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.

Deep learning-based conditional inpainting for restoration of artifact-affected 4D CT images.

BACKGROUND: 4D CT imaging is an essential component of radiotherapy of thoracic and abdominal tumors. 4D CT images are, however, often affected by artifacts that compromise treatment planning quality and image information reliability. PURPOSE: In this work, deep learning (DL)-based conditional inpainting is proposed to restore anatomically correct image information of artifact-affected areas. METHODS: The restoration approach consists of a two-stage process: DL-based detection of common interpolation (INT) and double structure (DS) artifacts, followed by conditional inpainting applied to the artifact areas. In this context, conditional refers to a guidance of the inpainting process by patient-specific image data to ensure anatomically reliable results. The study is based on 65 in-house 4D CT images of lung cancer patients (48 with only slight artifacts, 17 with pronounced artifacts) and two publicly available 4D CT data sets that serve as independent external test sets. RESULTS: Automated artifact detection revealed a ROC-AUC of 0.99 for INT and of 0.97 for DS artifacts (in-house data). The proposed inpainting method decreased the average root mean squared error (RMSE) by 52 % (INT) and 59 % (DS) for the in-house data. For the external test data sets, the RMSE improvement is similar (50 % and 59 %, respectively). Applied to 4D CT data with pronounced artifacts (not part of the training set), 72 % of the detectable artifacts were removed. CONCLUSIONS: The results highlight the potential of DL-based inpainting for restoration of artifact-affected 4D CT data. Compared to recent 4D CT inpainting and restoration approaches, the proposed methodology illustrates the advantages of exploiting patient-specific prior image information.

Dose reduction in sequence scanning 4D CT imaging through respiratory signal-guided tube current modulation: A feasibility study.

BACKGROUND: Respiratory signal-guided 4D CT sequence scanning such as the recently introduced Intelligent 4D CT (i4DCT) approach reduces image artifacts compared to conventional 4D CT, especially for irregular breathing. i4DCT selects beam-on periods during scanning such that data sufficiency conditions are fulfilled for each couch position. However, covering entire breathing cycles during beam-on periods leads to redundant projection data and unnecessary dose to the patient during long exhalation phases. PURPOSE: We propose and evaluate the feasibility of respiratory signal-guided dose modulation (i.e., temporary reduction of the CT tube current) to reduce the i4DCT imaging dose while maintaining high projection data coverage for image reconstruction. METHODS: The study is designed as an in-silico feasibility study. Dose down- and up-regulation criteria were defined based on the patients' breathing signals and their representative breathing cycle learned before and during scanning. The evaluation (including an analysis of the impact of the dose modulation criteria parameters) was based on 510 clinical 4D CT breathing curves. Dose reduction was determined as the fraction of the downregulated dose delivery time to the overall beam-on time. Furthermore, under the assumption of a 10-phase 4D CT and amplitude-based reconstruction, beam-on periods were considered negatively affected by dose modulation if the downregulation period covered an entire phase-specific amplitude range for a specific breathing phase (i.e., no appropriate reconstruction of the phase image possible for this specific beam-on period). Corresponding phase-specific amplitude bins are subsequently denoted as compromised bins. RESULTS: Dose modulation resulted in a median dose reduction of 10.4% (lower quartile: 7.4%, upper quartile: 13.8%, maximum: 28.6%; all values corresponding to a default parameterization of the dose modulation criteria). Compromised bins were observed in 1.0% of the beam-on periods (72 / 7370 periods) and affected 10.6% of the curves (54/510 curves). The extent of possible dose modulation depends strongly on the individual breathing patterns and is weakly correlated with the median breathing cycle length (Spearman correlation coefficient 0.22, p < 0.001). Moreover, the fraction of beam-on periods with compromised bins is weakly anti-correlated with the patient's median breathing cycle length (Spearman correlation coefficient -0.24; p < 0.001). Among the curves with the 17% longest average breathing cycles, no negatively affected beam-on periods were observed. CONCLUSION: Respiratory signal-guided dose modulation for i4DCT imaging is feasible and promises to significantly reduce the imaging dose with little impact on projection data coverage. However, the impact on image quality remains to be investigated in a follow-up study.

Discordant and Converting Receptor Expressions in Brain Metastases from Breast Cancer: MRI-Based Non-Invasive Receptor Status Tracking.

Discordance and conversion of receptor expressions in metastatic lesions and primary tumors is often observed in patients with brain metastases from breast cancer. Therefore, personalized therapy requires continuous monitoring of receptor expressions and dynamic adaptation of applied targeted treatment options. Radiological in vivo techniques may allow receptor status tracking at high frequencies at low risk and cost. The present study aims to investigate the potential of receptor status prediction through machine-learning-based analysis of radiomic MR image features. The analysis is based on 412 brain metastases samples from 106 patients acquired between 09/2007 and 09/2021. Inclusion criteria were as follows: diagnosed cerebral metastases from breast cancer; histopathology reports on progesterone (PR), estrogen (ER), and human epidermal growth factor 2 (HER2) receptor status; and availability of MR imaging data. In total, 3367 quantitative features of T1 contrast-enhanced, T1 non-enhanced, and FLAIR images and corresponding patient age were evaluated utilizing random forest algorithms. Feature importance was assessed using Gini impurity measures. Predictive performance was tested using 10 permuted 5-fold cross-validation sets employing the 30 most important features of each training set. Receiver operating characteristic areas under the curves of the validation sets were 0.82 (95% confidence interval [0.78; 0.85]) for ER+, 0.73 [0.69; 0.77] for PR+, and 0.74 [0.70; 0.78] for HER2+. Observations indicate that MR image features employed in a machine learning classifier could provide high discriminatory accuracy in predicting the receptor status of brain metastases from breast cancer.

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.

Risk management patterns in radiation oncology-results of a national survey within the framework of the Patient Safety in German Radiation Oncology (PaSaGeRO) project.

PURPOSE: Risk management (RM) is a key component of patient safety in radiation oncology (RO). We investigated current approaches on RM in German RO within the framework of the Patient Safety in German Radiation Oncology (PaSaGeRO) project. Aim was not only to evaluate a status quo of RM purposes but furthermore to discover challenges for sustainable RM that should be addressed in future research and recommendations. METHODS: An online survey was conducted from June to August 2021, consisting of 18 items on prospective and reactive RM, protagonists of RM, and self-assessment concerning RM. The survey was designed using LimeSurvey and invitations were sent by e­mail. Answers were requested once per institution. RESULTS: In all, 48 completed questionnaires from university hospitals, general and non-academic hospitals, and private practices were received and considered for evaluation. Prospective and reactive RM was commonly conducted within interprofessional teams; 88% of all institutions performed prospective risk analyses. Most institutions (71%) reported incidents or near-events using multiple reporting systems. Results were presented to the team in 71% for prospective analyses and 85% for analyses of incidents. Risk conferences take place in 46% of institutions. 42% nominated a manager/committee for RM. Knowledge concerning RM was mostly rated "satisfying" (44%). However, 65% of all institutions require more information about RM by professional societies. CONCLUSION: Our results revealed heterogeneous patterns of RM in RO departments, although most departments adhered to common recommendations. Identified mismatches between recommendations and implementation of RM provide baseline data for future research and support definition of teaching content.

Innovative radiation oncology Together - Precise, Personalized, Human : Vision 2030 for radiotherapy & radiation oncology in Germany.

PURPOSE: Scientific and clinical achievements in radiation, medical, and surgical oncology are changing the landscape of interdisciplinary oncology. The German Society for Radiation Oncology (DEGRO) working group of young clinicians and scientists (yDEGRO) and the DEGRO representation of associate and full professors (AKRO) are aware of the essential role of radiation oncology in multidisciplinary treatment approaches. Together, yDEGRO and AKRO endorsed developing a German radiotherapy & radiation oncology vision 2030 to address future challenges in patient care, research, and education. The vision 2030 aims to identify priorities and goals for the next decade in the field of radiation oncology. METHODS: The vision development comprised three phases. During the first phase, areas of interest, objectives, and the process of vision development were defined jointly by the yDEGRO, AKRO, and the DEGRO board. In the second phase, a one-day strategy retreat was held to develop AKRO and yDEGRO representatives' final vision from medicine, biology, and physics. The third phase was dedicated to vision interpretation and program development by yDEGRO representatives. RESULTS: The strategy retreat's development process resulted in conception of the final vision "Innovative radiation oncology Together - Precise, Personalized, Human." The first term "Innovative radiation oncology" comprises the promotion of preclinical research and clinical trials and highlights the development of a national committee for strategic development in radiation oncology research. The term "together" underpins collaborations within radiation oncology departments as well as with other partners in the clinical and scientific setting. "Precise" mainly covers technological precision in radiotherapy as well as targeted oncologic therapeutics. "Personalized" emphasizes biology-directed individualization of radiation treatment. Finally, "Human" underlines the patient-centered approach and points towards the need for individual longer-term career curricula for clinicians and researchers in the field. CONCLUSION: The vision 2030 balances the ambition of physical, technological, and biological innovation as well as a comprehensive, patient-centered, and collaborative approach towards radiotherapy & radiation oncology in Germany.

Current status and developments of German curriculum-based residency training programmes in radiation oncology.

PURPOSE: The current status of German residency training in the field of radiation oncology is provided and compared to programmes in other countries. In particular, we present the DEGRO-Academy within the international context. METHODS: Certified courses from 2018 and 2019 were systematically assigned to the DEGRO-Curriculum, retrospectively for 2018 and prospectively for 2019. In addition, questionnaires of course evaluations were provided, answered by course participants and collected centrally. RESULTS: Our data reveal a clear increase in curriculum coverage by certified courses from 57.6% in 2018 to 77.5% in 2019. The analyses enable potential improvements in German curriculum-based education. Specific topics of the DEGRO-Curriculum are still underrepresented, while others decreased in representation between 2018 and 2019. It was found that several topics in the DEGRO-Curriculum require more attention because of a low DEGRO-curriculum coverage. Evaluation results of certified courses improved significantly with a median grade of 1.62 in 2018 to 1.47 in 2019 (p = 0.0319). CONCLUSION: The increase of curriculum coverage and the simultaneous improvement of course evaluations are promising with respect to educational standards in Germany. Additionally, the early integration of radiation oncology into medical education is a prerequisite for resident training because of rising demands on quality control and increasing patient numbers. This intensified focus is a requirement for continued high standards and quality of curriculum-based education in radiation oncology both in Germany and other countries.

Carbon fiber-reinforced PEEK versus titanium implants: an in vitro comparison of susceptibility artifacts in CT and MR imaging.

Artifacts in computed tomography (CT) and magnetic resonance imaging (MRI) due to titanium implants in spine surgery are known to cause difficulties in follow-up imaging, radiation planning, and precise dose delivery in patients with spinal tumors. Carbon fiber-reinforced polyetheretherketon (CFRP) implants aim to reduce these artifacts. Our aim was to analyze susceptibility artifacts of these implants using a standardized in vitro model. Titanium and CFRP screw-rod phantoms were embedded in 3% agarose gel. Phantoms were scanned with Siemens Somatom AS Open and 3.0-T Siemens Skyra scanners. Regions of interest (ROIs) were plotted and analyzed for CT and MRI at clinically relevant localizations. CT voxel-based imaging analysis showed a significant difference of artifact intensity and central overlay between titanium and CFRP phantoms. For the virtual regions of the spinal canal, titanium implants (ti) presented - 30.7 HU vs. 33.4 HU mean for CFRP (p < 0.001), at the posterior margin of the vertebral body 68.9 HU (ti) vs. 59.8 HU (CFRP) (p < 0.001) and at the anterior part of the vertebral body 201.2 HU (ti) vs. 70.4 HU (CFRP) (p < 0.001), respectively. MRI data was only visually interpreted due to the low sample size and lack of an objective measuring system as Hounsfield units in CT. CT imaging of the phantom with typical implant configuration for thoracic stabilization could demonstrate a significant artifact reduction in CFRP implants compared with titanium implants for evaluation of index structures. Radiolucency with less artifacts provides a better interpretation of follow-up imaging, radiation planning, and more precise dose delivery.

Peer review analysis in the field of radiation oncology: results from a web-based survey of the Young DEGRO working group.

PURPOSE: To evaluate the reviewing behaviour in the German-speaking countries in order to provide recommendations to increase the attractiveness of reviewing activity in the field of radiation oncology. METHODS: In November 2019, a survey was conducted by the Young DEGRO working group (jDEGRO) using the online platform "eSurveyCreator". The questionnaire consisted of 29 items examining a broad range of factors that influence reviewing motivation and performance. RESULTS: A total of 281 responses were received. Of these, 154 (55%) were completed and included in the evaluation. The most important factors for journal selection criteria and peer review performance in the field of radiation oncology are the scientific background of the manuscript (85%), reputation of the journal (59%) and a high impact factor (IF; 40%). Reasons for declining an invitation to review include the scientific background of the article (60%), assumed effort (55%) and a low IF (27%). A double-blind review process is preferred by 70% of respondents to a single-blind (16%) or an open review process (14%). If compensation was offered, 59% of participants would review articles more often. Only 12% of the participants have received compensation for their reviewing activities so far. As compensation for the effort of reviewing, 55% of the respondents would prefer free access to the journal's articles, 45% a discount for their own manuscripts, 40% reduced congress fees and 39% compensation for expenses. CONCLUSION: The scientific content of the manuscript, reputation of the journal and a high IF determine the attractiveness for peer reviewing in the field of radiation oncology. The majority of participants prefer a double-blind peer review process and would conduct more reviews if compensation was available. Free access to journal articles, discounts for publication costs or congress fees, or an expense allowance were identified to increase attractiveness of the review process.

Comparison of intelligent 4D CT sequence scanning and conventional spiral 4D CT: a first comprehensive phantom study.

4D CT imaging is a cornerstone of 4D radiotherapy treatment. Clinical 4D CT data are, however, often affected by severe artifacts. The artifacts are mainly caused by breathing irregularity and retrospective correlation of breathing phase information and acquired projection data, which leads to insufficient projection data coverage to allow for proper reconstruction of 4D CT phase images. The recently introduced 4D CT approach i4DCT (intelligent 4D CT sequence scanning) aims to overcome this problem by breathing signal-driven tube control. The present motion phantom study describes the first in-depth evaluation of i4DCT in a real-world scenario. Twenty-eight 4D CT breathing curves of lung and liver tumor patients with pronounced breathing irregularity were selected to program the motion phantom. For every motion pattern, 4D CT imaging was performed with i4DCT and a conventional spiral 4D CT mode. For qualitative evaluation, the reconstructed 4D CT images were presented to clinical experts, who scored image quality. Further quantitative evaluation was based on established image intensity-based artifact metrics to measure (dis)similarity of neighboring image slices. In addition, beam-on and scan times of the scan modes were analyzed. The expert rating revealed a significantly higher image quality for the i4DCT data. The quantitative evaluation further supported the qualitative: While 20% of the slices of the conventional spiral 4D CT images were found to be artifact-affected, the corresponding fraction was only 4% for i4DCT. The beam-on time (surrogate of imaging dose) did not significantly differ between i4DCT and spiral 4D CT. Overall i4DCT scan times (time between first beam-on and last beam-on event, including scan breaks to compensate for breathing irregularity) were, on average, 53% longer compared to spiral CT. Thus, the results underline that i4DCT significantly improves 4D CT image quality compared to standard spiral CT scanning in the case of breathing irregularity during scanning.

Digital Health meets Hamburg integrated medical degree program iMED: concept and introduction of the new interdisciplinary 2nd track Digital Health.

Digitalization in medicine is transforming the everyday work and the environment of current and future physicians - and thereby brings new competencies required by the medical profession. The necessity for a curricular integration of related digital medicine and, in more general, digital health topics is mostly undisputed; however, few specific concepts and experience reports are available. Therefore, the present article reports on the aims, the implementation, and the initial experiences of the integration of the topic Digital Health as a longitudinal elective course (2nd track) into the integrated medical degree program iMED in Hamburg.

Self-contained deep learning-based boosting of 4D cone-beam CT reconstruction.

PURPOSE: Four-dimensional cone-beam computed tomography (4D CBCT) imaging has been suggested as a solution to account for interfraction motion variability of moving targets like lung and liver during radiotherapy (RT) of moving targets. However, due to severe sparse view sampling artifacts, current 4D CBCT data lack sufficient image quality for accurate motion quantification. In the present paper, we introduce a deep learning-based framework for boosting the image quality of 4D CBCT image data that can be combined with any CBCT reconstruction approach and clinical 4D CBCT workflow. METHODS: Boosting is achieved by learning the relationship between so-called sparse view pseudo-time-average CBCT images obtained by a projection selection scheme introduced to mimic phase image sparse view artifact characteristics and corresponding time-average CBCT images obtained by full view reconstruction. The employed convolutional neural network architecture is the residual dense network (RDN). The underlying hypothesis is that the RDN learns the appearance of the streaking artifacts that is typical for 4D CBCT phase images - and removes them without influencing the anatomical image information. After training the RDN, it can be applied to the 4D CBCT phase images to enhance the image quality without affecting the contained temporal and motion information. Different to existing approaches, no patient-specific prior knowledge about anatomy or motion characteristics is needed, that is, the proposed approach is self-contained. RESULTS: Application of the trained network to reconstructed phase images of an external (SPARE challenge) as well as in-house 4D CBCT patient and motion phantom data set reduces the phase image streak artifacts consistently for all patients and state-of-the-art reconstruction approaches. Using the SPARE data set, we show that the root mean squared error compared to ground truth data provided by the challenge is reduced by approximately 50% while normalized cross correlation of reconstruction and ground truth is improved up to 10%. Compared to direct deep learning-based 4D CBCT to 4D CT mapping, our proposed method performs better because inappropriate prior knowledge about the patient anatomy and physiology is taken into account. Moreover, the image quality enhancement leads to more plausible motion fields estimated by deformable image registration (DIR) in the 4D CBCT image sequences. CONCLUSIONS: The presented framework enables significantly boosting of 4D CBCT image quality as well as improved DIR and motion field consistency. Thus, the proposed method facilitates extraction of motion information from severely artifact-affected images, which is one of the key challenges of integrating 4D CBCT imaging into RT workflows.

4D CT image artifacts affect local control in SBRT of lung and liver metastases.

BACKGROUND AND PURPOSE: 4D CT images often contain artifacts that are suspected to affect treatment planning quality and clinical outcome of lung and liver SBRT. The present study investigates the correlation between the presence of artifacts in SBRT planning 4D CT data and local metastasis control. MATERIALS AND METHODS: The study includes 62 patients with 102 metastases (49 in the lung and 53 in the liver), treated between 2012 and 2016 with SBRT for mainly curative intent. For each patient, 10-phase 4D CT images were acquired and used for ITV definition and treatment planning. Follow-up intervals were 3 weeks after treatment and every 3-6 months thereafter. Based on the number and type of image artifacts, a strict rule-based two-class artifact score was introduced and assigned to the individual 4D CT data sets. Correlation between local control and artifact score (consensus rating based on two independent observers) were analyzed using uni- and multivariable Cox proportional hazards models with random effects. Metastatic site, target volume, metastasis motion, breathing irregularity-related measures, and clinical data (chemotherapy prior to SBRT, target dose, treatment fractionation) were considered as covariates. RESULTS: Local recurrence was observed in 17/102 (17%) metastases. Significant univariable factors for local control were artifact score (severe CT artifacts vs. few CT artifacts; hazard ratio 8.22; 95%-CI 2.04-33.18) and mean patient breathing period (>4.8 s vs. ≤4.8 s; hazard ratio 3.58; 95%-CI 1.18-10.84). Following multivariable analysis, artifact score remained as dominating prognostic factor, although statistically not significant (hazard ratio 10.28; 95%-CI 0.57-184.24). CONCLUSION: The results support the hypothesis that image artifacts in 4D CT treatment planning data negatively influence clinical outcome in SBRT of lung and liver metastases, underlining the need to account for 4D CT artifacts and improve image quality.

Definition and quality requirements for stereotactic radiotherapy: consensus statement from the DEGRO/DGMP Working Group Stereotactic Radiotherapy and Radiosurgery.

Stereotactic radiotherapy with its forms of intracranial stereotactic radiosurgery (SRS), intracranial fractionated stereotactic radiotherapy (FSRT) and stereotactic body radiotherapy (SBRT) is today a guideline-recommended treatment for malignant or benign tumors as well as neurological or vascular functional disorders. The working groups for radiosurgery and stereotactic radiotherapy of the German Society for Radiation Oncology (DEGRO) and for physics and technology in stereotactic radiotherapy of the German Society for Medical Physics (DGMP) have established a consensus statement about the definition and minimal quality requirements for stereotactic radiotherapy to achieve best clinical outcome and treatment quality in the implementation into routine clinical practice.

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.

Intelligent 4D CT sequence scanning (i4DCT): First scanner prototype implementation and phantom measurements of automated breathing signal-guided 4D CT.

PURPOSE: Four-dimensional (4D) computed tomography (CT) imaging is an essential part of current 4D radiotherapy treatment planning workflows, but clinical 4D CT images are often affected by artifacts. The artifacts are mainly caused by breathing irregularity during data acquisition, which leads to projection data coverage issues for currently available commercial 4D CT protocols. It was proposed to improve projection data coverage by online respiratory signal analysis and signal-guided CT tube control, but related work was always theoretical and presented as pure in silico studies. The present work demonstrates a first CT prototype implementation along with respective phantom measurements for the recently introduced intelligent 4D CT (i4DCT) sequence scanning concept (https://doi.org/10.1002/mp.13632). METHODS: Intelligent 4D CT was implemented on the Siemens SOMATOM go platform. Four-dimensional CT measurements were performed using the CIRS motion phantom. Motion curves were programmed to systematically vary from regular to very irregular, covering typical irregular patterns that are known to result in image artifacts using standard 4D CT imaging protocols. Corresponding measurements were performed using i4DCT and routine spiral 4D CT with similar imaging parameters (e.g., mAs setting and gantry rotation time, retrospective ten-phase reconstruction) to allow for a direct comparison of the image data. RESULTS: Following technological implementation of i4DCT on the clinical CT scanner platform, 4D CT motion artifacts were significantly reduced for all investigated levels of breathing irregularity when compared to routine spiral 4D CT scanning. CONCLUSIONS: The present study confirms feasibility of fully automated respiratory signal-guided 4D CT scanning by means of a first implementation of i4DCT on a CT scanner. The measurements thereby support the conclusions of respective in silico studies and demonstrate that respiratory signal-guided 4D CT (here: i4DCT) is ready for integration into clinical CT scanners.

Clinical relevance of metal artefact reduction in computed tomography (iMAR) in the pelvic and head and neck region: Multi-institutional contouring study of gross tumour volumes and organs at risk on clinical cases.

INTRODUCTION: Artefacts caused by dental implants and hip replacements may impede target volume definition and dose calculation accuracy. The iterative metal artefact reduction (iMAR) algorithm can provide a solution for this problem. The present study compares delineation of gross tumour volumes (GTVs) and organs at risk (OARs) in the pelvic and the head and neck (H & N) regions using computed tomography (CT) with and without iMAR, and thus the practical applicability of iMAR for routine clinical use. METHODS: The native planning CT and CT-iMAR data of two typical clinical cases with image-distorting artefacts were used for multi-institutional contouring and analysis using the Dice similarity coefficient (DSC). GTV/OAR contours were compared with an intraobserver approach and compared to predefined reference structures. RESULTS: Mean volume for GTVprostate in the intraobserver approach decreased from 87 ± 44 cm3 (native CT) to 75 ± 22 cm3 (CT-iMAR) (P = 0.168). Compared to the reference, DSC values for GTVP rostate increased from 0.68 ± 0.15 to 0.78 ± 0.07 (CT vs. iMAR) (P < 0.05). In the H & N region, the reference for GTVT ongue (34 cm3 ) was underestimated on both data sets. No significant improvement in DSC values (0.83 ± 0.06 (native CT) versus 0.86 ± 0.06 (CT-iMAR)) was observed. CONCLUSION: The use of iMAR improves the anatomical delineation at the transition of prostate and bladder in cases of bilateral hip replacement. In the H & N region, anatomical residual structures and experience were apparently sufficient for precise contouring.

Intelligent 4D CT sequence scanning (i4DCT): Concept and performance evaluation.

PURPOSE: Four-dimensional (4D) CT imaging is a central part of current treatment planning workflows in 4D radiotherapy (RT). However, clinical 4D CT image data often suffer from severe artifacts caused by insufficient projection data coverage due to the inability of current commercial 4D CT imaging protocols to adapt to breathing irregularity. We propose an intelligent sequence mode 4D CT imaging protocol (i4DCT) that builds on online breathing curve analysis and respiratory signal-guided selection of beam on/off periods during scan time in order to fulfill projection data coverage requirements. i4DCT performance is evaluated and compared to standard clinical sequence mode 4D CT (seq4DCT) and spiral 4D CT (spiral4DCT) approaches. METHODS: i4DCT consists of three main blocks: (a) an initial learning period to establish a patient-specific reference breathing cycle representation for data-driven i4DCT parameter selection, (b) online respiratory signal-guided sequence mode scanning (i4DCT core), (c) rapid breathing record analysis and quality control after scanning to trigger potential local rescanning (i4DCT rescan). Based on a phase space representation of the patient's breathing signal, i4DCT core implements real-time analysis of the signal to appropriately switch on and off projection data acquisition even during irregular breathing. Performance evaluation was based on 189 clinical breathing records acquired during spiral 4D CT scanning for RT planning (data acquisition period: 2013-2017; Siemens Somatom with Varian RPM system). For each breathing record, i4DCT, seq4DCT, and spiral4DCT scanning protocol variants were simulated. Evaluation measures were local projection data coverage ß cov ; number ϵ total of local projection data coverage failures; and number ϵ pat of patients with coverage failures; average beam on time t beam on as a surrogate for imaging dose and total patient on table time t table as the time between first and last beam on signal. RESULTS: Using i4DCT, mean inhalation and exhalation projection data coverage ß cov increased significantly compared to standard spiral 4D CT scanning as applied for the original clinical data acquisition and conventional 4D CT sequence scanning modes. The improved projection data coverage translated into a reduction of coverage failures ϵ total by 89% without and 93% when allowing for a rescanning at up to five z-positions compared to spiral scanning and between 76% and 82% without and 85% and 89% with rescanning when compared to seq4DCT. Similar numbers were observed for ϵ pat . Simultaneously, i4DCT (without rescanning) reduced the beam on time on average by 3%-17% compared to standard spiral 4D CT. In turn, the patient on table time increased by between 35% and 66%. Allowing for rescanning led on average to additional 5.9 s beam on and 10.6 s patient on table time. CONCLUSIONS: i4DCT outperformed currently implemented clinical fixed beam on period 4D CT scanning approaches by means of a significantly smaller data coverage failure rate without requiring additional beam on time compared to, for example, conventional spiral 4D CT protocols.