Feasibility of proton dosimetry overriding planning CT with daily CBCT elaborated through generative artificial intelligence tools

Radiotherapy commonly utilizes cone beam computed tomography (CBCT) for patient positioning and treatment monitoring. CBCT is deemed to be secure for patients, making it suitable for the delivery of fractional doses. However, limitations such as a narrow field of view, beam hardening, scattered radiation artifacts, and variability in pixel intensity hinder the direct use of raw CBCT for dose recalculation during treatment. To address this issue, reliable correction techniques are necessary to remove artifacts and remap pixel intensity into Hounsfield Units (HU) values. This study proposes a deep-learning framework for calibrating CBCT images acquired with narrow field of view (FOV) systems and demonstrates its potential use in proton treatment planning updates. Cycle-consistent generative adversarial networks (cGAN) processes raw CBCT to reduce scatter and remap HU. Monte Carlo simulation is used to generate CBCT scans, enabling the possibility to focus solely on the algorithm's ability to reduce artifacts and cupping effects without considering intra-patient longitudinal variability and producing a fair comparison between planning CT (pCT) and calibrated CBCT dosimetry. To showcase the viability of the approach using real-world data, experiments were also conducted using real CBCT. Tests were performed on a publicly available dataset of 40 patients who received ablative radiation therapy for pancreatic cancer. The simulated CBCT calibration led to a difference in proton dosimetry of less than 2%, compared to the planning CT. The potential toxicity effect on the organs at risk decreased from about 50% (uncalibrated) up the 2% (calibrated). The gamma pass rate at 3%/2 mm produced an improvement of about 37% in replicating the prescribed dose before and after calibration (53.78% vs 90.26%). Real data also confirmed this with slightly inferior performances for the same criteria (65.36% vs 87.20%). These results may confirm that generative artificial intelligence brings the use of narrow FOV CBCT scans incrementally closer to clinical translation in proton therapy planning updates.

Breathing Motion Pattern in Cyclists: Role of Inferior against Superior Thorax Compartment

The thoracoabdominal breathing motion pattern is being considered in sports training because of its contribution, along with other physiological adaptations, to overall performance. We examined whether and how experience with cycling training modifies the thoracoabdominal motion patterns. We utilized optoelectronic plethysmography to monitor ten trained male cyclists and compared them to ten physically active male participants performing breathing maneuvers. Cyclists then participated in a self-paced time trial to explore the similarity between that observed during resting breathing. From the 3D coordinates of 32 markers positioned on each participant's trunk, we calculated the percentage of contribution of the superior thorax, inferior thorax, and abdomen and the correlation coefficient among these compartments. During the rest maneuvers, the cyclists showed a thoracoabdominal motion pattern characterized by an increased role of the inferior thorax relative to the superior thorax (26.69±5.88%, 34.93±5.03%; p=0.002, respectively), in contrast to the control group (26.69±5.88%; 25.71±6.04%, p=0.4, respectively). In addition, the inferior thorax showed higher coordination in phase with the abdomen. Furthermore, the results of the time trial test underscored the same pattern found in cyclists breathing at rest, suggesting that the development of a permanent modification in respiratory mechanics may be associated with cycling practice.

Combined Edge Loss UNet for Optimized Segmentation in Total Knee Arthroplasty Preoperative Planning

Bone segmentation and 3D reconstruction are crucial for total knee arthroplasty (TKA) surgical planning with Personalized Surgical Instruments (PSIs). Traditional semi-automatic approaches are time-consuming and operator-dependent, although they provide reliable outcomes. Moreover, the recent expansion of artificial intelligence (AI) tools towards various medical domains is transforming modern healthcare. Accordingly, this study introduces an automated AI-based pipeline to replace the current operator-based tibia and femur 3D reconstruction procedure enhancing TKA preoperative planning. Leveraging an 822 CT image dataset, a novel patch-based method and an improved segmentation label generation algorithm were coupled to a Combined Edge Loss UNet (CEL-UNet), a novel CNN architecture featuring an additional decoding branch to boost the bone boundary segmentation. Root Mean Squared Errors and Hausdorff distances compared the predicted surfaces to the reference bones showing median and interquartile values of 0.26 (0.19-0.36) mm and 0.24 (0.18-0.32) mm, and of 1.06 (0.73-2.15) mm and 1.43 (0.82-2.86) mm for the tibia and femur, respectively, outperforming previous results of our group, state-of-the-art, and UNet models. A feasibility analysis for a PSI-based surgical plan revealed sub-millimetric distance errors and sub-angular alignment uncertainties in the PSI contact areas and the two cutting planes. Finally, operational environment testing underscored the pipeline's efficiency. More than half of the processed cases complied with the PSI prototyping requirements, reducing the overall time from 35 min to 13.1 s, while the remaining ones underwent a manual refinement step to achieve such PSI requirements, performing the procedure four to eleven times faster than the manufacturer standards. To conclude, this research advocates the need for real-world applicability and optimization of AI solutions in orthopedic surgical practice.

Preoperative infection risk assessment in hip arthroplasty a matched-pair study of the reliability of 3 validated risk scales

Peri-prosthetic infection (PJI) represents one of the most devastating complications of total hip arthroplasty (THA). The aim of this study is to assess the reliability of different PJI risk assessment scales between two matched pairs of THA groups. This study included 37 patients with PJI following THA performed between 2012 and 2020 (Group A). Each patient in this group was matched, based on sex, age, and follow-up duration, with a control patient who underwent the same surgical procedure without any septic complications (Group B) during the same period. Each patient's assessment included the American Society of Anesthesiologists (ASA) score and a retrospective evaluation using three different preoperative, specific PJI risk assessment scales: the International Consensus Meeting (ICM) Preoperative Risk Calculator for PJI, the Mayo PJI Risk Score, and the KLIC-score. The two groups were statistically compared using descriptive analyses, both for binomial data and numerical variables. Statistically significant higher values were observed in the preoperative ASA score and surgical time in Group A. Statistically different higher scores were determined only with the ICM risk calculator score in Group A. No significant differences were found using the KLIC score and Mayo score between the two groups. We emphasize the reliability of the ASA score as a nonspecific preoperative assessment scale for PJI. The ICM risk calculator was confirmed as a reliable, specific preoperative assessment scale for PJI, suggesting its routine adoption in THA clinical practice.

Mixed Reality and Artificial Intelligence: A Holistic Approach to Multimodal Visualization and Extended Interaction in Knee Osteotomy

OBJECTIVE

Recent advancements in augmented reality led to planning and navigation systems for orthopedic surgery. However little is known about mixed reality (MR) in orthopedics. Furthermore, artificial intelligence (AI) has the potential to boost the capabilities of MR by enabling automation and personalization. The purpose of this work is to assess Holoknee prototype, based on AI and MR for multimodal data visualization and surgical planning in knee osteotomy, developed to run on the HoloLens 2 headset.

METHODS

Two preclinical test sessions were performed with 11 participants (eight surgeons, two residents, and one medical student) executing three times six tasks, corresponding to a number of holographic data interactions and preoperative planning steps. At the end of each session, participants answered a questionnaire on user perception and usability.

RESULTS

During the second trial, the participants were faster in all tasks than in the first one, while in the third one, the time of execution decreased only for two tasks ("Patient selection" and "Scrolling through radiograph") with respect to the second attempt, but without statistically significant difference (respectively [Formula: see text] = 0.14 and [Formula: see text] = 0.13, [Formula: see text]). All subjects strongly agreed that MR can be used effectively for surgical training, whereas 10 (90.9%) strongly agreed that it can be used effectively for preoperative planning. Six (54.5%) agreed and two of them (18.2%) strongly agreed that it can be used effectively for intraoperative guidance.

DISCUSSION/CONCLUSION

In this work, we presented Holoknee, the first holistic application of AI and MR for surgical planning for knee osteotomy. It reported promising results on its potential translation to surgical training, preoperative planning, and surgical guidance. Clinical and Translational Impact Statement - Holoknee can be helpful to support surgeons in the preoperative planning of knee osteotomy. It has the potential to impact positively the training of the future generation of residents and aid surgeons in the intraoperative stage.

A new system for in-room image guidance in particle therapy at CNAO

This paper describes the design, installation, and commissioning of an in-room imaging device developed at the Centro Nazionale di Adroterapia Oncologica (CNAO, Pavia, Italy). The system is an upgraded version of the one previously installed in 2014, and its design accounted for the experience gained in a decade of clinical practice of patient setup verification and correction through robotic-supported, off-isocenter in-room image guidance. The system's basic feature consists of image-based setup correction through 2D/3D and 3D/3D registration through a dedicated HW/SW platform. The major update with respect to the device already under clinical usage resides in the implementation of a functionality for extending the field of view of the reconstructed Cone Beam CT (CBCT) volume, along with improved overall safety and functional optimization. We report here details on the procedures implemented for system calibration under all imaging modalities and the results of the technical and preclinical commissioning of the device performed on two different phantoms. In the technical commissioning, specific attention was given to the assessment of the accuracy with which the six-degrees-of-freedom correction vector computed at the off-isocenter imaging position was propagated to the planned isocentric irradiation geometry. During the preclinical commissioning, the entire clinical-like procedure for detecting and correcting imposed, known setup deviation was tested on an anthropomorphic radioequivalent phantom. Results showed system performance within the sub-millimeter and sub-degree range according to project specifications under each imaging modality, making it ready for clinical application.

SLEEP-SEE-THROUGH: Explainable Deep Learning for Sleep Event Detection and Quantification From Wearable Somnography

Evidence is rapidly accumulating that multifactorial nocturnal monitoring, through the coupling of wearable devices and deep learning, may be disruptive for early diagnosis and assessment of sleep disorders. In this work, optical, differential air-pressure and acceleration signals, acquired by a chest-worn sensor, are elaborated into five somnographic-like signals, which are then used to feed a deep network. This addresses a three-fold classification problem to predict the overall signal quality (normal, corrupted), three breathing-related patterns (normal, apnea, irregular) and three sleep-related patterns (normal, snoring, noise). In order to promote explainability, the developed architecture generates additional information in the form of qualitative (saliency maps) and quantitative (confidence indices) data, which helps to improve the interpretation of the predictions. Twenty healthy subjects enrolled in this study were monitored overnight for approximately ten hours during sleep. Somnographic-like signals were manually labeled according to the three class sets to build the training dataset. Both record- and subject-wise analyses were performed to evaluate the prediction performance and the coherence of the results. The network was accurate (0.96) in distinguishing normal from corrupted signals. Breathing patterns were predicted with higher accuracy (0.93) than sleep patterns (0.76). The prediction of irregular breathing was less accurate (0.88) than that of apnea (0.97). In the sleep pattern set, the distinction between snoring (0.73) and noise events (0.61) was less effective. The confidence index associated with the prediction allowed us to elucidate ambiguous predictions better. The saliency map analysis provided useful insights to relate predictions to the input signal content. While preliminary, this work supported the recent perspective on the use of deep learning to detect particular sleep events in multiple somnographic signals, thus representing a step towards bringing the use of AI-based tools for sleep disorder detection incrementally closer to clinical translation.

Analytical Challenges in Diabetes Management: Towards Glycated Albumin Point-of-Care Detection

Diabetes mellitus is a worldwide-spread chronic metabolic disease that occurs when the pancreas fails to produce enough insulin levels or when the body fails to effectively use the secreted pancreatic insulin, eventually resulting in hyperglycemia. Systematic glycemic control is the only procedure at our disposal to prevent diabetes long-term complications such as cardiovascular disorders, kidney diseases, nephropathy, neuropathy, and retinopathy. Glycated albumin (GA) has recently gained more and more attention as a control biomarker thanks to its shorter lifespan and wider reliability compared to glycated hemoglobin (HbA1c), currently the “gold standard” for diabetes screening and monitoring in clinics. Various techniques such as ion exchange, liquid or affinity-based chromatography and immunoassay can be employed to accurately measure GA levels in serum samples; nevertheless, due to the cost of the lab equipment and complexity of the procedures, these methods are not commonly available at clinical sites and are not suitable to home monitoring. The present review describes the most up-to-date advances in the field of glycemic control biomarkers, exploring in particular the GA with a special focus on the recent experimental analysis techniques, using enzymatic and affinity methods. Finally, analysis steps and fundamental reading technologies are integrated into a processing pipeline, paving the way for future point-of-care testing (POCT). In this view, we highlight how this setup might be employed outside a laboratory environment to reduce the time from measurement to clinical decision, and to provide diabetic patients with a brand-new set of tools for glycemic self-monitoring.

CEL-Unet: a novel CNN architecture for 3D Segmentation of Knee Bones affected by Severe Osteoarthritis for PSI-Based Surgical Planning

Unet architectures are promising deep learning networks exploited to perform the automatic segmentation of bone CT images, in line with their ability to deal with pathological deformations and size-varying anatomies. However, bone degeneration, like the development of irregular osteophytes as well as mineral density alterations might interfere with this automated process and demand extensive manual refinement. The aim of this work is to implement an innovative Unet variant, the CEL-Unet, to improve the femur and tibia segmentation outcomes in osteoarthritic knee joints. In this network the decoding path is split into a region and contour-aware branch to increase the prediction reliability in such pathological conditions. The comparison between the segmentation results achieved with a standard Unet and its novel variant (CEL-Unet) was performed as follows: the Unet was trained with 5 different loss functions: Dice Loss, Focal Loss, Exponential Logarithmic Loss, Double Cross Entropy Loss and Distanced Cross Entropy loss. The CEL-Unet was instead trained with two loss functions, one for each of the network outputs, namely Mask and Edge, yielding the so-called Combined Edge Loss (CEL) function. A set of 259 knee CT scans was used to train the model and test segmentation performance. The CEL-Unet outperformed all other Unet-based models, reaching the highest Jaccard values of about 0.97 and 0.96 on femur and tibia, respectively. Clinical Relevance- With the increasing rate of Total Knee Arthoplasty deep learning-based methods can achieve fast accurate and automatic 3D segmentation of the knee joint bones to enhance new costumized pre-operative planning.

Identification of Characteristic Points in Multivariate Physiological Signals by Sensor Fusion and Multi-Task Deep Networks

Identification of characteristic points in physiological signals, such as the peak of the R wave in the electrocardiogram and the peak of the systolic wave of the photopletismogram, is a fundamental step for the quantification of clinical parameters, such as the pulse transit time. In this work, we presented a novel neural architecture, called eMTUnet, to automate point identification in multivariate signals acquired with a chest-worn device. The eMTUnet consists of a single deep network capable of performing three tasks simultaneously: (i) localization in time of characteristic points (labeling task), (ii) evaluation of the quality of signals (classification task); (iii) estimation of the reliability of classification (reliability task). Preliminary results in overnight monitoring showcased the ability to detect characteristic points in the four signals with a recall index of about 1.00, 0.90, 0.90, and 0.80, respectively. The accuracy of the signal quality classification was about 0.90, on average over four different classes. The average confidence of the correctly classified signals, against the misclassifications, was 0.93 vs. 0.52, proving the worthiness of the confidence index, which may better qualify the point identification. From the achieved outcomes, we point out that high-quality segmentation and classification are both ensured, which brings the use of a multi-modal framework, composed of wearable sensors and artificial intelligence, incrementally closer to clinical translation.

Hybrid Convolutional Networks for End-to-End Event Detection in Concurrent PPG and PCG Signals Affected by Motion Artifacts

The accurate detection of physiologically-related events in photopletismographic (PPG) and phocardiographic (PCG) signals, recorded by wearable sensors, is mandatory to perform the estimation of relevant cardiovascular parameters like the heart rate and the blood pressure. However, the measurement performed in uncontrolled conditions without clinical supervision leaves the detection quality particularly susceptible to noise and motion artifacts. The performed work proposed a new fully-automatic computational framework, based on convolutional networks, to identify and localize fiducial points in time as the foot, maximum slope and peak in PPG signal and the S1 sound in the PCG signal, both acquired by a custom chest sensor, described recently in the literature by our group. The novelty entailing a custom neural architecture to process sequentially the PPG and PCG signals. Tests were performed analysing four different acquisition conditions (rest, cycling, rest recovery and walking). Cross-validation results for the three PPG fiducial points showed identification accuracy greater than 93 % and localization error (RMSE) less than 10 ms. As expected, cycling and walking conditions provided worse results than rest and recovery, however reaching an accuracy greater than 90 % and a localization error lower than 15 ms. Likewise, the identification and localization error for S1 sound were greater than 90 % and lower than 25 ms. Overall, this study showcased the ability of the proposed technique to detect events with high accuracy not only for steady acquisitions but also during subject movements. We also showed that the proposed network outperformed traditional Shannon-energy-envelope method in the detection of S1 sound. Therefore, we argue that coupling chest sensors and deep learning processing techniques may disclose wearable devices to unobtrusively acquire health information, being less affected by noise and motion artifacts.

Deep Learning for Automated Analysis of Cellular and Extracellular Components of the Foreign Body Response in Multiphoton Microscopy Images

The Foreign body response (FBR) is a major unresolved challenge that compromises medical implant integration and function by inflammation and fibrotic encapsulation. Mice implanted with polymeric scaffolds coupled to intravital non-linear multiphoton microscopy acquisition enable multiparametric, longitudinal investigation of the FBR evolution and interference strategies. However, follow-up analyses based on visual localization and manual segmentation are extremely time-consuming, subject to human error, and do not allow for automated parameter extraction. We developed an integrated computational pipeline based on an innovative and versatile variant of the U-Net neural network to segment and quantify cellular and extracellular structures of interest, which is maintained across different objectives without impairing accuracy. This software for automatically detecting the elements of the FBR shows promise to unravel the complexity of this pathophysiological process.

Femoral Neck Fractures in HIV-Positive Patients: Analysis of 10 Years Short-Term Post-operative Complications

Introduction:

Aging and effect of antiretroviral therapy on bone mass could increase the risk of femoral neck fractures (FNF) in HIV patient. The aim of this study was specifically to determine whether intracapsular FNF in HIV-positive patients are more prone to short-term post-operative complications than similar fractures occurring in HIV-negative patients.

Materials and methods:

A group of 25 HIV-positive patients with intracapsular FNF were enrolled and matched to HIV-negative patient with similar fractures according to gender, age, a modified Charlson Comorbidity Index (CCI), fracture classification, surgical treatment and time interval between fracture event and surgery. For each group, length of stay, surgical time, early clinical outcomes and short-term surgical and medical complications were compared to determine the impact on the early outcome.

Results:

At the time of the fracture occurrence, 56% of HIV-positive patients were on antiretroviral therapy and 12% started with therapy in the perioperative period. At three months follow-up, there were no statistically significant differences between the two study groups in length of stay, Harris hip score and total number of early complications. However, a statistically significant increase in urinary tract infections and longer surgical time using hip sliding screw fixation were seen in the HIV-positive group. The poorest post-operative result was seen in a patient who failed to adequately adhere to the HIV therapy protocol.

Conclusions:

This study failed to show any statistically significant increase in short-term complications or worse clinical outcomes for intracapsular FNF in HIV-positive patients compared to HIV-negative patients to recommend their treatment in dedicated centres.

Image-based shading correction for narrow-FOV truncated pelvic CBCT with deep convolutional neural networks and transfer learning

Purpose: Cone beam computed tomography (CBCT) is a standard solution for in‐room image guidance for radiation therapy. It is used to evaluate and compensate for anatomopathological changes between the dose delivery plan and the fraction delivery day. CBCT is a fast and versatile solution, but it suffers from drawbacks like low contrast and requires proper calibration to derive density values. Although these limitations are even more prominent with in‐room customized CBCT systems, strategies based on deep learning have shown potential in improving image quality. As such, this article presents a method based on a convolutional neural network and a novel two‐step supervised training based on the transfer learning paradigm for shading correction in CBCT volumes with narrow field of view (FOV) acquired with an ad hoc in‐room system.

Methods: We designed a U‐Net convolutional neural network, trained on axial slices of corresponding CT/CBCT couples. To improve the generalization capability of the network, we exploited two‐stage learning using two distinct data sets. At first, the network weights were trained using synthetic CBCT scans generated from a public data set, and then only the deepest layers of the network were trained again with real‐world clinical data to fine‐tune the weights. Synthetic data were generated according to real data acquisition parameters. The network takes a single grayscale volume as input and outputs the same volume with corrected shading and improved HU values.

Results: Evaluation was carried out with a leave‐one‐out cross‐validation, computed on 18 unique CT/CBCT pairs from six different patients from a real‐world dataset. Comparing original CBCT to CT and improved CBCT to CT, we obtained an average improvement of 6 dB on peak signal‐to‐noise ratio (PSNR), +2% on structural similarity index measure (SSIM). The median interquartile range (IQR) Hounsfield unit (HU) difference between CBCT and CT improved from 161.37 (162.54) HU to 49.41 (66.70) HU. Region of interest (ROI)‐based HU difference was narrowed by 75% in the spongy bone (femoral head), 89% in the bladder, 85% for fat, and 83% for muscle. The improvement in contrast‐to‐noise ratio for these ROIs was about 67%.

Conclusions: We demonstrated that shading correction obtaining CT‐compatible data from narrow‐FOV CBCTs acquired with a customized in‐room system is possible. Moreover, the transfer learning approach proved particularly beneficial for such a shading correction approach.

Comparison of Supervised and Unsupervised Approaches for the Generation of Synthetic CT from Cone-Beam CT

Due to major artifacts and uncalibrated Hounsfield units (HU), cone-beam computed tomography (CBCT) cannot be used readily for diagnostics and therapy planning purposes. This study addresses image-to-image translation by convolutional neural networks (CNNs) to convert CBCT to CT-like scans, comparing supervised to unsupervised training techniques, exploiting a pelvic CT/CBCT publicly available dataset. Interestingly, quantitative results were in favor of supervised against unsupervised approach showing improvements in the HU accuracy (62% vs. 50%), structural similarity index (2.5% vs. 1.1%) and peak signal-to-noise ratio (15% vs. 8%). Qualitative results conversely showcased higher anatomical artifacts in the synthetic CBCT generated by the supervised techniques. This was motivated by the higher sensitivity of the supervised training technique to the pixel-wise correspondence contained in the loss function. The unsupervised technique does not require correspondence and mitigates this drawback as it combines adversarial, cycle consistency, and identity loss functions. Overall, two main impacts qualify the paper: (a) the feasibility of CNN to generate accurate synthetic CT from CBCT images, which is fast and easy to use compared to traditional techniques applied in clinics; (b) the proposal of guidelines to drive the selection of the better training technique, which can be shifted to more general image-to-image translation.

Is age rating enough to investigate changes in breathing motion pattern associated with aging of physically active women?

The most common way to analyze the effect of aging on breathing is to divide subjects into age groups. However, in addition to the fact that there is no consensus in the literature regarding age group division, such design critically influences the interpretation of the effects attributed to aging. Thus, this study aimed to investigate the feasibility to distinguish different age groups from the 3D kinematic variables of breathing motion (i.e., markers' coordinate as a function of time allowing the calculation of compartmental volume variations) and to analyze whether the aging could influence these variables. Seventy-three physically active women aged 19-80 years performed quiet breathing and vital capacity maneuvers. To record the thoracoabdominal breathing motion, the 3D coordinates of 32 retroreflective markers positioned on the trunk were used to estimate the volume variation of the superior thorax, inferior thorax, and abdomen. The percentage of contribution and the correlation coefficient were calculated to analyze the breathing motion pattern from the estimated volumes. The k-means cluster analysis was performed to analyze the age group classification. Linear regression was performed to investigate whether age can predict changes in the breathing motion pattern. The results showed that physically active women could not be classified into age groups from breathing motion. Despite significant p values of the linear regression, the high variability of the data suggested that age itself is not enough to predict the changes in breathing motion pattern when non-sedentary women are considered.

Septic arthritis following joint injections: a 17 years retrospective study in an Academic General Hospital

BACKGROUND

Septic arthritis following intra-articular infiltrations is an uncommon devastating complication correlated  to high costs for the health service and often to poor outcomes. The purpose of this study is to assess a 17-years experience in a single academic multispecialist hospital managing this uncommon complication in Orthopaedic practice.

METHODS

Patients with diagnosis of septic arthritis following joint injections treated in our hospital from January 2002 to December 2019 were included in the study. Clinical and demographic data, pathogens, injected agent, conservative/surgical treatments were reviewed. Patient were classified according to the ore operative Charlson Comorbidity Index (CCI) and the Cierny-Mader Classification(CMC). Furthermore follow-up outcome and time occurred to infection eradication were registered.

RESULTS

We included in the study 11 patients with a median age of 74 years old (IQR= 61.5 - 79). The median CCI was 3  (IQR= 2 - 5) and the majority of patients belong to CMC = B class. Septic arthritis occurred mainly following corticosteroids injections and more frequently involving knees. The pathogen more often isolated was Staphylococcus aureus. Five (45%) patients referred an history of multiple intrarticular injections. 7 patients (64%) had a complete resolution following an arthroscopic debridement, 4 (36%) patients underwent to a 2-stage replacement and one of them hesitated in an arthrodesis because of a recurrent periprothesic joint infection and extensor apparatus insufficiency.

CONCLUSION

The authors observed a potential increased risk of septic arthritis following joint injection in patients with history of multiple injections and poor health/immunological conditions. They recommend an early arthroscopic debridement as the treatment of choice especially in septic knees  performed in a multispecialist dedicated center.

Humeral head necrosis associated to shaft non-union with massive bone loss: a case report

Humeral non-union is a rare complication in shaft fractures, as well as humeral head necrosis is a possible complication in fracture involving the proximal third especially in four-part fractures. The presence of head osteonecrosis and diaphyseal non-union in the same arm represents a formidable challenge for an orthopaedic surgeon. We could not find any similar report in the literature dealing with this issue thus far. We present a case of a 65 years old woman referred to our hospital being affected by an atrophic humeral diaphyseal non-union with a massive bone loss (>10cm) associated to a humeral head osteonecrosis following a previous surgical procedures with a clear loosening of the hardware. At our institution,she was treated with hardware removal and insertion of a diaphyseal antibiotic spacer with Gentamycin for 2 months suspecting an active septic process at the union site despite negative cultural exams. Finally, she was treated with a cemented modular humeral megaprosthesis. At 20 months follow up, the patient, despite a reduced shoulder range of motion, referred to a pain-free recovery to an almost normal lifestyle, including car driving with no major disturbances. This case suggests that, in extreme selected cases following several failed treatments, megaprosthesis can represent a viable solution, especially in huge bone loss associated to joint degeneration, to ensure an acceptable return to a normal lifestyle.

Predicting Knee Joint Instability Using a Tibio-Femoral Statistical Shape Model

Statistical shape models (SSMs) are a well established computational technique to represent the morphological variability spread in a set of matching surfaces by means of compact descriptive quantities, traditionally called "modes of variation" (MoVs). SSMs of bony surfaces have been proposed in biomechanics and orthopedic clinics to investigate the relation between bone shape and joint biomechanics. In this work, an SSM of the tibio-femoral joint has been developed to elucidate the relation between MoVs and bone angular deformities causing knee instability. The SSM was built using 99 bony shapes (distal femur and proximal tibia surfaces obtained from segmented CT scans) of osteoarthritic patients. Hip-knee-ankle (HKA) angle, femoral varus-valgus (FVV) angle, internal-external femoral rotation (IER), tibial varus-valgus (TVV) angles, and tibial slope (TS) were available across the patient set. Discriminant analysis (DA) and logistic regression (LR) classifiers were adopted to underline specific MoVs accounting for knee instability. First, it was found that thirty-four MoVs were enough to describe 95% of the shape variability in the dataset. The most relevant MoVs were the one encoding the height of the femoral and tibial shafts (MoV #2) and the one representing variations of the axial section of the femoral shaft and its bending in the frontal plane (MoV #5). Second, using quadratic DA, the sensitivity results of the classification were very accurate, being all >0.85 (HKA: 0.96, FVV: 0.99, IER: 0.88, TVV: 1, TS: 0.87). The results of the LR classifier were mostly in agreement with DA, confirming statistical significance for MoV #2 (p = 0.02) in correspondence to IER and MoV #5 in correspondence to HKA (p = 0.0001), FVV (p = 0.001), and TS (p = 0.02). We can argue that the SSM successfully identified specific MoVs encoding ranges of alignment variability between distal femur and proximal tibia. This discloses the opportunity to use the SSM to predict potential misalignment in the knee for a new patient by processing the bone shapes, removing the need for measuring clinical landmarks as the rotation centers and mechanical axes.

A Metal Oxide Gas Sensors Array for Lung Cancer Diagnosis Through Exhaled Breath Analysis

Lung cancer high mortality rate is mainly related to late-stage tumor diagnosis. Survival rates and treatments could be greatly improved with an effective early diagnosis. Volatile organic compounds (VOCs) in exhaled breath have been known for long to be linked to the presence of a disease. Exhaled breath analysis for early diagnosis of lung cancer represents a non-invasive, low-cost and user-friendly approach. In this paper we present the design and development of an electronic nose based on a metal oxide sensors array for the early diagnosis of lung cancer. Breath samples collected from healthy controls (n=10) and lung cancer subjects (n=6) were analyzed by the electronic nose, and classification was performed using an artificial neural network (ANN). A sensitivity of 85.7%, specificity of 100%, and accuracy of 93.8% were reached with leave one out cross validation (LOOCV). The presented device demonstrates that a simple, cost-effective, and non-invasive approach based on exhaled breath analysis has the potential to be of great help in decreasing lung cancer mortality.

Reducing the Number of Degrees of Freedom to Control an Eye Surgical Robot through Classification of Surgical Phases

This paper introduces an optimized input device workflow to control an eye surgical robot in a simulated vitreoretinal environment. The input device is a joystick with four Degrees of Freedom (DOF) that controls a six DOFs robot. This aim is achieved through a segmentation plan for an eye surgeon. In this study, the different surgical phases are defined while each phase includes their specific number of DOFs. The segmentation plan is divided into four surgical phases: Phase I: Approach with three DOFs; Phase II: Introduction with three DOFs; Phase III: Aim with 3+1 DOFs; and Phase IV: Injection with one DOF. Taking these phases into consideration, an eye surgical robot with six DOFs could be controlled through a joystick with only four DOFs intuitively. In this work we show that reducing the number of DOFs will decrease the complexity of the surgery with a robotic platform.

Non artheritic bilateral anterior ischaemic optic neuropathy (NAION) as devastating complication following Total Hip Arthroplasty: a case report

Introduction: Postoperative vision loss (PVL) is an extremely rare complication following major surgical procedures. Patients with systemic hypertension, diabetes, coronary diseases and smokers are generally predisposed to this complication. More frequently, it is caused by ischemic optic neuropathy (ION), central retinal artery occlusion or retinal vein occlusion. Rare cases of unilateral PVL following total joint arthroplasty surgery have been recently described in literature. Case report: This case report describes the first reported bilateral non-arteritic anterior ischemic optic neuropathy (NAION), which occurred 3 days following a total hip arthroplasty with a consequent post-operative hypotension. Conclusions: Orthopedic surgeons should be aware that in hip joint replacement procedures, selected patients present an higher risk of ION following intra/postoperative hypotension and prolonged surgical times. (www.actabiomedica.it)

Moving system with action sport cameras: 3D kinematics of the walking and running in a large volume

Traditionally, motion analysis in clinical laboratories using optoelectronic systems (MOCAP) is performed in acquisition volumes of limited size. Given the complexity and cost of MOCAP in larger volumes, action sports cameras (ASC) represent an alternative approach in which the cameras move along with the subject during the movement task. Thus, this study aims to compare ASC against a traditional MOCAP in the perspective of reconstructing walking and running movements in large spatial volumes, which extend over the common laboratory setup. The two systems, consisting of four cameras each, were closely mounted on a custom carrying structure endowed with wheels. Two different acquisition setups, namely steady and moving conditions, were taken into account. A devoted calibration procedure, using the same protocol for the two systems, enabled the reconstruction of surface markers, placed on voluntary subjects, during the two acquisition setups. The comparison was quantitatively expressed in terms of three-dimensional (3D) marker reconstruction and kinematic computation quality. The quality of the marker reconstruction quality was quantified by means of the mean absolute error (MAE) of inter-marker distance and two-stick angle. The kinematic computation quality was quantified by means of the measure of the knee angle reconstruction during walking and running trials. In order to evaluate the camera system and moving camera effects, we used a Wilcoxon rank sum test and a Kruskal Wallis test (post-hoc Tukey), respectively. The Spearman correlation coefficient (ρ) and the Wilcoxon rank sum test were applied to compare the kinematic data obtained by the two camera systems. We found small ASC MAE values (< 2.6mm and 1.3°), but they were significantly bigger than the MOCAP (< 0.7mm and 0.6°). However, for the human movement no significant differences were found between kinematic variables in walking and running acquisitions (p>0.05), and the motion patterns of the right-left knee angles between both systems were very similar (ρ>0.90, p<0.05). These results highlighted the promising results of a system that uses ASC based on the procedure of mobile cameras to follow the movement of the subject, allowing a less constrained movement in the direction in which the structure moves, compared to the traditional laboratory setup.

Pair-wise vs group-wise registration in statistical shape model construction: representation of physiological and pathological variability of bony surface morphology

Statistical shape models (SSM) of bony surfaces have been widely proposed in orthopedics, especially for anatomical bone modeling, joint kinematic analysis, staging of morphological abnormality, and pre- and intra-operative shape reconstruction. In the SSM computation, reference shape selection, shape registration and point correspondence computation are fundamental aspects determining the quality (generality, specificity and compactness) of the SSM. Such procedures can be made critical by the presence of large morphological dissimilarities within the surfaces, not only because of anthropometrical variability but also mainly due to pathological abnormalities. In this work, we proposed a SW pipeline for SSM construction based on pair-wise (PW) shape registration, which requires the a-priori selection of the reference shape, and on a custom iterative point correspondence algorithm. We addressed large morphological deformations in five different bony surface sets, namely proximal femur, distal femur, patella, proximal fibula and proximal tibia, extracted from a retrospective patient dataset. The technique was compared to a method from the literature, based on group-wise (GW) shape registration. As a main finding, the proposed technique provided generalization and specificity median errors, for all the five bony regions, lower than 2 mm. The comparative analysis provided basically similar results. Particularly, for the distal femur that was the shape affected by the largest pathological deformations, the differences in generalization, specificity and compactness were lower than 0.5 mm, 0.5 mm, and 1%, respectively. We can argue the proposed pipeline, along with the robust correspondence algorithm, is able to compute high-quality SSM of bony shapes, even affected by large morphological variability.

Model-Supported Radiotherapy Personalization: In silico Test of Hyper- and Hypo-Fractionation Effects

The need for radiotherapy personalization is now widely recognized, however, it would require considerations not only on the probability of control and survival of the tumor, but also on the possible toxic effects, on the quality of the expected life and the economic efficiency of the treatment. In this paper, we propose a simulation tool that can be integrated into a decision support system that allows selection of the most suitable irradiation regimen. We used a macroscale mathematical model, which includes active and necrotic tumor dynamics and the role of oxygenation to simulate the effects of different hypo-/hyper-fractional regimens using retrospective data of seven virtual patients from as many cervical cancer patients used for its training in a previous study. The results confirmed the heterogeneous response across the patients as a function of treatment regimen and suggested the tumor growth rate as a main factor in the final tumor regression. In addition to the maximum regression, another criterion was suggested to select the most suitable regimen (minimum number of fractions to achieve a regression of 80%) minimizing the toxicity and maximizing the cost-effectiveness ratio. Despite the lack of direct validation, the simulation results are in agreement with the literature findings that suggest the need for hypo-fractionated regimens in case of aggressive tumor phenotypes. Finally, the paper suggests a possible exploitation of the model within a tool to support clinical decisions.

Stacked sparse autoencoder networks and statistical shape models for automatic staging of distal femur trochlear dysplasia

BACKGROUND

The quantitative morphological analysis of the trochlear region in the distal femur and the precise staging of the potential dysplastic condition constitute a key point for the use of personalized treatment options for the patella-femoral joint. In this paper, we integrated statistical shape models (SSM), able to represent the individual morphology of the trochlea by means of a set of parameters and stacked sparse autoencoder (SSPA) networks, which exploit the parameters to discriminate among different levels of abnormalities.

METHODS

Two datasets of distal femur reconstructions were obtained from CT scans, including pathologic and physiologic shapes. Both of them were processed to compute SSM of healthy and dysplastic trochlear regions. The parameters obtained by the 3D-3D reconstruction of a femur shape were fed into a trained SSPA classifier to automatically establish the membership to one of three clinical conditions, namely, healthy, mild dysplasia, and severe dysplasia of the trochlea. The validation was performed on a subset of the shapes not used in the construction of the SSM, by verifying the occurrence of a correct classification.

RESULTS

A major finding of the work is that SSM are able to represent anomalies of the trochlear geometry by means of specific eigenmodes of variation and to model the interplay between morphologic features related to dysplasia. Exploiting the patient-specific morphing parameters of SSM, computed by means of a 3D-3D reconstruction, SSPA is demonstrated to outperform traditional discriminant analysis in classifying healthy, mild, and severe trochlear dysplasia providing 99%, 97%, and 98% accuracy for each of the three classes, respectively (discriminant analysis accuracy: 85%, 89%, and 77%).

CONCLUSIONS

From a clinical point of view, this paper contributes to support the increasing role of SSM, integrated with deep learning techniques, in diagnostics and therapy definition as quantitative and advanced visualization tools.

A Novel Wearable Apparatus to Measure Fingertip Forces in Manipulation Tasks Based on MEMS Barometric Sensors

Artificial tactile sensing is a challenging research topic in robotics, motor control, and rehabilitation engineering encompassing multi-disciplinary skills and different technologies. This paper presents the development of a wearable tactile thimble system using MEMS barometric sensors and flexible printed circuit board. Barometric sensors were carefully processed to make them able to transduce contact forces. Thumb, index, and medium fingers were equipped with an array of six sensing elements each, covering the central, lateral, and medial aspects of the fingertip. The sensor integration, signal read-out and processing, hardware architecture of the device, along with the calibration protocol, were described. The test results showed adequate sensitivity at very low forces with an almost linear transduction range up to about 4N (RMSE: 0.04N). Tests on object manipulation tasks highlighted the value of the proposed system demonstrating the ability of measuring both the force amplitude and contact points, demonstrating the suitability of barometric sensors for tactile applications.

Comparison between model-predicted tumor oxygenation dynamics and vascular-/flow-related Doppler indices

PURPOSE

Mathematical modeling is a powerful and flexible method to investigate complex phenomena. It discloses the possibility of reproducing expensive as well as invasive experiments in a safe environment with limited costs. This makes it suitable to mimic tumor evolution and response to radiotherapy although the reliability of the results remains an issue. Complexity reduction is therefore a critical aspect in order to be able to compare model outcomes to clinical data. Among the factors affecting treatment efficacy, tumor oxygenation is known to play a key role in radiotherapy response. In this work, we aim at relating the oxygenation dynamics, predicted by a macroscale model trained on tumor volumetric data of uterine cervical cancer patients, to vascularization and blood flux indices assessed on Ultrasound Doppler images.

METHODS

We propose a macroscale model of tumor evolution based on three dynamics, namely active portion, necrotic portion, and oxygenation. The model parameters were assessed on the volume size of seven cervical cancer patients administered with 28 fractions of intensity modulated radiation therapy (IMRT) (1.8 Gy/fraction). For each patient, five Doppler ultrasound tests were acquired before, during, and after the treatment. The lesion was manually contoured by an expert physician using 4D View^® (General Electric Company - Fairfield, Connecticut, United States), which automatically provided the overall tumor volume size along with three vascularization and/or blood flow indices. Volume data only were fed to the model for training purpose, while the predicted oxygenation was compared a posteriori to the measured Doppler indices.

RESULTS

The model was able to fit the tumor volume evolution within 8% error (range: 3-8%). A strong correlation between the intrapatient longitudinal indices from Doppler measurements and oxygen predicted by the model (about 90% or above) was found in three cases. Two patients showed an average correlation value (50-70%) and the remaining two presented poor correlations. The latter patients were the ones featuring the smallest tumor reduction throughout the treatment, typical of hypoxic conditions. Moreover, the average oxygenation value predicted by the model was close to the average vascularization-flow index (average difference: 7%).

CONCLUSIONS

The results suggest that the modeled relation between tumor evolution and oxygen dynamics was reasonable enough to provide realistic oxygenation curves in five cases (correlation greater than 50%) out of seven. In case of nonresponsive tumors, the model failed in predicting the oxygenation trend while succeeded in reproducing the average oxygenation value according to the mean vascularization-flow index. Despite the need for deeper investigations, the outcomes of the present work support the hypothesis that a simple macroscale model of tumor response to radiotherapy is able to predict the tumor oxygenation. The possibility of an objective and quantitative validation on imaging data discloses the possibility to translate them as decision support tools in clinical practice and to move a step forward in the treatment personalization.

2D/3D reconstruction of the distal femur using statistical shape models addressing personalized surgical instruments in knee arthroplasty: A feasibility analysis

BACKGROUND

Personalized surgical instruments (PSI) have gained success in the domain of total knee replacement, demonstrating clinical outcomes similar or even superior to both traditional and navigated surgeries. The key requirement for prototyping PSI is the availability of the digital bony surface. In this paper, we aim at verifying whether the 2D/3D reconstruction of the distal femur, based on statistical shape models (SSM), grants sufficient accuracy, especially in the condylar regions, to support a PSI technique.

METHODS

Computed tomographic knee datasets acquired on 100 patients with severe cartilage damage were retrospectively considered in this work. All the patients underwent total knee replacement using the PSI-based surgical technique. Eighty out of 100 reconstructed distal femur surfaces were used to build the statistical model. The remaining 20 surfaces were used for testing. The 2D/3D reconstruction process was based on digital reconstructed radiographies (DRRs) obtained with a simulated X-ray projection process. An iterative optimization procedure, based on an evolutionary algorithm, systematically morphed the statistical model to decrease the difference between the DRR, obtained by the original CT dataset, and the DRR obtained from the morphed surface.

RESULTS

Over the 80 variations, the first ten modes were found sufficient to reconstruct the distal femur surface with accuracy. Using three DRR, the maximum Hausdorff and RMS distance errors were lower than 1.50 and 0.75 mm, respectively. As expected, the reconstruction quality improved by increasing the number of DRRs. Statistical difference (P < 0.001) was found in the 2 vs 3, 2 vs 4 and 2 vs 5 DRR, thus proving that adding just a single displaced projection to the two traditional sagittal and coronal X-ray images improved significantly the reconstruction quality. The effect of the PSI contact area errors on the distal cut direction featured a maximum median error lower than 2° and 0.5° on the sagittal and frontal plane, respectively. Statistical difference was found (P < 0.0001) in the reconstruction accuracy when comparing SSM built using pathologic with respect to non-pathologic shapes (cadavers), meaning that, to improve the patient-specific reconstruction, the morphologic anomalies, specific to the pathology, must be embedded into the SSM.

CONCLUSIONS

We showed that the X-ray based reconstruction of the distal femur is reasonable also in presence of pathologic bony conditions, featuring accuracy results similar to earlier reports in the literature that reconstructed normal femurs. This finding discloses the chance of applying the proposed methodology to the reconstruction of bony surfaces used in the PSI surgical approach.

In-air versus underwater comparison of 3D reconstruction accuracy using action sport cameras

Action sport cameras (ASC) have achieved a large consensus for recreational purposes due to ongoing cost decrease, image resolution and frame rate increase, along with plug-and-play usability. Consequently, they have been recently considered for sport gesture studies and quantitative athletic performance evaluation. In this paper, we evaluated the potential of two ASCs (GoPro Hero3+) for in-air (laboratory) and underwater (swimming pool) three-dimensional (3D) motion analysis as a function of different camera setups involving the acquisition frequency, image resolution and field of view. This is motivated by the fact that in swimming, movement cycles are characterized by underwater and in-air phases what imposes the technical challenge of having a split volume configuration: an underwater measurement volume observed by underwater cameras and an in-air measurement volume observed by in-air cameras. The reconstruction of whole swimming cycles requires thus merging of simultaneous measurements acquired in both volumes. Characterizing and optimizing the instrumental errors of such a configuration makes mandatory the assessment of the instrumental errors of both volumes. In order to calibrate the camera stereo pair, black spherical markers placed on two calibration tools, used both in-air and underwater, and a two-step nonlinear optimization were exploited. The 3D reconstruction accuracy of testing markers and the repeatability of the estimated camera parameters accounted for system performance. For both environments, statistical tests were focused on the comparison of the different camera configurations. Then, each camera configuration was compared across the two environments. In all assessed resolutions, and in both environments, the reconstruction error (true distance between the two testing markers) was less than 3mm and the error related to the working volume diagonal was in the range of 1:2000 (3×1.3×1.5m³) to 1:7000 (4.5×2.2×1.5m³) in agreement with the literature. Statistically, the 3D accuracy obtained in the in-air environment was poorer (p<10^-5) than the one in the underwater environment, across all the tested camera configurations. Related to the repeatability of the camera parameters, we found a very low variability in both environments (1.7% and 2.9%, in-air and underwater). This result encourage the use of ASC technology to perform quantitative reconstruction both in-air and underwater environments.

Patient-specific modeling of the trochlear morphologic anomalies by means of hyperbolic paraboloids

Diagnostic and therapeutic purposes are issuing pressing demands to improve the evaluation of the dysplasia condition of the femoral trochlea. The traditional clinical assessment of the dysplasia, based on Dejour classification, recognized 4 increasing (A, B, C, D) levels of severity. It has been extensively questioned in the literature that this classification methodology can be defective suggesting that quantitative measures can ensure more reliable criteria for the dysplasia severity assessment. This study reports on a novel technique to model the trochlear surface (TS), digitally reconstructed by 3D volumetric imaging, using three hyperbolic paraboloids (HP), one to describe the global trochlear aspect, two to represent the local aspects of the medial and lateral compartments, respectively. Results on a cohort of 43 patients, affected by aspecific anterior knee pain, demonstrate the consistency of the estimated model parameters with the morphologic aspect of the TS. The obtained small fitting error (on average lower than 0.80 mm) demonstrated that the ventral aspect of the trochlear morphology can be modeled with high accuracy by HPs. We also showed that HP modeling provides a continuous representation of morphologic variations in shape parameter space while we found that similar morphologic anomalies of the trochlear aspect are actually attributed to different severity grades in the Dejour classification. This finding is in agreement with recent works in the literature reporting that morphometric parameters can only optimistically be used to discriminate between the Grade A and the remaining three grades. In conclusion, we can assert that the proposed methodology is a further step toward modeling of anatomical surfaces that can be used to quantify deviations to normality on a patient-specific basis.

Navigated "small implants" in knee reconstruction

PURPOSE

At the beginning of this century, unprecedented interest in the concept of using less invasive approaches for the treatment of knee degenerative diseases was ignited. Initial interest in this approach was about navigated and non-navigated knee reconstruction using small implants and conventional total knee arthroplasty.

METHODS

To this end, a review of the published literature relating to less invasive compartmental arthroplasty of the knee using computer-based alignment techniques and on soft tissue-dedicated small implants is presented. The authors present and compare their personal results using these techniques with those reported in the current literature. These involved the use of a shorter incision and an emphasis sparing. However, nowadays most surgeons look at compartmental knee resurfacing with the use of small implants as the new customized approach for younger and higher-demand patients. The aim of this paper is to stimulate further debate.

RESULTS

Since the beginning of 2000, computer-assisted surgery has been applied to total knee arthroplasty (TKA) and later to compartmental knee arthroplasty. Recent studies in the literature have reported better implant survivorship for younger patients using navigation in TKA at longer-term follow-up. Only one published report was identified showing superior clinical outcomes at short-term follow-up using computer-assisted technology compared with conventional alignment techniques in small implant surgery. No studies were found in the literature that demonstrated similar clinical advantages with navigated small implants at long-term follow-up. Two published meta-analyses were identified reporting better implant and limb alignment and no increase in complications using a navigated unicompartmental knee arthroplasty. However, neither meta-analysis showed superior clinical outcomes or survivorship with the navigated techniques.

CONCLUSION

In conclusion, we can assert that replacing just the damaged compartment and preserving the normal biomechanics will require not only new implant designs but also new technologies allowing the surgeon to make extremely precise adjustments to implant alignment and providing continuous feedback during surgery.

LEVEL OF EVIDENCE

IV.

Mathematical modeling of tumor response to radiation: radio-sensitivity correlation with BOLD, TOLD, ΔR1 and ΔR2* investigated in large Dunning R3327-AT1 rat prostate tumors

Tumor response to radiation therapy can vary highly across patients. Several factors, both tumor- and environment-specific, can influence the radio-sensitivity, one of the most well-known being hypoxia. In this work, we investigated possible correlations between the radio-sensitivity parameters determined by means of a simple mathematical model of tumor volume evolution, and the MRI-based indicators of oxygenation in Dunning R3327-AT1 rats. Prior to irradiation the rats were subjected to an oxygen-breathing challenge, which was evaluated by MRI. The tumors were administered a single irradiation dose (30 Gy), while breathing air or oxygen. Despite a poor fitting performance, the model was able to identify two different tumor volume regression patterns. Moreover, the radio-sensitivity of the oxygen-breathing group was found to correlate with the variation of the transverse relaxation rate ΔR2* (-0.89). This suggests that MRI-based indices of tumor oxygenation may provide information about radio-sensitivity.

Action Sport Cameras as an Instrument to Perform a 3D Underwater Motion Analysis

Action sport cameras (ASC) are currently adopted mainly for entertainment purposes but their uninterrupted technical improvements, in correspondence of cost decreases, are going to disclose them for three-dimensional (3D) motion analysis in sport gesture study and athletic performance evaluation quantitatively. Extending this technology to sport analysis however still requires a methodologic step-forward to making ASC a metric system, encompassing ad-hoc camera setup, image processing, feature tracking, calibration and 3D reconstruction. Despite traditional laboratory analysis, such requirements become an issue when coping with both indoor and outdoor motion acquisitions of athletes. In swimming analysis for example, the camera setup and the calibration protocol are particularly demanding since land and underwater cameras are mandatory. In particular, the underwater camera calibration can be an issue affecting the reconstruction accuracy. In this paper, the aim is to evaluate the feasibility of ASC for 3D underwater analysis by focusing on camera setup and data acquisition protocols. Two GoPro Hero3+ Black (frequency: 60Hz; image resolutions: 1280×720/1920×1080 pixels) were located underwater into a swimming pool, surveying a working volume of about 6m³. A two-step custom calibration procedure, consisting in the acquisition of one static triad and one moving wand, carrying nine and one spherical passive markers, respectively, was implemented. After assessing camera parameters, a rigid bar, carrying two markers at known distance, was acquired in several positions within the working volume. The average error upon the reconstructed inter-marker distances was less than 2.5mm (1280×720) and 1.5mm (1920×1080). The results of this study demonstrate that the calibration of underwater ASC is feasible enabling quantitative kinematic measurements with accuracy comparable to traditional motion capture systems.

Tumor radio-sensitivity assessment by means of volume data and magnetic resonance indices measured on prostate tumor bearing rats

PURPOSE

Radiation therapy is one of the most common treatments in the fight against prostate cancer, since it is used to control the tumor (early stages), to slow its progression, and even to control pain (metastasis). Although many factors (e.g., tumor oxygenation) are known to influence treatment efficacy, radiotherapy doses and fractionation schedules are often prescribed according to the principle "one-fits-all," with little personalization. Therefore, the authors aim at predicting the outcome of radiation therapy a priori starting from morphologic and functional information to move a step forward in the treatment customization.

METHODS

The authors propose a two-step protocol to predict the effects of radiation therapy on individual basis. First, one macroscopic mathematical model of tumor evolution was trained on tumor volume progression, measured by caliper, of eighteen Dunning R3327-AT1 bearing rats. Nine rats inhaled 100% O2 during irradiation (oxy), while the others were allowed to breathe air. Second, a supervised learning of the weight and biases of two feedforward neural networks was performed to predict the radio-sensitivity (target) from the initial volume and oxygenation-related information (inputs) for each rat group (air and oxygen breathing). To this purpose, four MRI-based indices related to blood and tissue oxygenation were computed, namely, the variation of signal intensity ΔSI in interleaved blood oxygen level dependent and tissue oxygen level dependent (IBT) sequences as well as changes in longitudinal ΔR1 and transverse ΔR2(*) relaxation rates.

RESULTS

An inverse correlation of the radio-sensitivity parameter, assessed by the model, was found with respect the ΔR2(*) (-0.65) for the oxy group. A further subdivision according to positive and negative values of ΔR2(*) showed a larger average radio-sensitivity for the oxy rats with ΔR2(*)<0 and a significant difference in the two distributions (p < 0.05). Finally, a leave-one-out procedure yielded a radio-sensitivity error lower than 20% in both neural networks.

CONCLUSIONS

While preliminary, these specific results suggest that subjects affected by the same pathology can benefit differently from the same irradiation modalities and support the usefulness of IBT in discriminating between different responses.

Adaptive Mathematical Model of Tumor Response to Radiotherapy Based on CBCT Data

Mathematical modeling of tumor response to radiotherapy has the potential of enhancing the quality of the treatment plan, which can be even tailored on an individual basis. Lack of extensive in vivo validation has prevented, however, reliable clinical translation of modeling outcomes. Image-guided radiotherapy is a consolidated treatment modality based on computed tomographic (CT) imaging for tumor delineation and volumetric cone beam CT data for periodic checks during treatment. In this study, a macroscopic model of tumor growth and radiation response is proposed, being able to adapt along the treatment course as volumetric tumor data become available. Model parameter learning was based on cone beam CT images in 13 uterine cervical cancer patients, subdivided into three groups (G1, G2, G3) according to tumor type and treatment. Three group-specific parameter sets (PS1, PS2, and PS3) on one general parameter set (PSa) were applied. The corresponding average model fitting errors were 14%, 18%, 13%, and 21%, respectively. The model adaptation testing was performed using volume data of three patients, other than the ones involved in the parameter learning. The extrapolation performance of the general model was improved, while comparable prediction errors were found for the group-specific approach. This suggests that an online parameter tuning can overcome the limitations of a suboptimal patient stratification, which appeared otherwise a critical issue.

Kinetic Models for Predicting Cervical Cancer Response to Radiation Therapy on Individual Basis Using Tumor Regression Measured In Vivo With Volumetric Imaging

This article describes a macroscopic mathematical modeling approach to capture the interplay between solid tumor evolution and cell damage during radiotherapy. Volume regression profiles of 15 patients with uterine cervical cancer were reconstructed from serial cone-beam computed tomography data sets, acquired for image-guided radiotherapy, and used for model parameter learning by means of a genetic-based optimization. Patients, diagnosed with either squamous cell carcinoma or adenocarcinoma, underwent different treatment modalities (image-guided radiotherapy and image-guided chemo-radiotherapy). The mean volume at the beginning of radiotherapy and the end of radiotherapy was on average 23.7 cm(3) (range: 12.7-44.4 cm(3)) and 8.6 cm(3) (range: 3.6-17.1 cm(3)), respectively. Two different tumor dynamics were taken into account in the model: the viable (active) and the necrotic cancer cells. However, according to the results of a preliminary volume regression analysis, we assumed a short dead cell resolving time and the model was simplified to the active tumor volume. Model learning was performed both on the complete patient cohort (cohort-based model learning) and on each single patient (patient-specific model learning). The fitting results (mean error: ∼ 16% and ∼ 6% for the cohort-based model and patient-specific model, respectively) highlighted the model ability to quantitatively reproduce tumor regression. Volume prediction errors of about 18% on average were obtained using cohort-based model computed on all but 1 patient at a time (leave-one-out technique). Finally, a sensitivity analysis was performed and the data uncertainty effects evaluated by simulating an average volume perturbation of about 1.5 cm(3) obtaining an error increase within 0.2%. In conclusion, we showed that simple time-continuous models can represent tumor regression curves both on a patient cohort and patient-specific basis; this discloses the opportunity in the future to exploit such models to predict how changes in the treatment schedule (number of fractions, doses, intervals among fractions) might affect the tumor regression on an individual basis.

Modeling the Interplay Between Tumor Volume Regression and Oxygenation in Uterine Cervical Cancer During Radiotherapy Treatment

This paper describes a patient-specific mathematical model to predict the evolution of uterine cervical tumors at a macroscopic scale, during fractionated external radiotherapy. The model provides estimates of tumor regrowth and dead-cell reabsorption, incorporating the interplay between tumor regression rate and radiosensitivity, as a function of the tumor oxygenation level. Model parameters were estimated by minimizing the difference between predicted and measured tumor volumes, these latter being obtained from a set of 154 serial cone-beam computed tomography scans acquired on 16 patients along the course of the therapy. The model stratified patients according to two different estimated dynamics of dead-cell removal and to the predicted initial value of the tumor oxygenation. The comparison with a simpler model demonstrated an improvement in fitting properties of this approach (fitting error average value <5%, p < 0.01), especially in case of tumor late responses, which can hardly be handled by models entailing a constant radiosensitivity, failing to model changes from initial severe hypoxia to aerobic conditions during the treatment course. The model predictive capabilities suggest the need of clustering patients accounting for cancer cell line, tumor staging, as well as microenvironment conditions (e.g., oxygenation level).

Automating the design of resection guides specific to patient anatomy in knee replacement surgery by enhanced 3D curvature and surface modeling of distal femur shape models

Personalized resection guides (PRG) have been recently proposed in the domain of knee replacement, demonstrating clinical outcome similar or even superior to both manual and navigated interventions. Among the mandatory pre-surgical steps for PRG prototyping, the measurement of clinical landmarks (CL) on the bony surfaces is recognized as a key issue due to lack of standardized methodologies, operator-dependent variability and time expenditure. In this paper, we focus on the reliability and repeatability of an anterior-posterior axis, also known as Whiteside line (WL), of the distal femur proposing automatic surface processing and modeling methods aimed at overcoming some of the major concerns related to the manual identification of such CL on 2D images and 3D models. We show that the measurement of WL, exploiting the principle of mean-shifting surface curvature, is highly repeatable and coherent with clinical knowledge.

Commissioning and Quality Assurance of an Integrated System for Patient Positioning and Setup Verification in Particle Therapy

In an increasing number of clinical indications, radiotherapy with accelerated particles shows relevant advantages when compared with high energy X-ray irradiation. However, due to the finite range of ions, particle therapy can be severely compromised by setup errors and geometric uncertainties. The purpose of this work is to describe the commissioning and the design of the quality assurance procedures for patient positioning and setup verification systems at the Italian National Center for Oncological Hadrontherapy (CNAO). The accuracy of systems installed in CNAO and devoted to patient positioning and setup verification have been assessed using a laser tracking device. The accuracy in calibration and image based setup verification relying on in room X-ray imaging system was also quantified. Quality assurance tests to check the integration among all patient setup systems were designed, and records of daily QA tests since the start of clinical operation (2011) are presented. The overall accuracy of the patient positioning system and the patient verification system motion was proved to be below 0.5 mm under all the examined conditions, with median values below the 0.3 mm threshold. Image based registration in phantom studies exhibited sub-millimetric accuracy in setup verification at both cranial and extra-cranial sites. The calibration residuals of the OTS were found consistent with the expectations, with peak values below 0.3 mm. Quality assurance tests, daily performed before clinical operation, confirm adequate integration and sub-millimetric setup accuracy. Robotic patient positioning was successfully integrated with optical tracking and stereoscopic X-ray verification for patient setup in particle therapy. Sub-millimetric setup accuracy was achieved and consistently verified in daily clinical operation.

A micro-optical system for endoscopy based on mechanical compensation paradigm using miniature piezo-actuation

The goal of the study was to investigate the feasibility of a novel miniaturized optical system for endoscopy. Fostering the mechanical compensation paradigm, the modeled optical system, composed by 14 lenses, separated in 4 different sets, had a total length of 15.55mm, an effective focal length ranging from 1.5 to 4.5mm with a zoom factor of about 2.8×, and an angular field of view up to 56°. Predicted maximum lens travel was less than 3.5mm. The consistency of the image plane height across the magnification range testified the zoom capability. The maximum predicted achromatic astigmatism, transverse spherical aberration, longitudinal spherical aberration and relative distortion were less than or equal to 25μm, 15μm, 35μm and 12%, respectively. Tests on tolerances showed that the manufacturing and opto-mechanics mounting are critical as little deviations from design dramatically decrease the optical performances. However, recent micro-fabrication technology can guarantee tolerances close to nominal design. A closed-loop actuation unit, devoted to move the zoom and the focus lens sets, was implemented adopting miniaturized squiggle piezo-motors and magnetic position encoders based on Hall effect. Performance results, using a prototypical test board, showed a positioning accuracy of less than 5μm along a lens travel path of 4.0mm, which was in agreement with the lens set motion features predicted by the analysis. In conclusion, this study demonstrated the feasibility of the optical design and the viability of the actuation approach while tolerances must be carefully taken into account.