HNF1B controls epithelial organization and cell polarity during ureteric bud branching and collecting duct morphogenesis.

Kidney development depends crucially on proper ureteric bud branching giving rise to the entire collecting duct system. The transcription factor HNF1B is required for the early steps of ureteric bud branching, yet the molecular and cellular events regulated by HNF1B are poorly understood. We report that specific removal of Hnf1b from the ureteric bud leads to defective cell-cell contacts and apicobasal polarity during the early branching events. High-resolution ex vivo imaging combined with a membranous fluorescent reporter strategy show decreased mutant cell rearrangements during mitosis-associated cell dispersal and severe epithelial disorganization. Molecular analysis reveals downregulation of Gdnf-Ret pathway components and suggests that HNF1B acts both upstream and downstream of Ret signaling by directly regulating Gfra1 and Etv5 Subsequently, Hnf1b deletion leads to massively mispatterned ureteric tree network, defective collecting duct differentiation and disrupted tissue architecture, which leads to cystogenesis. Consistently, mRNA-seq analysis shows that the most impacted genes encode intrinsic cell-membrane components with transporter activity. Our study uncovers a fundamental and recurring role of HNF1B in epithelial organization during early ureteric bud branching and in further patterning and differentiation of the collecting duct system in mouse.

Novel fixed z-direction (FiZD) kidney primordia and an organoid culture system for time-lapse confocal imaging.

Tissue, organ and organoid cultures provide suitable models for developmental studies, but our understanding of how the organs are assembled at the single-cell level still remains unclear. We describe here a novel fixed z-direction (FiZD) culture setup that permits high-resolution confocal imaging of organoids and embryonic tissues. In a FiZD culture a permeable membrane compresses the tissues onto a glass coverslip and the spacers adjust the thickness, enabling the tissue to grow for up to 12 days. Thus, the kidney rudiment and the organoids can adjust to the limited z-directional space and yet advance the process of kidney morphogenesis, enabling long-term time-lapse and high-resolution confocal imaging. As the data quality achieved was sufficient for computer-assisted cell segmentation and analysis, the method can be used for studying morphogenesis ex vivo at the level of the single constituent cells of a complex mammalian organogenesis model system.

A Star Sensor On-Orbit Calibration Method Based on Singular Value Decomposition.

The navigation accuracy of a star sensor depends on the estimation accuracy of its optical parameters, and so, the parameters should be updated in real time to obtain the best performance. Current on-orbit calibration methods for star sensors mainly rely on the angular distance between stars, and few studies have been devoted to seeking new calibration references. In this paper, an on-orbit calibration method using singular values as the calibration reference is introduced and studied. Firstly, the camera model of the star sensor is presented. Then, on the basis of the invariance of the singular values under coordinate transformation, an on-orbit calibration method based on the singular-value decomposition (SVD) method is proposed. By means of observability analysis, an optimal model of the star combinations for calibration is explored. According to the physical interpretation of the singular-value decomposition of the star vector matrix, the singular-value selection for calibration is discussed. Finally, to demonstrate the performance of the SVD method, simulation calibrations are conducted by both the SVD method and the conventional angular distance-based method. The results show that the accuracy and convergence speed of both methods are similar; however, the computational cost of the SVD method is heavily reduced. Furthermore, a field experiment is conducted to verify the feasibility of the SVD method. Therefore, the SVD method performs well in the calibration of star sensors, and in particular, it is suitable for star sensors with limited computing resources.

Geometrical uncertainty of heart position in deep-inspiration breath-hold radiotherapy of left-sided breast cancer patients.

BACKGROUND: This study aimed to determine the geometrical uncertainty of the position of the heart with daily cone beam computed tomography (CBCT) during deep-inspiration breath-hold (DIBH) treatment of the left breast. MATERIAL AND METHODS: A visually guided optical respiratory monitoring system was used in DIBH treatment of 15 breast cancer patients. Heart position was determined in relation to the planning target volume (PTV) in 225 fractions in which daily low-dose CBCT images were compared with planning CT images. In addition, the position of the left lung apex and diaphragm was measured to evaluate the success of the DIBH. RESULTS: The median shift of the heart was 1 mm to the left, 1 mm superiorly and 0 mm in the anterior-posterior (AP) direction during the treatment course when compared to the PTV position in planning CT. Based on these movements, an AP margin of 4 mm, a lateral (LR) margin of 3 mm, and a superior-inferior (SI) margin of 5 mm should be added to the heart contour to ensure avoiding the heart when planning treatment. The distance between the left lung apex and diaphragm, applied as a surrogate for lung volume, was 2mm (median) smaller during the CBCT acquisitions than during the planning CT acquisition. The correlation coefficient between the surrogate of lung volume and the distance between the heart and PTV was r = .46 in the AP, r = .72 in the LR and r = .79 in the SI directions. CONCLUSION: Residual variation was observed in the position of the heart in comparison to PTV, even with a visually guided DIBH technique. These geometrical uncertainties should be taken into account when planning radiotherapy treatment. The success of DIBH may make a major contribution to the variation of the heart position during treatment.

Hypofractionated stereotactic body radiotherapy for localized prostate cancer - first Nordic clinical experience.

BACKGROUND: The use of hypofractionated stereotactic body radiotherapy (SBRT) as primary treatment modality in clinically localized prostate cancer (PCa) is emerging, because the low α/ß-ratio favors the use of high dose per fraction in PCa. There is a need for more data about SBRT, especially in high-risk PCa patients. The purpose of this retrospective study was to evaluate the safety and the short-term efficacy of robotic SBRT in a clinical patient cohort with localized PCa including also high-risk patients (D'Amico risk stratification). MATERIALS AND METHODS: A total of 240 consecutive patients with clinically localized PCa were treated primarily with SBRT to total doses of 35 Gy or 36.25 Gy in 5 fractions using a robotic SBRT device (CyberKnife®). All risk groups (D'Amico risk stratification) were represented as follows: 48 (22%), 59 (27%) and 111 (51%) of the patients representing low-, intermediate- and high-risk group, respectively. Data on acute and intermediate-term toxicities and early PSA responses were analyzed. RESULTS: Neither acute grade 3 or higher GU nor rectal toxicity was observed. Regardless of the fact that 29 (13.3%) patients experienced intermediate-term toxicity requiring diagnostic interventions, the rates of intermediate-term grade 3 GU, rectal and infectious toxicity were low, 1.8%, 0.9% and 1.4%, respectively. A biochemical relapse was observed in ten (4.6%) patients. With the median follow-up time of 23 months the biochemical relapse-free survival (bRFS) rate was 100%, 96.6% and 92.8% in low-, intermediate- and high-risk group, respectively. CONCLUSIONS: The toxicity of robotic SBRT in a large clinical cohort of PCa patients was tolerable and the early PSA response was good in all risk groups. The hypofractionated SBRT offers a possibility to high dose per fraction and to provide the whole radiotherapy treatment within two to three weeks.

Volumetric modulated arc therapy for synchronous bilateral whole breast irradiation - A case study.

PURPOSE: The treatment planning of bilateral breast irradiation (BBI) is a challenging task. The overlapping of tangential fields is usually unavoidable without compromising the target coverage. The purpose of this study was to investigate the technical feasibility and benefits of a single isocentre volumetric modulated arc therapy (VMAT) in BBI. METHODS AND MATERIALS: Two women with bilateral breast cancer were included in this case study. The first patient (Pat#1) underwent a bilateral breast-conserving surgery and sentinel lymph node biopsy. The second patient (Pat#2) underwent a bilateral ablation and axillary lymph node dissection. Planning target volumes (PTV) and organs at risk were delineated on CT images. VMAT plans were created with four (two for both sides, Pat#1) or two (one for each breast, Pat#2) separate VMAT fields. Subsequently, traditional tangential field plans were generated for each patient and the dosimetric parameters were compared. RESULTS: The treatment times of the patients with VMAT were less than 15 min with daily CBCT imaging. When compared to the standard tangential field technique, the VMAT plans improved the PTV dose coverage and dose homogeneity with improved sparing of lungs and heart. With traditional field arrangement, the overlapping of the tangential fields was inevitable without significantly compromising the target coverage, whereas with VMAT the hotspots were avoided. The patients were treated with the VMAT technique and no acute skin toxicity was observed with either of the patients. CONCLUSIONS: A single isocentre VMAT technique has been implemented clinically for BBI. With the VMAT techniques, the dose delivery was quick and the hotspots in the field overlapping areas were avoided. The PTV dose coverage was superior in VMAT plans when compared with conventional tangential technique plans.

Cascaded and Generalizable Neural Radiance Fields for Fast View Synthesis.

We present CG-NeRF, a cascade and generalizable neural radiance fields method for view synthesis. Recent generalizing view synthesis methods can render high-quality novel views using a set of nearby input views. However, the rendering speed is still slow due to the nature of uniformly-point sampling of neural radiance fields. Existing scene-specific methods can train and render novel views efficiently but can not generalize to unseen data. Our approach addresses the problems of fast and generalizing view synthesis by proposing two novel modules: a coarse radiance fields predictor and a convolutional-based neural renderer. This architecture infers consistent scene geometry based on the implicit neural fields and renders new views efficiently using a single GPU. We first train CG-NeRF on multiple 3D scenes of the DTU dataset, and the network can produce high-quality and accurate novel views on unseen real and synthetic data using only photometric losses. Moreover, our method can leverage a denser set of reference images of a single scene to produce accurate novel views without relying on additional explicit representations and still maintains the high-speed rendering of the pre-trained model. Experimental results show that CG-NeRF outperforms state-of-the-art generalizable neural rendering methods on various synthetic and real datasets.

Deep learning-based prediction of the dose-volume histograms for volumetric modulated arc therapy of left-sided breast cancer.

BACKGROUND: The advancements in artificial intelligence and computational power have made deep learning an attractive tool for radiotherapy treatment planning. Deep learning has the potential to significantly simplify the trial-and-error process involved in inverse planning required by modern treatment techniques such as volumetric modulated arc therapy (VMAT). In this study, we explore the ability of deep learning to predict organ-at-risk (OAR) dose-volume histograms (DVHs) of left-sided breast cancer patients undergoing VMAT treatment based solely on their anatomical characteristics. The predicted DVHs could be used to derive patient-specific dose constraints and dose objectives, streamlining the treatment planning process, standardizing the quality of the plans, and personalizing the treatment planning. PURPOSE: This study aimed to develop a deep learning-based framework for the prediction of organ-specific dose-volume histograms (DVH) based on structures delineated for left-sided breast cancer treatment. METHODS: We used a dataset of 249 left-sided breast cancer patients treated with tangential VMAT fields. We extracted delineated structures and dose distributions for each patient and derived slice-by-slice DVHs for planning target volume (PTV) and organs-at-risk. The patients were divided into training (70%, n = 174), validation (10%, n = 24), and test (20%, n = 51) sets. Collected data were used to train a deep learning model for the prediction of the DVHs based on the delineated structures. The developed deep learning model comprised a modified DenseNet architecture followed by a recurrent neural network. RESULTS: In the independent test set (n = 51), the point-wise differences in the slice-by-slice DVHs between the clinical and predicted DVHs were small; the mean squared errors were 3.53, 1.58, 2.28, 3.37, and 1.44 [×10-4] for PTV, heart, ipsilateral lung, contralateral lung, and contralateral breast, respectively. With the derived cumulative DVHs, the mean absolute difference ± standard deviation of mean doses between the clinical and the predicted DVH were 0.08 ± 0.04 Gy, 0.24 ± 0.22 Gy, 0.73 ± 0.46 Gy, 0.07 ± 0.06 Gy, and 0.14 ± 0.14 Gy for PTV, heart, ipsilateral lung, contralateral lung, and contralateral breast, respectively. CONCLUSIONS: The deep learning-based approach enabled automatic and reliable prediction of the DVH based on delineated structures. The predicted DVHs could potentially serve as patient-specific clinical goals used to aid treatment planning and avoid suboptimal plans or to derive optimization objectives and constraints for automated treatment planning.

Unbiased Scene Graph Generation via Two-Stage Causal Modeling.

Despite the impressive performance of recent unbiased Scene Graph Generation (SGG) methods, the current debiasing literature mainly focuses on the long-tailed distribution problem, whereas it overlooks another source of bias, i.e., semantic confusion, which makes the SGG model prone to yield false predictions for similar relationships. In this paper, we explore a debiasing procedure for the SGG task leveraging causal inference. Our central insight is that the Sparse Mechanism Shift (SMS) in causality allows independent intervention on multiple biases, thereby potentially preserving head category performance while pursuing the prediction of high-informative tail relationships. However, the noisy datasets lead to unobserved confounders for the SGG task, and thus the constructed causal models are always causal-insufficient to benefit from SMS. To remedy this, we propose Two-stage Causal Modeling (TsCM) for the SGG task, which takes the long-tailed distribution and semantic confusion as confounders to the Structural Causal Model (SCM) and then decouples the causal intervention into two stages. The first stage is causal representation learning, where we use a novel Population Loss (P-Loss) to intervene in the semantic confusion confounder. The second stage introduces the Adaptive Logit Adjustment (AL-Adjustment) to eliminate the long-tailed distribution confounder to complete causal calibration learning. These two stages are model agnostic and thus can be used in any SGG model that seeks unbiased predictions. Comprehensive experiments conducted on the popular SGG backbones and benchmarks show that our TsCM can achieve state-of-the-art performance in terms of mean recall rate. Furthermore, TsCM can maintain a higher recall rate than other debiasing methods, which indicates that our method can achieve a better tradeoff between head and tail relationships.

Deletion of the murine ortholog of human 9p21.3 locus promotes atherosclerosis by increasing macrophage proinflammatory activity.

Background: Several genome-wide association studies have reported a risk locus for coronary artery disease (CAD) in the 9p21. 3 chromosomal region. This region encodes a lncRNA in the INK4 locus (ANRIL) and its genetic variance has a strong association with CAD, but its mechanisms in atherogenesis remain unclear. Objectives: This study aimed to investigate the role of the murine ortholog of human 9p21.3 locus in atherogenesis in hypercholesterolemic mice. Methods: Murine 9p21.3 ortholog knockout mice (Chr4Δ70kb/Δ70kb ) were crossbred with Ldlr -/- ApoB 100/100 mice, and atherosclerotic plaque size and morphology were analyzed on a standard or a high-fat diet (HFD). The hematopoietic cell-specific effect of Chr4Δ70kb/Δ70kb on atherosclerotic plaque development was studied via bone marrow (BM) transplantation, where Chr4Δ70kb/Δ70kb or wild-type BM was transplanted into Ldlr -/- ApoB 100/100 mice. The role of Chr4Δ70kb/Δ70kb in macrophage M1/M2 polarization was studied. In addition, single-cell sequencing data from human and mouse atheroma were analyzed to show the expression profiles of ANRIL and its murine equivalent, Ak148321, in the plaques. Results: Both systemic and hematopoietic Chr4Δ70kb/Δ70kb increased atherosclerosis in Ldlr -/- ApoB 100/100 mice after 12 weeks of HFD. The systemic Chr4Δ70kb/Δ70kb also elevated the number of circulating leukocytes. Chr4Δ70kb/Δ70kb BMDMs showed enhanced M1 polarization in vitro. Single-cell sequencing data from human and mouse atheroma revealed that ANRIL and Ak148321 were mainly expressed in the immune cells. Conclusion: These data demonstrate that both systemic and BM-specific deletion of the murine 9p21.3 risk locus ortholog promotes atherosclerosis and regulates macrophage pro-inflammatory activity, suggesting the inflammation-driven mechanisms of the risk locus on atherogenesis.

The robustness of VMAT radiotherapy for breast cancer with tissue deformations.

To investigate the near-surface doses and target coverage in modulated arc radiotherapy (RT) of the breast or chest wall in two treatment planning systems (TPS) in the presence of soft tissue deformations. This retrospective study consisted of 10 breast cancer patients with axillary lymph node inclusion. For each case, five RT plans were created: (1) tangential 3D conformal field-in-field (FinF) technique; (2) 200° to 240° arcs with optimization bolus (OB) in Eclipse (EB); (3) 243° to 250° arcs with an 8-mm OB in Monaco (MB); (4) 243° to 250° arcs with automatic skin flash tool (ASF) in Monaco TPS (MA); (5) 243° to 250° arcs with both ASF and OB in Monaco (MAB). Soft tissue deformation was simulated by editing CT-images with 4-, 8-, and 12-mm swelling and recalculating the dose. The increasing swelling from 0 to 12 mm caused the coverage (V95%) in clinical target volume to decrease from 96% ± 2% to 90% ± 6% for the FinF plans. For volumetric-modulated arc therapy (VMAT), the coverage decreased from 99% ± 1% to 92% ± 4% in the EB plans, and from 97% ± 1% to 68% ± 8%, 85% ± 6%, and 86% ± 5% for MA, MB, and MAB, respectively. The mean dose in the surface extending from 0 to 3 mm from the skin decreased on average 5%, 17%, 20%, 15%, and 8% in FinF, EB, MA, MB, and MAB, respectively. In the Monaco plans, the use of an OB(+ASF) provided better target coverage and lower dose maxima despite of tissue swelling than the ASF alone. With modulated arc therapy, we recommend the use of an OB instead of or in addition to the ASF. The use of 8 mm OB with VMAT plans is robust to account deformations extending outside up to 8mm. If soft tissue deformation is larger than 8 mm, the need for replanning should be evaluated.

Stereotactic body radiotherapy for localized prostate cancer - 5-year efficacy results.

BACKGROUND: The use of stereotactic body radiotherapy (SBRT) as the primary treatment modality in clinically localized prostate cancer (PCa) is emerging. The aim of the study was to analyze the long-term results of PCa patients treated with SBRT. METHODS: This non-selected, real-life patient cohort included 213 patients with localized PCa treated with a robotic SBRT device during 2012-2015. RESULTS: The median follow-up was 64 months (range, 10-85 months), and all risk-groups were represented as 47 (22.1%), 56 (26.3%) and 110 (51.6%) patients were classified into D'Amico risk stratification of low, intermediate and high-risk groups, respectively. Androgen deprivation therapy (ADT) was administered to 64.3% of the patients. At cut-off, the biochemical relapse-free survival (bRFS) was 100, 87.5 and 80.0% for patients at low, intermediate and high-risk (p = 0.004), and 92.5, 84.2 and 66.7% for patients with Gleason score ≤ 6, 7 and ≥ 8, respectively (p = 0.001). The actuarial 5-year overall survival (OS) rates were 97.9, 96.4 and 88.6% in the low, intermediate and high-risk groups, respectively, and at the cut-off, the disease-specific survival (DSS) rate of the whole cohort was high (99.1%), as only two high-risk patients died due to PCa. CONCLUSION: Our present results of SBRT delivered with CyberKnife produced excellent long-term bRFS, OS and DSS outcomes among patients with localized PCa. We conclude that SBRT provides an efficient and convenient treatment option for patients with localized PCa, irrespective of the risk-group.

A Deep Learning-Based Automated CT Segmentation of Prostate Cancer Anatomy for Radiation Therapy Planning-A Retrospective Multicenter Study.

A commercial deep learning (DL)-based automated segmentation tool (AST) for computed tomography (CT) is evaluated for accuracy and efficiency gain within prostate cancer patients. Thirty patients from six clinics were reviewed with manual- (MC), automated- (AC) and automated and edited (AEC) contouring methods. In the AEC group, created contours (prostate, seminal vesicles, bladder, rectum, femoral heads and penile bulb) were edited, whereas the MC group included empty datasets for MC. In one clinic, lymph node CTV delineations were evaluated for interobserver variability. Compared to MC, the mean time saved using the AST was 12 min for the whole data set (46%) and 12 min for the lymph node CTV (60%), respectively. The delineation consistency between MC and AEC groups according to the Dice similarity coefficient (DSC) improved from 0.78 to 0.94 for the whole data set and from 0.76 to 0.91 for the lymph nodes. The mean DSCs between MC and AC for all six clinics were 0.82 for prostate, 0.72 for seminal vesicles, 0.93 for bladder, 0.84 for rectum, 0.69 for femoral heads and 0.51 for penile bulb. This study proves that using a general DL-based AST for CT images saves time and improves consistency.

3D Multi-Resolution Optical Flow Analysis of Cardiovascular Pulse Propagation in Human Brain.

The brain is cleaned from waste by glymphatic clearance serving a similar purpose as the lymphatic system in the rest of the body. Impairment of the glymphatic brain clearance precedes protein accumulation and reduced cognitive function in Alzheimer's disease (AD). Cardiovascular pulsations are a primary driving force of the glymphatic brain clearance. We developed a method to quantify cardiovascular pulse propagation in the human brain with magnetic resonance encephalography (MREG). We extended a standard optical flow estimation method to three spatial dimensions, with a multi-resolution processing scheme. We added application-specific criteria for discarding inaccurate results. With the proposed method, it is now possible to estimate the propagation of cardiovascular pulse wavefronts from the whole brain MREG data sampled at 10 Hz. The results show that on average the cardiovascular pulse propagates from major arteries via cerebral spinal fluid spaces into all tissue compartments in the brain. We present an example, that cardiovascular pulsations are significantly altered in AD: coefficient of variation and sample entropy of the pulse propagation speed in the lateral ventricles change in AD. These changes are in line with the theory of glymphatic clearance impairment in AD. The proposed non-invasive method can assess a performance indicator related to the glymphatic clearance in the human brain.

Dosimetric evaluation of modern radiation therapy techniques for left breast in deep-inspiration breath-hold.

PURPOSE: The dosimetric differences between four radiation therapy techniques for left sided whole breast irradiation were evaluated side by side in the same patient population. METHODS: Radiotherapy treatment plans were retrospectively created with Accuray TomoDirect (TD), Elekta Volumetric Modulated Arc Therapy (E-VMAT), Varian RapidArc (RA) and Field-in-field (FinF) technique for 20 patients, who had received left breast irradiation during deep-inspiration breath-hold. Dose characteristics of planning target volume and organs at risk were compared. RESULTS: The E-VMAT, TD and RA treatment plans had higher target coverage (V95%) than FinF plans (97.7-98.3% vs. 96.6%). The low-dose spillage to contralateral breast and lung was smaller with FinF and TD (mean 0.1 and 0.3 Gy) compared to E-VMAT and RA (mean 0.6 and 0.9 Gy). E-VMAT, RA and TD techniques were more effective than FinF in sparing left anterior descending artery (mean 4.0, 4.2 and 4.7 Gy vs. 6.1 Gy, respectively). CONCLUSIONS: In whole breast irradiation TD, E-VMAT and RA plans generated in this study achieved higher dose coverage and sparing of organs from the high dose in the vicinity of the PTV. The advantage of calculated FinF plans is the lowest dose on contralateral organs. The choice of the technique used should be weighted by each institution taking into account the dose characteristics of each technique and its fit with patient anatomy bearing in mind the increased workload of using modulated techniques and the increased beam on time.

A photographic technique for quick assessment of mechanical isocenter of a linear accelerator.

Demands for mechanical accuracy of medical linear accelerators are increased due to the stereotactic and modulated rotational treatments. Mechanical inaccuracies affect the size and shape of the mechanical and radiation isocenters. In practice, the mechanical isocenter is defined by the intersection of rotational axes. However, there are no simple tools to check the properties of the mechanical isocenter in 3D. We introduce a new photography-based method for quick and sub-millimeter accurate determination of the mechanical isocenter. The method is based on image-processing algorithm and modified front pointer. The results demonstrate the quick measurement and visualization of the mechanical isocenter.

Simulations of localized harmonic motions on a blood vessel wall induced by an acoustic radiation force used in ultrasound elastography.

Many noninvasive techniques have been developed recently to explore the mechanical properties of soft tissue. In this paper, dynamic acoustic radiation force induced vibrations on a blood vessel wall were simulated using different stimulation frequencies and stiffness parameters for the vessel wall. The stimulation frequency was varied between 20 Hz and 20 kHz and the stiffness parameter (Young's modulus) was varied between 60 kPa and 360 kPa. The vibration simulations were computed using a finite-element method in a 3D geometry that contained a vessel wall surrounded by soft tissue. The results indicate that vibrations caused by acoustic stimulation are sensitive to the changes in mechanical properties of the vessel wall and that the vibrations are highly dependent on the stimulation frequency and target structure. Therefore, measurements of absolute stiffness parameters may not be accurately achieved because this method is so dependent on the whole target structure, whereas the monitoring of changes during some process may be feasible.

A new convexity measure based on a probabilistic interpretation of images.

In this paper, we present a novel convexity measure for object shape analysis. The proposed method is based on the idea of generating pairs of points from a set and measuring the probability that a point dividing the corresponding line segments belongs to the same set. The measure is directly applicable to image functions representing shapes and also to gray-scale images which approximate image binarizations. The approach introduced gives rise to a variety of convexity measures which make it possible to obtain more information about the object shape. The proposed measure turns out to be easy to implement using the Fast Fourier Transform and we will consider this in detail. Finally, we illustrate the behavior of our measure in different situations and compare it to other similar ones.

Flattening filter free technique in breath-hold treatments of left-sided breast cancer: The effect on beam-on time and dose distributions.

BACKGROUND AND PURPOSE: The use of flattening filter free (FFF) beams has potential to speed up deep-inspiration breath-hold treatments. In this study, the beam-on time and dose characteristics of left-sided breast treatment plans with FFF and flattened beams were evaluated. MATERIAL AND METHODS: Twelve plans were generated for 20 patients. The techniques utilized were volumetric modulated arc therapy with two limited tangential arcs (tVMAT) and tangential intensity modulated radiotherapy with dynamic (dIMRT) as well as step-and-shoot (FinF) dose delivery. Each technique was planned with FFF and flattened beams with 6 and 10MV photons. All plans were irradiated and the beam-on times were measured. Dose characteristics of planning target volume (PTV) and organs at risk (OAR) were evaluated. RESULTS: The mean beam-on times were reduced by 18-39% using FFF. Mean PTV dose coverage was least reduced with tVMAT (0.6-0.8%) compared to dIMRT (4%) and FinF (5.6-9.1%), when FFF beams were used instead of flattened beams. Only small differences were observed in OAR doses between equivalent plans (FFF vs. flattened). CONCLUSIONS: A significant reduction was observed in beam-on time when utilizing FFF beams with tVMAT, dIMRT and FinF. tVMAT was the only technique for which the use of FFF did not degrade the treatment plan dose distributions.

Affine invariant pattern recognition using Multiscale Autoconvolution.

This paper presents a new affine invariant image transform called Multiscale Autoconvolution (MSA). The proposed transform is based on a probabilistic interpretation of the image function. The method is directly applicable to isolated objects and does not require extraction of boundaries or interest points, and the computational load is significantly reduced using the Fast Fourier Transform. The transform values can be used as descriptors for affine invariant pattern classification and, in this article, we illustrate their performance in various object classification tasks. As shown by a comparison with other affine invariant techniques, the new method appears to be suitable for problems where image distortions can be approximated with affine transformations.