The document summarizes Giovanni Fattori's PhD thesis defense presentation on image-guided management of uncertainties in particle therapy. The presentation covers optical tracking and X-ray imaging used at CNAO, development and clinical implementation of these systems, and their impact on geometrical accuracy and dosimetry. It also discusses using optical tracking to monitor real-time motion of moving targets and experimental 4D dosimetry studies. Simulation results are presented showing the dosimetric impact of setup errors can be quantified through dose-volume histograms and indices.
Whitepaper: Image Quality Impact of SmartGrid Processing in Bedside Chest Ima...Carestream
Scattered radiation is known to degrade image quality in
diagnostic X-ray imaging. A new image processing tool, SmartGrid, has been developed that compensates for the effects of X-ray scatter in an image, and produces results comparable to those of a physical antiscatter grid. Read the white paper to learn more.
Smart Noise Cancellation Processing: New Level of Clarity in Digital RadiographyCarestream
Smart Noise Cancellation significantly reduces noise in diagnostic images while retaining fine spatial detail –there is no degradation of anatomical sharpness. When SNC is applied, it produces images that are significantly clearer than with standard processing. It also provides better contrast-to-noise ratio for images acquired at a broad range of exposures.
A COMPARATIVE STUDY ALGORITHM FOR NOISY IMAGE RESTORATION IN THE FIELD OF MED...ijait
This paper presents the performance analysis of different basic techniques used for the image restoration.
Restoration is a process by removing blur and noise from image and get back the original form. Medical
images play a vital role in dealing with the detection of various diseases in patients and they face the
problem of salt and pepper noise and Gausian noise. Hence restoration is performed based on different
image restoration techniques. In this paper, popular restoration techniques is applied and analyzed in the
recovery of medical images,.
Instant fracture detection using ir-raysijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Whitepaper: Image Quality Impact of SmartGrid Processing in Bedside Chest Ima...Carestream
Scattered radiation is known to degrade image quality in
diagnostic X-ray imaging. A new image processing tool, SmartGrid, has been developed that compensates for the effects of X-ray scatter in an image, and produces results comparable to those of a physical antiscatter grid. Read the white paper to learn more.
Smart Noise Cancellation Processing: New Level of Clarity in Digital RadiographyCarestream
Smart Noise Cancellation significantly reduces noise in diagnostic images while retaining fine spatial detail –there is no degradation of anatomical sharpness. When SNC is applied, it produces images that are significantly clearer than with standard processing. It also provides better contrast-to-noise ratio for images acquired at a broad range of exposures.
A COMPARATIVE STUDY ALGORITHM FOR NOISY IMAGE RESTORATION IN THE FIELD OF MED...ijait
This paper presents the performance analysis of different basic techniques used for the image restoration.
Restoration is a process by removing blur and noise from image and get back the original form. Medical
images play a vital role in dealing with the detection of various diseases in patients and they face the
problem of salt and pepper noise and Gausian noise. Hence restoration is performed based on different
image restoration techniques. In this paper, popular restoration techniques is applied and analyzed in the
recovery of medical images,.
Instant fracture detection using ir-raysijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Medical Images are regularly of low contrast and boisterous/Noisy (absence of clarity) because of
the circumstances they are being taken. De-noising these pictures is a troublesome undertaking as they
ought to exclude any antiquities or obscuring of edges in the pictures. The Bayesian shrinkage strategy has
been chosen for thresholding in light of its sub band reliance property. The spatial space and Wavelet
based de-noising systems utilizing delicate thresholding strategy are contrasted and the proposed technique
utilizing GA (Genetic Algorithm) is used. The GA procedure is proposed in view of PSNR and results are
contrasted and existing spatial space and wavelet based de-noising separating strategies. The proposed
calculation gives improved visual clarity to diagnosing the restorative pictures. The proposed strategy in
view of GA surveys the better execution on the premise of the quantitative metric i.e PSNR (Peak Signal
to Noise-Ratio) and visual impacts. Reenactment results demonstrate that the GA based proposed
technique beats the current de-noising separating strategies.
An Innovative Use of X-ray Computed Tomography in Composite Impact Damage Cha...Fabien Léonard
This study presents how X-ray computed tomography (CT) can be employed to obtain a more complete 3-dimensional description of damage in carbon fibre reinforced polymer (CFRP) composites. Impact damage was produced with energy ranging from 5 J to 20 J on coupon size (89 mm x 55 mm) composite laminates aimed for primary structures in aerospace applications. CT has been employed to characterise in 3D, non-destructively the impact damage generated. An innovative data processing methodology has been developed to obtain a better description of the complex damage structure. This data processing provides the through-thickness damage distribution of the full laminate and allows the individual ply-by-ply damage to be visualised and assessed.
An adaptive threshold segmentation for detection of nuclei in cervical cells ...csandit
PAP smear test is the most efficient and easy procedure to detect any abnormality in cervical
cells. It becomes difficult for the cytologist to analyse a large set of PAP smear test images
when there is a rapid increase in the incidence of cervical cancer. On the replacement, image
analysis could swap manual interpretation. This paper proposes a method for the detection of
cervical cells in pap smear images using wavelet based thresholding. First, Wiener filter is used
for smoothing to suppress the noise and to improve the contrast of the image. Second, optimal
threshold is been obtained for segmenting the cell by various Wavelet shrinkage techniques like
VisuShrink, BayesShrink and SureShrink thresholding which segment the foreground from the
background and detect cell component like nucleus from the clustered cell images. From the
results, it is proved that the performance of the adaptive Wiener filter with combination of
SureShrink thresholding performs better in terms of threshold values and Mean Squared Error
than the other comparative methods. The succeeding research work can be carried out based on
the size of the segmented nucleus which therefore helps in differentiating abnormality among
the cells.
Image De-noising on Strip Steel Surface Defect Using Improved Compressive Sen...TELKOMNIKA JOURNAL
De-noising for the strip steel surface defect image is conductive to the accurate detection of the strip steel
surface defects. In order to filter the Gaussian noise and salt and pepper noise of strip steel surface defect
images, an improved compressive sensing algorithm was applied to defect image de-noising in this paper.
First, the improved Regularized Orthogonal Matching Pursuit algorithm was described. Then, three typical
surface defects (scratch, scar, surface upwarping) images were selected as the experimental samples. Last,
detailed experimental tests were carried out to the strip steel surface defect image de-noising. Through
comparison and analysis of the test results, the Peak Signal to Noise Ratio value of the proposed algorithm
is higher compared with other traditional de-noising algorithm, and the running time of the proposed algorithm
is only26.6% of that of traditional Orthogonal Matching Pursuit algorithms. Therefore, it has better de-noising
effect and can meet the requirements of real-time image processing.
Adaptive Noise Reduction Scheme for Salt and Peppersipij
In this paper, a new adaptive noise reduction scheme for images corrupted by impulse noise is presented. The proposed scheme efficiently identifies and reduces salt and pepper noise. MAG (Mean Absolute Gradient) is used to identify pixels which are most likely corrupted by salt and pepper noise that are candidates for further median based noise reduction processing. Directional filtering is then applied after noise reduction to achieve a good tradeoff between detail preservation and noise removal. The proposed scheme can remove salt and pepper noise with noise density as high as 90% and produce better result in terms of qualitative and quantitative measures of images.
COMPUTING THE GROWTH RATE OF STEM CELLS USING DIGITAL IMAGE PROCESSING Pratyusha Mahavadi
The aim is to compute the growth rate of stem cells by using segmentation, feature extraction and pattern recognition which are the fundamental methods of digital image processing. DRLSE algorithm is applied for segmenting images. The DRLSE algorithm is an amalgamation of Canny Edge Detector algorithm and DRLSE method, which uses the four well potential function. Features are extracted from segmented images using GLCM method and finally Support Vector Machine (SVM) is used for pattern recognition and classification of stem cells.
Image De-noising and Enhancement for Salt and Pepper Noise using Genetic Algo...IDES Editor
Image Enhancement through De-noising is one of
the most important applications of Digital Image Processing
and is still a challenging problem. Images are often received
in defective conditions due to usage of Poor image sensors,
poor data acquisition process and transmission errors etc.,
which creates problems for the subsequent process to
understand such images. The proposed Genetic filter is capable
of removing noise while preserving the fine details, as well as
structural image content. It can be divided into: (i) de-noising
filtering, and (ii) enhancement filtering. Image Denoising
and enhancement are essential part of any image processing
system, whether the processed information is utilized for visual
interpretation or for automatic analysis. The Experimental
results performed on a set of standard test images for a wide
range of noise corruption levels shows that the proposed filter
outperforms standard procedures for salt and pepper removal
both visually and in terms of performance measures such as
PSNR.Genetic algorithms will definitely helpful in solving
various complex image processing tasks in the future.
Medical Images are regularly of low contrast and boisterous/Noisy (absence of clarity) because of
the circumstances they are being taken. De-noising these pictures is a troublesome undertaking as they
ought to exclude any antiquities or obscuring of edges in the pictures. The Bayesian shrinkage strategy has
been chosen for thresholding in light of its sub band reliance property. The spatial space and Wavelet
based de-noising systems utilizing delicate thresholding strategy are contrasted and the proposed technique
utilizing GA (Genetic Algorithm) is used. The GA procedure is proposed in view of PSNR and results are
contrasted and existing spatial space and wavelet based de-noising separating strategies. The proposed
calculation gives improved visual clarity to diagnosing the restorative pictures. The proposed strategy in
view of GA surveys the better execution on the premise of the quantitative metric i.e PSNR (Peak Signal
to Noise-Ratio) and visual impacts. Reenactment results demonstrate that the GA based proposed
technique beats the current de-noising separating strategies.
An Innovative Use of X-ray Computed Tomography in Composite Impact Damage Cha...Fabien Léonard
This study presents how X-ray computed tomography (CT) can be employed to obtain a more complete 3-dimensional description of damage in carbon fibre reinforced polymer (CFRP) composites. Impact damage was produced with energy ranging from 5 J to 20 J on coupon size (89 mm x 55 mm) composite laminates aimed for primary structures in aerospace applications. CT has been employed to characterise in 3D, non-destructively the impact damage generated. An innovative data processing methodology has been developed to obtain a better description of the complex damage structure. This data processing provides the through-thickness damage distribution of the full laminate and allows the individual ply-by-ply damage to be visualised and assessed.
An adaptive threshold segmentation for detection of nuclei in cervical cells ...csandit
PAP smear test is the most efficient and easy procedure to detect any abnormality in cervical
cells. It becomes difficult for the cytologist to analyse a large set of PAP smear test images
when there is a rapid increase in the incidence of cervical cancer. On the replacement, image
analysis could swap manual interpretation. This paper proposes a method for the detection of
cervical cells in pap smear images using wavelet based thresholding. First, Wiener filter is used
for smoothing to suppress the noise and to improve the contrast of the image. Second, optimal
threshold is been obtained for segmenting the cell by various Wavelet shrinkage techniques like
VisuShrink, BayesShrink and SureShrink thresholding which segment the foreground from the
background and detect cell component like nucleus from the clustered cell images. From the
results, it is proved that the performance of the adaptive Wiener filter with combination of
SureShrink thresholding performs better in terms of threshold values and Mean Squared Error
than the other comparative methods. The succeeding research work can be carried out based on
the size of the segmented nucleus which therefore helps in differentiating abnormality among
the cells.
Image De-noising on Strip Steel Surface Defect Using Improved Compressive Sen...TELKOMNIKA JOURNAL
De-noising for the strip steel surface defect image is conductive to the accurate detection of the strip steel
surface defects. In order to filter the Gaussian noise and salt and pepper noise of strip steel surface defect
images, an improved compressive sensing algorithm was applied to defect image de-noising in this paper.
First, the improved Regularized Orthogonal Matching Pursuit algorithm was described. Then, three typical
surface defects (scratch, scar, surface upwarping) images were selected as the experimental samples. Last,
detailed experimental tests were carried out to the strip steel surface defect image de-noising. Through
comparison and analysis of the test results, the Peak Signal to Noise Ratio value of the proposed algorithm
is higher compared with other traditional de-noising algorithm, and the running time of the proposed algorithm
is only26.6% of that of traditional Orthogonal Matching Pursuit algorithms. Therefore, it has better de-noising
effect and can meet the requirements of real-time image processing.
Adaptive Noise Reduction Scheme for Salt and Peppersipij
In this paper, a new adaptive noise reduction scheme for images corrupted by impulse noise is presented. The proposed scheme efficiently identifies and reduces salt and pepper noise. MAG (Mean Absolute Gradient) is used to identify pixels which are most likely corrupted by salt and pepper noise that are candidates for further median based noise reduction processing. Directional filtering is then applied after noise reduction to achieve a good tradeoff between detail preservation and noise removal. The proposed scheme can remove salt and pepper noise with noise density as high as 90% and produce better result in terms of qualitative and quantitative measures of images.
COMPUTING THE GROWTH RATE OF STEM CELLS USING DIGITAL IMAGE PROCESSING Pratyusha Mahavadi
The aim is to compute the growth rate of stem cells by using segmentation, feature extraction and pattern recognition which are the fundamental methods of digital image processing. DRLSE algorithm is applied for segmenting images. The DRLSE algorithm is an amalgamation of Canny Edge Detector algorithm and DRLSE method, which uses the four well potential function. Features are extracted from segmented images using GLCM method and finally Support Vector Machine (SVM) is used for pattern recognition and classification of stem cells.
Image De-noising and Enhancement for Salt and Pepper Noise using Genetic Algo...IDES Editor
Image Enhancement through De-noising is one of
the most important applications of Digital Image Processing
and is still a challenging problem. Images are often received
in defective conditions due to usage of Poor image sensors,
poor data acquisition process and transmission errors etc.,
which creates problems for the subsequent process to
understand such images. The proposed Genetic filter is capable
of removing noise while preserving the fine details, as well as
structural image content. It can be divided into: (i) de-noising
filtering, and (ii) enhancement filtering. Image Denoising
and enhancement are essential part of any image processing
system, whether the processed information is utilized for visual
interpretation or for automatic analysis. The Experimental
results performed on a set of standard test images for a wide
range of noise corruption levels shows that the proposed filter
outperforms standard procedures for salt and pepper removal
both visually and in terms of performance measures such as
PSNR.Genetic algorithms will definitely helpful in solving
various complex image processing tasks in the future.
A summary of recent innovations in radiation oncology focussing on the priniciples of different techniques and their application. An overview of clinical results has also been given
Digital Tomosynthesis: Theory of OperationCarestream
Digital Tomosynthesis (DT) is a new radiographic imaging technique that is revived from the nearly century-old traditional film-screen tomography. This rejuvenation is all made possible by the recent advances in high frame-rate, high-sensitivity flat-panel digital radiographic detector, rapid pulsed-exposure sequence-capable high-frequency x-ray generator, the widely available and low-cost computer GPU processing power, and the precision motion controls built in the digital radiography system hardware. Read the white paper.
Quantitative Image Analysis for Cancer Diagnosis and Radiation TherapyWookjin Choi
1.Lung Cancer Screening
1.1.Deep learning (feasible but not interpretable)
1.2.Radiomics (concise model)
1.3.Spiculation quantification (interpretable feature)
2.PET/CT Tumor Response
2.1.Aggressive Lung ADC subtype prediction (helpful for surgeons)
2.2.Pathologic response prediction (accurate but not concise)
2.3.Local tumor morphological changes (accurate and interpretable)
Contourlet Transform Based Method For Medical Image DenoisingCSCJournals
Noise is an important factor of the medical image quality, because the high noise of medical imaging will not give us the useful information of the medical diagnosis. Basically, medical diagnosis is based on normal or abnormal information provided diagnose conclusion. In this paper, we proposed a denoising algorithm based on Contourlet transform for medical images. Contourlet transform is an extension of the wavelet transform in two dimensions using the multiscale and directional filter banks. The Contourlet transform has the advantages of multiscale and time-frequency-localization properties of wavelets, but also provides a high degree of directionality. For verifying the denoising performance of the Contourlet transform, two kinds of noise are added into our samples; Gaussian noise and speckle noise. Soft thresholding value for the Contourlet coefficients of noisy image is computed. Finally, the experimental results of proposed algorithm are compared with the results of wavelet transform. We found that the proposed algorithm has achieved acceptable results compared with those achieved by wavelet transform.
A Study of Total-Variation Based Noise-Reduction Algorithms For Low-Dose Cone...CSCJournals
In low-dose cone-beam computed tomography, the reconstructed image is contaminated with
excessive quantum noise. In this work, we examined the performance of two popular noisereduction
algorithms—total-variation based on the split Bregman (TVSB) and total-variation based
on Nesterov’s method (TVN)—on noisy imaging data from a computer-simulated Shepp–Logan
phantom, a physical CATPHAN phantom and head-and-neck patient. Up to 15% Gaussian noise
was added to the Shepp–Logan phantom. The CATPHAN phantom was scanned by a Varian OBI
system with scanning parameters 100 kVp, 4 ms, and 20 mA. Images from the head-and-neck
patient were generated by the same scanner, but with a 20-ms pulse time. The 4-ms low-dose
image of the head-and-neck patient was simulated by adding Poisson noise to the 20-ms image.
The performance of these two algorithms was quantitatively compared by computing the peak
signal-to-noise ratio (PSNR), contrast-to-noise ratio (CNR) and the total computational time. For
CATPHAN, PSNR improved by 2.3 dB and 3.1 dB with respect to the low-dose noisy image for the
TVSB and TVN based methods, respectively. The maximum enhancement ratio of CNR for
CATPHAN was 4.6 and 4.8 for TVSB and TVN respectively. For data for head-and-neck patient,
the PSNR improvement was 2.7 dB and 3.4 dB for TVSB and TVN respectively. Convergence
speed for the TVSB-based method was comparatively slower than TVN method. We conclude that
TVN algorithm has more desirable properties than TVSB for image denoising.
this slide sharer contents are basic principle of CT fluoroscopy , software and hardware parts of equipment and image aqua cation and radiation dose comparison and videos related to equipment .
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Ve...kevinkariuki227
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
HOT NEW PRODUCT! BIG SALES FAST SHIPPING NOW FROM CHINA!! EU KU DB BK substit...GL Anaacs
Contact us if you are interested:
Email / Skype : kefaya1771@gmail.com
Threema: PXHY5PDH
New BATCH Ku !!! MUCH IN DEMAND FAST SALE EVERY BATCH HAPPY GOOD EFFECT BIG BATCH !
Contact me on Threema or skype to start big business!!
Hot-sale products:
NEW HOT EUTYLONE WHITE CRYSTAL!!
5cl-adba precursor (semi finished )
5cl-adba raw materials
ADBB precursor (semi finished )
ADBB raw materials
APVP powder
5fadb/4f-adb
Jwh018 / Jwh210
Eutylone crystal
Protonitazene (hydrochloride) CAS: 119276-01-6
Flubrotizolam CAS: 57801-95-3
Metonitazene CAS: 14680-51-4
Payment terms: Western Union,MoneyGram,Bitcoin or USDT.
Deliver Time: Usually 7-15days
Shipping method: FedEx, TNT, DHL,UPS etc.Our deliveries are 100% safe, fast, reliable and discreet.
Samples will be sent for your evaluation!If you are interested in, please contact me, let's talk details.
We specializes in exporting high quality Research chemical, medical intermediate, Pharmaceutical chemicals and so on. Products are exported to USA, Canada, France, Korea, Japan,Russia, Southeast Asia and other countries.
Knee anatomy and clinical tests 2024.pdfvimalpl1234
This includes all relevant anatomy and clinical tests compiled from standard textbooks, Campbell,netter etc..It is comprehensive and best suited for orthopaedicians and orthopaedic residents.
MANAGEMENT OF ATRIOVENTRICULAR CONDUCTION BLOCK.pdfJim Jacob Roy
Cardiac conduction defects can occur due to various causes.
Atrioventricular conduction blocks ( AV blocks ) are classified into 3 types.
This document describes the acute management of AV block.
Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
NVBDCP.pptx Nation vector borne disease control programSapna Thakur
NVBDCP was launched in 2003-2004 . Vector-Borne Disease: Disease that results from an infection transmitted to humans and other animals by blood-feeding arthropods, such as mosquitoes, ticks, and fleas. Examples of vector-borne diseases include Dengue fever, West Nile Virus, Lyme disease, and malaria.
New Drug Discovery and Development .....NEHA GUPTA
The "New Drug Discovery and Development" process involves the identification, design, testing, and manufacturing of novel pharmaceutical compounds with the aim of introducing new and improved treatments for various medical conditions. This comprehensive endeavor encompasses various stages, including target identification, preclinical studies, clinical trials, regulatory approval, and post-market surveillance. It involves multidisciplinary collaboration among scientists, researchers, clinicians, regulatory experts, and pharmaceutical companies to bring innovative therapies to market and address unmet medical needs.
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
Dehradun #ℂall #gIRLS Oyo Hotel 9719300533 #ℂall #gIRL in Dehradun
Image-guided management of uncertainties in scanned particle therapy
1. Image-guided management of uncertainties
in scanned particle therapy
13 March 2014
Giovanni Fattori
Department of Electronics, Information and Bioengineering
Politecnico di Milano
-- PhD Thesis Defense --
2. Presentation outline
Image-guided particle therapy @ CNAO
Optical tracking and X-ray imaging
Development and clinical implementation
Geometrical accuracy
Dosimetrical aspects
Treatment of moving targets
Optical tracking for real time motion monitoring
4D dosimetry (experimental studies)
2 / 35
3. Particle radiotherapy
1. PHYSICS
Favorable tissue depth-dose distribution
2. RELATIVE BIOLOGICAL EFFECTIVENESS
Microscopic spatial energy distribution
DOSE DELIVERY
Active scanning
Kramer et al. [2012 J. Phys]
Kramer et al. [2010 Eur. Phys. J. D ]
Durante et al. [2009 Nat Rev Clin Oncol]
3 / 35
RBE =
Dphoton
Dion iso
4. Uncertainties in therapy
Challenge
Development of technologies for therapy to manage treatment
uncertainties
Setup errors
Moving targets
Critical issues
Particle sensitivity to tissue density
variation
Organ motion
• Inter-fractional
• Intra-fractional
Treatment of moving targets
Questionable cost-benefit ratio
4 / 35
5. IGRT: the case of CNAO
In-room imaging
6 DOF treatment couch (PPS)
Solutions for real time monitoring
OTS OPTICAL TRACKING SYSTEM
• Non-invasive
• Real time patient monitoring
PVS PATIENT VERIFICATION SYSTEM
• Schaer-engineering: isocentric double
projection
• Custom system for robotic imaging:
Radiograph and Cone Beam CT
TREATMENT ROOM 1-3 TREATMENT ROOM 2
TREATMENT
SETUP
PPS
OTS
PVS
NOMINAL
POSITION
5 / 35
6. Optical tracking
Point-based patient registration and motion
monitoring
SMART, BTS Bioengineering
3 free-standing infrared TVC cameras
15 min calibration procedure
• Accuracy = 0.3 mm in 1 m3 volume
• Frequency: 70/100 Hz
PATIENT MODEL OPTICAL DATA
6 / 35
What is needed: nominal point-based geometry from planning CT images
Sub millimeter scale accuracy (1-3 mm slice thickness in CT)
Manual segmentation
Low 3D accuracy
Inter-operator variability
Automatic fiducials localization in CT images
7. Automatic fiducials localization in CT images
SURFACE
EXTRACTION
SURFACE
PROCESSING
MARKER
RECOGNITION
Geometric filters
1. 10 mm < diag < 22 mm
2. Hausdorff < 20 mm2
3. n° triangles < 650
4. Side difference < 5 mm
CANDIDATE SURFACE
Geometrical prior knowledge: aluminum spheres. (1cm diameter, 1800 HU)
Fattori et al. [2012 IEEE TBME]
7 / 35
8. Automatic fiducials localization in CT images
LOCALIZATION ACCURACY
3 mm 1 mm
I 0.1640 0.2113
II 0.2374 0.0322
II
I
0.1414 0.2054
LOCALIZATION ROBUSTNESS
Accuracy: 20 μm
N° patients CT resolution
Fiducials per
patient
N° of fiducials Fiducials found
10 head
25 thorax
1.27x1.27x3 mm
6/8 233 215
3 head 0 0 0
92.3%
LEICA LTD 500
** CLINICAL USE SINCE 2011 **
Fattori et al. [2012 IEEE TBME]
High true positive ratio
No false positive
High accuracy
3D error [mm]
8 / 35
9. In-room imaging system for CNAO central room
Specifications:
Limited operating space (Horizontal & Vertical beam lines)
X-ray radiographs and Cone Beam CT
Registration performance comparable with lateral rooms
Geometric residual error < 1mm / 1
Patient setup procedure < 2 min
Integration with existing technologies (PPS, PACS)
Project leader:
Image registration:
Robot & Safety:
Software:
G. Baroni
M. Riboldi
G. Fattori
M. Peroni
P. Cerveri
A. Pella
G. Fattori
G. Fattori
M. Riboldi
Treatment Isocenter
Imaging Isocenter
9 / 35
2 YEARS PROJECT
1st year: 2D-3D
2nd year: 3D-3D
10. Hardware components 10 / 35
Custom C-arm
SID=1772.2 mm;
SAD=1272.2mm
Robotic arm
Kawasaki
ZX300S
Flat panel detector
Varian 4030D
(30 Hz, 2048x1536 pixels
193.8x194 um pitch)
X-ray source
Varian A277
Generator
Sedecal HF
series
Exposure controlGantry pendant Intrusion detection
11. X-ray Patient Positioning Verification software
Gantry control
Exposure settings
Automatic registration
PPS communication
PACS integration
ROI
Interactive
checkerboard
Images
overlay
Visualization
settings
Automatic
registration
Manual
registration
ROI
Load/Save
11 / 35
12. Geometry calibration
Calibration phantom (Brandis
Medizintechnik Vertriebs GmbH,
Weinheim, Germany)
36 metal bearings
Optimization of on-plane
projection error
Free parameters:
• Image center
• Panel orientation
System geometric daily QA
SOURCE
(0,0)
Center of
rotation
(-cx,-cy)
Panel
rotations
Imaging Isocenter
12 / 35
13. System installation in CNAO Room 2
CLINICAL WORKFLOW
PPS MOTION TO TREATMENT ISOCENTER
OTS Point-based registration at treatment isocenter
(≈1min)
PPS MOTION TO IMAGING ISOCENTER (≈3min)
Image-based registration at imaging isocenter
PPS MOTION TO TREATMENT ISOCENTER
(≈3min)
Treatment start
1. Images acquisition
2. Automatic registration
(≈1min)
3. PPS correction
4. Images acquisition
(verification)
5. Automatic registration
Image-based registration
13 / 35
20. Dosimetric consequences of setup errors after IGRT
Nominal
Setup errors
D95
D105D05
HU-WE
Setup
CI =
Vol95%
VolCTV
IC =
(MaxDose - MinDose)
MinDose
Purpose
To provide clinicians with dosimetric information
about treatment setup besides the residual
geometric error
Range
Interpretation of results
Indexes clearly readable by clinicians
• Envelope DVH (ΔD95CTV, ΔD105CTV, ΔD05OAR)
• Conformity Index for CTV
• Inhomogeneity Coefficient for CTV
Materials and Methods
• Image processing & TRiP98 (M. Krämer)
• Comparison of treatment delivery in nominal
situation and in presence of uncertainty
(Optimum=1)
(Optimum =0)
20 / 35
21. Simulation of setup errors
ERROR SPACE SAMPLING
Orthogonal sampling (64 simulations)
• 6 Dimensions (translations,
rotations)
Implementation of isocentric 6DOF Correction vector on patient CT
1. Image resampling T
2. Dose calculation
3. Dose cube resampling Tinv
Figure 2. Plan no 299. (Upper part) Convergence of the various algorithms as function of iteration
steps (left hand side) and computation time (right hand side). (Lower part) DVH (left hand side)
and dose distribution in a CT-slice (right hand side). Only results of CGFR optimization are shown.
The indicated isodoses are in percent of the prescribed dose.
For the sake of completeness we additionally present the Levenberg–Marquardt
minimization (LMM) which we also investigated. As far as the number of iterations are
concerned (see figure 1, upper left part) LMM looks quite promising but the computation
times are extremely large (see figure 1, upper right part). The disadvantage of LMM is that in
every iteration step a large system of linear equations has to be solved. Solving the system of
linear equations with the Cholesky decomposition requires about 60 times more computation
time compared with CGFR (figure 1). We investigated alternative equation solvers, for
example the iterative Krylov subspace methods. With the Krylov subspace methods the
computation times could be decreased by a factor of approximately 3 (Buschbacher 2009),
which is by far not enough to allow the usage of LMM in our context.
We further investigated the distribution of the resultant particle numbers on the raster
grid. This is important because large fluctuations of particle numbers between rasterspots
might require changing of the particle intensities by the ion accelerator system. This is time
consuming and could potentially decrease the number of patients treated per day. We examined
some treatment plans and independently from the chosen algorithm we did not observe large
fluctuations of particle numbers between neighbouring rasterspots.
20%
40%
60%
80%
95%
105%
> 105%Figure 2. Plan no 299. (Upper part) Convergence of the various algorithms as function of iteration
steps (left hand side) and computation time (right hand side). (Lower part) DVH (left hand side)
and dose distribution in a CT-slice (right hand side). Only results of CGFR optimization are shown.
The indicated isodoses are in percent of the prescribed dose.
For the sake of completeness we additionally present the Levenberg–Marquardt
minimization (LMM) which we also investigated. As far as the number of iterations are
concerned (see figure 1, upper left part) LMM looks quite promising but the computation
times are extremely large (see figure 1, upper right part). The disadvantage of LMM is that in
every iteration step a large system of linear equations has to be solved. Solving the system of
linear equations with the Cholesky decomposition requires about 60 times more computation
time compared with CGFR (figure 1). We investigated alternative equation solvers, for
example the iterative Krylov subspace methods. With the Krylov subspace methods the
computation times could be decreased by a factor of approximately 3 (Buschbacher 2009),
which is by far not enough to allow the usage of LMM in our context.
We further investigated the distribution of the resultant particle numbers on the raster
grid. This is important because large fluctuations of particle numbers between rasterspots
might require changing of the particle intensities by the ion accelerator system. This is time
consuming and could potentially decrease the number of patients treated per day. We examined
some treatment plans and independently from the chosen algorithm we did not observe large
fluctuations of particle numbers between neighbouring rasterspots.
6. Summary and conclusion
The task for the optimization of RBE-weighted dose is
depending nonlinearly on the particle numbers
20%
40%
60%
80%
95%
105%
> 105%
-30° Dos
+30° CT
1 mm 1 setup error
21 / 35
23. Patient 1: worst case simulation for D95CTV
ce of the various algorithms as function of iteration
ght hand side). (Lower part) DVH (left hand side)
de). Only results of CGFR optimization are shown.
scribed dose.
20%
40%
60%
80%
95%
105%
> 105%
rgence of the various algorithms as function of iteration
me (right hand side). (Lower part) DVH (left hand side)
nd side). Only results of CGFR optimization are shown.
e prescribed dose.
ionally present the Levenberg–Marquardt
ed. As far as the number of iterations are
looks quite promising but the computation
ht part). The disadvantage of LMM is that in
20%
40%
60%
80%
95%
105%
> 105%
SETUP ERROR
SETUP AND RANGE ERROR
(Expected) Results:
Dose coverage remains acceptable
Conformity is reduced
Inhomogeneity is increased
Quantification of dosimetric deviations wrt nominal condition
23 / 35
LL:
AP:
SI:
Pitch:
Rotate:
Roll:
-0.34mm
-0.87mm
-0.97mm
-0.19°
-0.97°
0.78°
Setup error
+
Rel WEL +2.6%
24. IGRT: Conclusion and Limitations
Tools for automated fiducials localization in treatment planning CT images
Development of a custom robotic in-room imaging system
Implementation at CNAO
Double projection: Clinical use since April 2013
CBCT: foreseen for April 2014
Overall residual setup error following CNAO IGRT strategy (OTS + in-
room imaging): Millimeter and degree scale.
Tool to provide valuable dosimetric information to clinicians
Not far from pre-treatment setup verification: about 10 mins (single
simulation)
Treatment plan robustness test: 2 hours
24 / 35
25. From static to moving target with active scanning
X-RAY SOFT-TISSUE IMAGING
US MRI
PHASE1
4D IMAGING
(4D CT)
PHASEN
OPTICAL TRACKING +
CORRELATION MODELS
BASIC ASSUMPTION:
TARGET MOTION REPEATIBILTY
4D TREATMENT PLAN (TP)
TIME RESOLVED TREATMENT DELIVERY
ENERGY ADAPTATIONLATERAL DEFLECTION
MOTION MONITORING
DOSE DELIVERY (beam tracking)
MOTION MONITORING SYSTEMS
Real time feedback to TCS to drive
the treatment delivery
Verify consistency wrt TP
Trigger image acquisition
MOTION MITIGATION STRATEGIES
BEAM TRACKING
GATING
RESCANNING
Direct observation Surrogate signal
25 / 35
26. Optical tracking for time resolved treatment
PURPOSE
To interface a commercial solution for optical tracking with a Therapy Control System
for particles: beam tracking and gating
WHAT IS REQUIRED
Real time monitoring of multiple surrogates
Compatibility with 4DCT acquisition protocols
Real time communication with TCS: delay compensation
Static
Residual
Interplay
26 / 35
27. The tracking code package
Optical Tracking System Therapy Control System
Correction
vector
3D OTS DATA
BTU
Wedge
range shifter
Steering
magnets
Depth
compensation
Lateral
compensation100 Hz frame rate
LABELLER
TARGE
T
FRAMES
INTERPOLATION
POLYNOMIAL
COEFFICIENTS
TIMECRITICALTHREADOTSDRIVENTHREAD
BREATHING
SIGNAL
MOTION PHASE
DETECTION
CORRELATION MODELS [ x y z ]
[ MP ]
PATIENT
MODEL
MOTION
PHASE TABLE
RCS
TRANSFORM
MATRIX
SHARED RESOURCES
DIGITALCOMMUNICATION(UDPSOCKET)
Treatment
plan
Fattori et al. [2012 AAPM]
KEY FEATURES:
Phase/Amplitude 4DCT
Ethernet link (UDP)
Signal time prediction
Ready for Gating and Beam
tracking experiments
27 / 35
28. Procedure for system latencies quantification
DEPTH
WE
compensation
Mean
Std.Dev
1 mm 27.43 7.51
9 mm 34.1 6.29
5measurements(0,
DEPTH
Calculatedbydiffere
TOTAL–LATERAL
Laser distantiometer
OTS marker
Fattori et al. [2013 TCRT Express]
LAT ERAL
OTSbenchmark with Laser distantiometer (1KHz frm.rate)
5 measurements(0,5,10,15,20 msec.advance prediction)
DEPT H
Calculated by difference
TOTAL– LATERAL
Motion OTS
LATERAL
OTSbenchmark with Laser distantiometer (1KHz frm.rate)
5 measurements(0,5,10,15,20 msec.advance prediction)
DEPTH
Calculated by difference
TOTAL– LATERAL
Motion OTS TCS
34
(mea
1
MAGNETS
WEDGEFILTER
LATERAL
14.6 msec
DEPT H
Calculated by difference
TOTAL– LATERAL
Motion OTS
28 / 35
29. Signal time prediction accuracy
Polynomial fitting: Ist order
5 samples history, 100 Hz data
Time compensated Vs. Reference
Fattori et al. [2013 TCRT Express]
REFERENCE
NON COMPENSATED
TIME COMPENSATED
• Reference = Non-compensated + δ (=14.6ms)
• 10 mins acquisition:
RMS = 0.05 mm
RMS = 0.1 mm
30. Beam tracking @ GSI: Setup
Purpose:
To evaluate the feasibility of
OTS driven 4D treatment
Steidl et al. [2012 PMB]
Breathing phantom
Correlated target/thorax motion
10x5x10 cm (x,y,depth)
Treatment plan
1 Gy homogeneous, 12C
35 mm side
4DCT: 8 MPh, phase
binned
Motion monitoring:
SMART-DX100, 2 TVC
Dose measurement
16 PTW Pinpoint ionization
30 / 35
31. Beam tracking @ GSI: Results
Fattori et al. [2013 TCRT Express]
Note:
Pure translational target motion
No soft tissue material inside the thorax
Excellent target and thorax motion repeatibility
Median(IQR) 2.0 (25.9) % -0.3 (2.3) % -1.2 (9.3) %
Measured delta wrt static irradiation
31 / 35
32. Lateral beam tracking @ CNAO
Phantom
- Planar target motion (2D)
- 25 mm (lat) 18 mm (vert) peak-to-peak
- Planarity: median 0.038 mm (IQR:0.09)
- Repeatibility: mean std 0.18 0.3 mm
‘Treatment plan’
- Squared PTV
- 6 cm side
Purpose
Proof the OTS/TCS integration
STATIC TRACKING INTERPLAY GATING
Average flatness 4 % 5.7 % 24 % 9.5 %
Average penumbra 9.2 mm 9 mm 19 mm 9.1 mm
32 / 35
Pella, Fattori et al. [PTCOG52]
33. Moving targets: Conclusion and Limitations
General solution for OTS/TCS integration was described
Ethernet link, UDP protocol
Procedure for delays quantification
The tracking code package available for research (CNAO, GSI,… )
Development and benchmark of int-ext correlation model (M. Seregni)
Functional gating and beam tracking modules
GSI: 3D optical driven beam tracking
CNAO: 2D optical driven beam tracking and gating
BEAM TRACKING: how to deal with deviations from treatment plan?
1. Real time dose compensation with beam tracking [Lüchtenborg 2012, Med Phys]
2. Dose changes outside the VOIs (inverse interplay effect)
33 / 35
34. Final remarks
IGRT
1. Development and implementation of state-of-the art methods for IGRT
• Point based
• Anatomical information (bone anatomy + soft tissue imaging)
2. CNAO Room 2: Custom system for robotic imaging:
(!) 2D-3D available for clinical use
(!) CBCT almost available for clinical use
(!) Double projection & CBCT dataset: 2D-3D / 3D-3D Comparison
Treatment of moving targets
1. GSI: beam tracking, lateral and depth compensation
2. CNAO:
• lateral compensation
• ready for gated treatment:
(!) strategy to compensate for residual motion in the gating window
34 / 35
35. Future directions
IGRT @ CNAO
1. ‘CT-of-the-day’ software module
• Pre-treatment dose simulation on the updated CT
• Anatomical information in perspective of PET in-room
2. 4D CBCT
Treatment of moving targets
Tailored treatment on patient specific basis:
• Motion reduction: gating + rescanning/overlapped pencil
beams
• Motion compensation: multiple points + tumor tracking
• Adequate strategy for margin definition
35 / 35