In vivo MRI and PET imaging in a translational ILD mouse model expressing non-resolving fibrosis and bronchiectasis-like pathology after repeated systemic exposure to bleomycin.

Drug-induced interstitial lung disease (ILD) is crucial to detect early to achieve the best treatment outcome. Optimally, non-invasive imaging biomarkers can be used for early detection of disease progression and treatment follow-up. Therefore, reliable in vivo models are warranted in new imaging biomarker development to accelerate better-targeted treatment options. Single-dose bleomycin models have, for a long time, served as a reference model in fibrosis and lung injury research. Here, we aimed to use a clinically more relevant animal model by systemic exposure to bleomycin and assessing disease progression over time by combined magnetic resonance imaging (MRI) and positron emission tomography (PET) imaging. Methods: C57BL/6 mice received bleomycin (i.p. 35iU/kg) or saline as control twice per week for 4 weeks. Mice were monitored until 2 weeks after cessation of bleomycin administration (w4 + 1 and w4 + 2), referred to as the resting period. MRI scans were performed in weeks 3 and 4 and during the resting weeks. [18F]FDG-PET was performed at the last week of dosing (w4) and 2 weeks after the last dosing (w4 + 2). Lung tissue sections were stained with Masson's trichrome and evaluated by modified Ashcroft scoring. Lung volume and lesion volumes were assessed using MRI, as well as 3D mapping of the central airways. Results and discussion: Bleomycin-challenged mice showed increased lung weights (p < 0.05), while total lung volume was unchanged (w4 and onward). Histology analysis demonstrated fibrotic lesions emanating from the distal parts of the lung. Fibrosis progression was visualized by MRI with significantly increased high signal in bleomycin-exposed lungs compared to controls (p < 0.05). In addition, a significant increase in central airway diameter (p < 0.01) was displayed in bleomycin-exposed animals compared to controls and further continued to dilate as the disease progressed, comparing the bleomycin groups over time (p < 0.05-0.001). Lung [18F]FDG uptake was significantly elevated in bleomycin-exposed mice compared to controls (p < 0.05). Conclusion: Non-invasive imaging displayed progressing lesions in the lungs of bleomycin-exposed mice, using two distinct MRI sequences and [18F]FDG-PET. With observed fibrosis progression emanating from distal lung areas, dilation of the central airways was evident. Taken together, this chronic bleomycin-exposure model is translationally more relevant for studying lung injury in ILD and particularly in the context of DIILD.

Evaluation of a deep learning magnetic resonance imaging reconstruction method for synthetic computed tomography generation in prostate radiotherapy.

Background and Purpose: In magnetic resonance imaging (MRI) only radiotherapy computed tomography (CT) is excluded. The method relies entirely on synthetic CT images generated from MRI. This study evaluates the compatibility of a commercial synthetic CT (sCT) with an accelerated commercial deep learning reconstruction (DLR) in MRI-only prostate radiotherapy. Materials and Methods: For a group of 24 patients (cohort 1) the effects of DLR were studied in isolation. MRI data were reconstructed conventionally and with DLR from identical k-space data, and sCTs were generated for both reconstructions. The sCT quality, Hounsfield Unit (HU) and dosimetric impact were investigated. In another group of 15 patients (cohort 2) effects on sCT generation using accelerated MRI acquisition (40 % time reduction) reconstructed with DLR were investigated. Results: sCT images from both cohorts, generated from DLR MRI data, were of clinically expected image quality. The mean dose differences for targets and organs at risks in cohort 1 were <0.06 Gy, corresponding to a 0.1 % prescribed dose difference. Similar dose differences were observed in cohort 2. Gamma pass rates for cohort 1 were 100 % for criteria 3 %/3mm, 2 %/2mm and 1 %/1mm for all dose levels. Mean error and mean absolute error inside the body, between sCTs, averaged over all cohort 1 subjects, were -1.1 ± 0.6 [-2.4 0.2] and 2.9 ± 0.4 [2.3 3.9] HU, respectively. Conclusions: DLR was suitable for sCT generation with clinically negligible differences in HU and calculated dose compared to the conventional MRI reconstruction method. For sCT generation DLR enables scan time reduction, without compromised sCT quality.

An Experimental Cold Gas Cannon for the Study of Porcine Lung Contusion and Behind Armor Blunt Trauma.

Behind armor blunt trauma (BABT) is a non-penetrating injury caused by the rapid deformation of body armor, by a projectile, which may in extreme circumstances cause death. The understanding of the mechanisms is still low, in relation to what is needed for safety threshold levels. Few models of graded kinetic energy transfer to the body exist. We established an experimental model for graded BABT. The cold gas cannon was air-driven, consisted of a pressure vessel, a barrel, and a pressure actuator. It required short training to operate and was constructed by standard components. It produced standardized expulsion of plastic projectiles with 65 mm and weight 58 g. Velocity correlated linearly to pressure (R 0.9602, p < 0.0001), equation Y = 6.558*X + 46.50. Maximum tested pressure was 10 bar, velocity 110 m/s and kinetic energy (Ek) 351 J. Crossbred male swine (n = 10) mean weight (SD) 56 ± 3 kg, were subjected to BABT, mean Ek (SD) 318 (61) J, to a fix point on the right lateral thorax. Pulmonary contusion was confirmed by physiological parameters pO2 (p < 0.05), SaO2 (p < 0.01), pCO2 (p < 0.01), etCO2 (p < 0.01), MPAP (p < 0.01), Cstat (p < 0.01), intrapulmonary shunt (Q's/Q't) (p < 0.05), and qualified trans-thoracic ultrasound (p < 0.0001). The consistent injury profile enabled for the addition of future experimental interventions.

Geometric impact and dose estimation of on-patient placement of a lightweight receiver coil in a clinical magnetic resonance imaging-only radiotherapy workflow for prostate cancer.

Background and Purpose: For pelvic magnetic resonance imaging (MRI)-only radiotherapy the use of receiver coil bridges (CB) is recommended to avoid deformation of the patient. Development in coil technology has enabled lightweight, flexible coils. In this work we evaluate the effects of a lightweight coil in a pelvic MRI-only radiotherapy workflow. Materials and Methods: Twenty-one patients, referred to prostate MRI-only radiotherapy, were included. Images were acquired with and without CB. Anatomical deformation from the on-patient coil placement was measured in the anterior-posterior (AP) and left-right (LR) direction. The change in signal-to-noise ratio (SNR) was measured in phantom and in vivo.The clinical treatment plan, created on the image with CB, was transferred and recalculated on the image without the CB. Dose metrics for the targets (planning- and clinical target volume) and organs at risks (OAR) were analyzed. Results: There was a statistically significant increase in SNR in-vivo (median 21 %, p = 0.002) when removing the CB. Anatomical differences after removing the CB in patients were -1.5 mm in AP (median change) and + 2.5 mm in LR direction. Dosimetric differences for the target structures were clinically negligible, but statistically significant. The difference in target mean doses were 0.2 % (both p = 0.004) of the prescribed dose. No dosimetric differences were observed for the OAR, except for the penile bulb. Conclusions: We concluded that anatomical change and dosimetric differences, originating from scanning without CB were minor. The CB can thereby be removed from the workflow, enabling easier patient positioning and increased SNR when using lightweight coils.

Nowhere to go - Effects on elderly's travel during Covid-19.

The COVID-19 pandemic has presented numerous, significant challenges for elderly in their daily life. In order to reach a deeper understanding of the feelings and thoughts of the elderly related to their possibilities to travel and engage in activities during the pandemic, this study takes a qualitative approach to exploring the views of the elderly themselves. The study focuses on experiences during the COVID-19 pandemic. A number of in-depth semi-structured interviews with elderly aged 70 and above, were conducted in June 2020. Applied Thematic Analysis (ATA) was applied, as a first stage, to investigate meaningful segments of data. In a second stage these identified segments were combined into a number of themes. This study reports the outcome of the ATA analysis. More specifically we report experiences, motivations and barriers for travel and activity participation, and discuss how these relate to the health and well-being of elderly, and vice versa. These findings highlight the strong need to develop a transport system that to a higher extent addresses the physical as well as the mental health of old people, with a particular focus on facilitating social interactions.

Divergent selection predating the Last Glacial Maximum mainly acted on macro-phenotypes in Norway spruce.

The current distribution and population structure of many species were, to a large extent, shaped by cycles of isolation in glacial refugia and subsequent population expansions. Isolation in and postglacial expansion through heterogeneous environments led to either neutral or adaptive divergence. Norway spruce is no exception, and its current distribution is the consequence of a constant interplay between evolutionary and demographic processes. We investigated population differentiation and adaptation of Norway spruce for juvenile growth, diameter of the stem, wood density, and tracheid traits at breast height. Data from 4461 phenotyped and genotyped Norway spruce from 396 half-sib families in two progeny tests were used to test for divergent selection in the framework of Q ST vs. F ST. We show that the macroscopic resultant trait (stem diameter), unlike its microscopic components (tracheid dimensions) and juvenile growth, was under divergent selection that predated the Last Glacial Maximum. Altogether, the current variation in these phenotypic traits in Norway spruce is better explained by local adaptation to ancestral environments than to current ones, where populations were partly preadapted, mainly through growth-related traits.

Pandemic impacts on public transport safety and stress perceptions in Nordic cities.

COVID-19 has brought severe disruption and demand suppression to mobility, especially to public transport (PT). A key challenge now is to restore trust that PT is safe again. This paper investigates pandemic impacts on PT safety and stress perceptions in three Nordic cities, drawing on 2018 and 2020 survey data analysed in structural equation models. While finding modest pandemic effects on safety and stress perceptions overall, strong heterogeneities exist across gender, age and geographic categories. Women perceive less PT safety and more stress, especially during the pandemic. Older adults reduced PT more during the pandemic and perceived no stress reduction like younger adults. Stockholm travellers feel less safe and more stressed than in Oslo and Bergen, whilst pandemic PT use and perceived safety reductions are least in Bergen. The paper discusses the long-term implications for theory and policy across multiple mobility scenarios accounting for modal change and travel demand uncertainties.

MRI-only radiotherapy from an economic perspective: Can new techniques in prostate cancer treatment be cost saving?

Background and Purpose: The aim of this study was to analyze a magnetic resonance imaging (MRI)-only radiotherapy workflow from an economic perspective in terms of reduced time, costs and systematic uncertainties. Material/Methods: A documented Swedish clinical implementation of MRI-only radiotherapy was used as template for cost assessments compared to a combined computed tomography (CT)/MRI workflow. The costs were taken from official regional price lists from 2021. MRI-only specific quality assurance (QA) was assumed necessary in an initial phase. Treatment plans for target volumes with margins of 5-10 mm were created for ten prostate cancer patients prescribed 78 Gy in 39 fractions. The risk of Grade ≥ 2 rectal toxicity or rectal bleeding was calculated using the QUANTEC recommended NTCP model and costs estimated based on subsequent diagnostic examinations. Results: The exclusion of the CT-examination and faster target delineation were the main contributors to cost reductions. Additional QA procedures limited the initial cost reduction to 14 EUR/patient. Long-term MRI-only reduced the costs by 209 EUR/patient. Reducing margins resulted in Grade ≥ 2 rectal toxicity or rectal bleeding probability of 9.7 % for 7 mm margin and 6.0 % for 5 mm margin. This margin reduction resulted in an additional cost reduction of 46 EUR/patient. Conclusion: An MRI-only workflow implementation is associated with reduced costs when the workflow tasks are more time efficient and side effects are reduced as a result of margin reduction. The short-term economic benefits are limited due to extra costs of QA procedures. The economic benefits of MRI-only will make impact first when the workflow is well established, and margin reduction has been included.

Tensor-valued diffusion magnetic resonance imaging in a radiotherapy setting.

Background and purpose: Diagnostic information about cell density variations and microscopic tissue anisotropy can be gained from tensor-valued diffusion magnetic resonance imaging (MRI). These properties of tissue microstructure have the potential to become novel imaging biomarkers for radiotherapy response. However, tensor-valued diffusion encoding is more demanding than conventional encoding, and its compatibility with MR scanners that are dedicated to radiotherapy has not been established. Thus, our aim was to investigate the feasibility of tensor-valued diffusion MRI with radiotherapy dedicated MR equipment. Material and methods: A tensor-valued diffusion protocol was implemented, and five healthy volunteers were scanned with different resolutions using conventional head coil and radiotherapy coil setup with fixation masks. Signal-to-noise-ratio (SNR) was evaluated to assess the risk of signal bias due to rectified noise floor. We also evaluated the repeatability and reproducibility of the microstructure parameters. One patient with brain metastasis was scanned to investigate the image quality and the transferability of the setup to diseased tissue. Results: A resolution of 3 × 3 × 3 mm3 provided images with SNR > 3 for 93 % of the voxels using radiotherapy coil setup. The parameter maps and repeatability characteristics were comparable to those observed with a conventional head coil. The patient evaluation demonstrated successful parameter analysis also in tumor tissue, with SNR > 3 for 93 % of the voxels. Conclusion: We demonstrate that tensor-valued diffusion MRI is compatible with radiotherapy fixation masks and coil setup for investigations of microstructure parameters. The reported reproducibility may be used to plan future investigations of imaging biomarkers in brain cancer radiotherapy.

Pelvic U-Net: multi-label semantic segmentation of pelvic organs at risk for radiation therapy anal cancer patients using a deeply supervised shuffle attention convolutional neural network.

BACKGROUND: Delineation of organs at risk (OAR) for anal cancer radiation therapy treatment planning is a manual and time-consuming process. Deep learning-based methods can accelerate and partially automate this task. The aim of this study was to develop and evaluate a deep learning model for automated and improved segmentations of OAR in the pelvic region. METHODS: A 3D, deeply supervised U-Net architecture with shuffle attention, referred to as Pelvic U-Net, was trained on 143 computed tomography (CT) volumes, to segment OAR in the pelvic region, such as total bone marrow, rectum, bladder, and bowel structures. Model predictions were evaluated on an independent test dataset (n = 15) using the Dice similarity coefficient (DSC), the 95th percentile of the Hausdorff distance (HD95), and the mean surface distance (MSD). In addition, three experienced radiation oncologists rated model predictions on a scale between 1-4 (excellent, good, acceptable, not acceptable). Model performance was also evaluated with respect to segmentation time, by comparing complete manual delineation time against model prediction time without and with manual correction of the predictions. Furthermore, dosimetric implications to treatment plans were evaluated using different dose-volume histogram (DVH) indices. RESULTS: Without any manual corrections, mean DSC values of 97%, 87% and 94% were found for total bone marrow, rectum, and bladder. Mean DSC values for bowel cavity, all bowel, small bowel, and large bowel were 95%, 91%, 87% and 81%, respectively. Total bone marrow, bladder, and bowel cavity segmentations derived from our model were rated excellent (89%, 93%, 42%), good (9%, 5%, 42%), or acceptable (2%, 2%, 16%) on average. For almost all the evaluated DVH indices, no significant difference between model predictions and manual delineations was found. Delineation time per patient could be reduced from 40 to 12 min, including manual corrections of model predictions, and to 4 min without corrections. CONCLUSIONS: Our Pelvic U-Net led to credible and clinically applicable OAR segmentations and showed improved performance compared to previous studies. Even though manual adjustments were needed for some predicted structures, segmentation time could be reduced by 70% on average. This allows for an accelerated radiation therapy treatment planning workflow for anal cancer patients.

Favorable fatty acid composition in adipose tissue in healthy Iraqi- compared to Swedish-born men - a pilot study using MRI assessment.

Middle Eastern immigrants are at high-risk for insulin resistance. Fatty acid composition (FAC) plays an important role in the development of insulin resistance but has not been investigated in people of Middle Eastern ancestry. Here, the aim was to assess the FAC in visceral and subcutaneous adipose tissue (VAT and SAT) in healthy Iraqi- and Swedish-born men using a magnetic resonance imaging (MRI) method.This case-control study included 23 Iraqi- and 15 Swedish-born middle-aged men, without cardiometabolic disease. Using multi-echo MRI of the abdomen, the fractions of saturated, monounsaturated, and polyunsaturated fatty acids (fSFA, fMUFA, and fPUFA) were estimated in VAT and SAT. SAT was further analyzed in deep and superficial compartments (dSAT and sSAT).In all depots, fPUFA was significantly higher and fSFA significantly lower in Iraqi men, independently of age and BMI. In both Iraqi- and Swedish-born men, higher fPUFA and lower fMUFA were found in sSAT vs. dSAT. Among Iraqi men only, higher fPUFA and lower fMUFA were found in SAT vs. VAT.Iraqi-born men presented a more favorable abdominal FAC compared to Swedish-born men. This MRI method also revealed different FACs in different abdominal depots. Our results may reflect a beneficial FAC in Middle Eastern immigrants.

A sense of being rejected: Patients' lived experiences of cancelled knee or hip replacement surgery.

BACKGROUND: Growing care queues, reduced access to care and cancelled surgery are realities for some patients being treated with total hip or knee replacement surgery in Sweden. Most of the patients on the waiting lists have experienced pain and limited motion for a varying period of time, with a negative effect on their everyday lives. Overbooked surgical schedules are already contributing to the lengthy waiting times, but, with the addition of cancellations, longer waiting times will increase still further and may affect patients' well-being. METHODS: In the present study, we aimed to illuminate the experience of having planned surgery cancelled, based on narratives from 10 participants. The interview transcriptions were analysed using a phenomenological hermeneutic approach. RESULTS: The comprehensive analyses revealed that the participants described the agony of being deselected and the additional impression of being excluded. Metaphors of being damaged and feeling physical pain were used and the interpretations referred to the cancellations as unpleasant. Additionally, the important relationship and the trust between the health workers and the patient were negatively affected by the cancellation. CONCLUSION: After the cancellation, the participants expressed being vulnerable and from their perspective the cancelled surgery affected them deeply; in fact, much more than the healthcare workers appeared to understand. Therefore, information around the cancellation must be given respectfully and with dignity, in a dialogue between the patient and the healthcare workers. Taken together, to enable an opportunity to be involved in the continued care. The cancellations should be seen as an interruption, in which the patients' chance of living a pain-free, active life is postponed.

Investigation of the clinical inter-observer bias in prostate fiducial marker image registration between CT and MR images.

BACKGROUND AND PURPOSE: Inter-modality image registration between computed tomography (CT) and magnetic resonance (MR) images is associated with systematic uncertainties and the magnitude of these uncertainties is not well documented. The purpose of this study was to investigate the potential uncertainty of gold fiducial marker (GFM) registration for localized prostate cancer and to estimate the inter-observer bias in a clinical setting. METHODS: Four experienced observers registered CT and MR images for 42 prostate cancer patients. Manual GFM identification was followed by a landmark-based registration. The absolute difference between observers in GFM identification and the displacement of the clinical target volume (CTV) was investigated. The CTV center of mass (CoM) vector displacements, DICE-index and Hausdorff distances for the observer registrations were compared against a clinical baseline registration. The time allocated for the manual registrations was compared. RESULTS: Absolute difference in GFM identification between observers ranged from 0.0 to 3.0 mm. The maximum CTV CoM displacement from the clinical baseline was 3.1 mm. Displacements larger than or equal to 1 mm, 2 mm and 3 mm were 46%, 18% and 4%, respectively. No statistically significant difference was detected between observers in terms of CTV displacement. Median DICE-index and Hausdorff distance for the CTV, with their respective ranges were 0.94 [0.70-1.00] and 2.5 mm [0.7-8.7]. CONCLUSIONS: Registration of CT and MR images using GFMs for localized prostate cancer patients was subject to inter-observer bias on an individual patient level. A CTV displacement as large as 3 mm occurred for individual patients. These results show that GFM registration in a clinical setting is associated with uncertainties, which motivates the removal of inter-modality registrations in the radiotherapy workflow and a transition to an MRI-only workflow for localized prostate cancer.

Volumetric modulated arc therapy dose prediction and deliverable treatment plan generation for prostate cancer patients using a densely connected deep learning model.

BACKGROUND AND PURPOSE: Radiation therapy treatment planning is a manual, time-consuming task that might be accelerated using machine learning algorithms. In this study, we aimed to evaluate if a triplet-based deep learning model can predict volumetric modulated arc therapy (VMAT) dose distributions for prostate cancer patients. MATERIALS AND METHODS: A modified U-Net was trained on triplets, a combination of three consecutive image slices and corresponding segmentations, from 160 patients, and compared to a baseline U-Net. Dose predictions from 17 test patients were transformed into deliverable treatment plans using a novel planning workflow. RESULTS: The model achieved a mean absolute dose error of 1.3%, 1.9%, 1.0% and ≤ 2.6% for clinical target volume (CTV) CTV_D100%, planning target volume (PTV) PTV_D98%, PTV_D95% and organs at risk (OAR) respectively, when compared to the clinical ground truth (GT) dose distributions. All predicted distributions were successfully transformed into deliverable treatment plans and tested on a phantom, resulting in a passing rate of 100% (global gamma, 3%, 2 mm, 15% cutoff). The dose difference between deliverable treatment plans and GT dose distributions was within 4.4%. The difference between the baseline model and our improved model was statistically significant (p < 0.05) for CVT_D100%, PTV_D98% and PTV_D95%. CONCLUSION: Triplet-based training improved VMAT dose distribution predictions when compared to 2D. Dose predictions were successfully transformed into deliverable treatment plans using our proposed treatment planning procedure. Our method may automate parts of the workflow for external beam prostate radiation therapy and improve the overall treatment speed and plan quality.

Artificial intelligence and the medical physics profession - A Swedish perspective.

BACKGROUND: There is a continuous and dynamic discussion on artificial intelligence (AI) in present-day society. AI is expected to impact on healthcare processes and could contribute to a more sustainable use of resources allocated to healthcare in the future. The aim for this work was to establish a foundation for a Swedish perspective on the potential effect of AI on the medical physics profession. MATERIALS AND METHODS: We designed a survey to gauge viewpoints regarding AI in the Swedish medical physics community. Based on the survey results and present-day situation in Sweden, a SWOT analysis was performed on the implications of AI for the medical physics profession. RESULTS: Out of 411 survey recipients, 163 responded (40%). The Swedish medical physicists with a professional license believed (90%) that AI would change the practice of medical physics but did not foresee (81%) that AI would pose a risk to their practice and career. The respondents were largely positive to the inclusion of AI in educational programmes. According to self-assessment, the respondents' knowledge of and workplace preparedness for AI was generally low. CONCLUSIONS: From the survey and SWOT analysis we conclude that AI will change the medical physics profession and that there are opportunities for the profession associated with the adoption of AI in healthcare. To overcome the weakness of limited AI knowledge, potentially threatening the role of medical physicists, and build upon the strong position in Swedish healthcare, medical physics education and training should include learning objectives on AI.

Correction: Lättman, K., et al. Perceived Accessibility, Satisfaction with Daily Travel, and Life Satisfaction among the Elderly. Int. J. Environ. Res. Public Health 2019, 16, 4498.

There were errors in the original article [...].

Clinical validation of a commercially available deep learning software for synthetic CT generation for brain.

BACKGROUND: Most studies on synthetic computed tomography (sCT) generation for brain rely on in-house developed methods. They often focus on performance rather than clinical feasibility. Therefore, the aim of this work was to validate sCT images generated using a commercially available software, based on a convolutional neural network (CNN) algorithm, to enable MRI-only treatment planning for the brain in a clinical setting. METHODS: This prospective study included 20 patients with brain malignancies of which 14 had areas of resected skull bone due to surgery. A Dixon magnetic resonance (MR) acquisition sequence for sCT generation was added to the clinical brain MR-protocol. The corresponding sCT images were provided by the software MRI Planner (Spectronic Medical AB, Sweden). sCT images were rigidly registered and resampled to CT for each patient. Treatment plans were optimized on CT and recalculated on sCT images for evaluation of dosimetric and geometric endpoints. Further analysis was also performed for the post-surgical cases. Clinical robustness in patient setup verification was assessed by rigidly registering cone beam CT (CBCT) to sCT and CT images, respectively. RESULTS: All sCT images were successfully generated. Areas of bone resection due to surgery were accurately depicted. Mean absolute error of the sCT images within the body contour for all patients was 62.2 ± 4.1 HU. Average absorbed dose differences were below 0.2% for parameters evaluated for both targets and organs at risk. Mean pass rate of global gamma (1%/1 mm) for all patients was 100.0 ± 0.0% within PTV and 99.1 ± 0.6% for the full dose distribution. No clinically relevant deviations were found in the CBCT-sCT vs CBCT-CT image registrations. In addition, mean values of voxel-wise patient specific geometric distortion in the Dixon images for sCT generation were below 0.1 mm for soft tissue, and below 0.2 mm for air and bone. CONCLUSIONS: This work successfully validated a commercially available CNN-based software for sCT generation. Results were comparable for sCT and CT images in both dosimetric and geometric evaluation, for both patients with and without anatomical anomalies. Thus, MRI Planner is feasible to use for radiotherapy treatment planning of brain tumours.

Airspace Dimension Assessment (AiDA) by inhaled nanoparticles: benchmarking with hyperpolarised 129Xe diffusion-weighted lung MRI.

Enlargements of distal airspaces can indicate pathological changes in the lung, but accessible and precise techniques able to measure these regions are lacking. Airspace Dimension Assessment with inhaled nanoparticles (AiDA) is a new method developed for in vivo measurement of distal airspace dimensions. The aim of this study was to benchmark the AiDA method against quantitative measurements of distal airspaces from hyperpolarised 129Xe diffusion-weighted (DW)-lung magnetic resonance imaging (MRI). AiDA and 129Xe DW-MRI measurements were performed in 23 healthy volunteers who spanned an age range of 23-70 years. The relationship between the 129Xe DW-MRI and AiDA metrics was tested using Spearman's rank correlation coefficient. Significant correlations were observed between AiDA distal airspace radius (rAiDA) and mean 129Xe apparent diffusion coefficient (ADC) (p < 0.005), distributed diffusivity coefficient (DDC) (p < 0.001) and distal airspace dimension (LmD) (p < 0.001). A mean bias of - 1.2 µm towards rAiDA was observed between 129Xe LmD and rAiDA, indicating that rAiDA is a measure of distal airspace dimension. The AiDA R0 intercept correlated with MRI 129Xe α (p = 0.02), a marker of distal airspace heterogeneity. This study demonstrates that AiDA has potential to characterize the distal airspace microstructures and may serve as an alternative method for clinical examination of the lungs.

Prospective Clinical Feasibility Study for MRI-Only Brain Radiotherapy.

OBJECTIVES: MRI-only radiotherapy (RT) provides a workflow to decrease the geometric uncertainty introduced by the image registration process between MRI and CT data and to streamline the RT planning. Despite the recent availability of validated synthetic CT (sCT) methods for the head region, there are no clinical implementations reported for brain tumors. Based on a preceding validation study of sCT, this study aims to investigate MRI-only brain RT through a prospective clinical feasibility study with endpoints for dosimetry and patient setup. MATERIAL AND METHODS: Twenty-one glioma patients were included. MRI Dixon images were used to generate sCT images using a CE-marked deep learning-based software. RT treatment plans were generated based on MRI delineated anatomical structures and sCT for absorbed dose calculations. CT scans were acquired but strictly used for sCT quality assurance (QA). Prospective QA was performed prior to MRI-only treatment approval, comparing sCT and CT image characteristics and calculated dose distributions. Additional retrospective analysis of patient positioning and dose distribution gamma evaluation was performed. RESULTS: Twenty out of 21 patients were treated using the MRI-only workflow. A single patient was excluded due to an MRI artifact caused by a hemostatic substance injected near the target during surgery preceding radiotherapy. All other patients fulfilled the acceptance criteria. Dose deviations in target were within ±1% for all patients in the prospective analysis. Retrospective analysis yielded gamma pass rates (2%, 2 mm) above 99%. Patient positioning using CBCT images was within ± 1 mm for registrations with sCT compared to CT. CONCLUSION: We report a successful clinical study of MRI-only brain radiotherapy, conducted using both prospective and retrospective analysis. Synthetic CT images generated using the CE-marked deep learning-based software were clinically robust based on endpoints for dosimetry and patient positioning.

Development and evaluation of a deep learning based artificial intelligence for automatic identification of gold fiducial markers in an MRI-only prostate radiotherapy workflow.

Identification of prostate gold fiducial markers in magnetic resonance imaging (MRI) images is challenging when CT images are not available, due to misclassifications from intra-prostatic calcifications. It is also a time consuming task and automated identification methods have been suggested as an improvement for both objectives. Multi-echo gradient echo (MEGRE) images have been utilized for manual fiducial identification with 100% detection accuracy. The aim is therefore to develop an automatic deep learning based method for fiducial identification in MRI images intended for MRI-only prostate radiotherapy. MEGRE images from 326 prostate cancer patients with fiducials were acquired on a 3T MRI, post-processed with N4 bias correction, and the fiducial center of mass (CoM) was identified. A 9 mm radius sphere was created around the CoM as ground truth. A deep learning HighRes3DNet model for semantic segmentation was trained using image augmentation. The model was applied to 39 MRI-only patients and 3D probability maps for fiducial location and segmentation were produced and spatially smoothed. In each of the three largest probability peaks, a 9 mm radius sphere was defined. Detection sensitivity and geometric accuracy was assessed. To raise awareness of potential false findings a 'BeAware' score was developed, calculated from the total number and quality of the probability peaks. All datasets, annotations and source code used were made publicly available. The detection sensitivity for all fiducials were 97.4%. Thirty-six out of thirty-nine patients had all fiducial markers correctly identified. All three failed patients generated a user notification using the BeAware score. The mean absolute difference between the detected fiducial and ground truth CoM was 0.7 ± 0.9 [0 3.1] mm. A deep learning method for automatic fiducial identification in MRI images was developed and evaluated with state-of-the-art results. The BeAware score has the potential to notify the user regarding patients where the proposed method is uncertain.