Assessment of the axial resolution of a compact gamma camera with coded aperture collimator.

PURPOSE: Handheld gamma cameras with coded aperture collimators are under investigation for intraoperative imaging in nuclear medicine. Coded apertures are a promising collimation technique for applications such as lymph node localization due to their high sensitivity and the possibility of 3D imaging. We evaluated the axial resolution and computational performance of two reconstruction methods. METHODS: An experimental gamma camera was set up consisting of the pixelated semiconductor detector Timepix3 and MURA mask of rank 31 with round holes of 0.08 mm in diameter in a 0.11 mm thick Tungsten sheet. A set of measurements was taken where a point-like gamma source was placed centrally at 21 different positions within the range of 12-100 mm. For each source position, the detector image was reconstructed in 0.5 mm steps around the true source position, resulting in an image stack. The axial resolution was assessed by the full width at half maximum (FWHM) of the contrast-to-noise ratio (CNR) profile along the z-axis of the stack. Two reconstruction methods were compared: MURA Decoding and a 3D maximum likelihood expectation maximization algorithm (3D-MLEM). RESULTS: While taking 4400 times longer in computation, 3D-MLEM yielded a smaller axial FWHM and a higher CNR. The axial resolution degraded from 5.3 mm and 1.8 mm at 12 mm to 42.2 mm and 13.5 mm at 100 mm for MURA Decoding and 3D-MLEM respectively. CONCLUSION: Our results show that the coded aperture enables the depth estimation of single point-like sources in the near field. Here, 3D-MLEM offered a better axial resolution but was computationally much slower than MURA Decoding, whose reconstruction time is compatible with real-time imaging.

Point Cloud Registration for Measuring Shape Dependence of Soft Tissue Deformation by Digital Twins in Head and Neck Surgery.

Introduction: A 2½ D point cloud registration method was developed to generate digital twins of different tissue shapes and resection cavities by applying a machine learning (ML) approach. This demonstrates the feasibility of quantifying soft tissue shifts. Methods: An ML model was trained using simulated surface scan data obtained from tumor resections in a pig head cadaver model. It hereby uses 438 2½ D scans of the tissue surface. Tissue shift was induced by a temperature change from 7.91 ± 4.1°C to 36.37 ± 1.28°C. Results: Digital twins were generated from various branched and compact resection cavities (RCs) and cut tissues (CT). A temperature increase induced a tissue shift with a significant volume increase of 6 mL and 2 mL in branched and compact RCs, respectively (p = 0.0443; 0.0157). The volumes of branched and compact CT were decreased by 3 and 4 mL (p < 0.001). In the warm state, RC and CT no longer fit together because of the significant tissue deformation. Although not significant, the compact RC showed a greater tissue deformation of 1 µL than the branched RC with 0.5 µL induced by the temperature change (p = 0.7874). The branched and compact CT forms responded almost equally to changes in temperature (p = 0.1461). Conclusions: The simulation experiment of induced soft tissue deformation using digital twins based on 2½ D point cloud models proved that our method helps to quantify shape-dependent tissue shifts.

Real-time definition of single seed placement sensitivity in low-dose-rate prostate brachytherapy.

PURPOSE: In low-dose-rate brachytherapy, iodine-125 seeds are implanted based on a treatment plan, generated with respect to different dose constraints. The quality of the dose distribution depends on a precise seed placement, however, during treatment planning the impact on the dose parameters when certain seeds fail to be placed precisely is not clear. METHODS AND MATERIALS: We developed a method using automatic differentiation to calculate gradients of dose parameters with regard to the seeds' positions. Thus, we understand their sensitivity with respect to the seed placement. A statistical analysis is performed on a data set with 35 prostate brachytherapy patients. RESULTS: The most sensitive seeds regarding the dosimetric parameters of both rectum and urethra are close to the corresponding organ. Their gradient directions are mainly orthogonal to their surfaces. However, not all seeds close to the surface are equally sensitive with regard to the dose parameter. The most sensitive seeds regarding the prostate's dose parameters are distributed throughout the prostate and the direction of the gradients are mainly parallel to its surface. A linear regression with respect to different patient parameters shows that dose constraints which are barely fulfilled have large gradients and thus are additionally sensitive to misplacement. CONCLUSION: Automatic differentiation can be used to analyze dose parameter sensitivity with respect to seed placement. Integrating this into treatment planning systems is valuable as it speeds up the planning procedure, making it more robust and less dependent on user experience while showing the operating physician which needle placements require greater accuracy than others.

Artificial intelligence directed development of a digital twin to measure soft tissue shift during head and neck surgery.

Digital twins derived from 3D scanning data were developed to measure soft tissue deformation in head and neck surgery by an artificial intelligence approach. This framework was applied suggesting feasibility of soft tissue shift detection as a hitherto unsolved problem. In a pig head cadaver model 104 soft tissue resection had been performed. The surface of the removed soft tissue (RTP) and the corresponding resection cavity (RC) was scanned (N = 416) to train an artificial intelligence (AI) with two different 3D object detectors (HoloLens 2; ArtecEva). An artificial tissue shift (TS) was created by changing the tissue temperature from 7,91±4,1°C to 36,37±1,28°C. Digital twins of RTP and RC in cold and warm conditions had been generated and volumes were calculated based on 3D surface meshes. Significant differences in number of vertices created by the different 3D scanners (HoloLens2 51313 vs. ArtecEva 21694, p<0.0001) hence result in differences in volume measurement of the RTC (p = 0.0015). A significant TS could be induced by changing the temperature of the tissue of RC (p = 0.0027) and RTP (p = <0.0001). RC showed more correlation in TS by heating than RTP with a volume increase of 3.1 µl or 9.09% (p = 0.449). Cadaver models are suitable for training a machine learning model for deformable registration through creation of a digital twin. Despite different point cloud densities, HoloLens and ArtecEva provide only slightly different estimates of volume. This means that both devices can be used for the task.TS can be simulated and measured by temperature change, in which RC and RTP react differently. This corresponds to the clinical behaviour of tumour and resection cavity during surgeries, which could be used for frozen section management and a range of other clinical applications.

A theoretical study of H2 O2 as the surrogate of dose in minibeam radiotherapy, with a diffusion model considering radical removal process.

BACKGROUND: Minibeam radiation therapy (MBRT) is an innovative dose delivery method with the potential to spare normal tissue while achieving similar tumor control as conventional radiotherapy. However, it is difficult to use a single dose parameter, such as mean dose, to compare different patterns of MBRT due to the spatially fractionated radiation. Also, the mechanism leading to the biological effects is still unknown. PURPOSE: This study aims to demonstrate that the hydrogen peroxide (H2 O2 ) distribution could serve as a surrogate of dose distribution when comparing different patterns of MBRT. METHODS: A free diffusion model (FDM) for H2 O2 developed with Fick's second law was compared with a previously published model based on Monte Carlo & convolution method. Since cells form separate compartments that can eliminate H2 O2 radicals diffusing inside the cell, a term describing the elimination was introduced into the equation. The FDM and the diffusion model considering removal (DMCR) were compared by simulating various dose rate irradiation schemes and uniform irradiation. Finally, the DMCR was compared with previous microbeam and minibeam animal experiments. RESULTS: Compared with a previous Monte Carlo & Convolution method, this analytical method provides more accurate results. Furthermore, the new model shows H2 O2 concentration distribution instead of the time to achieve a certain H2 O2 uniformity. The comparison between FDM and DMCR showed that H2 O2 distribution from FDM varied with dose rate irradiation, while DMCR had consistent results. For uniform irradiation, FDM resulted in a Gaussian distribution, while the H2 O2 distribution from DMCR was close to the dose distribution. The animal studies' evaluation showed a correlation between the H2 O2 concentration in the valley region and treatment outcomes. CONCLUSION: DMCR is a more realistic model for H2 O2 simulation than the FDM. In addition, the H2 O2 distribution can be a good surrogate of dose distribution when the minibeam effect could be observed.

Augmented reality during parotid surgery: real-life evaluation of voice control of a head mounted display.

PURPOSE: Augmented Reality can improve surgical planning and performance in parotid surgery. For easier application we implemented a voice control manual for our augmented reality system. The aim of the study was to evaluate the feasibility of the voice control in real-life situations. METHODS: We used the HoloLens 1® (Microsoft Corporation) with a special speech recognition software for parotid surgery. The evaluation took place in a audiometry cubicle and during real surgical procedures. Voice commands were used to display various 3D structures of the patient with the HoloLens 1®. Commands had different variations (male/female, 65 dB SPL)/louder, various structures). RESULTS: In silence, 100% of commands were recognized. If the volume of the operation room (OR) background noise exceeds 42 dB, the recognition rate decreases significantly, and it drops below 40% at > 60 dB SPL. With constant speech volume at 65 dB SPL male speakers had a significant better recognition rate than female speakers (p = 0.046). Higher speech volumes can compensate this effect. The recognition rate depends on the type of background noise. Mixed OR noise (52 dB(A)) reduced the detection rate significantly compared to single suction noise at 52 dB(A) (p ≤ 0.00001). The recognition rate was significantly better in the OR than in the audio cubicle (p = 0.00013 both genders, 0.0086 female, and 0.0036 male). CONCLUSIONS: The recognition rate of voice commands can be enhanced by increasing the speech volume and by singularizing ambient noises. The detection rate depends on the loudness of the OR noise. Male voices are understood significantly better than female voices.

Are We There Yet? The Value of Deep Learning in a Multicenter Setting for Response Prediction of Locally Advanced Rectal Cancer to Neoadjuvant Chemoradiotherapy.

This retrospective study aims to evaluate the generalizability of a promising state-of-the-art multitask deep learning (DL) model for predicting the response of locally advanced rectal cancer (LARC) to neoadjuvant chemoradiotherapy (nCRT) using a multicenter dataset. To this end, we retrained and validated a Siamese network with two U-Nets joined at multiple layers using pre- and post-therapeutic T2-weighted (T2w), diffusion-weighted (DW) images and apparent diffusion coefficient (ADC) maps of 83 LARC patients acquired under study conditions at four different medical centers. To assess the predictive performance of the model, the trained network was then applied to an external clinical routine dataset of 46 LARC patients imaged without study conditions. The training and test datasets differed significantly in terms of their composition, e.g., T-/N-staging, the time interval between initial staging/nCRT/re-staging and surgery, as well as with respect to acquisition parameters, such as resolution, echo/repetition time, flip angle and field strength. We found that even after dedicated data pre-processing, the predictive performance dropped significantly in this multicenter setting compared to a previously published single- or two-center setting. Testing the network on the external clinical routine dataset yielded an area under the receiver operating characteristic curve of 0.54 (95% confidence interval [CI]: 0.41, 0.65), when using only pre- and post-therapeutic T2w images as input, and 0.60 (95% CI: 0.48, 0.71), when using the combination of pre- and post-therapeutic T2w, DW images, and ADC maps as input. Our study highlights the importance of data quality and harmonization in clinical trials using machine learning. Only in a joint, cross-center effort, involving a multidisciplinary team can we generate large enough curated and annotated datasets and develop the necessary pre-processing pipelines for data harmonization to successfully apply DL models clinically.

Normalization of HE-stained histological images using cycle consistent generative adversarial networks.

BACKGROUND: Histological images show strong variance (e.g. illumination, color, staining quality) due to differences in image acquisition, tissue processing, staining, etc. This can impede downstream image analysis such as staining intensity evaluation or classification. Methods to reduce these variances are called image normalization techniques. METHODS: In this paper, we investigate the potential of CycleGAN (cycle consistent Generative Adversarial Network) for color normalization in hematoxylin-eosin stained histological images using daily clinical data with consideration of the variability of internal staining protocol variations. The network consists of a generator network GB that learns to map an image X from a source domain A to a target domain B, i.e. GB:XA→XB. In addition, a discriminator network DB is trained to distinguish whether an image from domain B is real or generated. The same process is applied to another generator-discriminator pair (GA,DA), for the inverse mapping GA:XB→XA. Cycle consistency ensures that a generated image is close to its original when being mapped backwards (GA(GB(XA))≈XA and vice versa). We validate the CycleGAN approach on a breast cancer challenge and a follicular thyroid carcinoma data set for various stain variations. We evaluate the quality of the generated images compared to the original images using similarity measures. In addition, we apply stain normalization on pathological lymph node data from our institute and test the gain from normalization on a ResNet classifier pre-trained on the Camelyon16 data set. RESULTS: Qualitative results of the images generated by our network are compared to original color distributions. Our evaluation indicates that by mapping images to a target domain, the similarity training images from that domain improves up to 96%. We also achieve a high cycle consistency for the generator networks by obtaining similarity indices greater than 0.9. When applying the CycleGAN normalization to HE-stain images from our institute the kappa-value of the ResNet-model that is only trained on Camelyon16 data is increased more than 50%. CONCLUSIONS: CycleGANs have proven to efficiently normalize HE-stained images. The approach compensates for deviations resulting from image acquisition (e.g. different scanning devices) as well as from tissue staining (e.g. different staining protocols), and thus overcomes the staining variations in images from various institutions.The code is publicly available at https://github.com/m4ln/stainTransfer_CycleGAN_pytorch . The data set supporting the solutions is available at https://doi.org/10.11588/data/8LKEZF .

Noise-robust breathing-phase estimation on marker-free, ultra low dose X-ray projections for real-time tumor localization via surrogate structures.

PURPOSE: This paper presents a novel strategy for feature-based breathing-phase estimation on ultra low-dose X-ray projections for tumor motion control in radiation therapy. METHODS: Coarse-scaled Curvelet coefficients are identified as motion sensitive but noise-robust features for this purpose. For feature-based breathing-phase estimation, an ensemble strategy with two classifiers is used. This consensus-based estimation substantially increases tracking reliability by rejection of false positives. The algorithm is evaluated on both synthetic and measured phantom data: Monte Carlo simulated ultra low dose projections for a C-arm X-ray and on the basis of 4D-chest-CTs of eight patients on one hand side and real measurements based on a motion phantom. RESULTS: To achieve an accuracy of breathing-phase estimation of more than 95% a fluence between 20 and 400 photons per pixel (open field) is required depending on the patient. Furthermore, the algorithm is evaluated on real ultra low dose projections from an XVI R5.0 system (Elekta AB, Stockholm, Sweden) using an additional lead filter to reduce fluence. The classifiers-consensus-based-gating method estimated the correct position of the test projections in all test cases at a fluence of ∼180 photons per pixel and 92% at a fluence of ∼40 photons per pixel. The deposited dose to patient per image is in the range of nGy. CONCLUSIONS: A novel method is presented for estimation of breathing-phases for real-time tumor localization at ultra low dose both on a simulation and a phantom basis. Its accuracy is comparable to state of the art X-ray based algorithms while the released dose to patients is reduced by two to three orders of magnitude compared to conventional template-based approaches. This allows for continuous motion control during irradiation without the need of external markers.

Augmented Reality with HoloLens® in Parotid Tumor Surgery: A Prospective Feasibility Study.

INTRODUCTION: Augmented reality can improve planning and execution of surgical procedures. Head-mounted devices such as the HoloLens® (Microsoft, Redmond, WA, USA) are particularly suitable to achieve these aims because they are controlled by hand gestures and enable contactless handling in a sterile environment. OBJECTIVES: So far, these systems have not yet found their way into the operating room for surgery of the parotid gland. This study explored the feasibility and accuracy of augmented reality-assisted parotid surgery. METHODS: 2D MRI holographic images were created, and 3D holograms were reconstructed from MRI DICOM files and made visible via the HoloLens. 2D MRI slices were scrolled through, 3D images were rotated, and 3D structures were shown and hidden only using hand gestures. The 3D model and the patient were aligned manually. RESULTS: The use of augmented reality with the HoloLens in parotic surgery was feasible. Gestures were recognized correctly. Mean accuracy of superimposition of the holographic model and patient's anatomy was 1.3 cm. Highly significant differences were seen in position error of registration between central and peripheral structures (p = 0.0059), with a least deviation of 10.9 mm (centrally) and highest deviation for the peripheral parts (19.6-mm deviation). CONCLUSION: This pilot study offers a first proof of concept of the clinical feasibility of the HoloLens for parotid tumor surgery. Workflow is not affected, but additional information is provided. The surgical performance could become safer through the navigation-like application of reality-fused 3D holograms, and it improves ergonomics without compromising sterility. Superimposition of the 3D holograms with the surgical field was possible, but further invention is necessary to improve the accuracy.

Augmented reality with HoloLens in parotid surgery: how to assess and to improve accuracy.

PURPOSE: Augmented reality improves planning and execution of surgical procedures. The aim of this study was to evaluate the feasibility of a 3D augmented reality hologram in live parotic surgery. Another goal was to develop an accuracy measuring instrument and to determine the accuracy of the system. METHODS: We created a software to build and manually align 2D and 3D augmented reality models generated from MRI data onto the patient during surgery using the HoloLens® 1 (Microsoft Corporation, Redmond, USA). To assess the accuracy of the system, we developed a specific measuring tool applying a standard electromagnetic navigation device (Fiagon GmbH, Hennigsdorf, Germany). RESULTS: The accuracy of our system was measured during real surgical procedures. Training of the experimenters and the use of fiducial markers significantly reduced the accuracy of holographic system (p = 0.0166 and p = 0.0132). Precision of the developed measuring system was very high with a mean error of the basic system of 1.3 mm. Feedback evaluation demonstrated 86% of participants agreed or strongly agreed that the HoloLens will play a role in surgical education. Furthermore, 80% of participants agreed or strongly agreed that the HoloLens is feasible to be introduced in clinical routine and will play a role within surgery in the future. CONCLUSION: The use of fiducial markers and repeated training reduces the positional error between the hologram and the real structures. The developed measuring device under the use of the Fiagon navigation system is suitable to measure accuracies of holographic augmented reality images of the HoloLens.

Feasibility of interstitial stepping-source electronic brachytherapy to locally inoperable tumors.

PURPOSE: Radiotherapy is the mainstay in the treatment of locally inoperable tumors. Interstitial electronic needle-based kilovoltage brachytherapy (EBT) could be an economic alternative to high-dose-rate (HDR) brachytherapy or permanent seed implantation (PSI). In this work, we evaluated if locally inoperable tumors treated with PSI at our institution may be suitable for EBT. MATERIAL AND METHODS: A total of 10 post-interventional computed tomography (CT) scans of patients, who received PSI and simulated stepping-source EBT applied with Intrabeam system and needle applicator were used. EBT treatment planning software with 3-dimensional image and projection of applicator were applied for designing trajectories and establishing dwell positions. Dwell position doses were summarized, and doses covering 90% of the target volume (D90) achieved with stepping-source EBT were compared to those of PSI. Additionally, conformality of dose distributions and total irradiation time were assessed using conformation number (CN) or conformal index (COIN). RESULTS: In all patients, D90 of EBT exceeded the prescribed dose or D90 of PSI on average by 4.7% or 21.3% relative to the prescribed dose, respectively. Mean number of trajectories was 5.0 for EBT and 6.9 for PSI. Average CN/COIN for EBT was 0.69, with a mean irradiation time of 27.8 minutes for standardized dose of 13 Gy. CONCLUSIONS: Stepping-source EBT allowed for a conformal treatment of inoperable interstitial tumors with similar D90. Fewer trajectories were required for EBT in majority of cases.

Imaging and Characterization of Sustained Gadolinium Nanoparticle Release from Next Generation Radiotherapy Biomaterial.

Smart radiotherapy biomaterials (SRBs) present a new opportunity to enhance image-guided radiotherapy while replacing routinely used inert radiotherapy biomaterials like fiducials. In this study the potential of SRBs loaded with gadolinium-based nanoparticles (GdNPs) is investigated for magnetic resonance imaging (MRI) contrast. GdNP release from SRB is quantified and modelled for accurate prediction. SRBs were manufactured similar to fiducials, with a cylindrical shell consisting of poly(lactic-co-glycolic) acid (PLGA) and a core loaded with GdNPs. Magnetic resonance imaging (MRI) contrast was investigated at 7T in vitro (in agar) and in vivo in subcutaneous tumors grown with the LLC1 lung cancer cell line in C57/BL6 mice. GdNPs were quantified in-phantom and in tumor and their release was modelled by the Weibull distribution. Gd concentration was linearly fitted to the R1 relaxation rate with a detection limit of 0.004 mmol/L and high confidence level (R2 = 0.9843). GdNP loaded SRBs in tumor were clearly visible up to at least 14 days post-implantation. Signal decrease during this time showed GdNP release in vivo, which was calculated as 3.86 ± 0.34 µg GdNPs release into the tumor. This study demonstrates potential and feasibility for SRBs with MRI-contrast, and sensitive GdNP quantification and release from SRBs in a preclinical animal model. The feasibility of monitoring nanoparticle (NP) concentration during treatment, allowing dynamic quantitative treatment planning, is also discussed.

Diffuse domain method for needle insertion simulations.

We present a new strategy for needle insertion simulations without the necessity of meshing. A diffuse domain approach on a regular grid is applied to overcome the need for an explicit representation of organ boundaries. A phase field function captures the transition of tissue parameters and boundary conditions are imposed implicitly. Uncertainties of a volume segmentation are translated in the width of the phase field, an approach that is novel and overcomes the problem of defining an accurate segmentation boundary. We perform a convergence analysis of the diffuse elastic equation for decreasing phase field width, compare our results to deformation fields received from conforming mesh simulations and analyze the diffuse linear elastic equation for different widths of material interfaces. Then, the approach is applied to computed tomography data of a patient with liver tumors. A three-class U-Net is used to automatically generate tissue probability maps serving as phase field functions for the transition of elastic parameters between different tissues. The needle tissue interaction forces are approximated by the absolute gradient of a phase field function, which eliminates the need for explicit boundary parameterization and collision detection at the needle-tissue interface. The results show that the deformation field of the diffuse domain approach is comparable to the deformation of a conforming mesh simulation. Uncertainties of tissue boundaries are included in the model and the simulation can be directly performed on the automatically generated voxel-based probability maps. Thus, it is possible to perform easily implementable patient-specific elastomechanical simulations directly on voxel data.

Prospective trial on telemonitoring of geriatric cancer patients using handheld devices.

PURPOSE AND OBJECTIVE: Randomized trials indicate that electronic or app-based assessment of patient-reported outcomes may improve outcomes in cancer patients. To analyze if an app-based follow-up would be accepted by elderly cancer patients, we conducted a single-center prospective feasibility study (NCT03196050). MATERIALS AND METHODS: Cancer patients (≥60 years) without concurrent uncontrolled severe medical conditions and a Karnofsky performance status (KPS) ≥70 were eligible if they were able to use the smartphone app. The primary endpoint was compliance over 1 year, calculated as patient-specific and study date-specific response rate to questions sent as push notifications; in this interim analysis, we report on 4­month data. Secondary outcomes included a comparison of a subjective health status item (SPHS) with the physician-rated KPS. RESULTS: Out of 225 patients screened, 54 patients agreed to participate and 29 activated the app and participated in the study. The mean age was 66 years (61-78). The individual compliance rate averaged at 58.3% (standard deviation SD = 35%). Daily compliance was 53.3% on average (SD = 10.8%) and declined over time. The average percentage of patients who sent answers at least weekly was 75.0% (SD = 14.8%) and declined from 100% in week 1 to 53.8% in week 17 post-enrollment. Secondary outcomes indicated that questionnaires such as the EORTC-QLQ-C30 are accepted via app and that there is a significant moderate correlation between the SPHS and KPS scores (r = 0.566; p < 0.001). CONCLUSION: Our data indicate that an app-based follow-up incorporating EORTC questionnaires might be possible in highly selected elderly cancer patients with modest compliance rates. Further trials should aim at an increased participation rate.

Analysis of Impact of Humidity and Temperature on Excimer Laser Ablation of Polyethylene Terephthalate, Polymethylmethacrylate, and Porcine Corneal Tissue.

BACKGROUND AND OBJECTIVES: To analyze the impact of humidity and temperature on excimer laser ablation of polyethylene terephthalate (PET), polymethylmethacrylate (PMMA) and porcine corneal tissue, and an ablation model to compensate for the temperature and humidity changes on ablation efficiency. STUDY DESIGN/MATERIALS AND METHODS: The study was conducted using an AMARIS 1050RS (Schwind eye-tech-solutions) placed inside a climate chamber at ACTS. Ablations were performed on PET, PMMA, and porcine cornea. The impact of a wide range of temperature (~18°C to ~30°C) and relative humidity (~25% to ~80%) on laser ablation outcomes was tested using nine climate test settings. For porcine eyes, change in defocus was calculated from the difference of post-ablation to pre-ablation average keratometry readings. Laser scanning deflectometry was performed to measure refractive change achieved in PMMA. Multiple linear regression was performed using the least square method with predictive factors: temperature, relative humidity, time stamp. Influence of climate settings was modeled for pulse energy, pulse fluence, ablation efficiency on PMMA and porcine cornea tissue. RESULTS: Temperature changes did not affect laser pulse energy, pulse fluence (PET), and ablation efficiency (on PMMA or porcine corneal tissue) significantly. Changes in relative humidity were critical and significantly affected laser pulse energy, high fluence and low fluence. The opposite trend was observed between the ablation performance on PMMA and porcine cornea. CONCLUSIONS: The proposed well-fitting multi-linear model can be utilized for compensation of temperature and humidity changes on ablation efficiency. Based on this model, a working window for optimum operation has been found (temperature 18°C to 28°C and relative humidity 25% to 65%) for a maximum deviation of ±2.5% in ablation efficiency in PMMA and porcine corneal tissue. Lasers Surg. Med. © 2019 The Authors. Lasers in Surgery and Medicine Published by Wiley Periodicals, Inc.

Evaluation of a new image reconstruction method for digital breast tomosynthesis: effects on the visibility of breast lesions and breast density.

OBJECTIVE: To compare image quality and breast density of two reconstruction methods, the widely-used filtered-back projection (FBP) reconstruction and the iterative heuristic Bayesian inference reconstruction (Bayesian inference reconstruction plus the method of total variation applied, HBI). METHODS: Thirty-two clinical DBT data sets with malignant and benign findings, n = 27 and 17, respectively, were reconstructed using FBP and HBI. Three experienced radiologists evaluated the images independently using a 5-point visual grading scale and classified breast density according to the American College of Radiology Breast Imaging-Reporting And Data System Atlas, fifth edition. Image quality metrics included lesion conspicuity, clarity of lesion borders and spicules, noise level, artifacts surrounding the lesion, visibility of parenchyma and breast density. RESULTS: For masses, the image quality of HBI reconstructions was superior to that of FBP in terms of conspicuity,clarity of lesion borders and spicules (p < 0.01). HBI and FBP were not significantly different in calcification conspicuity. Overall, HBI reduced noise and supressed artifacts surrounding the lesions better (p < 0.01). The visibility of fibroglandular parenchyma increased using the HBI method (p < 0.01). On average, five cases per radiologist were downgraded from BI-RADS breast density category C/D to A/B. CONCLUSION: HBI significantly improves lesion visibility compared to FBP. HBI-visibility of breast parenchyma increased, leading to a lower breast density rating. Applying the HBIR algorithm should improve the diagnostic performance of DBT and decrease the need for additional imaging in patients with dense breasts. ADVANCES IN KNOWLEDGE: Iterative heuristic Bayesian inference (HBI) image reconstruction substantially improves the image quality of breast tomosynthesis leading to a better visibility of breast carcinomas and reduction of the perceived breast density compared to the widely-used filtered-back projection (FPB) reconstruction. Applying HBI should improve the accuracy of breast tomosynthesis and reduce the number of unnecessary breast biopsies. It may also reduce the radiation dose for the patients, which is especially important in the screening context.

Conditional random fields for phase-based lung feature tracking with ultra-low-dose x-rays.

PURPOSE: During radiation therapy, a continuous internal tumor monitoring without additional imaging dose is desirable. In this study, a sequential feature-based position estimation with ultra-low-dose (ULD) kV x rays using linear-chain conditional random fields (CRFs) is performed. METHODS: Four-dimensional computed tomography (4D-CTs) of eight patients serve as a-priori information from which ULD projections are simulated using a Monte Carlo method. CRFs are trained with Local Energy-based Shape Histogram features extracted from the ULD images to estimate one out of ten breathing phases from the 4D-CT associated with the tumor position. RESULTS: Compared to a mean accuracy for ±1 breathing phase of 0.867 using a support vector machine (SVM), a mean accuracy of 0.958 results for the CRF with ten incident photons per pixel. This corresponds to a position estimation with a discretization error of 2.4-5.3 mm assuming a linear displacement relation between the breathing phases and a systematic error of 2.0-4.4 mm due to motion underestimation of the 4D-CT. CONCLUSIONS: The tumor position estimation is comparable to state-of-the-art methods despite its low imaging dose. Training CRFs further allows a prediction of the following phase and offers a precise post-treatment evaluation tool when decoding the full image sequence.

Modeling sphere dynamics in blood vessels for SIRT pre-planning - To fathom the potential and limitations.

For selective internal radiation therapy (SIRT) the calculation of the 3D distribution of spheres based on individual blood flow properties is still an open and relevant research question. The purpose of this work is to develop and analyze a new treatment planning method for SIRT to calculate the absorbed dose distribution. For this intention, flow dynamics of the SIRT-spheres inside the blood vessels was simulated. The challenge is treatment planning solely using high-resolution imaging data available before treatment. The resolution required to reliably predict the sphere distribution and hence the dose was investigated. For this purpose, arteries of the liver were segmented from a contrast-enhanced angiographic CT. Due to the limited resolution of the given CT, smaller vessels were generated via a vessel model. A combined 1D/3D-flow simulation model was implemented to simulate the final 3D distribution of spheres and dose. Results were evaluated against experimental data from Y90-PET. Analysis showed that the resolution of the vessels within the angiographic CT of about 0.5mm should be improved to a limit of about 150µm to reach a reliable prediction.

MIRACUM: Medical Informatics in Research and Care in University Medicine.

INTRODUCTION: This article is part of the Focus Theme of Methods of Information in Medicine on the German Medical Informatics Initiative. Similar to other large international data sharing networks (e.g. OHDSI, PCORnet, eMerge, RD-Connect) MIRACUM is a consortium of academic and hospital partners as well as one industrial partner in eight German cities which have joined forces to create interoperable data integration centres (DIC) and make data within those DIC available for innovative new IT solutions in patient care and medical research. OBJECTIVES: Sharing data shall be supported by common interoperable tools and services, in order to leverage the power of such data for biomedical discovery and moving towards a learning health system. This paper aims at illustrating the major building blocks and concepts which MIRACUM will apply to achieve this goal. GOVERNANCE AND POLICIES: Besides establishing an efficient governance structure within the MIRACUM consortium (based on the steering board, a central administrative office, the general MIRACUM assembly, six working groups and the international scientific advisory board), defining DIC governance rules and data sharing policies, as well as establishing (at each MIRACUM DIC site, but also for MIRACUM in total) use and access committees are major building blocks for the success of such an endeavor. ARCHITECTURAL FRAMEWORK AND METHODOLOGY: The MIRACUM DIC architecture builds on a comprehensive ecosystem of reusable open source tools (MIRACOLIX), which are linkable and interoperable amongst each other, but also with the existing software environment of the MIRACUM hospitals. Efficient data protection measures, considering patient consent, data harmonization and a MIRACUM metadata repository as well as a common data model are major pillars of this framework. The methodological approach for shared data usage relies on a federated querying and analysis concept. USE CASES: MIRACUM aims at proving the value of their DIC with three use cases: IT support for patient recruitment into clinical trials, the development and routine care implementation of a clinico-molecular predictive knowledge tool, and molecular-guided therapy recommendations in molecular tumor boards. RESULTS: Based on the MIRACUM DIC release in the nine months conceptual phase first large scale analysis for stroke and colorectal cancer cohorts have been pursued. DISCUSSION: Beyond all technological challenges successfully applying the MIRACUM tools for the enrichment of our knowledge about diagnostic and therapeutic concepts, thus supporting the concept of a Learning Health System will be crucial for the acceptance and sustainability in the medical community and the MIRACUM university hospitals.