Prediction and classification of respiratory motion:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Heidelberg ; New York, NY ; Dordrecht ; London ; Berlin
Springer
2014
|
| Schriftenreihe: | Studies in computational intelligence
525 |
| Schlagworte: | |
| Online-Zugang: | Inhaltstext Inhaltsverzeichnis Klappentext |
| Beschreibung: | IX, 167 S. graph. Darst. 24 cm |
| ISBN: | 9783642415081 3642415083 |
Internformat
MARC
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| 100 | 1 | |a Lee, Suk Jin |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Prediction and classification of respiratory motion |c Suk Jin Lee ; Yuichi Motai |
| 264 | 1 | |a Heidelberg ; New York, NY ; Dordrecht ; London ; Berlin |b Springer |c 2014 | |
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| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |t Prediction and Classification of Respiratory Motion |
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Datensatz im Suchindex
| _version_ | 1809769952868040704 |
|---|---|
| adam_text |
Contents
1
Introduction
. 1
References
.
З
2
Review: Prediction of Respiratory Motion
. 7
2.1
Tools for Measuring Target Position During Radiotherapy
. 7
2.1.1
Radiographs
. 8
2.1.2
Fiducial Markers
. 8
2.1.3
Fluoroscopy
. 8
2.1.4
Computed Tomography
. 8
2.1.5
Magnetic Resonance Imaging
. 9
2.1.6
Optical Imaging
. 9
2.2
Tracking-Based Delivery Systems
. 10
2.2.1
Linear Accelerator
. 10
2.2.2
Multileaf Collimator
. 11
2.2.3
Robotic Couch
. 12
2.3
Prediction Algorithms for Respiratory Motion
. 13
2.3.1
Model-Based Prediction Algorithms
. 14
2.3.2
Model-Free Prediction Algorithms
. 22
2.3.3
Hybrid Prediction Algorithms
. 25
2.4
Open Questions for Prediction of Respiratory Motion
. 30
2.4.1
Changes of Respiratory Patterns
. 31
2.4.2
Tumor Deformation and Target Dosimetry
. 31
2.4.3
Irregular Pattern Detection
. 31
2.5
Summary
. 31
References
. 32
3
Phantom: Prediction of Human Motion with Distributed
Body Sensors
. 39
3.1
Introduction
. 39
3.2
Related Work
. 41
3.2.1
Kalman
Filter
. 41
3.2.2
Interacting Multiple Model Framework
. 42
3.2.3
Cluster Number Selection Using Gaussian Mixture
Model and Expectation-Maximization Algorithm
. 43
3.3
Proposed Grouping Criteria with Distributed Sensors
. 45
3.3.1
Collaborative Grouping with Distributed
Body Sensors
. 45
3.3-2
Estimated Parameters Used for Interacting
Multiple Model Estimator
. 47
3.4
Sensors Multi-Channel
IMME:
Proposed System Design
. 48
3.4.1 MC
Mixed Initial Condition and the Associated
Covariance
. 49
3.4.2 MC
Likelihood Update
. 50
3.4.3
Switching Probability Update
. 50
3.4.4
Feedback from Switching Probability Update to Stage
1
for Grouping Criteria with Distributed Sensors
. 50
3.4.5
Combination of
MC
Conditioned Estimates
and Covariance
. 51
3.4.6
Computational Time
. 51
3.5
Experimental Results
. 52
3.5.1
Motion Data
. 53
3.5.2
Collaborative Grouping Initialization
. 53
3.5.3
Comparison of Grouping Methods
with Other Techniques
. 57
3.5.4
Multi-Channel
IMME. 58
3.5.5
Prediction Overshoot
. 61
3.5.6
Computational Time
. 62
3.6
Summary
. 64
References
. 64
Respiratory Motion Estimation with Hybrid Implementation
. 67
4.1
Introduction
. 67
4.2
Related Work
. 69
4.2.1
Recurrent Neural Network
. 69
4.2.2
Extended
Kalman
Filter for Recurrent
Neural Networks
. 71
4.3
Multi-Channel Coupled EKF-RNN
. 72
4.3.1
Decoupled Extended
Kalman
Filter
. 72
4.3.2
Hybrid Estimation Based on EKF
for Neural Network
. 74
4.3.3
Optimized Group Number for Recurrent
Multilayer Perceptron
. 75
4.3.4
Prediction Overshoot Analysis
. 77
4.3.5
Comparisons on Computational Complexity
and Storage Requirement
. 78
4.4
Experimental Results
. 79
4.4.1
Motion Data Captured
. 79
4.4.2
Optimized Group Number for RMLP
. 80
4.4.3
Prediction Overshoot Analysis
. 81
4.4.4
Comparison on Estimation Performance
. 82
4.4.5
Error Performance Over Prediction Time Horizon
. 84
4.4.6
Comparisons on Computational Complexity
. 84
4.5
Summary
. 86
References
. 87
Customized Prediction of Respiratory Motion
. 91
5.1
Introduction
. 91
5.2
Prediction Process for Each Patient
. 92
5.3
Proposed Filter Design for Multiple Patients
. 94
5.3.1
Grouping Breathing Pattern for Prediction Process
. 95
5.3.2
Neuron Number Selection
. 97
5.4
Experimental Results
. 98
5.4.1
Breathing Motion Data
. 98
5.4.2
Feature Selection Metrics
. 98
5.4.3
Comparison on Estimation Performance
. 99
5.4.4
Prediction Accuracy with Time Horizontal Window
. 100
5.4.5
Prediction Overshoot Analysis
. 102
5.4.6
Comparisons on Computational Complexity
. 104
5.5
Summary
. 104
References
. 105
Irregular Breathing Classification from Multiple
Patient
Datasets
. 109
6.1
Introduction
. 109
6.2
Related Work
.
Ill
6.2.1
Expectation—Maximization Based on Gaussian
Mixture Model
.
Ill
6.2.2
Neural Network
. 112
6.3
Proposed Algorithms on Irregular Breathing Classifier
. 113
6.3.1
Feature Extraction from Breathing Analysis
. 113
6.3.2
Clustering of Respiratory Patterns Based on EM
. 115
6.3.3
Reconstruction Error for Each Cluster Using
NN. 116
6.3.4
Detection of Irregularity Based
on Reconstruction Error
. 117
6.4
Evaluation Criteria for Irregular Breathing Classifier
. 119
6.4.1
Sensitivity and Specificity
. 119
6.4.2
Receiver Operating Characteristics
. 120
6.5
Experimental Results
. 121
6.5.1
Breathing Motion Data
. 121
6.5.2
Selection of the Estimated Feature Metrics (ic)
. 122
6.5.3
Clustering of Respiratory Patterns Based on EM
. 123
6.5.4
Breathing Pattern Analysis to Detect Irregular Pattern.
. . 123
6.5.5
Classifier Performance
. 127
6.6
Summary
. 130
References
. 131
7
Conclusions and Contributions
. 135
7.1
Conclusions
. 135
7.1.1
Hybrid Implementation of Extended
Kalman
Filter
. 135
7.1.2
Customized Prediction of Respiratory Motion
with Clustering
. 135
7.1.3
Irregular Breathing Classification from Multiple
Patient
Datasets
. 136
7.2
Contributions
. 136
Appendix A
. 139
Appendix
В
. 145
This book describes recent racliotherapv technologies including tools tor moasurine
target position during radiolherapv and tracking based cicliverv svslcms.
i lii
s
book presents a customized prediction
oí
resr^iratory motion with clustering
írom
multiple patient interactions. The proposed method contributes to the improvement ot
patient treatments by considering breathing pattern
ror lhe
accurate Jose calculation in
radioiherapv svstems. Real-time tiimor-trackint* where the valediction
oí
'irregularities
becomes relevant, has yet to be clinically established. The statistical quantitative
modeling tor irregular breathing classification, in which commercial respiration traces
are retrospect ivelv categorized into several classes based on breath
і пц
pattern are
discussed as well. The proposed statistical classification may provide clinical i\d\ 'antages
to adjust the dose rate before and during the external beam radiotherapy lor minimizing
the safety margin.
In the tirst chapter follow ing the Introduction to this book, we review three prediction
approaches
oí
respiratory motion: model-based methods, model-
í
ree
heuristic learning
algorithms, and hybrid methods. In the follow ing chapter, we present a phantom study
—
prediction
oí
human motion with distributed bodv sensors
—
usino
a Pol
h
emus Libertv
AC magnetic tracker. Next we describe respn atorv motion estimation with hvbrid
implementation of extended
Kalman
filter. The
inveii
method assigns the recurrent
neu
ra)
network the role
Ы
the predictor and the extended
Kalman
filter the role of
the corrector. Alter that, we present customized prediction of respiratory motion
w
ith clustering from multiple patient interactions. For the customized prediction, we
construct the clustering based on breathing patterns t)i multiple patients
usine,
the
feature selection metrics that are composed of a variety of breathing features.We
Ьал'с
exaluated the new algorithm b\'
comparine
the prediction overshoot and the tracking
estimation
чаїне.
The experimental results
ol
448
patients breathing patterns validated
the proposed irregular breathing classifier in the last chapter. |
| any_adam_object | 1 |
| author | Lee, Suk Jin Motai, Yuichi |
| author_facet | Lee, Suk Jin Motai, Yuichi |
| author_role | aut aut |
| author_sort | Lee, Suk Jin |
| author_variant | s j l sj sjl y m ym |
| building | Verbundindex |
| bvnumber | BV041946233 |
| classification_rvk | ST 330 |
| ctrlnum | (OCoLC)864560133 (DE-599)DNB1041798172 |
| dewey-full | 616.2407572028563 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 616 - Diseases |
| dewey-raw | 616.2407572028563 |
| dewey-search | 616.2407572028563 |
| dewey-sort | 3616.2407572028563 |
| dewey-tens | 610 - Medicine and health |
| discipline | Informatik Medizin |
| format | Book |
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| id | DE-604.BV041946233 |
| illustrated | Illustrated |
| indexdate | 2024-09-10T01:18:42Z |
| institution | BVB |
| isbn | 9783642415081 3642415083 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027389306 |
| oclc_num | 864560133 |
| open_access_boolean | |
| owner | DE-739 |
| owner_facet | DE-739 |
| physical | IX, 167 S. graph. Darst. 24 cm |
| publishDate | 2014 |
| publishDateSearch | 2014 |
| publishDateSort | 2014 |
| publisher | Springer |
| record_format | marc |
| series | Studies in computational intelligence |
| series2 | Studies in computational intelligence |
| spelling | Lee, Suk Jin Verfasser aut Prediction and classification of respiratory motion Suk Jin Lee ; Yuichi Motai Heidelberg ; New York, NY ; Dordrecht ; London ; Berlin Springer 2014 IX, 167 S. graph. Darst. 24 cm txt rdacontent n rdamedia nc rdacarrier Studies in computational intelligence 525 Sensorsystem (DE-588)4307964-7 gnd rswk-swf Soft Computing (DE-588)4455833-8 gnd rswk-swf Bewegungsanalyse (DE-588)4122917-4 gnd rswk-swf Strahlendosis (DE-588)4057821-5 gnd rswk-swf Atmung (DE-588)4003404-5 gnd rswk-swf Kalman-Filter (DE-588)4130759-8 gnd rswk-swf Automatische Klassifikation (DE-588)4120957-6 gnd rswk-swf Atmung (DE-588)4003404-5 s Bewegungsanalyse (DE-588)4122917-4 s Sensorsystem (DE-588)4307964-7 s Automatische Klassifikation (DE-588)4120957-6 s Kalman-Filter (DE-588)4130759-8 s Strahlendosis (DE-588)4057821-5 s Soft Computing (DE-588)4455833-8 s DE-604 Motai, Yuichi Verfasser aut Erscheint auch als Online-Ausgabe Prediction and Classification of Respiratory Motion Studies in computational intelligence 525 (DE-604)BV020822171 525 X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=4443077&prov=M&dok_var=1&dok_ext=htm Inhaltstext Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027389306&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027389306&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Lee, Suk Jin Motai, Yuichi Prediction and classification of respiratory motion Studies in computational intelligence Sensorsystem (DE-588)4307964-7 gnd Soft Computing (DE-588)4455833-8 gnd Bewegungsanalyse (DE-588)4122917-4 gnd Strahlendosis (DE-588)4057821-5 gnd Atmung (DE-588)4003404-5 gnd Kalman-Filter (DE-588)4130759-8 gnd Automatische Klassifikation (DE-588)4120957-6 gnd |
| subject_GND | (DE-588)4307964-7 (DE-588)4455833-8 (DE-588)4122917-4 (DE-588)4057821-5 (DE-588)4003404-5 (DE-588)4130759-8 (DE-588)4120957-6 |
| title | Prediction and classification of respiratory motion |
| title_auth | Prediction and classification of respiratory motion |
| title_exact_search | Prediction and classification of respiratory motion |
| title_full | Prediction and classification of respiratory motion Suk Jin Lee ; Yuichi Motai |
| title_fullStr | Prediction and classification of respiratory motion Suk Jin Lee ; Yuichi Motai |
| title_full_unstemmed | Prediction and classification of respiratory motion Suk Jin Lee ; Yuichi Motai |
| title_short | Prediction and classification of respiratory motion |
| title_sort | prediction and classification of respiratory motion |
| topic | Sensorsystem (DE-588)4307964-7 gnd Soft Computing (DE-588)4455833-8 gnd Bewegungsanalyse (DE-588)4122917-4 gnd Strahlendosis (DE-588)4057821-5 gnd Atmung (DE-588)4003404-5 gnd Kalman-Filter (DE-588)4130759-8 gnd Automatische Klassifikation (DE-588)4120957-6 gnd |
| topic_facet | Sensorsystem Soft Computing Bewegungsanalyse Strahlendosis Atmung Kalman-Filter Automatische Klassifikation |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=4443077&prov=M&dok_var=1&dok_ext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027389306&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027389306&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV020822171 |
| work_keys_str_mv | AT leesukjin predictionandclassificationofrespiratorymotion AT motaiyuichi predictionandclassificationofrespiratorymotion |