling of the clinical energy nor an independent image prediction algorithm are necessary. The main advantage of this method is that portal dose prediction is calculated with the same algorithm and beam model used for patient dose distribution calculation. This method was independently validated with an ionization chamber matrix.

The Effect of Acoustic Impedance on Subsurface Absorber Geometry Reconstruction using 1D Frequency-Domain Photoacoustics

This paper considers the effect of an impedance mismatch between the absorber and its surroundings on the aborber reconstructions from the photoacoustic signal profile, in particular when a non-delta input pulse is used. A transfer function approach is taken, demonstrating in the case of impedance mismatch how the total response can be modeled using the sum of the mismatch-free response and its time-delayed, time-reversed replicas, which may or may not overlap. It is shown how this approach can be exploited to accommodate the effects of non-delta pulses and/or pulse-equivalent waveforms such as linear-frequency-modulated (LFM) chirps, and impedance mismatches in any inversion algorithms, even in the presence of large reflection coefficients. As a consequence, for simple-absorber reconstruction algorithms that assume impulses or 'short enough' pulses, the compressive portion of the measured response may be used in reconstruction formulas that do not model the impedance mismatch, regardless of the size of the mismatch. For longer-duration input waveforms, it is demonstrated how existing reconstruction methods can be successfully adapted to include the effect of the impedance mismatch. Simulations are used to illustrate these ideas. The gained physical insight into how components of the generated pressure wave carry absorber information is then exploited for signal inversion and absorber reconstruction in the frequency domain when multi-frequency modulation chirps are used for photoacoustic radar pressure measurements. The foundational theoretical developments ultimately address impendance mismatch issues germane to the major photoacoustic frequency-domain imaging modality to-date, which is the photoacoustic radar.

Material discrimination based on K-edge characteristics

Spectral/multienergy CT employing the state-of-the-art energy-discriminative photon-counting detector can identify absorption features in the multiple ranges of photon energies and has the potential to distinguish different materials based on K-edge characteristics. K-edge characteristics involve the sudden attenuation increase in the attenuation profile of a relatively high atomic number material. Hence, spectral CT can utilize material K-edge characteristics (sudden attenuation increase) to capture images in available energy bins (levels/windows) to distinguish different material components. In this paper, we propose an imaging model based on K-edge characteristics for maximum material discrimination with spectral CT. The wider the energy bin width is, the lower the noise level is, but the poorer the reconstructed image contrast is. Here, we introduce the contrast-to-noise ratio (CNR) criterion to optimize the energy bin width after the K-edge jump for the maximum CNR. In the simulation, we analyze the reconstructed image quality in different energy bins and demonstrate that our proposed optimization approach can maximize CNR between target region and background region in reconstructed image.