Comprehensive framework of GPU-accelerated image reconstruction for photoacoustic computed tomography.

Significance: Photoacoustic computed tomography (PACT) is a promising non-invasive imaging technique for both life science and clinical implementations. To achieve fast imaging speed, modern PACT systems have equipped arrays that have hundreds to thousands of ultrasound transducer (UST) elements, and the element number continues to increase. However, large number of UST elements with parallel data acquisition could generate a massive data size, making it very challenging to realize fast image reconstruction. Although several research groups have developed GPU-accelerated method for PACT, there lacks an explicit and feasible step-by-step description of GPU-based algorithms for various hardware platforms. Aim: In this study, we propose a comprehensive framework for developing GPU-accelerated PACT image reconstruction (GPU-accelerated photoacoustic computed tomography), to help the research community to grasp this advanced image reconstruction method. Approach: We leverage widely accessible open-source parallel computing tools, including Python multiprocessing-based parallelism, Taichi Lang for Python, CUDA, and possible other backends. We demonstrate that our framework promotes significant performance of PACT reconstruction, enabling faster analysis and real-time applications. Besides, we also described how to realize parallel computing on various hardware configurations, including multicore CPU, single GPU, and multiple GPUs platform. Results: Notably, our framework can achieve an effective rate of ∼ 871 times when reconstructing extremely large-scale three-dimensional PACT images on a dual-GPU platform compared to a 24-core workstation CPU. In this paper, we share example codes via GitHub. Conclusions: Our approach allows for easy adoption and adaptation by the research community, fostering implementations of PACT for both life science and medicine.

The impact of the combat method on radiomics feature compensation and analysis of scanners from different manufacturers.

BACKGROUND: This study investigated whether the Combat compensation method can remove the variability of radiomic features extracted from different scanners, while also examining its impact on the subsequent predictive performance of machine learning models. MATERIALS AND METHODS: 135 CT images of Credence Cartridge Radiomic phantoms were collected and screened from three scanners manufactured by Siemens, Philips, and GE. 100 radiomic features were extracted and 20 radiomic features were screened according to the Lasso regression method. The radiomic features extracted from the rubber and resin-filled regions in the cartridges were labeled into different categories for evaluating the performance of the machine learning model. Radiomics features were divided into three groups based on the different scanner manufacturers. The radiomic features were randomly divided into training and test sets with a ratio of 8:2. Five machine learning models (lasso, logistic regression, random forest, support vector machine, neural network) were employed to evaluate the impact of Combat on radiomic features. The variability among radiomic features were assessed using analysis of variance (ANOVA) and principal component analysis (PCA). Accuracy, precision, recall, and area under the receiver curve (AUC) were used as evaluation metrics for model classification. RESULTS: The principal component and ANOVA analysis results show that the variability of different scanner manufacturers in radiomic features was removed (P˃0.05). After harmonization with the Combat algorithm, the distributions of radiomic features were aligned in terms of location and scale. The performance of machine learning models for classification improved, with the Random Forest model showing the most significant enhancement. The AUC value increased from 0.88 to 0.92. CONCLUSIONS: The Combat algorithm has reduced variability in radiomic features from different scanners. In the phantom CT dataset, it appears that the machine learning model's classification performance may have improved after Combat harmonization. However, further investigation and validation are required to fully comprehend Combat's impact on radiomic features in medical imaging.

Fine structural human phantom in dentistry and instance tooth segmentation.

In this study, we present the development of a fine structural human phantom designed specifically for applications in dentistry. This research focused on assessing the viability of applying medical computer vision techniques to the task of segmenting individual teeth within a phantom. Using a virtual cone-beam computed tomography (CBCT) system, we generated over 170,000 training datasets. These datasets were produced by varying the elemental densities and tooth sizes within the human phantom, as well as varying the X-ray spectrum, noise intensity, and projection cutoff intensity in the virtual CBCT system. The deep-learning (DL) based tooth segmentation model was trained using the generated datasets. The results demonstrate an agreement with manual contouring when applied to clinical CBCT data. Specifically, the Dice similarity coefficient exceeded 0.87, indicating the robust performance of the developed segmentation model even when virtual imaging was used. The present results show the practical utility of virtual imaging techniques in dentistry and highlight the potential of medical computer vision for enhancing precision and efficiency in dental imaging processes.

Augmented-reality-based surgical navigation for endoscope retrograde cholangiopancreatography: A phantom study.

BACKGROUND: Endoscope retrograde cholangiopancreatography is a standard surgical treatment for gallbladder and pancreatic diseases. However, surgeons is at high risk and require sufficient surgical experience and skills. METHODS: (1) The simultaneous localisation and mapping technique to reconstruct the surgical environment. (2) The preoperative 3D model is transformed into the intraoperative video environment to implement the multi-modal fusion. (3) A framework for virtual-to-real projection based on hand-eye alignment. For the purpose of projecting the 3D model onto the imaging plane of the camera, it uses position data from electromagnetic sensors. RESULTS: Our AR-assisted navigation system can accurately guide physicians, which means a distance of registration error to be restricted to under 5 mm and a projection error of 5.76 ± 2.13, and the intubation procedure is done at 30 frames per second. CONCLUSIONS: Coupled with clinical validation and user studies, both the quantitative and qualitative results indicate that our navigation system has the potential to be highly useful in clinical practice.

Impact of deep learning image reconstruction on volumetric accuracy and image quality of pulmonary nodules with different morphologies in low-dose CT.

BACKGROUND: This study systematically compares the impact of innovative deep learning image reconstruction (DLIR, TrueFidelity) to conventionally used iterative reconstruction (IR) on nodule volumetry and subjective image quality (IQ) at highly reduced radiation doses. This is essential in the context of low-dose CT lung cancer screening where accurate volumetry and characterization of pulmonary nodules in repeated CT scanning are indispensable. MATERIALS AND METHODS: A standardized CT dataset was established using an anthropomorphic chest phantom (Lungman, Kyoto Kaguku Inc., Kyoto, Japan) containing a set of 3D-printed lung nodules including six diameters (4 to 9 mm) and three morphology classes (lobular, spiculated, smooth), with an established ground truth. Images were acquired at varying radiation doses (6.04, 3.03, 1.54, 0.77, 0.41 and 0.20 mGy) and reconstructed with combinations of reconstruction kernels (soft and hard kernel) and reconstruction algorithms (ASIR-V and DLIR at low, medium and high strength). Semi-automatic volumetry measurements and subjective image quality scores recorded by five radiologists were analyzed with multiple linear regression and mixed-effect ordinal logistic regression models. RESULTS: Volumetric errors of nodules imaged with DLIR are up to 50% lower compared to ASIR-V, especially at radiation doses below 1 mGy and when reconstructed with a hard kernel. Also, across all nodule diameters and morphologies, volumetric errors are commonly lower with DLIR. Furthermore, DLIR renders higher subjective IQ, especially at the sub-mGy doses. Radiologists were up to nine times more likely to score the highest IQ-score to these images compared to those reconstructed with ASIR-V. Lung nodules with irregular margins and small diameters also had an increased likelihood (up to five times more likely) to be ascribed the best IQ scores when reconstructed with DLIR. CONCLUSION: We observed that DLIR performs as good as or even outperforms conventionally used reconstruction algorithms in terms of volumetric accuracy and subjective IQ of nodules in an anthropomorphic chest phantom. As such, DLIR potentially allows to lower the radiation dose to participants of lung cancer screening without compromising accurate measurement and characterization of lung nodules.

Implementing an electromagnetic tracking navigation system improves the precision of endoscopic transgastric necrosectomy in an ex vivo model.

Endoscopic transgastric necrosectomy is crucial in the management of complications resulting from necrotizing pancreatitis. However, both real-time and visual-spatial information is lacking during the procedure, thereby jeopardizing a precise positioning of the endoscope. We conducted a proof-of-concept study with the aim of overcoming these technical difficulties. For this purpose, a three-dimensional (3D) phantom of a stomach and pancreatic necroses was 3D-printed based on spatial information from individual patient CT scans and subsequently integrated into a silicone torso. An electromagnetic (EM) sensor was adjusted inside the endoscope´s working channel. A software interface enabled real time visualization. The accuracy of this novel assistant system was tested ex vivo by four experienced interventional endoscopists who were supposed to reach seven targets inside the phantom in six different experimental runs of simulated endoscopic transgastric necrosectomy. Supported by endoscopic camera view combined with real-time 3D visualization, all endoscopists reached the targets with a targeting error ranging between 2.6 and 6.5 mm in a maximum of eight minutes. In summary, the EM tracking system might increase efficacy and safety of endoscopic transgastric necrosectomy at the experimental level by enhancing visualization. Yet, a broader feasibility study and further technical improvements are mandatory before aiming at implementation into clinical setting.

Artificial neural network for enhancing signal-to-noise ratio and contrast in photothermal optical coherence tomography.

Optical coherence tomography (OCT) is a medical imaging method that generates micron-resolution 3D volumetric images of tissues in-vivo. Photothermal (PT)-OCT is a functional extension of OCT with the potential to provide depth-resolved molecular information complementary to the OCT structural images. PT-OCT typically requires long acquisition times to measure small fluctuations in the OCT phase signal. Here, we use machine learning with a neural network to infer the amplitude of the photothermal phase modulation from a short signal trace, trained in a supervised fashion with the ground truth signal obtained by conventional reconstruction of the PT-OCT signal from a longer acquisition trace. Results from phantom and tissue studies show that the developed network improves signal to noise ratio (SNR) and contrast, enabling PT-OCT imaging with short acquisition times and without any hardware modification to the PT-OCT system. The developed network removes one of the key barriers in translation of PT-OCT (i.e., long acquisition time) to the clinic.

Effects of phantom microstructure on their optical properties.

Significance: Developing stable, robust, and affordable tissue-mimicking phantoms is a prerequisite for any new clinical application within biomedical optics. To this end, a thorough understanding of the phantom structure and optical properties is paramount. Aim: We characterized the structural and optical properties of PlatSil SiliGlass phantoms using experimental and numerical approaches to examine the effects of phantom microstructure on their overall optical properties. Approach: We employed scanning electron microscope (SEM), hyperspectral imaging (HSI), and spectroscopy in combination with Mie theory modeling and inverse Monte Carlo to investigate the relationship between phantom constituent and overall phantom optical properties. Results: SEM revealed that microspheres had a broad range of sizes with average (13.47±5.98) µm and were also aggregated, which may affect overall optical properties and warrants careful preparation to minimize these effects. Spectroscopy was used to measure pigment and SiliGlass absorption coefficient in the VIS-NIR range. Size distribution was used to calculate scattering coefficients and observe the impact of phantom microstructure on scattering properties. The results were surmised in an inverse problem solution that enabled absolute determination of component volume fractions that agree with values obtained during preparation and explained experimentally observed spectral features. HSI microscopy revealed pronounced single-scattering effects that agree with single-scattering events. Conclusions: We show that knowledge of phantom microstructure enables absolute measurements of phantom constitution without prior calibration. Further, we show a connection across different length scales where knowledge of precise phantom component constitution can help understand macroscopically observable optical properties.

Flexural pulse wave velocity in blood vessels.

Arteriosclerosis is a major risk factor for cardiovascular disease and results in arterial vessel stiffening. Velocity estimation of the pulse wave sent by the heart and propagating into the arteries is a widely accepted biomarker. This symmetrical pulse wave propagates at a speed which is related to the Young's modulus through the Moens Korteweg (MK) equation. Recently, an antisymmetric flexural wave has been observed in vivo. Unlike the symmetrical wave, it is highly dispersive. This property offers promising applications for monitoring arterial stiffness and early detection of atheromatous plaque. However, as far as it is known, no equivalent of the MK equation exists for flexural pulse waves. To bridge this gap, a beam based theory was developed, and approximate analytical solutions were reached. An experiment in soft polymer artery phantoms was built to observe the dispersion of flexural waves. A good agreement was found between the analytical expression derived from beam theory and experiments. Moreover, numerical simulations validated wave speed dependence on the elastic and geometric parameters at low frequencies. Clinical applications, such as arterial age estimation and arterial pressure measurement, are foreseen.

Non-telecentric photoacoustic microscopy for multi-scale imaging.

Optical-resolution photoacoustic microscopy (OR-PAM) excels in precisely imaging a biological tissue based on absorption contrast. However, existing OR-PAMs are confined by fixed compromises between spatial resolution and field of view (FOV), preventing the integration of large FOV and local high-resolution within one system. Here, we present a non-telecentric OR-PAM (nTC-PAM) that empowers efficient adaptation of FOV and spatial resolution to match the multi-scale requirement of diverse biological imaging. Our method allows for a large-scale transformation in FOV and even surpassing the nominal FOV of the objective with minimal marginal degradation of the lateral resolution. We demonstrate the advantage of nTC-PAM through multi-scale imaging of the leaf phantom, mouse ear, and cortex. The results reveal that nTC-PAM can switch the FOV and spatial resolution to meet the requirements of different biological tissues, such as large-scale imaging of the whole cerebral cortex and high-resolution imaging of microvascular structures in local brain regions.

Optical coherence elastography under homolateral parallel acoustic radiation force excitation for ocular elasticity quantification.

Alteration in the elastic properties of biological tissues may indicate changes in the structure and components. Acoustic radiation force optical coherence elastography (ARF-OCE) can assess the elastic properties of the ocular tissues non-invasively. However, coupling the ultrasound beam and the optical beam remains challenging. In this Letter, we proposed an OCE method incorporating homolateral parallel ARF excitation for measuring the elasticity of the ocular tissues. An acoustic-optic coupling unit was established to reflect the ultrasound beam while transmitting the light beam. The ARF excited the ocular tissue in the direction parallel to the light beam from the same side of the light beam. We demonstrated the method on the agar phantoms, the porcine cornea, and the porcine retina. The results show that the ARF-OCE method can measure the elasticity of the cornea and the retina, resulting in higher detection sensitivity and a more extensive scanning range.

A modular motion compensation pipeline for prospective respiratory motion correction of multi-nuclear MR spectroscopy.

Magnetic resonance (MR) acquisitions of the torso are frequently affected by respiratory motion with detrimental effects on signal quality. The motion of organs inside the body is typically decoupled from surface motion and is best captured using rapid MR imaging (MRI). We propose a pipeline for prospective motion correction of the target organ using MR image navigators providing absolute motion estimates in millimeters. Our method is designed to feature multi-nuclear interleaving for non-proton MR acquisitions and to tolerate local transmit coils with inhomogeneous field and sensitivity distributions. OpenCV object tracking was introduced for rapid estimation of in-plane displacements in 2D MR images. A full three-dimensional translation vector was derived by combining displacements from slices of multiple and arbitrary orientations. The pipeline was implemented on 3 T and 7 T MR scanners and tested in phantoms and volunteers. Fast motion handling was achieved with low-resolution 2D MR image navigators and direct implementation of OpenCV into the MR scanner's reconstruction pipeline. Motion-phantom measurements demonstrate high tracking precision and accuracy with minor processing latency. The feasibility of the pipeline for reliable in-vivo motion extraction was shown on heart and kidney data. Organ motion was manually assessed by independent operators to quantify tracking performance. Object tracking performed convincingly on 7774 navigator images from phantom scans and different organs in volunteers. In particular the kernelized correlation filter (KCF) achieved similar accuracy (74%) as scored from inter-operator comparison (82%) while processing at a rate of over 100 frames per second. We conclude that fast 2D MR navigator images and computer vision object tracking can be used for accurate and rapid prospective motion correction. This and the modular structure of the pipeline allows for the proposed method to be used in imaging of moving organs and in challenging applications like cardiac magnetic resonance spectroscopy (MRS) or magnetic resonance imaging (MRI) guided radiotherapy.

Imperfect refractive index matching in scanning laser optical tomography and a method for digital correction.

Significance: Scanning laser optical tomography (SLOT) is a volumetric multi-modal imaging technique that is comparable to optical projection tomography and computer tomography. Image quality is crucially dependent on matching the refractive indexes (RIs) of the sample and surrounding medium, but RI matching often requires some effort and is never perfect. Aim: Reducing the burden of RI matching between the immersion medium and sample in biomedical imaging is a challenging and interesting task. We aim at implementing a post processing strategy for correcting SLOT measurements that have errors caused by RI mismatch. Approach: To better understand the problems with poorly matched Ris, simulated SLOT measurements with imperfect RI matching of the sample and medium are performed and presented here. A method to correct distorted measurements was developed and is presented and evaluated. This method is then applied to a sample containing fluorescent polystyrene beads and a sample made of olydimethylsiloxane with embedded fluorescent nanoparticles. Results: From the simulations, it is evident that measurements with an RI mismatch larger than 0.02 and no correction yield considerably worse results compared to perfectly matched measurements. RI mismatches larger than 0.05 make it almost impossible to resolve finer details and structures. By contrast, the simulations imply that a measurement with an RI mismatch of up to 0.1 can still yield reasonable results if the presented correction method is applied. The experiments validate the simulated results for an RI mismatch of about 0.09. Conclusions: The method significantly improves the SLOT image quality for samples with imperfectly matched Ris. Although the absolutely best imaging quality will be achieved with perfect RI matching, these results pave the way for imaging in SLOT with RI mismatches while maintaining high image quality.

Thermal damage to the skin from 8.2 and 95 GHz microwave exposures in swine.

A study of burn thresholds from superficially penetrating radio-frequency (RF) energy at 8.2 and 95 GHz for swine skin was conducted. The study determined the thresholds for superficial, partial-thickness, and full-thickness burn severities after 5 seconds of exposure at power densities of 4-30 W/cm2and 2-15 W/cm2at 8.2 and 95 GHz, respectively. There were significant differences in he burn thresholds at the different severities between the two frequencies due to the large difference in energy penetration depths. Biopsies were collected from each burn site at 1, 24, 72, and 168 hr post exposure. Each sample was assessed by a burn pathologist against 20 histological factors to characterize the damage resulting from these RF overexposures. A one-dimensional, layered digital phantom that utilized realistic values for dielectric and thermal properties was used to explain some observed thresholds. The results of the heating and cooling response of the animal model and histology scores of each exposure are provided to enhance future efforts at simulation of RF overexposures and to establish damage thresholds.

Three-dimensional assessment of image distortion induced by active cardiac implants in 3.0T CMR.

CMR at 3.0T in the presence of active cardiac implants remains a challenge due to susceptibility artifacts. Beyond a signal void that cancels image information, magnetic field inhomogeneities may cause distorted appearances of anatomical structures. Understanding influencing factors and the extent of distortion are a first step towards optimizing the image quality of CMR with active implants at 3.0T. All measurements were obtained at a clinical 3.0T scanner. An in-house designed phantom with a 3D cartesian grid of water filled spheres was used to analyze the distortion caused by four representative active cardiac devices (cardiac loop recorder, pacemaker, 2 ICDs). For imaging a gradient echo (3D-TFE) sequence and a turbo spin echo (2D-TSE) sequence were used. The work defines metrics to quantify the different features of distortion such as changes in size, location and signal intensity. It introduces a specialized segmentation technique based on a reaction-diffusion-equation. The distortion features are dependent on the amount of magnetic material in the active implants and showed a significant increase when measured with the 3D TFE compared to the 2D TSE. This work presents a quantitative approach for the evaluation of image distortion at 3.0T caused by active cardiac implants and serves as foundation for both further optimization of sequences and devices but also for planning of imaging procedures.

Dosimetric benefits and preclinical performance of steerable needles in HDR prostate brachytherapy.

Prostate cancer patients with an enlarged prostate and/or excessive pubic arch interference (PAI) are generally considered non-eligible for high-dose-rate (HDR) brachytherapy (BT). Steerable needles have been developed to make these patients eligible again. This study aims to validate the dosimetric impact and performance of steerable needles within the conventional clinical setting. HDR BT treatment plans were generated, needle implantations were performed in a prostate phantom, with prostate volume > 55 cm3 and excessive PAI of 10 mm, and pre- and post-implant dosimetry were compared considering the dosimetric constraints: prostate V100 > 95 % (13.50 Gy), urethra D0.1cm3 < 115 % (15.53 Gy) and rectum D1cm3 < 75 % (10.13 Gy). The inclusion of steerable needles resulted in a notable enhancement of the dose distribution and prostate V100 compared to treatment plans exclusively employing rigid needles to address PAI. Furthermore, the steerable needle plan demonstrated better agreement between pre- and post-implant dosimetry (prostate V100: 96.24 % vs. 93.74 %) compared to the rigid needle plans (79.13 % vs. 72.86 % and 87.70 % vs. 81.76 %), with no major changes in the clinical workflow and no changes in the clinical set-up. The steerable needle approach allows for more flexibility in needle positioning, ensuring a highly conformal dose distribution, and hence, HDR BT is a feasible treatment option again for prostate cancer patients with an enlarged prostate and/or excessive PAI.

Fat quantification in dual-layer detector spectral CT: How to handle iron overload, varying tube voltage and radiation dose Indices.

OBJECTIVES: Opposed to other spectral CT techniques, fat quantification in dual-layer detector CT (dlCT) has only recently been developed. The impact of concomitant iron overload and dlCT-specific protocol settings such as the dose right index (DRI), a measure of image noise and tube current, on dlCT fat quantification was unclear. Further, spectral information became newly available <120 kV. Therefore, this study's objective was to evaluate the impact of iron, changing tube voltage, and DRI on dlCT fat quantification. MATERIAL AND METHODS: Phantoms with 0 and 8mg/cm3 iron; 0 and 5mg/cm3 iodine; 0, 10, 20, 35, 50, and 100% fat and liver equivalent, respectively, were scanned with a dlCT (CT7500, Philips, the Netherlands) at 100kV/20DRI, 120kV/20DRI, 140kV/20DRI, and at 120kV/16DRI, 120kV/24DRI. Material decomposition was done for fat, liver, and iodine (A1); for fat, liver, and iron (A2); and for fat, liver, and combined reference values of iodine and iron (A3). All scans were analyzed with reference values from 120kV/20DRI. For statistics, the intraclass correlation coefficient (ICC) and Bland-Altman analyses were used. RESULTS: In phantoms with iron and iodine, results were best for A3 with a mean deviation to phantom fat of 1.3±2.6% (ICC 0.999 [95%-confidence interval 0.996-1]). The standard approach A1 yielded a deviation of -2.5±3.0% (0.998[0.994-0.999]), A2 of 6.1±4.8% (0.991[0.974-0.997]). With A3 and changing tube voltage, the maximal difference between quantified fat and the phantom ground truth occurred at 100kV with 4.6±2.1%. Differences between scans were largest between 100kV and 140kV (2.0%[-7.1-11.2]). The maximal difference of changing DRI occurred between 16 and 24 DRI with 0.4%[-2.2-3.0]. CONCLUSION: For dlCT fat quantification in the presence of iron, material decomposition with combined reference values for iodine and iron delivers the most accurate results. Tube voltage-specific calibration of reference values is advisable while the impact of the DRI on dlCT fat quantification is neglectable.

A comprehensive Study of the Out of Field Non Target Dose Associated with 6 and 10 MV Flattened and Flattening Filter Free X Ray Beam in a True Beam Linear Accelerator.

AIM: To evaluate the out-of-field dose associated with flattened (FF) and flattening filter-free (FFF) 6 and 10 MV X-ray beams in a TrueBeam linear accelerator (Linac). MATERIALS AND METHODS: Measurements were taken in a slab phantom using the metal oxide semiconductor field effect transistor (MOSFET) detector at varying depths (dmax, 5 cm, and 10 cm) for clinically relevant field sizes and up to 30 cm from the field edges for 6 and 10 MV FF and FFF beams in TrueBeam Linac. Dose calculation accuracy of the analytic anisotropic algorithm (AAA) and Acuros algorithm was investigated in the out-of-field region. Similarly, the out-of-field dose associated with volumetric modulated arc therapy (VMAT) head-and-neck plan delivered to a body phantom was evaluated. RESULTS: The out-of-field dose for both FF and FFF photon beams (6 and 10 MV) decreased with increasing distance from the field boundary and size. Furthermore, regardless of FF in the field, higher-energy photon beams were associated with lower out-of-field dose. Both algorithms underestimated the dose in the out-of-field region, with AAA failing to calculate the out-of-field dose at 15 cm from the field edge and Acuros failing to calculate out-of-field radiation at 20 cm. At 5 cm from the field edge, an average of 50% underestimation was observed, and at 10 cm, an average of 60% underestimation was observed for both FF and FFF (6 and 10 MV) beams. The VMAT head-and-neck plan performed with the FFF beam resulted in a lower out-of-field dose than the FF beam for a comparable dose distribution. CONCLUSION: Compared with flattened beams, the FFF modes on TrueBeam Linac exhibited a clinically relevant reduction in the out-of-field dose. Further dosimetric studies are warranted to determine the significant benefit of FFF beams across different cancer sites.

Dosimetric Validation of Treatment Planning System for Volumetric Modulated Arc Therapy Using AAPM Medical Physics Practice Guideline 5.b.

AIM: To assess the precision of dose calculations for Volumetric Modulated Arc Therapy (VMAT) using megavoltage (MV) photon beams, we validated the accuracy of two algorithms: AUROS XB and Analytical Anisotropic Algorithm (AAA). This validation will encompass both flattening filter (FF) and flattening filter-free beam (FFF) modes, using AAPM Medical Physics Practice Guideline (MPPG 5b). MATERIALS AND METHODS: VMAT validation tests were generated for 6 MV FF and 6 MV FFF beams using the AAA and AXB algorithms in the Eclipse V.15.1 treatment planning system (TPS). Corresponding measurements were performed on a linear accelerator using a diode detector and a radiation field analyzer. Point dose (PD) and in-vivo measurements were conducted using an A1SL ion chamber and (TLD) from Thermofisher, respectively. The Rando Phantom was employed for end-to-end (E2E) tests. RESULTS: The mean difference (MD) between the TPS-calculated values and the measured values for the PDD and output factors were within 1% and 0.5%, respectively, for both 6 MV FF and 6 MV FFF. In the TG 119 sets, the MD for PD with both AAA and AXB was <0.9%. For the TG 244 sets, the minimum, maximum, and mean deviations in PD for both 6 MV FF and 6 MV FFF beams were 0.3%, 1.4% and 0.8% respectively. In the E2E test, using the Rando Phantom, the MD between the TLD dose and the TPS dose was within 0.08% for both 6 MV FF (p=1.0) and 6 MV FFF (0.018) beams. CONCLUSION: The accuracy of the TPS and its algorithms (AAA and AXB) has been successfully validated. The recommended tests included in the VMAT/IMRT validation section proved invaluable for verifying the PDD, output factors, and the feasibility of complex clinical cases. E2E tests were instrumental in validating the entire workflow from CT simulation to treatment delivery.

Functional ultrasound imaging of human brain activity through an acoustically transparent cranial window.

Visualization of human brain activity is crucial for understanding normal and aberrant brain function. Currently available neural activity recording methods are highly invasive, have low sensitivity, and cannot be conducted outside of an operating room. Functional ultrasound imaging (fUSI) is an emerging technique that offers sensitive, large-scale, high-resolution neural imaging; however, fUSI cannot be performed through the adult human skull. Here, we used a polymeric skull replacement material to create an acoustic window compatible with fUSI to monitor adult human brain activity in a single individual. Using an in vitro cerebrovascular phantom to mimic brain vasculature and an in vivo rodent cranial defect model, first, we evaluated the fUSI signal intensity and signal-to-noise ratio through polymethyl methacrylate (PMMA) cranial implants of different thicknesses or a titanium mesh implant. We found that rat brain neural activity could be recorded with high sensitivity through a PMMA implant using a dedicated fUSI pulse sequence. We then designed a custom ultrasound-transparent cranial window implant for an adult patient undergoing reconstructive skull surgery after traumatic brain injury. We showed that fUSI could record brain activity in an awake human outside of the operating room. In a video game "connect the dots" task, we demonstrated mapping and decoding of task-modulated cortical activity in this individual. In a guitar-strumming task, we mapped additional task-specific cortical responses. Our proof-of-principle study shows that fUSI can be used as a high-resolution (200 µm) functional imaging modality for measuring adult human brain activity through an acoustically transparent cranial window.