Shear wave generation from non-spherical bubble collapse in a tissue phantom.

Elastography is a non-invasive technique to detect tissue anomalies via the local elastic modulus using shear waves. Commonly shear waves are produced via acoustic focusing or the use of mechanical external sources, shear waves may result also naturally from cavitation bubbles during medical intervention, for example from thermal ablation. Here, we measure the shear wave emitted from a well-controlled single laser-induced cavitation bubble oscillating near a rigid boundary. The bubbles are generated in a transparent tissue-mimicking hydrogel embedded with tracer particles. High-speed imaging of the tracer particles and the bubble shape allow quantifying the shear wave and relate it to the bubble dynamics. It is found that different stages of the bubble dynamics contribute to the shear wave generation and the mechanism of shear wave emission, its direction and the efficiency of energy converted into the shear wave depend crucially on the bubble to wall stand-off distance.

Rapid safety assessment and mitigation of radiofrequency induced implant heating using small root mean square sensors and the sensor matrix Qs.

PURPOSE: Rapid detection and mitigation of radiofrequency (RF)-induced implant heating during MRI based on small and low-cost embedded sensors. THEORY AND METHODS: A diode and a thermistor are embedded at the tip of an elongated mock implant. RF-induced voltages or temperature change measured by these root mean square (RMS) sensors are used to construct the sensor Q-Matrix (QS ). Hazard prediction, monitoring and parallel transmit (pTx)-based mitigation using these sensors is demonstrated in benchtop measurements at 300 MHz and within a 3T MRI. RESULTS: QS acquisition and mitigation can be performed in <20 ms demonstrating real-time capability. The acquisitions can be performed using safe low powers (<3 W) due to the high reading precision of the diode (126 µV) and thermistor (26 µK). The orthogonal projection method used for pTx mitigation was able to reduce the induced signals and temperatures in all 155 investigated locations. Using the QS approach in a pTx capable 3T MRI with either a two-channel body coil or an eight-channel head coil, RF-induced heating was successfully assessed, monitored and mitigated while the image quality outside the implant region was preserved. CONCLUSION: Small (<1.5 mm3 ) and low-cost (<1 €) RMS sensors embedded in an implant can provide all relevant information to predict, monitor and mitigate RF-induced heating in implants, while preserving image quality. The proposed pTx-based QS approach is independent of simulations or in vitro testing and therefore complements these existing safety assessments.

Fourier-based decomposition for simultaneous 2-voxel MRS acquisition with 2SPECIAL.

PURPOSE: To simultaneously acquire spectroscopic signals from two MRS voxels using a multi-banded 2 spin-echo, full-intensity acquired localized (2SPECIAL) sequence, and to decompose the signal to their respective regions by a novel voxel-GRAPPA (vGRAPPA) decomposition approach for in vivo brain applications at 7 T. METHODS: A wideband, uniform rate, smooth truncation (WURST) multi-banded pulse was incorporated into SPECIAL to implement 2SPECIAL for simultaneous multi-voxel spectroscopy (sMVS). To decompose the acquired data, the voxel-GRAPPA decomposition algorithm is introduced, and its performance is compared to the SENSE-based decomposition. Furthermore, the limitations of two-voxel excitation concerning the multi-banded adiabatic inversion pulse, as well as of the combined B0 shim and B1 + adjustments, are evaluated. RESULTS: It was successfully shown that the 2SPECIAL sequence enables sMVS without a significant loss in SNR while reducing the total scan time by 21.6% compared to two consecutive acquisitions. The proposed voxel-GRAPPA algorithm properly reassigns the signal components to their respective origin region and shows no significant differences to the well-established SENSE-based algorithm in terms of leakage (both <10%) or Cramér-Rao lower bounds (CRLB) for in vivo applications, while not requiring the acquisition of additional sensitivity maps and thus decreasing motion sensitivity. CONCLUSION: The use of 2SPECIAL in combination with the novel voxel-GRAPPA decomposition technique allows a substantial reduction of measurement time compared to the consecutive acquisition of two single voxels without a significant decrease in spectral quality or metabolite quantification accuracy and thus provides a new option for multiple-voxel applications.

Assessment of measurement precision in single-voxel spectroscopy at 7 T: Toward minimal detectable changes of metabolite concentrations in the human brain in vivo.

PURPOSE: To introduce a study design and statistical analysis framework to assess the repeatability, reproducibility, and minimal detectable changes (MDCs) of metabolite concentrations determined by in vivo MRS. METHODS: An unbalanced nested study design was chosen to acquire in vivo MRS data within different repeatability and reproducibility scenarios. A spin-echo, full-intensity acquired localized (SPECIAL) sequence was employed at 7 T utlizing three different inversion pulses: a hyperbolic secant (HS), a gradient offset independent adiabaticity (GOIA), and a wideband, uniform rate, smooth truncation (WURST) pulse. Metabolite concentrations, Cramér-Rao lower bounds (CRLBs) and coefficients of variation (CVs) were calculated. Both Bland-Altman analysis and a restricted maximum-likelihood estimation (REML) analysis were performed to estimate the different variance contributions of the repeatability and reproducibility of the measured concentration. A Bland-Altmann analysis of the spectral shape was performed to assess the variance of the spectral shape, independent of quantification model influences. RESULTS: For the used setup, minimal detectable changes of brain metabolite concentrations were found to be between 0.40 µmol/g and 2.23 µmol/g. CRLBs account for only 16 % to 74 % of the total variance of the metabolite concentrations. The application of gradient-modulated inversion pulses in SPECIAL led to slightly improved repeatability, but overall reproducibility appeared to be limited by differences in positioning, calibration, and other day-to-day variations throughout different sessions. CONCLUSION: A framework is introduced to estimate the precision of metabolite concentrations obtained by MRS in vivo, and the minimal detectable changes for 13 metabolite concentrations measured at 7 T using SPECIAL are obtained.

Investigation of Microwave Ablation Process in Sweet Potatoes as Substitute Liver.

The microwave ablation technique to destroy cancer tissues in liver is practiced clinically and is the subject of ongoing research, e.g., ablation monitoring. For studies, liver tissue from cattle or pigs is often used as a substitute material. In this work, sweet potato is presented as an alternative material for microwave ablation experiments in liver due to similar material properties. Sweet potatoes as a substitute for liver have the advantages of better handling, easy procurement and stable material properties over time for microwave ablation experiments. The dielectric constant and electrical conductivity of sweet potato are characterized for temperature variation with the help of high-temperature dielectric probe. Furthermore, a test setup is presented for microwave ablation experiments in which a bowtie slot antenna matched to sweet potato is placed on its surface to directly receive the microwave power from a self-developed microwave applicator inserted into a sweet potato 4 cm below the surface antenna. A high-power source was used to excite the microwave powers up to 80 W and a spectrum analyzer was used to measure the signal received by the surface antenna. The experiments were performed in an anechoic chamber for safety reasons. Power at 50 W and 80 W was stimulated for a maximum of 600 s at the 2.45 GHz ISM band in different sweet potato experiments. A correlation is found between the power received by the surface antenna and rise of temperature inside sweet potato; relative received power drops from 1 at 76 ∘C to 0.6 at 88 ∘C (max. temperature) represents a 40% relative change in a 50 W microwave ablation experiment. The received power envelope at the surface antenna is between 10 mW and 32 mW during 50 W microwave ablation. Other important results for 10 min, 80 W microwave ablation include: a maximum ablation zone short axis diameter of 4.5 cm and a maximum ablation temperature reached at 99 ∘C, 3 mm away from the applicator's slot. The results are compared with the state of the art in microwave ablation in animal liver. The dielectric constant and electrical conductivity evolution of sweet potato with rising temperature is comparable to animal liver in 50-60 ∘C range. The reflection loss of self-developed applicator in sweet potato is below 15 dB which is equal to reflection loss in liver experiments for 600 s. The temperature rise for the first 90 s in sweet potato is 76 ∘C as compared to 73 ∘C in liver with 50 W microwave ablation. Similarly, with 80-75 W microwave ablation, for the first 60 s, the temperature is 98 ∘C in sweet potato as compared to 100 ∘C in liver. The ablation zone short-axis diameter after 600 s is 3.3 cm for 50 W microwave ablation in sweet potato as compared to 3.5 cm for 30 W microwave ablation in liver. The reasons for difference in microwave ablation results in sweet potato and animal liver are discussed. This is the first study to directly receive a signal from microwave applicator during a microwave ablation process with the help of a surface antenna. The work can be extended to multiple array antennas for microwave ablation monitoring.

Gait Event Detection for Stroke Patients during Robot-Assisted Gait Training.

Functional electrical stimulation and robot-assisted gait training are techniques which are used in a clinical routine to enhance the rehabilitation process of stroke patients. By combining these technologies, therapy effects could be further improved and the rehabilitation process can be supported. In order to combine these technologies, a novel algorithm was developed, which aims to extract gait events based on movement data recorded with inertial measurement units. In perspective, the extracted gait events can be used to trigger functional electrical stimulation during robot-assisted gait training. This approach offers the possibility of equipping a broad range of potential robot-assisted gait trainers with functional electrical stimulation. In particular, the aim of this study was to test the robustness of the previously developed algorithm in a clinical setting with patients who suffered a stroke. A total amount of N = 10 stroke patients participated in the study, with written consent. The patients were assigned to two different robot-assisted gait trainers (Lyra and Lokomat) according to their performance level, resulting in five recording sessions for each gait-trainer. A previously developed algorithm was applied and further optimized in order to extract the gait events. A mean detection rate across all patients of 95.8% ± 7.5% for the Lyra and 98.7% ± 2.6% for the Lokomat was achieved. The mean type 1 error across all patients was 1.0% ± 2.0% for the Lyra and 0.9% ± 2.3% for the Lokomat. As a result, the developed algorithm was robust against patient specific movements, and provided promising results for the further development of a technique that can detect gait events during robot-assisted gait training, with the future aim to trigger functional electrical stimulation.

Feasibility of a Sensor-Based Gait Event Detection Algorithm for Triggering Functional Electrical Stimulation during Robot-Assisted Gait Training.

Technologies such as robot-assisted gait trainers or functional electrical stimulation can improve the rehabilitation process of people affected with gait disorders due to stroke or other neurological defects. By combining both technologies, the potential disadvantages of each technology could be compensated and simultaneously, therapy effects could be improved. Thus, an algorithm was designed that aims to detect the gait cycle of a robot-assisted gait trainer. Based on movement data recorded with inertial measurement units, gait events can be detected. These events can further be used to trigger functional electrical stimulation. This novel setup offers the possibility of equipping a broad range of potential robot-assisted gait trainers with functional electrical stimulation. The aim of this paper in particular was to test the feasibility of a system using inertial measurement units for gait event detection during robot-assisted gait training. Thus, a 39-year-old healthy male adult executed a total of six training sessions with two robot-assisted gait trainers (Lokomat and Lyra). The measured data from the sensors were analyzed by a custom-made gait event detection algorithm. An overall detection rate of 98.1% ± 5.2% for the Lokomat and 94.1% ± 6.8% for the Lyra was achieved. The mean type-1 error was 0.3% ± 1.2% for the Lokomat and 1.9% ± 4.3% for the Lyra. As a result, the setup provides promising results for further research and a technique that can enhance robot-assisted gait trainers by adding functional electrical stimulation to the rehabilitation process.

Prospective motion correction enables highest resolution time-of-flight angiography at 7T.

PURPOSE: Higher magnetic field strengths enable time-of-flight (TOF) angiography with higher resolution to depict small-vessel pathologies. However, this potential is limited by the subject's ability to remain motionless. Even small-scale, involuntary motion can degrade vessel depiction, thus limiting the effective resolution. The aim of this study was to overcome this resolution limit by deploying prospectively motion-corrected (PMC) TOF. METHODS: An optical, marker-based, in-bore tracking system was used to update the imaging volume prospectively according to the subject's head motion. PMC TOF was evaluated in 12 healthy, cooperative subjects at isotropic resolution of up to 150 µm. Image quality was assessed qualitatively through reader rating and quantitatively with the average edge-strength metric. RESULTS: PMC significantly increased the average edge strength and qualitatively improved the vessel depiction in nine out of 11 cases. Image quality was never degraded by motion correction. PMC also enabled acquisition of the highest resolution human brain in vivo TOF angiography to date. CONCLUSION: With PMC enabled, high-resolution TOF is able to visualize brain vasculature beyond the effective resolution limit. Magn Reson Med 80:248-258, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

A Newly Developed mm-Wave Sensor for Detecting Plaques of Arterial Vessels.

BACKGROUND: Microcalcifications within the fibrous cap of the arteriosclerotic plaques lead to the accrual of plaque-destabilizing mechanical stress. New techniques for plaque screening with small detectors and the ability to differentiate between the smooth and hard elements of plaque formation are necessary. METHOD: Vascular plaque formations are characterized as calcium phosphate containing structures organized as hydroxylapatite resembling the mineral whitlockite. In transmission and reflexion studies with a simple millimeter wave (mm-wave)-demonstrator, we found that there is a narrow window for plaque detection in arterial vessels because of the tissue water content, the differentiation to fatty tissue, and the dielectric property of air or water, respectively. RESULT: The new sensor is based on a sensing oscillator working around 27 GHz. The open-stub capacitance determines the operating frequency of the sensor oscillator. The capacitance depends on the dielectric properties of the surrounding material. The sensor components were completely built up in surface mount technique. CONCLUSION: Completed with a catheter, the sensor based on microwave technology appears as a robust tool ready for further clinical use.

Evaluation of exposure to (ultra) high static magnetic fields during activities around human MRI scanners.

OBJECTIVE: To assess the individual exposure to the static magnetic field (SMF) and the motion-induced time-varying magnetic field (TVMF) generated by activities in an inhomogeneous SMF near high and ultra-high field magnetic resonance imaging (MRI) scanners. The study provides information on the level of exposure to high and ultra-high field MRI scanners during research activities. MATERIALS AND METHODS: A three-axis Hall magnetometer was used to determine the SMF and TVMF around human 3- and 7-Tesla (T) MRI systems. The 7-T MRI scanner used in this study was passively shielded and the 3-T scanner was actively shielded and both were from the same manufacturer. The results were compared with the exposure restrictions given by the International Commission on Non-Ionizing Radiation Protection (ICNIRP). RESULTS: The recorded exposure was highly variable between individuals, although they followed the same instructions for moving near the scanners. Maximum exposure values of B = 2057 mT and dB/dt = 4347 mT/s for the 3-T scanner and B = 2890 mT, dB/dt = 3900 mT/s for 7 T were recorded. No correlation was found between reporting the MRI-related sensory effects and exceeding the reference values. CONCLUSIONS: According to the results of our single-center study with five subjects, violation of the ICNIRP restrictions for max B in MRI research environments was quite unlikely at 3 and 7 T. Occasions of exceeding the dB/dt limit at 3 and 7 T were almost similar (30% of 60 exposure scenarios) and highly variable among the individuals.

Effects of grain shape on packing and dilatancy of sheared granular materials.

A granular material exposed to shear shows a variety of unique phenomena: Reynolds dilatancy, positional order and orientational order effects may compete in the shear zone. We study granular packing consisting of macroscopic prolate, oblate and spherical grains and compare their behaviour. X-ray tomography is used to determine the particle positions and orientations in a cylindrical split bottom shear cell. Packing densities and the arrangements of individual particles in the shear zone are evaluated. For anisometric particles, we observe the competition of two opposite effects. On the one hand, the sheared granules are dilated, on the other hand the particles reorient and align with respect to the streamlines. Even though aligned cylinders in principle may achieve higher packing densities, this alignment compensates for the effect of dilatancy only partially. The complex rearrangements lead to a depression of the surface above the well oriented region while neighbouring parts still show the effect of dilation in the form of heaps. For grains with isotropic shapes, the surface remains rather flat. Perfect monodisperse spheres crystallize in the shear zone, whereby positional order partially overcompensates dilatancy effects. However, even slight deviations from the ideal monodisperse sphere shape inhibit crystallization.

Joint B0 and Image Reconstruction in Low-Field MRI by Physics-Informed Deep-Learning.

OBJECTIVE: We present a model-based image reconstruction approach based on unrolled neural networks which corrects for image distortion and noise in low-field ( B0  âˆ¼  50mT) MRI. METHODS: Utilising knowledge about the underlying physics, a novel network architecture (SH-Net) is introduced which involves the estimation of spherical harmonic coefficients to guarantee a spatially smooth field map estimate. The SH-Net is integrated in an end-to-end trainable model which jointly estimates the B0-field map as well as the image. Experiments were conducted on retrospectively simulated low-field data of human knees. RESULTS: We compare our model to different model-based approaches at distinct noise levels and various B0-field distributions. Our results show that our physics-informed neural network approach outperforms the purely model-based methods by improving the PSNR up to 11.7% and the RMSE up to 86.3%. CONCLUSION: Our end-to-end trained model-based approach outperforms existing methods in reconstructing image and B0-field maps in the low-field regime. SIGNIFICANCE: low-field MRI is becoming increasingly more popular as it enables access to MR in challenging situations such as intensive care units or resource poor areas. Our method allows for fast and accurate image reconstruction in such low-field imaging with B0-inhomogeneity compensation under a wide range of various environmental conditions.

MRI-compatible abdomen phantom to mimic respiratory-triggered organ movement while performing needle-based interventions.

PURPOSE: In vivo studies are often required to prove the functionality and safety of medical devices. Clinical trials are costly and complex, adding to ethical scrutiny of animal testing. Anthropomorphic phantoms with versatile functionalities can overcome these issues with regard to medical education or an effective development of assistance systems during image-guided interventions (e.g., robotics, navigation/registration algorithms). In this work, an MRI-compatible and customizable motion phantom is presented to mimic respiratory-triggered organ movement as well as human anatomy. METHODS: For this purpose, polyvinyl alcohol cryogel (PVA-C) was the foundation for muscles, liver, kidneys, tumors, and remaining abdominal tissue in different sizes of the abdominal phantom body (APB) with the ability to mimic human tissue in various properties. In addition, a semi-flexible rib cage was 3D-printed. The motion unit (MU) with an electromagnetically shielded stepper motor and mechanical extensions simulated a respiration pattern to move the APB. RESULTS: Each compartment of the APB complied the relaxation times, dielectricity, and elasticity of human tissue. It showed resistance against mold and provided a resealable behavior after needle punctures. During long-term storage, the APB had a weight loss of 2.3%, followed by changes to relaxation times of 9.3% and elasticity up to 79%. The MU was able to physiologically appropriately mimic the organ displacement without reducing the MRI quality. CONCLUSION: This work presents a novel modularizable and low-cost PVA-C based APB to mimic fundamental organ motion. Beside a further organ motion analysis, an optimization of APB's chemical composition is needed to ensure a realistic motion simulation and reproducible long-term use. This phantom enhances diverse and varied training environments for prospective physicians as well as effective R&D of medical devices with the possibility to reduce in vivo experiments.

Deformable 3D/3D CT-to-digital-tomosynthesis image registration in image-guided bronchoscopy interventions.

Traditional navigational bronchoscopy procedures rely on preprocedural computed tomography (CT) and intraoperative chest radiography and cone-beam CT (CBCT) to biopsy peripheral lung lesions. This navigational approach is challenging due to the projective nature of radiography, and the high radiation dose, long imaging time, and large footprints of CBCT. Digital tomosynthesis (DTS) is considered an attractive alternative combining the advantages of radiography and CBCT. Only the depth resolution cannot match a full CBCT image due to the limited angle acquisition. To address this issue, preoperative CT is a good auxiliary in guiding bronchoscopy interventions. Nevertheless, CT-to-body divergence caused by anatomic changes and respiratory motion, hinders the effective use of CT imaging. To mitigate CT-to-body divergence, we propose a novel deformable 3D/3D CT-to-DTS registration algorithm employing a multistage, multiresolution approach and using affine and elastic B-spline transformation models with bone and lung mask images. A multiresolution strategy with a Gaussian image pyramid and a multigrid strategy within the B-spline model are applied. The normalized correlation coefficient is included in the cost function for the affine model and a multimetric weighted cost function is used for the B-spline model, with weights determined heuristically. Tested on simulated and real patient bronchoscopy data, the algorithm yields promising results. Assessed qualitatively by visual inspection and quantitatively by computing the Dice coefficient (DC) and the average symmetric surface distance (ASSD), the algorithm achieves mean DC of 0.82±0.05 and 0.74±0.05, and mean ASSD of 0.65±0.29mm and 0.93±0.43mm for simulated and real data, respectively. This algorithm lays the groundwork for CT-aided intraoperative DTS imaging in image-guided bronchoscopy interventions with future studies focusing on automated metric weight setting.

Exploration of Interpretability Techniques for Deep COVID-19 Classification Using Chest X-ray Images.

The outbreak of COVID-19 has shocked the entire world with its fairly rapid spread, and has challenged different sectors. One of the most effective ways to limit its spread is the early and accurate diagnosing of infected patients. Medical imaging, such as X-ray and computed tomography (CT), combined with the potential of artificial intelligence (AI), plays an essential role in supporting medical personnel in the diagnosis process. Thus, in this article, five different deep learning models (ResNet18, ResNet34, InceptionV3, InceptionResNetV2, and DenseNet161) and their ensemble, using majority voting, have been used to classify COVID-19, pneumoniæ and healthy subjects using chest X-ray images. Multilabel classification was performed to predict multiple pathologies for each patient, if present. Firstly, the interpretability of each of the networks was thoroughly studied using local interpretability methods-occlusion, saliency, input X gradient, guided backpropagation, integrated gradients, and DeepLIFT-and using a global technique-neuron activation profiles. The mean micro F1 score of the models for COVID-19 classifications ranged from 0.66 to 0.875, and was 0.89 for the ensemble of the network models. The qualitative results showed that the ResNets were the most interpretable models. This research demonstrates the importance of using interpretability methods to compare different models before making a decision regarding the best performing model.

ECG-based gating in ultra high field cardiovascular magnetic resonance using an independent component analysis approach.

BACKGROUND: In Cardiovascular Magnetic Resonance (CMR), the synchronization of image acquisition with heart motion is performed in clinical practice by processing the electrocardiogram (ECG). The ECG-based synchronization is well established for MR scanners with magnetic fields up to 3 T. However, this technique is prone to errors in ultra high field environments, e.g. in 7 T MR scanners as used in research applications. The high magnetic fields cause severe magnetohydrodynamic (MHD) effects which disturb the ECG signal. Image synchronization is thus less reliable and yields artefacts in CMR images. METHODS: A strategy based on Independent Component Analysis (ICA) was pursued in this work to enhance the ECG contribution and attenuate the MHD effect. ICA was applied to 12-lead ECG signals recorded inside a 7 T MR scanner. An automatic source identification procedure was proposed to identify an independent component (IC) dominated by the ECG signal. The identified IC was then used for detecting the R-peaks. The presented ICA-based method was compared to other R-peak detection methods using 1) the raw ECG signal, 2) the raw vectorcardiogram (VCG), 3) the state-of-the-art gating technique based on the VCG, 4) an updated version of the VCG-based approach and 5) the ICA of the VCG. RESULTS: ECG signals from eight volunteers were recorded inside the MR scanner. Recordings with an overall length of 87 min accounting for 5457 QRS complexes were available for the analysis. The records were divided into a training and a test dataset. In terms of R-peak detection within the test dataset, the proposed ICA-based algorithm achieved a detection performance with an average sensitivity (Se) of 99.2%, a positive predictive value (+P) of 99.1%, with an average trigger delay and jitter of 5.8 ms and 5.0 ms, respectively. Long term stability of the demixing matrix was shown based on two measurements of the same subject, each being separated by one year, whereas an averaged detection performance of Se = 99.4% and +P = 99.7% was achieved.Compared to the state-of-the-art VCG-based gating technique at 7 T, the proposed method increased the sensitivity and positive predictive value within the test dataset by 27.1% and 42.7%, respectively. CONCLUSIONS: The presented ICA-based method allows the estimation and identification of an IC dominated by the ECG signal. R-peak detection based on this IC outperforms the state-of-the-art VCG-based technique in a 7 T MR scanner environment.

Sinogram upsampling using Primal-Dual UNet for undersampled CT and radial MRI reconstruction.

Computed tomography (CT) and magnetic resonance imaging (MRI) are two widely used clinical imaging modalities for non-invasive diagnosis. However, both of these modalities come with certain problems. CT uses harmful ionising radiation, and MRI suffers from slow acquisition speed. Both problems can be tackled by undersampling, such as sparse sampling. However, such undersampled data leads to lower resolution and introduces artefacts. Several techniques, including deep learning based methods, have been proposed to reconstruct such data. However, the undersampled reconstruction problem for these two modalities was always considered as two different problems and tackled separately by different research works. This paper proposes a unified solution for both sparse CT and undersampled radial MRI reconstruction, achieved by applying Fourier transform-based pre-processing on the radial MRI and then finally reconstructing both modalities using sinogram upsampling combined with filtered back-projection. The Primal-Dual network is a deep learning based method for reconstructing sparsely-sampled CT data. This paper introduces Primal-Dual UNet, which improves the Primal-Dual network in terms of accuracy and reconstruction speed. The proposed method resulted in an average SSIM of 0.932±0.021 while performing sparse CT reconstruction for fan-beam geometry with a sparsity level of 16, achieving a statistically significant improvement over the previous model, which resulted in 0.919±0.016. Furthermore, the proposed model resulted in 0.903±0.019 and 0.957±0.023 average SSIM while reconstructing undersampled brain and abdominal MRI data with an acceleration factor of 16, respectively - statistically significant improvements over the original model, which resulted in 0.867±0.025 and 0.949±0.025. Finally, this paper shows that the proposed network not only improves the overall image quality, but also improves the image quality for the regions-of-interest: liver, kidneys, and spleen; as well as generalises better than the baselines in presence the of a needle.

Accurate determination of hip implant wear, cup anteversion and inclination through AI automated 2D-3D registration.

The precise and accurate measurement of implant wear, acetabular cup anteversion and inclination from routine anterior-posterior radiographs still poses a challenge. Current approaches suffer from time-consuming procedures accompanied by low and observer-dependent accuracy and precision. We present and validate a novel, automated method for determining total hip arthroplasty parameters by comparing its accuracy and precision with methods in contemporary scientific literature. The algorithm uses CAD-model-based two dimensional-three dimensional (2D-3D)-registration supported by convolutional neural networks. Two in-vitro experimental set-ups were designed to validate the proposed 2D-3D-method. The set-ups provided 84 predefined wear values and 24 configurations of anteversion and inclination in 114 radiographs. Accuracy and precision were evaluated by systematically comparing the predefined ground truth and the automatically calculated values from in-vitro X-rays. In addition, an algorithm was developed and validated against physician's measurements on clinical X-rays to determine the inclination of the interteardrop (ITL) and biischial line (BL) to account for the individual patient's pelvic rotation in the frontal plane. Using X-rays from experimental set-ups, the determined mean error was 0.014 mm (standard deviation: 0.020 mm; root-mean-square-error: 0.024 mm) for wear in pelvic position, -0.01° (0.24°; 0.23°) for radiographic cup anteversion, and 0.11° (0.38°; 0.39°) for radiographic cup inclination. The inclination of ITL and BL was automatically determined in all clinical X-rays with excellent interclass correlation coefficients of 0.95 and 0.91, respectively. The presented algorithm allows the accurate and precise evaluation of total hip arthroplasty parameters without additional equipment. The method might help to investigate different implant designs, biomaterials, and surgical techniques with greater objectivity.

Determining of Ablation Zone in Ex Vivo Bovine Liver Using Time-Shift Measurements.

This study presents a measurement principle for determining the size of the ablation zone in MWA, which could ultimately form an alternative to more expensive monitoring approaches like CT. The measurement method is based on a microwave transmission measurement. A MWA is performed experimentally on ex vivo bovine liver to determine the ablation zone. This setup uses a custom slot applicator performing the MWA at an operating frequency of 2.45 GHz and a custom bowtie antenna measuring the waves transmitted from the applicator. Furthermore, a custom measurement probe is used to determine the dielectric properties. A time-shift analysis is used to determine the radial extent of the ablation zone. Several measurements are carried out with a power of 50 W for 10 min to show the reproducibility. The results show that this method can provide reproducible outcomes to determine the ablation zone with a maximum error of 4.11%.

Clinical prototype implementation enabling an improved day-to-day mammography compression.

PURPOSE: In mammography, breast compression is achieved by lowering a compression paddle on the breast. Despite the directive that compression is needed, there is no concrete guideline on its execution. To estimate the degree of compression, current mammography units only provide compression force and breast thickness as parameters. Therefore, radiographers could be induced to mainly determine the level of compression based on compression force and apply the same value to all breast sizes. In this case, smaller breast sizes are exposed to higher pressure. This results in a highly varying perception of discomfort or even pain during the procedure, depending on the breast size. METHODS: To overcome this imbalance, current research results suggest that pressure might be a more qualified parameter for a more uniform compression among all breast sizes. To utilize pressure, the contact area between breast and compression paddle must be determined. In this paper, we present an easy-to-implement prototype enabling a real-time pressure-based measure without the need of direct patient contact. Using an optical camera, the contact area between the breast and the compression paddle is automatically segmented by a deep learning model. RESULTS: The model provides a mean pixel accuracy of 96.7% (SD: 2.3%), mean frequency-weighted intersection over union of 88.5% (SD: 6.3%), and a Dice score of 93.6% (SD: 2.2%). The subsequent pressure display is updated more than five times per second which enables the use in clinical routines to set the compression level. CONCLUSION: This prototype could help guiding to an improved breast compression routine in mammography procedures.