Clinical application of breathing-adapted 4D CT: image quality comparison to conventional 4D CT.

PURPOSE: 4D CT imaging is an integral part of 4D radiotherapy workflows. However, 4D CT data often contain motion artifacts that mitigate treatment planning. Recently, breathing-adapted 4D CT (i4DCT) was introduced into clinical practice, promising artifact reduction in in-silico and phantom studies. Here, we present an image quality comparison study, pooling clinical patient data from two centers: a new i4DCT and a conventional spiral 4D CT patient cohort. METHODS: The i4DCT cohort comprises 129 and the conventional spiral 4D CT cohort 417 4D CT data sets of lung and liver tumor patients. All data were acquired for treatment planning. The study consists of three parts: illustration of image quality in selected patients of the two cohorts with similar breathing patterns; an image quality expert rater study; and automated analysis of the artifact frequency. RESULTS: Image data of the patients with similar breathing patterns underline artifact reduction by i4DCT compared to conventional spiral 4D CT. Based on a subgroup of 50 patients with irregular breathing patterns, the rater study reveals a fraction of almost artifact-free scans of 89% for i4DCT and only 25% for conventional 4D CT; the quantitative analysis indicated a reduction of artifact frequency by 31% for i4DCT. CONCLUSION: The results demonstrate 4D CT image quality improvement for patients with irregular breathing patterns by breathing-adapted 4D CT in this first corresponding clinical data image quality comparison study.

Deep Learning-Based Automated Thrombolysis in Cerebral Infarction Scoring: A Timely Proof-of-Principle Study.

Background and Purpose: Mechanical thrombectomy is an established procedure for treatment of acute ischemic stroke. Mechanical thrombectomy success is commonly assessed by the Thrombolysis in Cerebral Infarction (TICI) score, assigned by visual inspection of X-ray digital subtraction angiography data. However, expert-based TICI scoring is highly observer-dependent. This represents a major obstacle for mechanical thrombectomy outcome comparison in, for instance, multicentric clinical studies. Focusing on occlusions of the M1 segment of the middle cerebral artery, the present study aimed to develop a deep learning (DL) solution to automated and, therefore, objective TICI scoring, to evaluate the agreement of DL- and expert-based scoring, and to compare corresponding numbers to published scoring variability of clinical experts. Methods: The study comprises 2 independent datasets. For DL system training and initial evaluation, an in-house dataset of 491 digital subtraction angiography series and modified TICI scores of 236 patients with M1 occlusions was collected. To test the model generalization capability, an independent external dataset with 95 digital subtraction angiography series was analyzed. Characteristics of the DL system were modeling TICI scoring as ordinal regression, explicit consideration of the temporal image information, integration of physiological knowledge, and modeling of inherent TICI scoring uncertainties. Results: For the in-house dataset, the DL system yields Cohen's kappa, overall accuracy, and specific agreement values of 0.61, 71%, and 63% to 84%, respectively, compared with the gold standard: the expert rating. Values slightly drop to 0.52/64%/43% to 87% when the model is, without changes, applied to the external dataset. After model updating, they increase to 0.65/74%/60% to 90%. Literature Cohen's kappa values for expert-based TICI scoring agreement are in the order of 0.6. Conclusions: The agreement of DL- and expert-based modified TICI scores in the range of published interobserver variability of clinical experts highlights the potential of the proposed DL solution to automated TICI scoring.

Time-Dependent Image Restoration of Low-SNR Live-Cell Ca2 Fluorescence Microscopy Data.

Live-cell Ca2+ fluorescence microscopy is a cornerstone of cellular signaling analysis and imaging. The demand for high spatial and temporal imaging resolution is, however, intrinsically linked to a low signal-to-noise ratio (SNR) of the acquired spatio-temporal image data, which impedes on the subsequent image analysis. Advanced deconvolution and image restoration algorithms can partly mitigate the corresponding problems but are usually defined only for static images. Frame-by-frame application to spatio-temporal image data neglects inter-frame contextual relationships and temporal consistency of the imaged biological processes. Here, we propose a variational approach to time-dependent image restoration built on entropy-based regularization specifically suited to process low- and lowest-SNR fluorescence microscopy data. The advantage of the presented approach is demonstrated by means of four datasets: synthetic data for in-depth evaluation of the algorithm behavior; two datasets acquired for analysis of initial Ca2+ microdomains in T-cells; finally, to illustrate the transferability of the methodical concept to different applications, one dataset depicting spontaneous Ca2+ signaling in jGCaMP7b-expressing astrocytes. To foster re-use and reproducibility, the source code is made publicly available.

Technological quality requirements for stereotactic radiotherapy : Expert review group consensus from the DGMP Working Group for Physics and Technology in Stereotactic Radiotherapy.

This review details and discusses the technological quality requirements to ensure the desired quality for stereotactic radiotherapy using photon external beam radiotherapy as defined by the DEGRO Working Group Radiosurgery and Stereotactic Radiotherapy and the DGMP Working Group for Physics and Technology in Stereotactic Radiotherapy. The covered aspects of this review are 1) imaging for target volume definition, 2) patient positioning and target volume localization, 3) motion management, 4) collimation of the irradiation and beam directions, 5) dose calculation, 6) treatment unit accuracy, and 7) dedicated quality assurance measures. For each part, an expert review for current state-of-the-art techniques and their particular technological quality requirement to reach the necessary accuracy for stereotactic radiotherapy divided into intracranial stereotactic radiosurgery in one single fraction (SRS), intracranial fractionated stereotactic radiotherapy (FSRT), and extracranial stereotactic body radiotherapy (SBRT) is presented. All recommendations and suggestions for all mentioned aspects of stereotactic radiotherapy are formulated and related uncertainties and potential sources of error discussed. Additionally, further research and development needs in terms of insufficient data and unsolved problems for stereotactic radiotherapy are identified, which will serve as a basis for the future assignments of the DGMP Working Group for Physics and Technology in Stereotactic Radiotherapy. The review was group peer-reviewed, and consensus was obtained through multiple working group meetings.

Radiomics of Brain MRI: Utility in Prediction of Metastatic Tumor Type.

Purpose To investigate the feasibility of tumor type prediction with MRI radiomic image features of different brain metastases in a multiclass machine learning approach for patients with unknown primary lesion at the time of diagnosis. Materials and methods This single-center retrospective analysis included radiomic features of 658 brain metastases from T1-weighted contrast material-enhanced, T1-weighted nonenhanced, and fluid-attenuated inversion recovery (FLAIR) images in 189 patients (101 women, 88 men; mean age, 61 years; age range, 32-85 years). Images were acquired over a 9-year period (from September 2007 through December 2016) with different MRI units, reflecting heterogeneous image data. Included metastases originated from breast cancer (n = 143), small cell lung cancer (n = 151), non-small cell lung cancer (n = 225), gastrointestinal cancer (n = 50), and melanoma (n = 89). A total of 1423 quantitative image features and basic clinical data were evaluated by using random forest machine learning algorithms. Validation was performed with model-external fivefold cross validation. Comparative analysis of 10 randomly drawn cross-validation sets verified the stability of the results. The classifier performance was compared with predictions from a respective conventional reading by two radiologists. Results Areas under the receiver operating characteristic curve of the five-class problem ranged between 0.64 (for non-small cell lung cancer) and 0.82 (for melanoma); all P values were less than .01. Prediction performance of the classifier was superior to the radiologists' readings. Highest differences were observed for melanoma, with a 17-percentage-point gain in sensitivity compared with the sensitivity of both readers; P values were less than .02. Conclusion Quantitative features of routine brain MR images used in a machine learning classifier provided high discriminatory accuracy in predicting the tumor type of brain metastases. © RSNA, 2018 Online supplemental material is available for this article.

[Artificial Intelligence in Endoscopy: Deep Neural Nets for Endoscopic Computer Vision - Methods & Perspectives]. / Künstliche Intelligenz in der Endoskopie: Neuronale Netze und maschinelles Sehen ­ Techniken und Perspektiven.

Artificial neural networks, as a specific approach towards artificial intelligence (AI), can open up a variety of new perspectives for endoscopy, such as automated lesion detection and the precise prediction of a lesion's histology by its endoscopic appearance. Whilst early experiments do suggest an enormous potential for these methods, public expectations on their application in various fields of medicine sometimes appear to be grounded on general fascination rather than detailed understanding of their inner workings. Based on a selective review of the literature, this article shall convey an intuitive understanding of the underlying methods in order to help close the gap between functioning and fascination and allow for a realistic discussion of their perspectives and limitations in endoscopy.After decades of research, the success of deep neuronal networks in image classification has provoked rising interest for AI during recent years. We quickly touch upon the developments surrounding this breakthrough and the reasons for their impact on various disciplines much beyond computer science. Through a comparison with the functioning of the human vision system, we aim to understand the mechanisms of these techniques and their success in computer vision tasks in detail. Based on these considerations, we analyse the functioning of some important AI applications in endoscopy, deduce specific limitations and perspectives, discuss the current state of their evaluation in practical endoscopy and make a plea for the need for additional and realistic tests. Moreover, we seek to give an impression of some further specific applications that can currently be foreseen and how these can shape the role that AI might finally acquire in the routine clinical practice of GI endoscopy.

Under-reported dosimetry errors due to interplay effects during VMAT dose delivery in extreme hypofractionated stereotactic radiotherapy.

BACKGROUND AND PURPOSE: Radiotherapy of extracranial metastases changed from normofractioned 3D CRT to extreme hypofractionated stereotactic treatment using VMAT beam techniques. Random interaction between tumour motion and dynamically changing beam parameters might result in underdosage of the CTV even for an appropriately dimensioned ITV (interplay effect). This study presents a clinical scenario of extreme hypofractionated stereotactic treatment and analyses the impact of interplay effects on CTV dose coverage. METHODS: For a thoracic/abdominal phantom with an integrated high-resolution detector array placed on a 4D motion platform, dual-arc treatment plans with homogenous target coverage were created using a common VMAT technique and delivered in a single fraction. CTV underdosage through interplay effects was investigated by comparing dose measurements with and without tumour motion during plan delivery. RESULTS: Our study agrees with previous works that pointed out insignificant interplay effects on target coverage for very regular tumour motion patterns like simple sinusoidal motion. However, we identified and illustrated scenarios that are likely to result in a clinically relevant CTV underdosage. For tumour motion with abnormal variability, target coverage quantified by the CTV area receiving more than 98% of the prescribed dose decreased to 78% compared to 100% at static dose measurement. CONCLUSION: This study is further proof of considerable influence of interplay effects on VMAT dose delivery in stereotactic radiotherapy. For selected conditions of an exemplary scenario, interplay effects and related motion-induced target underdosage primarily occurred in tumour motion pattern with increased motion variability and VMAT plan delivery using complex MLC dose modulation.

Regional Lung Ventilation Analysis Using Temporally Resolved Magnetic Resonance Imaging.

OBJECTIVE: We propose a computer-aided method for regional ventilation analysis and observation of lung diseases in temporally resolved magnetic resonance imaging (4D MRI). METHODS: A shape model-based segmentation and registration workflow was used to create an atlas-derived reference system in which regional tissue motion can be quantified and multimodal image data can be compared regionally. Model-based temporal registration of the lung surfaces in 4D MRI data was compared with the registration of 4D computed tomography (CT) images. A ventilation analysis was performed on 4D MR images of patients with lung fibrosis; 4D MR ventilation maps were compared with corresponding diagnostic 3D CT images of the patients and 4D CT maps of subjects without impaired lung function (serving as reference). RESULTS: Comparison between the computed patient-specific 4D MR regional ventilation maps and diagnostic CT images shows good correlation in conspicuous regions. Comparison to 4D CT-derived ventilation maps supports the plausibility of the 4D MR maps. Dynamic MRI-based flow-volume loops and spirograms further visualize the free-breathing behavior. CONCLUSIONS: The proposed methods allow for 4D MR-based regional analysis of tissue dynamics and ventilation in spontaneous breathing and comparison of patient data. The proposed atlas-based reference coordinate system provides an automated manner of annotating and comparing multimodal lung image data.

Comparison of 3D and 4D Monte Carlo optimization in robotic tracking stereotactic body radiotherapy of lung cancer.

PURPOSE: To investigate the adequacy of three-dimensional (3D) Monte Carlo (MC) optimization (3DMCO) and the potential of four-dimensional (4D) dose renormalization (4DMCrenorm) and optimization (4DMCO) for CyberKnife (Accuray Inc., Sunnyvale, CA) radiotherapy planning in lung cancer. MATERIALS AND METHODS: For 20 lung tumors, 3DMCO and 4DMCO plans were generated with planning target volume (PTV5 mm) = gross tumor volume (GTV) plus 5 mm, assuming 3 mm for tracking errors (PTV3 mm) and 2 mm for residual organ deformations. Three fractions of 60 Gy were prescribed to ≥ 95 % of the PTV5 mm. Each 3DMCO plan was recalculated by 4D MC dose calculation (4DMCrecal) to assess the dosimetric impact of organ deformations. The 4DMCrecal plans were renormalized (4DMCrenorm) to 95 % dose coverage of the PTV5 mm for comparisons with the 4DMCO plans. A 3DMCO plan was considered adequate if the 4DMCrecal plan showed ≥ 95 % of the PTV3 mm receiving 60 Gy and doses to other organs at risk (OARs) were below the limits. RESULTS: In seven lesions, 3DMCO was inadequate, providing < 95 % dose coverage to the PTV3 mm. Comparison of 4DMCrecal and 3DMCO plans showed that organ deformations resulted in lower OAR doses. Renormalizing the 4DMCrecal plans could produce OAR doses higher than the tolerances in some 4DMCrenorm plans. Dose conformity of the 4DMCrenorm plans was inferior to that of the 3DMCO and 4DMCO plans. The 4DMCO plans did not always achieve OAR dose reductions compared to 3DMCO and 4DMCrenorm plans. CONCLUSION: This study indicates that 3DMCO with 2 mm margins for organ deformations may be inadequate for Cyberknife-based lung stereotactic body radiotherapy (SBRT). Renormalizing the 4DMCrecal plans could produce degraded dose conformity and increased OAR doses; 4DMCO can resolve this problem.

Stability analysis of patient-specific 4DCT- and 4DCBCT-based correspondence models.

BACKGROUND: Surrogate-based motion compensation in stereotactic body radiation therapy (SBRT) strongly relies on a constant relationship between an external breathing signal and the internal tumor motion over the course of treatment, that is, a stable patient-specific correspondence model. PURPOSE: This study aims to develop methods for analyzing the stability of correspondence models by integrating planning 4DCT and pretreatment 4D cone-beam computed tomography (4DCBCT) data and assessing the relation to patient-specific clinical parameters. METHODS: For correspondence modeling, a regression-based approach is applied, correlating patient-specific internal motion (vector fields computed by deformable image registration) and external breathing signals (recorded by Varian's RPM and RGSC system). To analyze correspondence model stability, two complementary methods are proposed. (1) Target volume-based analysis: 4DCBCT-based correspondence models predict clinical target volumes (GTV and internal target volume [ITV]) within the planning 4DCT, which are evaluated by overlap and distance measures (Dice similarity coefficient [DSC]/average symmetric surface distance [ASSD]). (2) System matrix-based analysis: 4DCBCT-based regression models are compared to 4DCT-based models using mean squared difference (MSD) and principal component analysis of the system matrices. Stability analysis results are correlated with clinical parameters. Both methods are applied to a dataset of 214 pretreatment 4DCBCT scans (Varian TrueBeam) from a cohort of 46 lung tumor patients treated with ITV-based SBRT (planning 4DCTs acquired with Siemens AS Open and SOMATOM go.OPEN Pro CT scanners). RESULTS: Consistent results across the two complementary analysis approaches (Spearman correlation coefficient of 0.6 / 0.7 $0.6/ 0.7$ between system matrix-based MSD and GTV-based DSC/ASSD) were observed. Analysis showed that stability was not predominant, with 114/214 fraction-wise models not surpassing a threshold of D S C > 0.7 $DSC > 0.7$ for the GTV, and only 14/46 patients demonstrating a D S C > 0.7 $DSC > 0.7$ in all fractions. Model stability did not degrade over the course of treatment. The mean GTV-based DSC is 0.59 ± 0.26 $0.59\pm 0.26$ (mean ASSD of 2.83 ± 3.37 $2.83\pm 3.37$ ) and the respective ITV-based DSC is 0.69 ± 0.20 $0.69\pm 0.20$ (mean ASSD of 2.35 ± 1.81 $2.35\pm 1.81$ ). The clinical parameters showed a strong correlation between smaller tumor motion ranges and increased stability. CONCLUSIONS: The proposed methods identify patients with unstable correspondence models prior to each treatment fraction, serving as direct indicators for the necessity of replanning and adaptive treatment approaches to account for internal-external motion variations throughout the course of treatment.

Monte Carlo-based simulation of virtual 3 and 4-dimensional cone-beam computed tomography from computed tomography images: An end-to-end framework and a deep learning-based speedup strategy.

Background and purpose: In radiotherapy, precise comparison of fan-beam computed tomography (CT) and cone-beam CT (CBCT) arises as a commonplace, yet intricate task. This paper proposes a publicly available end-to-end pipeline featuring an intrinsic deep-learning-based speedup technique for generating virtual 3D and 4D CBCT from CT images. Materials and methods: Physical properties, derived from CT intensity information, are obtained through automated whole-body segmentation of organs and tissues. Subsequently, Monte Carlo (MC) simulations generate CBCT X-ray projections for a full circular arc around the patient employing acquisition settings matched with a clinical CBCT scanner (modeled according to Varian TrueBeam specifications). In addition to 3D CBCT reconstruction, a 4D CBCT can be simulated with a fully time-resolved MC simulation by incorporating respiratory correspondence modeling. To address the computational complexity of MC simulations, a deep-learning-based speedup technique is developed and integrated that uses projection data simulated with a reduced number of photon histories to predict a projection that matches the image characteristics and signal-to-noise ratio of the reference simulation. Results: MC simulations with default parameter setting yield CBCT images with high agreement to ground truth data acquired by a clinical CBCT scanner. Furthermore, the proposed speedup technique achieves up to 20-fold speedup while preserving image features and resolution compared to the reference simulation. Conclusion: The presented MC pipeline and speedup approach provide an openly accessible end-to-end framework for researchers and clinicians to investigate limitations of image-guided radiation therapy workflows built on both (4D) CT and CBCT images.

Benchmarking machine learning-based real-time respiratory signal predictors in 4D SBRT.

BACKGROUND: Stereotactic body radiotherapy of thoracic and abdominal tumors has to account for respiratory intrafractional tumor motion. Commonly, an external breathing signal is continuously acquired that serves as a surrogate of the tumor motion and forms the basis of strategies like breathing-guided imaging and gated dose delivery. However, due to inherent system latencies, there exists a temporal lag between the acquired respiratory signal and the system response. Respiratory signal prediction models aim to compensate for the time delays and to improve imaging and dose delivery. PURPOSE: The present study explores and compares six state-of-the-art machine and deep learning-based prediction models, focusing on real-time and real-world applicability. All models and data are provided as open source and data to ensure reproducibility of the results and foster reuse. METHODS: The study was based on 2502 breathing signals ( t t o t a l ≈ 90 $t_{total} \approx 90$  h) acquired during clinical routine, split into independent training (50%), validation (20%), and test sets (30%). Input signal values were sampled from noisy signals, and the target signal values were selected from corresponding denoised signals. A standard linear prediction model (Linear), two state-of-the-art models in general univariate signal prediction (Dlinear, Xgboost), and three deep learning models (Lstm, Trans-Enc, Trans-TSF) were chosen. The prediction performance was evaluated for three different prediction horizons (480, 680, and 920 ms). Moreover, the robustness of the different models when applied to atypical, that is, out-of-distribution (OOD) signals, was analyzed. RESULTS: The Lstm model achieved the lowest normalized root mean square error for all prediction horizons. The prediction errors only slightly increased for longer horizons. However, a substantial spread of the error values across the test signals was observed. Compared to typical, that is, in-distribution test signals, the prediction accuracy of all models decreased when applied to OOD signals. The more complex deep learning models Lstm and Trans-Enc showed the least performance loss, while the performance of simpler models like Linear dropped the most. Except for Trans-Enc, inference times for the different models allowed for real-time application. CONCLUSION: The application of the Lstm model achieved the lowest prediction errors. Simpler prediction filters suffer from limited signal history access, resulting in a drop in performance for OOD signals.

Impact of breathing signal-guided dose modulation on step-and-shoot 4D CT image reconstruction.

BACKGROUND: Breathing signal-guided 4D CT sequence scanning such as the intelligent 4D CT (i4DCT) approach reduces imaging artifacts compared to conventional 4D CT. By design, i4DCT captures entire breathing cycles during beam-on periods, leading to redundant projection data and increased radiation exposure to patients exhibiting prolonged exhalation phases. A recently proposed breathing-guided dose modulation (DM) algorithm promises to lower the imaging dose by temporarily reducing the CT tube current, but the impact on image reconstruction and the resulting images have not been investigated. PURPOSE: We evaluate the impact of breathing signal-guided DM on 4D CT image reconstruction and corresponding images. METHODS: This study is designed as a comparative and retrospective analysis based on 104 4D CT datasets. Each dataset underwent retrospective reconstruction twice: (a) utilizing the acquired clinical projection data for reconstruction, which yields reference image data, and (b) excluding projections acquired during potential DM phases from image reconstruction, resulting in DM-affected image data. Resulting images underwent automatic organ segmentation (lung/liver). (Dis)Similarity of reference and DM-affected images were quantified by the Dice coefficient of the entire organ masks and the organ overlaps within the DM-affected slices. Further, for lung cases, (a) and (b) were deformably registered and median magnitudes of the obtained displacement field were computed. Eventually, for 17 lung cases, gross tumor volumes (GTV) were recontoured on both (a) and (b). Target volume similarity was quantified by the Hausdorff distance. RESULTS: DM resulted in a median imaging dose reduction of 15.4% (interquartile range [IQR]: 11.3%-19.9%) for the present patient cohort. Dice coefficients for lung ( n = 73 $n=73$ ) and liver ( n = 31 $n=31$ ) patients were consistently high for both the entire organs and the DM-affected slices (IQR lung: 0.985 / 0.982 $0.985/0.982$ [entire lung/DM-affected slices only] to 0.992 / 0.989 $0.992/0.989$ ; IQR liver: 0.977 / 0.972 $0.977/0.972$ to 0.986 / 0.986 $0.986/0.986$ ), demonstrating that DM did not cause organ distortions or alterations. Median displacements for DM-affected to reference image registration varied; however, only two out of 73 cases exhibited a median displacement larger than one isotropic 1 mm 3 ${\rm mm}^3$ voxel size. The impact on GTV definition for the end-exhalation phase was also minor (median Hausdorff distance: 0.38 mm, IQR: 0.15-0.46 mm). CONCLUSION: This study demonstrates that breathing signal-guided DM has a minimal impact on image reconstruction and image appearance while improving patient safety by reducing dose exposure.

Spatial normalization for voxel-based lesion symptom mapping: impact of registration approaches.

Background: Voxel-based lesion symptom mapping (VLSM) assesses the relation of lesion location at a voxel level with a specific clinical or functional outcome measure at a population level. Spatial normalization, that is, mapping the patient images into an atlas coordinate system, is an essential pre-processing step of VLSM. However, no consensus exists on the optimal registration approach to compute the transformation nor are downstream effects on VLSM statistics explored. In this work, we evaluate four registration approaches commonly used in VLSM pipelines: affine (AR), nonlinear (NLR), nonlinear with cost function masking (CFM), and enantiomorphic registration (ENR). The evaluation is based on a standard VLSM scenario: the analysis of statistical relations of brain voxels and regions in imaging data acquired early after stroke onset with follow-up modified Rankin Scale (mRS) values. Materials and methods: Fluid-attenuated inversion recovery (FLAIR) MRI data from 122 acute ischemic stroke patients acquired between 2 and 3 days after stroke onset and corresponding lesion segmentations, and 30 days mRS values from a European multicenter stroke imaging study (I-KNOW) were available and used in this study. The relation of the voxel location with follow-up mRS was assessed by uni- as well as multi-variate statistical testing based on the lesion segmentations registered using the four different methods (AR, NLR, CFM, ENR; implementation based on the ANTs toolkit). Results: The brain areas evaluated as important for follow-up mRS were largely consistent across the registration approaches. However, NLR, CFM, and ENR led to distortions in the patient images after the corresponding nonlinear transformations were applied. In addition, local structures (for instance the lateral ventricles) and adjacent brain areas remained insufficiently aligned with corresponding atlas structures even after nonlinear registration. Conclusions: For VLSM study designs and imaging data similar to the present work, an additional benefit of nonlinear registration variants for spatial normalization seems questionable. Related distortions in the normalized images lead to uncertainties in the VLSM analyses and may offset the theoretical benefits of nonlinear registration.

Imaging Initial Ca2+ Microdomains in Primary T Cells.

Local, sub-second Ca2+ signals, termed Ca2+ microdomains, are highly dynamic and short-lived Ca2+ signals, which result in a global [Ca2+]i elevation and might already determine the fate of a T cell. Upon T cell receptor activation, NAADP is formed rapidly, binding to NAADP binding proteins (HN1L/JPT2, LSM12) and their respective receptors (RyR1, TPC2) sitting on intracellular Ca2+ stores, like the ER and lysosomes, and leading to subsequent release and elevation of [Ca2+]i. To capture these fast and dynamically occurring Ca2+ signals, we developed a high-resolution imaging technique using a combination of two Ca2+ indicators, Fluo-4 AM and FuraRed AM. For postprocessing, an open-source, semi-automated Ca2+ microdomain detection approach was developed based on the programming language Python. Using this workflow, we are able to reliably detect Ca2+ microdomains on a subcellular level in primary murine and human T cells in high temporal and spatial resolution fluorescence videos. This method can also be applied to other cell types, like NK cells and murine neuronal cell lines.

Time-resolved role of P2X4 and P2X7 during CD8+ T cell activation.

CD8+ T cells are a crucial part of the adaptive immune system, responsible for combating intracellular pathogens and tumor cells. The initial activation of T cells involves the formation of highly dynamic Ca2+ microdomains. Recently, purinergic signaling was shown to be involved in the formation of the initial Ca2+ microdomains in CD4+ T cells. In this study, the role of purinergic cation channels, particularly P2X4 and P2X7, in CD8+ T cell signaling from initial events to downstream responses was investigated, focusing on various aspects of T cell activation, including Ca2+ microdomains, global Ca2+ responses, NFAT-1 translocation, cytokine expression, and proliferation. While Ca2+ microdomain formation was significantly reduced in the first milliseconds to seconds in CD8+ T cells lacking P2X4 and P2X7 channels, global Ca2+ responses over minutes were comparable between wild-type (WT) and knockout cells. However, the onset velocity was reduced in P2X4-deficient cells, and P2X4, as well as P2X7-deficient cells, exhibited a delayed response to reach a certain level of free cytosolic Ca2+ concentration ([Ca2+]i). NFAT-1 translocation, a crucial transcription factor in T cell activation, was also impaired in CD8+ T cells lacking P2X4 and P2X7. In addition, the expression of IFN-γ, a major pro-inflammatory cytokine produced by activated CD8+ T cells, and Nur77, a negative regulator of T cell activation, was significantly reduced 18h post-stimulation in the knockout cells. In line, the proliferation of T cells after 3 days was also impaired in the absence of P2X4 and P2X7 channels. In summary, the study demonstrates that purinergic signaling through P2X4 and P2X7 enhances initial Ca2+ events during CD8+ T cell activation and plays a crucial role in regulating downstream responses, including NFAT-1 translocation, cytokine expression, and proliferation on multiple timescales. These findings suggest that targeting purinergic signaling pathways may offer potential therapeutic interventions.

MASTER-NAADP: a membrane permeable precursor of the Ca2+ mobilizing second messenger NAADP.

Upon stimulation of membrane receptors, nicotinic acid adenine dinucleotide phosphate (NAADP) is formed as second messenger within seconds and evokes Ca2+ signaling in many different cell types. Here, to directly stimulate NAADP signaling, MASTER-NAADP, a Membrane permeAble, STabilized, bio-rEversibly pRotected precursor of NAADP is synthesized and release of its active NAADP mimetic, benzoic acid C-nucleoside, 2'-phospho-3'F-adenosine-diphosphate, by esterase digestion is confirmed. In the presence of NAADP receptor HN1L/JPT2 (hematological and neurological expressed 1-like protein, HN1L, also known as Jupiter microtubule-associated homolog 2, JPT2), this active NAADP mimetic releases Ca2+ and increases the open probability of type 1 ryanodine receptor. When added to intact cells, MASTER-NAADP initially evokes single local Ca2+ signals of low amplitude. Subsequently, also global Ca2+ signaling is observed in T cells, natural killer cells, and Neuro2A cells. In contrast, control compound MASTER-NADP does not stimulate Ca2+ signaling. Likewise, in cells devoid of HN1L/JPT2, MASTER-NAADP does not affect Ca2+ signaling, confirming that the product released from MASTER-NAADP is a bona fide NAADP mimetic.

Self-Supervision for Medical Image Classification: State-of-the-Art Performance with ~100 Labeled Training Samples per Class.

Is self-supervised deep learning (DL) for medical image analysis already a serious alternative to the de facto standard of end-to-end trained supervised DL? We tackle this question for medical image classification, with a particular focus on one of the currently most limiting factor of the field: the (non-)availability of labeled data. Based on three common medical imaging modalities (bone marrow microscopy, gastrointestinal endoscopy, dermoscopy) and publicly available data sets, we analyze the performance of self-supervised DL within the self-distillation with no labels (DINO) framework. After learning an image representation without use of image labels, conventional machine learning classifiers are applied. The classifiers are fit using a systematically varied number of labeled data (1-1000 samples per class). Exploiting the learned image representation, we achieve state-of-the-art classification performance for all three imaging modalities and data sets with only a fraction of between 1% and 10% of the available labeled data and about 100 labeled samples per class.