Fiducial visibility on planar images during motion-synchronized tomotherapy treatments

Objective: Synchrony® is a motion management system on the Radixact® that uses planar kV radiographs to locate the target during treatment. The purpose of this work is to quantify the visibility of fiducials on these radiographs. Approach: A custom acrylic slab was machined to hold 8 gold fiducials of various lengths, diameters, and orientations with respect to imaging axis. The slab was placed on the couch at the imaging isocenter and planar radiographs were acquired perpendicular to the custom slab with varying thicknesses of acrylic on each side. Fiducial signal to noise ratio (SNR) and detected fiducial position error in millimeters were quantified. Main Results: The minimum output protocol (100 kVp, 0.8 mAs) was sufficient to detect all fiducials on both Radixact configurations when the thickness of the phantom was 20 cm. However, no fiducials for any protocol were detected when the phantom was 50 cm thick. The algorithm accurately detected fiducials on the image when the SNR was larger than 4. The MV beam was observed to cause RFI artifacts on the kV images and to decrease SNR by an average of 10%. Significance: This work provides the first data on fiducial visibility on kV radiographs from Radixact Synchrony treatments. The Synchrony fiducial detection algorithm was determined to be very accurate when sufficient SNR is achieved. However, a higher output protocol may need to be added for use with larger patients. This work provided groundwork for investigating visibility of fiducial-free solid targets in future studies and provided a direct comparison of fiducial visibility on the two Radixact configurations, which will allow for intercomparison of results between configurations.

Effect of inaccurate small field output factors on brain SRS plans

External beam radiotherapy often includes the use of field sizes 3 × 3 cm2 or less, which can be defined as small fields. Dosimetry is a difficult, yet important part of the radiotherapy process. The dosimetry of small fields has additional challenges, which can lead to treatment inconsistencies if not done properly. Most important is the use of an appropriate detector, as well as the application of the necessary corrections. The International Atomic Energy Agency and the American Association of Physicists in Medicine provide the International Code of Practice (CoP) TRS-483 for the dosimetry of small static fields used in external MV photon beams. It gives guidelines on how to apply small-field correction factors for small field dosimetry. The purpose of this study was to evaluate the impact of inaccurate small-field output factors on clinical brain stereotactic radiosurgery plans with and without applying the small-field correction factors as suggested in the CoP. Small-field correction factors for a Varian TrueBeam linear accelerator were applied to uncorrected relative dose factors. Uncorrected and corrected clinical plans were created with two different beam configurations, 6 MV with a flattening filter (6 WFF) and 6 MV without a flattening filter (6 FFF). For the corrected plans, the planning target volume mean dose was 1.6 ± 0.9% lower with p < 0.001 for 6 WFF and 1.8 ± 1.5% lower with p < 0.001 for 6 FFF. For brainstem, a major organ at risk, the corrected plans had a dose that was 1.6 ± 0.9% lower with p = 0.03 for 6 WFF and 1.8 ± 1.5% lower with p = 0.10 for 6 FFF. This represents a systematic error that should and can be corrected.

Measurement of scattered rays from different materials using an inorganic scintillator based optical fiber sensor and its application in radiotherapy

Measurements using an Optical Fiber OFS including an inorganic scintillator placed on the surface of a phantom show that the particle energy distribution inside the phantom remains unchanged. The backscattered intensity measured using an Optical Fiber Sensor (OFS) exhibits a linear relationship with the total radiation dose delivered to the phantom, and this relationship shows that the OFS can be used for indirect dose measurement when located on the surface of the phantom i.e. that arising from the energetic backscattered electrons and photons. Such a device can therefore be used as a clinical in-vivo dosimeter, being located on the patient’s body surface. In addition, the measurement results for the same OFS located inside and outside the radiation field of a compound water based phantom are analyzed. The differences in measurement of the fluorescence signal in response to various tissue materials representing bone or tumor tissue in the irradiation field are strongly related to the material's ability to block the scattered rays from the water phantom, as well as the scattered X-rays generated by the material located within the phantom.

Deep learning-based extended field of view computed tomography image reconstruction: influence of network design on image estimation outside the scan field of view

The problem of data truncation in Computed Tomography (CT) is caused by the missing data when the patient exceeds the Scan Field of View (SFOV) of a CT scanner. The reconstruction of a truncated scan produces severe truncation artifacts both inside and outside the SFOV. We have employed a deep learning-based approach to extend the field of view and suppress truncation artifacts. Thereby, our aim is to generate a good estimate of the real patient data and not to provide a perfect and diagnostic image even in regions beyond the SFOV of the CT scanner. This estimate could then be used as an input to higher order reconstruction algorithms [1]. To evaluate the influence of the network structure and layout on the results, three convolutional neural networks (CNNs), in particular a general CNN called ConvNet, an autoencoder, and the U-Net architecture have been investigated in this paper. Additionally, the impact of L1, L2, structural dissimilarity and perceptual loss functions on the neural network’s learning have been assessed and evaluated. The evaluation of data set comprising 12 truncated test patients demonstrated that the U-Net in combination with the structural dissimilarity loss showed the best performance in terms of image restoration in regions beyond the SFOV of the CT scanner. Moreover, this network produced the best mean absolute error, L1, L2, and structural dissimilarity evaluation measures on the test set compared to other applied networks. Therefore, it is possible to achieve truncation artifact removal using deep learning techniques.

Alterations of tibialis anterior muscle activation pattern in subjects with type 2 diabetes and diabetic peripheral neuropathy

Diabetic peripheral neuropathy (DPN) is associated with loss of motor units (MUs), which can cause changes in the activation pattern of muscle fibres. This study investigated the pattern of muscle activation using high-density surface electromyography (HD-sEMG) signals from subjects with type 2 diabetes mellitus (T2DM) and DPN. Thirty-five adults participated in the study: 12 healthy subjects (HV), 12 patients with T2DM without DPN (No-DPN) and 11 patients with T2DM with DPN (DPN). HD-sEMG signals were recorded in the tibialis anterior muscle during an isometric contraction of ankle dorsiflexion at 50% of the maximum voluntary isometric contraction (MVIC) during 30-s. The calculated HD-sEMG signals parameters were the normalised root mean square (RMS), normalised median frequency (MDF), coefficient of variation (CoV) and modified entropy (ME). The RMS increased significantly (p = 0.001) with time only for the DPN group, while the MDF decreased significantly (p < 0.01) with time for the three groups. Moreover, the ME was significantly lower (p = 0.005), and CoV was significantly higher (p = 0.003) for the DPN group than the HV group. Using HD-sEMG, we have demonstrated a reduction in the number of MU recruited by individuals with DPN. This study provides proof of concept for the clinical utility of this technique for identifying neuromuscular impairment caused by DPN.

Improved cellular automata model shows that indirect apoptotic cell death due to vascular damage enhances the local control of tumors by single fraction high-dose irradiation

We investigated the effects of indirect apoptotic cell death due to vascular damage on tumor response to a single large dose with an improved two-dimensional cellular automata model. The tumor growth was simulated by considering the oxygen and nutrients supplied to the tumor through the blood vessels. The cell damage processes were modeled by taking account of the direct cell death and the indirect death due to the radiation-induced vascular damages. The radiation increased the permeation of oxygen and nutrients through the blood vessel or caused the breakdown of the vasculature. The amount of oxygen in cancer cells affected the response of cancer cells to radiation and the tumor growth rate after irradiation. The lack of oxygen led to the apoptotic death of cancer cells. We calculated the tumor control probability (TCP) at different radiation doses, D, the probability of apoptotic death, PO2_ap, the threshold of the oxygen level for indirect apoptotic death, O2t, the average oxygen level in cancer cells, [O2]av, and the vessel survival probability after radiation damage, Pv. Due to the vessel damage, indirect cell death led to a 4% increase in TCP for the dose ranging from 15 Gy to 20 Gy. TCP increased with increasing PO2_ap and O2t due to increased apoptotic death. The variation of TCP as a function of [O2]av exhibited the minimum at [O2]av of 2.7%. The apoptosis increased as [O2]av decreased, leading to an increasing TCP. On the other hand, the direct radiation damage increased, and the apoptosis decreased for higher [O2]av, resulting in a higher TCP. We showed by modeling the radiation damage of blood vessels in a 2D CA simulation that the indirect apoptotic death of cancer cells, caused by the reduction of the oxygen level due to vascular damage after high dose irradiation, increased TCP.

Virtual monochromatic imaging reduces beam hardening artefacts in cardiac interior photon counting computed tomography: a phantom study with cadaveric specimens

In interior cardiac computed tomography (CT) imaging, the x-ray beam is collimated to a limited field-of-view covering the heart volume, which decreases the radiation exposure to surrounding tissues. Spectral CT enables the creation of virtual monochromatic images (VMIs) through a computational material decomposition process. This study investigates the utility of VMIs for beam hardening (BH) reduction in interior cardiac CT, and further, the suitability of VMIs for coronary artery calcium (CAC) scoring and volume assessment is studied using spectral photon counting detector CT (PCD-CT). Ex vivo coronary artery samples (N=18) were inserted in an epoxy rod phantom. The rod was scanned in the conventional CT geometry, and subsequently, the rod was positioned in a torso phantom and re-measured in the interior PCD-CT geometry. The total energy (TE) 10-100 keV reconstructions from PCD-CT were used as a reference. The low energy 10-60 keV and high energy 60-100 keV data were used to perform projection domain material decomposition to polymethyl methacrylate and calcium hydroxylapatite basis. The truncated basis-material sinograms were extended using the adaptive detruncation method. VMIs from 30-180 keV range were computed from the detruncated virtual monochromatic sinograms using filtered back projection. Detrending was applied as a post-processing method prior to CAC scoring. The results showed that BH artefacts from the exterior structures can be suppressed with high (≥100 keV) VMIs. With appropriate selection of the monoenergy (46 keV), the underestimation trend of CAC scores and volumes shown in Bland-Altman (BA) plots for TE interior PCD-CT was mitigated, as the BA slope values were -0.02 for the 46 keV VMI compared to -0.21 the conventional TE image. To conclude, spectral PCD-CT imaging using VMIs could be applied to reduce BH artefacts interior CT geometry, and further, optimal selection of VMI may improve the accuracy of CAC scoring assessment in interior PCD-CT.

A Monte Carlo investigation of some important radiation parameters and tissue equivalency for photons below 1 keV in human tissues

The attenuation coefficients are important input values in estimating not only the dose and exposure in radiotherapy and medical imaging, but also in the proper design of photon shields. While studies are widely available above 1 keV, the attenuation coefficients of human tissues for photon energies less than 1 keV have not been studied yet. In this study, the attenuation coefficients of water and some human tissues were estimated for low energy photons using the MCNP6.1 code in the energy region 0.1 keV-1 keV. Mass attenuation coefficients were estimated at photon energies of 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950 and 1000 eV for water and ten human tissues (Soft, Breast, Lung, Bone, Brain, Eye lens, Ovary, Skin, Thyroid and Prostate). Results were compared with those available in literature and a fairly good agreement has been obtained. These data were then used to calculate the mean free path, half value layer, tenth value layer, effective atomic number and specific gamma-ray constant (useful for calculation of dose rate) as well. Moreover, for comparison the effective atomic number of the water has been obtained using the results of this work and using the data available in NIST database from 0.1 to 1 keV. In addition, the human tissues were compared with some tissue equivalent materials in terms of effective atomic number and specific gamma-ray constant to study the tissue equivalency from the results, the muscle-equivalent liquid with sucrose has been found to be the best tissue equivalent material for soft tissue, eye lens and brain with relative difference below 4.1%.

Single-trial detection of EEG error-related potentials in serial visual presentation paradigm

When the outcome of an event is not the same as expected, the cognitive state that monitors performance elicits a time-locked brain response termed as Error-Related Potential (ErrP). Objective – In the existing work, ErrP is not recorded when there is a disassociation between an object and its description. The objective of this work is to propose a Serial Visual Presentation (SVP) experimental paradigm to record ErrP when an image and its label are disassociated. Additionally, this work aims to propose a novel method for detecting ErrP on a single-trial basis. Method – The method followed in this work includes designing of SVP paradigm in which labeled images from six categories (bike, car, flower, fruit, cat, and dog) are presented serially. In this work, a text (visual) or an audio clip describing the image in one word is presented as the label. Further, the ErrP is detected on a single-trial basis using novel electrode-averaged features. Results - The ErrP data recorded from 11 subjects’ have consistent characteristics compared to existing ErrP literature. Detection of ErrP on a single-trial basis is carried out using a novel feature extraction method on two type labeling types separately. The best average classification accuracy achieved is 69.09±4.70% and 63.33±4.56% for the audio and visual type of labeling the image, respectively. The proposed feature extraction method achieved higher classification accuracy when compared with two existing feature extraction methods. Significance - The significance of this work is that it can be used as a Brain-Computer Interface (BCI) system for quantitative evaluation and treatment of mild cognitive impairment. This work can also find non-clinical BCI applications such as image annotation.

Capillary network formation and structure in a modified discrete mathematical model of angiogenesis

Angiogenesis, as part of cancer development, involves hierarchical complicated events and processes. Multiple studies have revealed the significance of the formation and structure of tumor-induced capillary networks. In this study, a discrete mathematical model of angiogenesis is studied and modified to capture the realistic physics of capillary network formation. Modifications are performed on the mathematical foundations of an existing discrete model of angiogenesis. The main modifications are the imposition of the matrix density effect, implementation of realistic boundary and initial conditions, and improvement of the method of governing equations based on physical observation. Results show that endothelial cells accelerate angiogenesis and capillary formation as they migrate toward the tumor and clearly exhibit the physical concept of haptotactic movement. On the other hand, consideration of blood flow-induced stress leads to a dynamic adaptive vascular network of capillaries which intelligibly reflects the brush border effect . The present modified model of capillary network formation is based on the physical rationale that defines a clear mathematical and physical interpretation of angiogenesis, which is likely to be used in cancer development modeling and anti-angiogenic therapies.