The Platysma Contraction Pattern in Healthy Adults: A Vector Analysis Study.

BACKGROUND: The platysma muscle's role in lower face dynamics is complex. Multiple insertion points to soft-tissue structures at various levels in the lower face create a multifaceted contraction pattern. To avoid adverse effects in cosmetic procedures when targeting the platysma, its anatomy and physiology must be understood. Clinical observations hint at a bidirectional contraction pattern. METHODS: Eighteen healthy volunteers (13 women and five men) with a mean age of 44.2 ± 10.1 years were enrolled. Skin displacement vector analysis was used on maximal platysma contraction to characterize and calculate the movement of the neck and lower face skin. RESULTS: In all of the participants, a bidirectional movement of the skin was observed: the skin of the lower face and inferior to the jawline moved caudally, whereas the skin of the lower neck moved cephalad. Both movements converged at a line situated at 54% ± 10% and 55% ± 8% of the length between the clavicle and the inferior base of the ear lobe in men and women, respectively ( P = 0.70). CONCLUSIONS: The platysma is a bidirectional muscle with a line of convergence. Whereas the superior portion acts as lip depressor, the lower portion elevates the skin of the upper chest and lower neck. This transition can explain some of the clinically observed adverse effects of neuromodulation of the neck area. It can potentially direct neuromodulation injections to focus above the convergence line to better address lower face descent.

A kinematics-based method for creating deformed patient-derived head and neck CT scans.

CT scans of the head and neck have multiple clinical uses, and simulating deformation of these CT scans allows for predicting patient motion and data augmentation for machine-learning methods. Current methods for creating patient-derived deformed CT scans require multiple scans or use unrealistic head and neck motion. This paper describes the CTHeadDeformation software package which allows for realistic synthetic deformation of head and neck CT scans for small amounts of motion. CTHeadDeformation is a python-based package that uses a kinematics-based approach using anatomical landmarks, and rigid/non-rigid registration to create a realistic patient-derived deformed CT scan. CTHeadDeformation is also designed for simple clinical implementation. The CTHeadDeformation software package was demonstrated on a head and neck CT scan of one patient. The CT scan was deformed in the anterior-posterior, superior-inferior, and left-right directions. Internal organ motion and more complex combination motions were also simulated. The results showed the patient's CT scan was able to be deformed in a way that preserved the shape and location of the anatomy.Clinical Relevance- This method allows for the realistic simulation of head and neck motion in CT scans. Clinical applications including simulating how patient motion affects radiation therapy treatment effectiveness. The CTHeadDeformation software can also be used to train machine-learning networks that are robust to patient motion, or to generate ground truth images for imaging or segmentation grand challenges.

Improving plan quality in cervical brachytherapy using a simple knowledge-based prediction tool for OAR dose (D2cm3).

PURPOSE: Toxicity from cervical brachytherapy has been demonstrated to correlate with the D2cm3 of the bladder, rectum, and bowel. This suggests a simplified version of knowledge-based planning investigating the relationship of the overlap distance for 2cm3 and the D2cm3 from planning may be possible. This work demonstrates the feasibility of simple knowledge-based planning to predict the D2cm3, detect suboptimal plans, and improve plan quality. METHODS AND MATERIALS: The overlap volume histogram (OVH) method was used to determine the distance for 2cm3 of overlap between the OAR and CTV_HR. Linear plots modeled the OAR D2cm3 and 2cm3 overlap distance. Two datasets of 20 patients (plans from 43 insertions in each dataset) were used to create two independent models, and the performance of each model was compared using cross-validation. Doses were scaled to ensure consistent CTV_HR D90 values. The predicted D2cm3 is entered as the maximum constraint in the inverse planning algorithm. RESULTS: Mean bladder D2cm3 decreased by 2.9% for the models from each dataset, mean rectal D2cm3 decreased 14.9% for the model from dataset 1 and 6.0% for the model from dataset 2, mean sigmoid D2cm3 decreased 10.7% for the model from dataset 1 and 6.1% for the model from dataset 2, mean bowel D2cm3 decreased 4.1% for the model from dataset 1 but no statistically significant difference was observed for the model from dataset 2. CONCLUSIONS: A simplified knowledge-based planning method was used to predict D2cm3 and was able to automate optimization of brachytherapy plans for locally advanced cervical cancer.

Realistic CT data augmentation for accurate deep-learning based segmentation of head and neck tumors in kV images acquired during radiation therapy.

BACKGROUND: Using radiation therapy (RT) to treat head and neck (H&N) cancers requires precise targeting of the tumor to avoid damaging the surrounding healthy organs. Immobilisation masks and planning target volume margins are used to attempt to mitigate patient motion during treatment, however patient motion can still occur. Patient motion during RT can lead to decreased treatment effectiveness and a higher chance of treatment related side effects. Tracking tumor motion would enable motion compensation during RT, leading to more accurate dose delivery. PURPOSE: The purpose of this paper is to develop a method to detect and segment the tumor in kV images acquired during RT. Unlike previous tumor segmentation methods for kV images, in this paper, a process for generating realistic and synthetic CT deformations was developed to augment the training data and make the segmentation method robust to patient motion. Detecting the tumor in 2D kV images is a necessary step toward 3D tracking of the tumor position during treatment. METHOD: In this paper, a conditional generative adversarial network (cGAN) is presented that can detect and segment the gross tumor volume (GTV) in kV images acquired during H&N RT. Retrospective data from 15 H&N cancer patients obtained from the Cancer Imaging Archive were used to train and test patient-specific cGANs. The training data consisted of digitally reconstructed radiographs (DRRs) generated from each patient's planning CT and contoured GTV. Training data was augmented by using synthetically deformed CTs to generate additional DRRs (in total 39 600 DRRs per patient or 25 200 DRRs for nasopharyngeal patients) containing realistic patient motion. The method for deforming the CTs was a novel deformation method based on simulating head rotation and internal tumor motion. The testing dataset consisted of 1080 DRRs for each patient, obtained by deforming the planning CT and GTV at different magnitudes to the training data. The accuracy of the generated segmentations was evaluated by measuring the segmentation centroid error, Dice similarity coefficient (DSC) and mean surface distance (MSD). This paper evaluated the hypothesis that when patient motion occurs, using a cGAN to segment the GTV would create a more accurate segmentation than no-tracking segmentations from the original contoured GTV, the current standard-of-care. This hypothesis was tested using the 1-tailed Mann-Whitney U-test. RESULTS: The magnitude of our cGAN segmentation centroid error was (mean ± standard deviation) 1.1 ± 0.8 mm and the DSC and MSD values were 0.90 ± 0.03 and 1.6 ± 0.5 mm, respectively. Our cGAN segmentation method reduced the segmentation centroid error (p < 0.001), and MSD (p = 0.031) when compared to the no-tracking segmentation, but did not significantly increase the DSC (p = 0.294). CONCLUSIONS: The accuracy of our cGAN segmentation method demonstrates the feasibility of this method for H&N cancer patients during RT. Accurate tumor segmentation of H&N tumors would allow for intrafraction monitoring methods to compensate for tumor motion during treatment, ensuring more accurate dose delivery and enabling better H&N cancer patient outcomes.

Credit for Truly Novel Ideas? A Critical Approach to the Intellectual Authorship in Surgery.

The formation of new ideas and techniques in medicine and surgery is crucial to bettering the medical field and the quality of medical care. The transmission of these new ideas is a source of pride and recognition for physicians who devote their lives to patient care. The quality and integrity of the medical literature that results from seminal medical ideas are an essential but unregulated field. From time to time, there are discussions in the medical literature about the authorship of an idea/strategy/technique. In this digital era, where communication works at an unmeasurable speed, the authenticity of medical communication requires honesty and verification. The possibility of unreliable or false information exists, and the need to verify "new" information as accurate and honest is crucial. Rhythm, genuine, and fake (fair/unfair) information circulates at high speed, and suddenly everything one encounters is represented as "true and often represented as new." Regarding medical science and particularly surgery - we are overloaded daily with new techniques, new names, new strategies, and everything. Several questions regarding the authenticity of any publication or scientific communication exist. A critical approach is done in this article.

Standardising Breast Radiotherapy Structure Naming Conventions: A Machine Learning Approach.

In progressing the use of big data in health systems, standardised nomenclature is required to enable data pooling and analyses. In many radiotherapy planning systems and their data archives, target volumes (TV) and organ-at-risk (OAR) structure nomenclature has not been standardised. Machine learning (ML) has been utilised to standardise volumes nomenclature in retrospective datasets. However, only subsets of the structures have been targeted. Within this paper, we proposed a new approach for standardising all the structures nomenclature by using multi-modal artificial neural networks. A cohort consisting of 1613 breast cancer patients treated with radiotherapy was identified from Liverpool & Macarthur Cancer Therapy Centres, NSW, Australia. Four types of volume characteristics were generated to represent each target and OAR volume: textual features, geometric features, dosimetry features, and imaging data. Five datasets were created from the original cohort, the first four represented different subsets of volumes and the last one represented the whole list of volumes. For each dataset, 15 sets of combinations of features were generated to investigate the effect of using different characteristics on the standardisation performance. The best model reported 99.416% classification accuracy over the hold-out sample when used to standardise all the nomenclatures in a breast cancer radiotherapy plan into 21 classes. Our results showed that ML based automation methods can be used for standardising naming conventions in a radiotherapy plan taking into consideration the inclusion of multiple modalities to better represent each volume.

Proton-to-photon comparative treatment planning guidelines for the Australian context.

Proton-to-photon comparative treatment planning is a current requirement of Australian Government funding for patients to receive proton beam therapy (PBT) overseas, and a future requirement for Medicare funding of PBT in Australia. Because of the fundamental differences in treatment plan creation and evaluation between PBT and conventional radiation therapy with x-rays (XRT), there is the potential for a lack of consistency in the process of comparing PBT and XRT treatment plans. This may have an impact on patient eligibility assessment for PBT. The objective of these guidelines is to provide a practical reference document for centres performing proton-to-photon comparative planning and thereby facilitate national uniformity.

Infrastructure platform for privacy-preserving distributed machine learning development of computer-assisted theragnostics in cancer.

INTRODUCTION: Emerging evidence suggests that data-driven support tools have found their way into clinical decision-making in a number of areas, including cancer care. Improving them and widening their scope of availability in various differing clinical scenarios, including for prognostic models derived from retrospective data, requires co-ordinated data sharing between clinical centres, secondary analyses of large multi-institutional clinical trial data, or distributed (federated) learning infrastructures. A systematic approach to utilizing routinely collected data across cancer care clinics remains a significant challenge due to privacy, administrative and political barriers. METHODS: An information technology infrastructure and web service software was developed and implemented which uses machine learning to construct clinical decision support systems in a privacy-preserving manner across datasets geographically distributed in different hospitals. The infrastructure was deployed in a network of Australian hospitals. A harmonized, international ontology-linked, set of lung cancer databases were built with the routine clinical and imaging data at each centre. The infrastructure was demonstrated with the development of logistic regression models to predict major cardiovascular events following radiation therapy. RESULTS: The infrastructure implemented forms the basis of the Australian computer-assisted theragnostics (AusCAT) network for radiation oncology data extraction, reporting and distributed learning. Four radiation oncology departments (across seven hospitals) in New South Wales (NSW) participated in this demonstration study. Infrastructure was deployed at each centre and used to develop a model predicting for cardiovascular admission within a year of receiving curative radiotherapy for non-small cell lung cancer. A total of 10,417 lung cancer patients were identified with 802 being eligible for the model. Twenty features were chosen for analysis from the clinical record and linked registries. After selection, 8 features were included and a logistic regression model achieved an area under the receiver operating characteristic (AUROC) curve of 0.70 and C-index of 0.65 on out-of-sample data. CONCLUSION: The infrastructure developed was demonstrated to be usable in practice between clinical centres to harmonize routinely collected oncology data and develop models with federated learning. It provides a promising approach to enable further research studies in radiation oncology using real world clinical data.

Understanding the Vascular Anatomy of the Face: Introducing the X-Y-Z-Concept.

The demand and use of injectable fillers are increasing and performed by practitioners at different levels. They are frequently used to rejuvenate and volumize the face, but there are well-known complications that can occur with intraluminal injection or compression of the facial vessels. The knowledge of the vascular anatomy of the face, particularly the predictable depth, can prevent these devastating complications.

Safety culture and incident learning systems in radiation oncology: Staff perceptions across Australia and New Zealand.

INTRODUCTION: Radiation therapy has a highly complex pathway and uses detailed quality assurance protocols and incident learning systems (ILSs) to mitigate risk; however, errors can still occur. The safety culture (SC) in a department influences its commitment and effectiveness in maintaining patient safety. METHODS: Perceptions of SC and knowledge and understanding of ILSs and their use were evaluated for radiation oncology staff across Australia and New Zealand (ANZ). A validated healthcare survey tool (the Hospital Survey on Patient Safety Culture) was used, with additional specialty-focussed supporting questions. A total of 220 radiation oncologists, radiation therapists and radiation oncology medical physicists participated. RESULTS: An overall positive SC was indicated, with strength in teamwork (83.7%), supervisor/manager/leader support (83.3%) and reporting events (77.1%). The weakest areas related to communication about error (63.9%), hospital-level management support (60.5%) and handovers and information exchange (58.0%). Barriers to ILS use included 'it takes too long' and that many respondents must use multiple reporting systems, including mandatory hospital-level systems. These are generally not optimal for specific radiation oncology needs. Varied understanding was indicated of what and when to report. CONCLUSION: The findings report the ANZ perspective on ILS and SC, highlighting weaknesses, barriers and areas for further investigation. Differences observed in some areas suggest that a unified state, national or bi-national ILS specific to radiation oncology might eliminate multiple reporting systems and reduce reporting time. It could also provide more consistent and robust approaches to incident reporting, information sharing and analysis.

A statistical, voxelised model of prostate cancer for biologically optimised radiotherapy.

Background and purpose: Radiation therapy (RT) is commonly indicated for treatment of prostate cancer (PC). Biologicallyoptimised RT for PC may improve disease-free survival. This requires accurate spatial localisation and characterisation of tumour lesions. We aimed to generate a statistical, voxelised biological model to complement in vivomultiparametric MRI data to facilitate biologically-optimised RT. Material and methods: Ex vivo prostate MRI and histopathological imaging were acquired for 63 PC patients. These data were co-registered to derive three-dimensional distributions of graded tumour lesions and cell density. Novel registration processes were used to map these data to a common reference geometry. Voxelised statistical models of tumour probability and cell density were generated to create the PC biological atlas. Cell density models were analysed using the Kullback-Leibler divergence to compare normal vs. lognormal approximations to empirical data. Results: A reference geometry was constructed using ex vivo MRI space, patient data were deformably registered using a novel anatomy-guided process. Substructure correspondence was maintained using peripheral zone definitions to address spatial variability in prostate anatomy between patients. Three distinct approaches to interpolation were designed to map contours, tumour annotations and cell density maps from histology into ex vivo MRI space. Analysis suggests a log-normal model provides a more consistent representation of cell density when compared to a linear-normal model. Conclusion: A biological model has been created that combines spatial distributions of tumour characteristics from a population into three-dimensional, voxelised, statistical models. This tool will be used to aid the development of biologically-optimised RT for PC patients.

PCXMC cone beam computed tomography dosimetry investigations.

With cone beam computed tomography (CBCT) in image guided radiation therapy being amongst the most widely used imaging modalities, there has been an increasing interest in quantifying the concomitant dose and risk. Whilst there have been several studies on this topic, there remains a lack of standardisation and knowledge on dose variations and the impact of patient size. Recently, PCXMC (a Monte Carlo simulator) has been used to assess both the concomitant dose and dosimetric impact of patient size variations for CBCT. The scopes of these studies, however, have included only a limited range of imaging manufacturers, protocols, and patient sizes. An approach using PCXMC and MATLAB was developed to enable a generalised method for rapidly quantifying and formulating the concomitant dose as a function of patient size across numerous CBCT vendors and protocols. The method was investigated using the Varian on board imaging 1.6 default pelvis and pelvis spotlight protocols, for 94 adult and paediatric phantoms over 6 age groups with extensive height and mass variations. It was found that dose varies significantly with patient size, as much as doubling and halving the average for patients of lower and higher mass, respectively. These variations, however, can be formulated and accounted for using the method developed, across a wide range of patient sizes for all CBCT vendors and protocols. This will enable the development of a comprehensive catalogue to account for concomitant doses in almost any clinically relevant scenario.

Multimodal Facial Aesthetic Treatment on the Appearance of Aging, Social Confidence, and Psychological Well-being: HARMONY Study.

BACKGROUND: A global approach to facial rejuvenation involves multiple treatment modalities. OBJECTIVES: The aim of this study was to evaluate the impact of multimodal facial aesthetic treatment on self-reported psychological and social outcomes. METHODS: HARMONY, a prospective, multicenter, 4-month study, enrolled patients aged 35 to 65 years to receive on-label treatment with a combination of hyaluronic fillers (VYC-20L, HYC-24L, and/or HYC-24L+), onabotulinumtoxinA, and bimatoprost. Fillers were injected on Day 1, with touch-ups performed on Day 14. OnabotulinumtoxinA was injected at Month 3 into glabellar lines and/or crow's feet lines. Patients applied bimatoprost to eyelashes once daily for 17 weeks. Mean change from baseline on FACE-Q Psychological Well-being and Social Confidence Scales, FACE-Q Aging Appearance Appraisal Scale, and FACE-Q Age Appraisal Visual Analog Scale were assessed. RESULTS: Of 100 patients treated, 93 were evaluated at 4 months posttreatment. Significant improvement vs baseline was observed on the FACE-Q Scales for Psychological Well-being (mean change, -19.9; P < 0.00001), Social Confidence (mean change, -18.2; P < 0.00001), and Aging Appearance (mean change, -28.5; P < 0.0001). On average, patients' self-assessed age was 0.1 years older than actual age at baseline and 4.5 years younger at Month 4 (P < 0.001 vs baseline). Forty-two patients experienced adverse events, all mild to moderate. CONCLUSIONS: Multimodal, full facial aesthetic treatment improves patients' self-reported psychological well-being, social confidence, aging appearance, and perceptions of chronologic age.

Cleft Rhinoplasty.

An understanding of anatomy and pathophysiology of the cleft nasal deformity is crucial to its management, including selection of correct surgical techniques for repair. Timing of intermediate and definitive rhinoplasty should be considered carefully, with definitive rhinoplasty occurring after management of facial skeletal deformities. At the time of definitive rhinoplasty, the septum, external and internal nasal valves, alar base malposition (and corresponding bony deficiency), and position and shape of the lower lateral cartilage and the columella all must be individually considered. Thorough knowledge of rhinoplasty techniques is crucial to address the cleft nasal deformity with optimal functional and aesthetic outcomes.

Evaluating incident learning systems and safety culture in two radiation oncology departments.

INTRODUCTION: Radiation oncology patient pathways are complex. This complexity creates risk and potential for error to occur. Comprehensive safety and quality management programmes have been developed alongside the use of incident learning systems (ILSs) to mitigate risks and errors reaching patients. Robust ILSs rely on the safety culture (SC) within a department. The aim of this study was to assess perceptions and understanding of SC and ILSs in two closely linked radiation oncology departments and to use the results to consider possible quality improvement (QI) of department ILSs and SC. METHODS: A survey to assess perceptions of SC and the currently used ILSs was distributed to radiation oncologists, radiation therapists and radiation oncology medical physicists in the two departments. The responses of 95 staff were evaluated (63% of staff). The findings were used to determine any areas for improvement in SC and local ILSs. RESULTS: Differences were shown between the professional cohorts. Barriers to current ILS use were indicated by 67% of respondents. Positive SC was shown in each area assessed: 69% indicated the departments practised a no-blame culture. Barriers identified in one department prompted a QI project to develop a new reporting system and process, improve departmental learning and modify the overall ILS. CONCLUSION: An understanding of SC and attitudes to ILSs has been established and used to improve ILS reporting, feedback on incidents, departmental learning and the QA program. This can be used for future comparisons as the systems develop.

Anatomy behind the Paramedian Platysmal Band: A Combined Cadaveric and Computed Tomographic Study.

BACKGROUND: The muscular hypothesis explanation of the pathophysiology behind paramedian platysmal bands does not seem to provide a sufficient explanation for the clinical presentation of these platysmal bands in aged individuals with cervical soft-tissue laxity. The purpose of this study was to investigate the fascial relationships of the anterior neck to enhance appropriate surgical treatment. METHODS: Retrospective analysis of computed tomographic scans of 50 Caucasian individuals (mean age, 55.84 ± 17.5 years) and anatomical dissections of 20 Caucasian and 10 Asian body donors (mean age, 75.88 ± 10.6 years) were conducted. Fascial adhesions were classified according to platysma fusion types, and platysma mobility was tested during dissection procedures. RESULTS: Fusion between the left and right platysma muscles occurred in 64 percent in the suprahyoid region and in 20 percent at the thyroid cartilage, and in 16 percent, the platysma attached to the mandible without fusion. In the absence of muscular fibers, a fascial adhesion zone with limited mobility was observed where the superficial cervical fascia fused with the investing layer of the deep cervical fascia. CONCLUSIONS: Muscular contraction of the platysma results in elevation of the most medial platysma muscle fibers, which are not attached in the fascial adhesion zone. The presence of a fatty layer deep to the platysma enables platysmal movement and anterior and inferior gliding of the skin and platysma when cervical soft-tissue laxity exists. Surgical treatments should include transection of the fascial adhesion; this could potentially prevent the recurrence of paramedian platysmal bands.

Implementation of the Australian Computer-Assisted Theragnostics (AusCAT) network for radiation oncology data extraction, reporting and distributed learning.

INTRODUCTION: There is significant potential to analyse and model routinely collected data for radiotherapy patients to provide evidence to support clinical decisions, particularly where clinical trials evidence is limited or non-existent. However, in practice there are administrative, ethical, technical, logistical and legislative barriers to having coordinated data analysis platforms across radiation oncology centres. METHODS: A distributed learning network of computer systems is presented, with software tools to extract and report on oncology data and to enable statistical model development. A distributed or federated learning approach keeps data in the local centre, but models are developed from the entire cohort. RESULTS: The feasibility of this approach is demonstrated across six Australian oncology centres, using routinely collected lung cancer data from oncology information systems. The infrastructure was used to validate and develop machine learning for model-based clinical decision support and for one centre to assess patient eligibility criteria for two major lung cancer radiotherapy clinical trials (RTOG-9410, RTOG-0617). External validation of a 2-year overall survival model for non-small cell lung cancer (NSCLC) gave an AUC of 0.65 and C-index of 0.62 across the network. For one centre, 65% of Stage III NSCLC patients did not meet eligibility criteria for either of the two practice-changing clinical trials, and these patients had poorer survival than eligible patients (10.6 m vs. 15.8 m, P = 0.024). CONCLUSION: Population-based studies on routine data are possible using a distributed learning approach. This has the potential for decision support models for patients for whom supporting clinical trial evidence is not applicable.

A novel method to determine linac mechanical isocenter position and size and examples of specific QA applications.

The most important geometric characteristic of stereotactic treatment is the accuracy of positioning the target at the treatment isocenter and the accuracy of directing the radiation beam at the treatment isocenter. Commonly, the radiation isocenter is used as the reference for the treatment isocenter, but its method of localization is not strictly defined, and it depends on the linac-specific beam steering parameters. A novel method is presented for determining the linac mechanical isocenter position and size based on the localization of the collimator axis of rotation at arbitrary gantry angle. The collimator axis of rotation position is determined from the radiation beam center position corrected for the focal spot offset. The focal spot offset is determined using the image center shift method with a custom-design rigid phantom with two sets of ball-bearings. Three specific quality assurance (QA) applications and assessment methods are also presented to demonstrate the functionality of linac mechanical isocenter position and size determination in clinical practice. The first is a mechanical and radiation isocenters coincidence test suitable for quick congruence assessment of these two isocenters for a selected energy, usually required after a nonroutine linac repair and/or energy adjustment. The second is a stereotactic beam isocentricity assessment suitable for pretreatment stereotactic QA. The third is a comprehensive linac geometrical performance test suitable for routine linac QA. The uncertainties of the method for determining mechanical isocenter position and size were measured to be 0.05 mm and 0.04 mm, respectively, using four available photon energies, and were significantly smaller than those of determining the radiation isocenter position and size, which were 0.36 mm and 0.12 mm respectively. It is therefore recommended that the mechanical isocenter position and size be used as the reference linac treatment isocenter and a linac mechanical characteristic parameter respectively.

Anatomic Trends and Directions in Periorbital Aesthetic Surgery.

The anatomy of the eyelids and periorbital region is delicate. The individual anatomic variations determine each person's eyelid appearance and function. It is essential that every surgeon that evaluates and treats the aesthetic conditions of patients desiring periorbital enhancement understands the association of anatomy and diagnosis. Each periorbital aesthetic diagnosis has an anatomic basis, and knowledge of the applied anatomy allows a targeted treatment plan. This article outlines the layered anatomy with its clinical significance for the eyelids and periorbital region. Specific examples are used to illustrate the applied anatomy. A contemporary treatment plan for each anatomic problem is given.

Towards zero radiation isocentre size: minimising radiation beam isocentricity on Elekta linear accelerators by means of optimising look-up tables.

The most important geometric characteristics of SRS/SBRT treatments are precise target localisation and precise aiming of the radiation beam at the target. The AAPM-RSS Medical Physics Practice Guideline 9.a. for SRS/SBRT recommends that the radiation isocentricity (i.e. beam deviation from the isocentre) should not exceed 1 mm for SRS and 1.5 mm for SBRT. Minimising the beam deviations from the treatment target, largely due to the gantry sag, can improve the accuracy of radiosurgery and stereotactic treatments and commonly beam steering parameters are optimised to achieve this objective. This study aims to investigate, as a proof of concept, if it is possible to eliminate the beam deviations on Elekta linear accelerators altogether by optimising gantry angle dependent beam steering parameters, as stored in look-up tables. The investigation used the EPID-based Winston-Lutz test at 13 gantry angles separated every 30° (from - 180° to + 180°). Elekta linacs have two look-up tables that can be customised explicitly for radial beam angle and transverse beam position. Modifications of the radial look-up table were limited by the radial beam asymmetry inhibit of more than 5%, as measured by the linac in-built ionisation chamber. Therefore, only small radial beam deviation reductions of 0.1 mm were achieved (on average from 0.37 to 0.26 mm) while radial beam symmetry changed significantly by up to ± 7%, depending on the gantry angle as measured by the IC Profiler™. The optimised transverse look-up table resulted in reduction of transverse beam deviations to almost zero (on average from 0.20 to 0.03 mm), however, that changed the transverse beam symmetry by almost a constant value of 1%, as measured by the IC Profiler™. Ideally, two additional look-up tables are needed for effective beam steering, one for radial beam position and one for transverse beam angle. Four look-up tables in total would enable customising beam centre position and beam symmetry at any gantry angle that would minimize radiation isocentre size without compromising beam symmetry.