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Objectives: Aim of this study was to assess the value of virtual non-contrast (VNC) reconstructions in differentiating between adrenal adenomas and metastases on a photon-counting detector CT (PCD-CT). Material and methods: Patients with adrenal masses and contrast-enhanced CT scans in portal venous phase were included. Image reconstructions were performed, including conventional VNC (VNCConv) and PureCalcium VNC (VNCPC), as well as virtual monochromatic images (VMI, 40–90 keV) and iodine maps. We analyzed images using semi-automatic segmentation of adrenal lesions and extracted quantitative data. Logistic regression models, non-parametric tests, Bland–Altman plots, and a random forest classifier were used for statistical analyses. Results: The final study cohort consisted of 90 patients (36 female, mean age 67.8 years [range 39–87]) with adrenal lesions (45 adenomas, 45 metastases). Compared to metastases, adrenal adenomas showed significantly lower CT-values in VNCConv and VNCPC (p = 0.007). Mean difference between VNC and true non-contrast (TNC) was 17.67 for VNCConv and 14.85 for VNCPC. Random forest classifier and logistic regression models both identified VNCConv and VNCPC as the best discriminators. When using 26 HU as the threshold in VNCConv reconstructions, adenomas could be discriminated from metastases with a sensitivity of 86.7% and a specificity of 75.6%. Conclusion: VNC algorithms overestimate CT values compared to TNC in the assessment of adrenal lesions. However, they allow a reliable discrimination between adrenal adenomas and metastases and could be used in clinical routine in near future with an increased threshold (e.g., 26 HU). Further (multi-center) studies with larger patient cohorts and standardized protocols are required. Clinical relevance statement: VNC reconstructions overestimate CT values compared to TNC. Using a different threshold (e.g., 26 HU compared to the established 10 HU), VNC has a high diagnostic accuracy for the discrimination between adrenal adenomas and metastases. Key Points: • Virtual non-contrast reconstructions may be promising tools to differentiate adrenal lesions and might save further diagnostic tests. • The conventional and a new calcium-preserving virtual non-contrast algorithm tend to systematically overestimate CT-values compared to true non-contrast images. • Therefore, increasing the established threshold for true non-contrast images (e.g., 10HU) may help to differentiate between adrenal adenomas and metastases on contrast-enhanced CT. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this review is to give an overview of the current status of molecular image–guided surgery in gynaecological malignancies, from both clinical and technological points of view. Methods: A narrative approach was taken to describe the relevant literature, focusing on clinical applications of molecular image–guided surgery in gynaecology, preoperative imaging as surgical roadmap, and intraoperative devices. Results: The most common clinical application in gynaecology is sentinel node biopsy (SNB). Other promising approaches are receptor-target modalities and occult lesion localisation. Preoperative SPECT/CT and PET/CT permit a roadmap for adequate surgical planning. Intraoperative detection modalities span from 1D probes to 2D portable cameras and 3D freehand imaging. Conclusion: After successful application of radio-guided SNB and SPECT, innovation is leaning towards hybrid modalities, such as hybrid tracer and fusion of imaging approaches including SPECT/CT and PET/CT. Robotic surgery, as well as augmented reality and virtual reality techniques, is leading to application of these innovative technologies to the clinical setting, guiding surgeons towards a precise, personalised, and minimally invasive approach. [ABSTRACT FROM AUTHOR]

Purpose: Ultrasound (US) imaging, while advantageous for its radiation-free nature, is challenging to interpret due to only partially visible organs and a lack of complete 3D information. While performing US-based diagnosis or investigation, medical professionals therefore create a mental map of the 3D anatomy. In this work, we aim to replicate this process and enhance the visual representation of anatomical structures. Methods: We introduce a point cloud-based probabilistic deep learning (DL) method to complete occluded anatomical structures through 3D shape completion and choose US-based spine examinations as our application. To enable training, we generate synthetic 3D representations of partially occluded spinal views by mimicking US physics and accounting for inherent artifacts. Results: The proposed model performs consistently on synthetic and patient data, with mean and median differences of 2.02 and 0.03 in Chamfer Distance (CD), respectively. Our ablation study demonstrates the importance of US physics-based data generation, reflected in the large mean and median difference of 11.8 CD and 9.55 CD, respectively. Additionally, we demonstrate that anatomical landmarks, such as the spinous process (with reconstruction CD of 4.73) and the facet joints (mean distance to ground truth (GT) of 4.96 mm), are preserved in the 3D completion. Conclusion: Our work establishes the feasibility of 3D shape completion for lumbar vertebrae, ensuring the preservation of level-wise characteristics and successful generalization from synthetic to real data. The incorporation of US physics contributes to more accurate patient data completions. Notably, our method preserves essential anatomical landmarks and reconstructs crucial injections sites at their correct locations. [ABSTRACT FROM AUTHOR]

Background: Dosimetry-based personalized therapy was shown to have clinical benefits e.g. in liver selective internal radiation therapy (SIRT). Yet, there is no consensus about its introduction into clinical practice, mainly as Monte Carlo simulations (gold standard for dosimetry) involve massive computation time. We addressed the problem of computation time and tested a patch-based approach for Monte Carlo simulations for internal dosimetry to improve parallelization. We introduce a physics-inspired cropping layout for patch-based MC dosimetry, and compare it to cropping layouts of the literature as well as dosimetry using organ-S-values, and dose kernels, taking whole-body Monte Carlo simulations as ground truth. This was evaluated in five patients receiving Yttrium-90 liver SIRT. Results: The patch-based Monte Carlo approach yielded the closest results to the ground truth, making it a valid alternative to the conventional approach. Our physics-inspired cropping layout and mosaicking scheme yielded a voxel-wise error of < 2% compared to whole-body Monte Carlo in soft tissue, while requiring only ≈ 10% of the time. Conclusions: This work demonstrates the feasibility and accuracy of physics-inspired cropping layouts for patch-based Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

The numbers of diagnostic and therapeutic nuclear medicine agents under investigation are rapidly increasing. Both novel emitters and novel carrier molecules require careful selection of measurement procedures. This document provides guidance relevant to dosimetry for first-in human and early phase clinical trials of such novel agents. The guideline includes a short introduction to different emitters and carrier molecules, followed by recommendations on the methods for activity measurement, pharmacokinetic analyses, as well as absorbed dose calculations and uncertainty analyses. The optimal use of preclinical information and studies involving diagnostic analogues is discussed. Good practice reporting is emphasised, and relevant dosimetry parameters and method descriptions to be included are listed. Three examples of first-in-human dosimetry studies, both for diagnostic tracers and radionuclide therapies, are given. [ABSTRACT FROM AUTHOR]

When dealing with a newly emerging disease such as COVID-19, the impact of patient- and disease-specific factors (e.g., body weight or known co-morbidities) on the immediate course of the disease is largely unknown. An accurate prediction of the most likely individual disease progression can improve the planning of limited resources and finding the optimal treatment for patients. In the case of COVID-19, the need for intensive care unit (ICU) admission of pneumonia patients can often only be determined on short notice by acute indicators such as vital signs (e.g., breathing rate, blood oxygen levels), whereas statistical analysis and decision support systems that integrate all of the available data could enable an earlier prognosis. To this end, we propose a holistic, multimodal graph-based approach combining imaging and non-imaging information. Specifically, we introduce a multimodal similarity metric to build a population graph that shows a clustering of patients. For each patient in the graph, we extract radiomic features from a segmentation network that also serves as a latent image feature encoder. Together with clinical patient data like vital signs, demographics, and lab results, these modalities are combined into a multimodal representation of each patient. This feature extraction is trained end-to-end with an image-based Graph Attention Network to process the population graph and predict the COVID-19 patient outcomes: admission to ICU, need for ventilation, and mortality. To combine multiple modalities, radiomic features are extracted from chest CTs using a segmentation neural network. Results on a dataset collected in Klinikum rechts der Isar in Munich, Germany and the publicly available iCTCF dataset show that our approach outperforms single modality and non-graph baselines. Moreover, our clustering and graph attention increases understanding of the patient relationships within the population graph and provides insight into the network's decision-making process. [ABSTRACT FROM AUTHOR]

Segmentation of abdominal Computed Tomography (CT) scan is essential for analyzing, diagnosing, and treating visceral organ diseases (e.g., hepatocellular carcinoma). This paper proposes a novel neural network (Res-PAC-UNet) that employs a fixed-width residual UNet backbone and Pyramid Atrous Convolutions, providing a low disk utilization method for precise liver CT segmentation. The proposed network is trained on medical segmentation decathlon dataset using a modified surface loss function. Additionally, we evaluate its quantitative and qualitative performance; the Res16-PAC-UNet achieves a Dice coefficient of 0.950 ± 0.019 with less than half a million parameters. Alternatively, the Res32-PAC-UNet obtains a Dice coefficient of 0.958 ± 0.015 with an acceptable parameter count of approximately 1.2 million. [ABSTRACT FROM AUTHOR]

Molecular imaging is one of the pillars of precision surgery. Its applications range from early diagnostics to therapy planning, execution, and the accurate assessment of outcomes. In particular, molecular imaging solutions are in high demand in minimally invasive surgical strategies, such as the substantially increasing field of robotic surgery. This review aims at connecting the molecular imaging and nuclear medicine community to the rapidly expanding armory of surgical medical devices. Such devices entail technologies ranging from artificial intelligence and computer-aided visualization technologies (software) to innovative molecular imaging modalities and surgical navigation (hardware). We discuss technologies based on their role at different steps of the surgical workflow, i.e., from surgical decision and planning, over to target localization and excision guidance, all the way to (back table) surgical verification. This provides a glimpse of how innovations from the technology fields can realize an exciting future for the molecular imaging and surgery communities. [ABSTRACT FROM AUTHOR]

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The sentinel lymph node (SLN) biopsy (SLNB) is currently being developed for uterine cancers. The uterus is not easily accessible for tracer application; furthermore, the lymphatic drainage of the uterus is complex, which makes laparoscopic localization of the SLN challenging. The freehand single photon emission computed tomography (SPECT) is a 3D laparoscopic imaging modality which has been developed to overcome these problems. The aim of the study was to evaluate the freehand SPECT to detect the SLN in patients with uterine cancers. Twenty-nine patients with endometrial or cervical cancers were included into the LAPSENT study. Freehand SPECT was utilized to detect the SLN and guide its resection following intracervical injection of the tracer in patients with cervical cancer or hysteroscopy-guided injection of the tracer in patients with endometrial cancers. SLNs were detected in 18 of 24 patients with endometrial cancer and in 5 of 5 patients with cervical cancer. An average of 1.1 SLNs was detected in patients with endometrial cancers and 2 SLNs in patients with cervical cancers. The SLN was located in the pelvic lymphatic basin in 16 patients (12 unilateral and 4 bilateral). Three patients had paraaortic SLNs. Only one metastasis was found in any of the SLNs removed (endometrial cancer case) resulting in an upstaging. We demonstrated that SLNB in endometrial and cervical cancers using the freehand SPECT is feasible and that hysteroscopy-guided injection of the tracer is reasonable in endometrial cancers. Combining both techniques single-anesthesia SLNB of endometrial cancer is feasible. [ABSTRACT FROM AUTHOR]

Freehand SPECT is a 3-D tomographic imaging modality based on data acquisition with a hand-held detector that is moved freely, in contrast to conventional, fixed gamma camera systems. In this pilot study, the feasibility of freehand SPECT for 3-D lymphatic mapping in breast cancer was evaluated. A total of 85 patients (age: 29–88 years) with an initial diagnosis of invasive breast cancer and no clinical evidence of nodal involvement prospectively underwent sentinel lymph node (SLN) biopsy. Preoperative lymphatic mapping (35–87 MBq 99mTc-Nanocoll) included tomographic imaging with a SPECT/CT device (Siemens Symbia T6) serving as reference. Initially, the freehand SPECT approach was assessed in a pilot study consisting of 50 patients. The quality of each freehand SPECT acquisition was assessed and ranked as good, intermediate or poor. In another series comprising a further 35 patients (validation study), a guidance system for the acquisition was implemented based on the results of the pilot study, ensuring acquisitions with good quality. For 3-D tomographic image reconstruction, ad hoc models and iterative reconstruction algorithms were used in all 85 patients. To allow for adequate comparison, SPECT/CT data and freehand SPECT data were registered within the same coordinate system. In the pilot study, freehand SPECT enabled mapping of 24 of 83 SLNs in 20 of 44 patients (3 dropouts, 3 patients without SLN either in SPECT/CT or in freehand SPECT). Using SPECT/CT as reference, the accuracy of freehand SPECT was 77.8% (7/9 nodes) in scans with good quality, while for intermediate and poor quality scans, the accuracy was reduced to 34.3 and 12.8%, respectively. In the validation study, quality feedback improved the results significantly and freehand SPECT enabled the mapping of at least one SLN in 87.5% of the patients (28/32 − 3 dropouts). Compared to the reference method, freehand SPECT showed a sensitivity of 83.3% (35/42 nodes). False-negative findings were related to insufficient scanning time, insufficient coverage of the axillary region, close proximity of the SLN to the injection site and low tracer uptake in the SLNs. In this preliminary study, we could demonstrate that 3-D localization of SLNs is feasible using freehand SPECT technology. Prerequisites for acquisition of a good scan quality, most likely allowing precise SLN mapping, have been defined. This approach has high potential to allow image-guided biopsy and further standardization of SLN dissection, thus bringing 3-D nuclear imaging into the operating room. [ABSTRACT FROM AUTHOR]

Background: Assessment of lymphatic status via sentinel lymph node (SLN) biopsy is an integral and crucial part of melanoma surgical oncology. The most common technique for sentinel node mapping is preoperative planar scintigraphy of an injected gamma-emitting lymphatic tracer followed by intraoperative node localization using a non-imaging gamma probe with auditory feedback. In recent years, intraoperative visualization of SLNs in 3D has become possible by coupling the probe to an external system capable of tracking its location and orientation as it is read out, thereby enabling computation of the 3D distribution of the tracer (freehand SPECT). In this project, the non-imaging probe of the fhSPECT system was replaced by a unique handheld gamma camera containing an array of sodium iodide crystals optically coupled to an array of silicon photomultipliers (SiPMs). A feasibility study was performed in which preoperative SLN mapping was performed using camera fhSPECT and the number of detected nodes was compared to that visualized by lymphoscintigraphy, probe fhSPECT, and to the number ultimately excised under non-imaging probe guidance. Results: Among five subjects, SLNs were detected in nine lymphatic basins, with one to five SLNs detected per basin. A basin-by-basin comparison showed that the number of SLNs detected using camera fhSPECT exceeded that using lymphoscintigraphy and probe fhSPECT in seven of nine basins and five of five basins, respectively. (Probe fhSPECT scans were not performed for four basins.) It exceeded the number excised under non-imaging probe guidance for seven of nine basins and equaled the number excised for the other two basins. Conclusions: Freehand SPECT using a prototype SiPM-based gamma camera demonstrates high sensitivity for detection of SLNs in a preoperative setting. Camera fhSPECT is a potential means for efficiently obtaining real-time 3D activity distribution maps in applications such as image-guided percutaneous biopsy, and surgical SLN biopsy or radioguided tumor excision. [ABSTRACT FROM AUTHOR]

This presentation describes a research project investigating the suitability of model-based Workflow Management Systems (WfMS) to support radiological process. The following aspects are covered: process modeling, process enactment, and architecture of workflow-enabled application systems. [ABSTRACT FROM AUTHOR]