A progressively dual reconstruction network for plane wave beamforming with both paired and unpaired training data

High-frame-rate plane wave (PW) imaging suffers from unsatisfactory image quality due to the absence of focus in transmission. Although coherent compounding from tens of PWs can improve PW image quality, it in turn results in a decreased frame rate, which is limited for tracking fast moving tissues....
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Gao, Junling [verfasserIn] Xu, Lei [verfasserIn] Zou, Qin [verfasserIn] Zhang, Bo [verfasserIn] Wang, Diya [verfasserIn] Wan, Mingxi [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Ultrasound plane wave imaging Beamforming Coherent compounding Progressively dual reconstruction network Transfer learning

Übergeordnetes Werk:	Enthalten in: Ultrasonics - Amsterdam [u.a.] : Elsevier, 1963, 127
Übergeordnetes Werk:	volume:127

DOI / URN:	10.1016/j.ultras.2022.106833

Katalog-ID:	ELV008580758

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520			\|a High-frame-rate plane wave (PW) imaging suffers from unsatisfactory image quality due to the absence of focus in transmission. Although coherent compounding from tens of PWs can improve PW image quality, it in turn results in a decreased frame rate, which is limited for tracking fast moving tissues. To overcome the trade-off between frame rate and image quality, we propose a progressively dual reconstruction network (PDRN) to achieve adaptive beamforming and enhance the image quality via both supervised and transfer learning in the condition of single or a few PWs transmission. Specifically, the proposed model contains a progressive network and a dual network to form a close loop and provide collaborative supervision for model optimization. The progressive network takes the channel delay of each spatial point as input and progressively learns coherent compounding beamformed data with increased numbers of steered PWs step by step. The dual network learns the downsampling process and reconstructs the beamformed data with decreased numbers of steered PWs, which reduces the space of the possible learning functions and improves the model’s discriminative ability. In addition, the dual network is leveraged to perform transfer learning for the training data without sufficient steered PWs. The simulated, in vivo, vocal cords (VCs), and public available CUBDL dataset are collected for model evaluation.
650		4	\|a Ultrasound plane wave imaging
650		4	\|a Beamforming
650		4	\|a Coherent compounding
650		4	\|a Progressively dual reconstruction network
650		4	\|a Transfer learning
700	1		\|a Xu, Lei \|e verfasserin \|4 aut
700	1		\|a Zou, Qin \|e verfasserin \|4 aut
700	1		\|a Zhang, Bo \|e verfasserin \|4 aut
700	1		\|a Wang, Diya \|e verfasserin \|4 aut
700	1		\|a Wan, Mingxi \|e verfasserin \|4 aut
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936	b	k	\|a 50.36 \|j Technische Akustik
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publishDate	2022
allfields	10.1016/j.ultras.2022.106833 doi (DE-627)ELV008580758 (ELSEVIER)S0041-624X(22)00139-1 DE-627 ger DE-627 rda eng 530 DE-600 50.36 bkl Gao, Junling verfasserin aut A progressively dual reconstruction network for plane wave beamforming with both paired and unpaired training data 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier High-frame-rate plane wave (PW) imaging suffers from unsatisfactory image quality due to the absence of focus in transmission. Although coherent compounding from tens of PWs can improve PW image quality, it in turn results in a decreased frame rate, which is limited for tracking fast moving tissues. To overcome the trade-off between frame rate and image quality, we propose a progressively dual reconstruction network (PDRN) to achieve adaptive beamforming and enhance the image quality via both supervised and transfer learning in the condition of single or a few PWs transmission. Specifically, the proposed model contains a progressive network and a dual network to form a close loop and provide collaborative supervision for model optimization. The progressive network takes the channel delay of each spatial point as input and progressively learns coherent compounding beamformed data with increased numbers of steered PWs step by step. The dual network learns the downsampling process and reconstructs the beamformed data with decreased numbers of steered PWs, which reduces the space of the possible learning functions and improves the model’s discriminative ability. In addition, the dual network is leveraged to perform transfer learning for the training data without sufficient steered PWs. The simulated, in vivo, vocal cords (VCs), and public available CUBDL dataset are collected for model evaluation. Ultrasound plane wave imaging Beamforming Coherent compounding Progressively dual reconstruction network Transfer learning Xu, Lei verfasserin aut Zou, Qin verfasserin aut Zhang, Bo verfasserin aut Wang, Diya verfasserin aut Wan, Mingxi verfasserin aut Enthalten in Ultrasonics Amsterdam [u.a.] : Elsevier, 1963 127 Online-Ressource (DE-627)320499251 (DE-600)2012001-1 (DE-576)093886101 1874-9968 nnns volume:127 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.36 Technische Akustik AR 127
spelling	10.1016/j.ultras.2022.106833 doi (DE-627)ELV008580758 (ELSEVIER)S0041-624X(22)00139-1 DE-627 ger DE-627 rda eng 530 DE-600 50.36 bkl Gao, Junling verfasserin aut A progressively dual reconstruction network for plane wave beamforming with both paired and unpaired training data 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier High-frame-rate plane wave (PW) imaging suffers from unsatisfactory image quality due to the absence of focus in transmission. Although coherent compounding from tens of PWs can improve PW image quality, it in turn results in a decreased frame rate, which is limited for tracking fast moving tissues. To overcome the trade-off between frame rate and image quality, we propose a progressively dual reconstruction network (PDRN) to achieve adaptive beamforming and enhance the image quality via both supervised and transfer learning in the condition of single or a few PWs transmission. Specifically, the proposed model contains a progressive network and a dual network to form a close loop and provide collaborative supervision for model optimization. The progressive network takes the channel delay of each spatial point as input and progressively learns coherent compounding beamformed data with increased numbers of steered PWs step by step. The dual network learns the downsampling process and reconstructs the beamformed data with decreased numbers of steered PWs, which reduces the space of the possible learning functions and improves the model’s discriminative ability. In addition, the dual network is leveraged to perform transfer learning for the training data without sufficient steered PWs. The simulated, in vivo, vocal cords (VCs), and public available CUBDL dataset are collected for model evaluation. Ultrasound plane wave imaging Beamforming Coherent compounding Progressively dual reconstruction network Transfer learning Xu, Lei verfasserin aut Zou, Qin verfasserin aut Zhang, Bo verfasserin aut Wang, Diya verfasserin aut Wan, Mingxi verfasserin aut Enthalten in Ultrasonics Amsterdam [u.a.] : Elsevier, 1963 127 Online-Ressource (DE-627)320499251 (DE-600)2012001-1 (DE-576)093886101 1874-9968 nnns volume:127 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.36 Technische Akustik AR 127
allfields_unstemmed	10.1016/j.ultras.2022.106833 doi (DE-627)ELV008580758 (ELSEVIER)S0041-624X(22)00139-1 DE-627 ger DE-627 rda eng 530 DE-600 50.36 bkl Gao, Junling verfasserin aut A progressively dual reconstruction network for plane wave beamforming with both paired and unpaired training data 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier High-frame-rate plane wave (PW) imaging suffers from unsatisfactory image quality due to the absence of focus in transmission. Although coherent compounding from tens of PWs can improve PW image quality, it in turn results in a decreased frame rate, which is limited for tracking fast moving tissues. To overcome the trade-off between frame rate and image quality, we propose a progressively dual reconstruction network (PDRN) to achieve adaptive beamforming and enhance the image quality via both supervised and transfer learning in the condition of single or a few PWs transmission. Specifically, the proposed model contains a progressive network and a dual network to form a close loop and provide collaborative supervision for model optimization. The progressive network takes the channel delay of each spatial point as input and progressively learns coherent compounding beamformed data with increased numbers of steered PWs step by step. The dual network learns the downsampling process and reconstructs the beamformed data with decreased numbers of steered PWs, which reduces the space of the possible learning functions and improves the model’s discriminative ability. In addition, the dual network is leveraged to perform transfer learning for the training data without sufficient steered PWs. The simulated, in vivo, vocal cords (VCs), and public available CUBDL dataset are collected for model evaluation. Ultrasound plane wave imaging Beamforming Coherent compounding Progressively dual reconstruction network Transfer learning Xu, Lei verfasserin aut Zou, Qin verfasserin aut Zhang, Bo verfasserin aut Wang, Diya verfasserin aut Wan, Mingxi verfasserin aut Enthalten in Ultrasonics Amsterdam [u.a.] : Elsevier, 1963 127 Online-Ressource (DE-627)320499251 (DE-600)2012001-1 (DE-576)093886101 1874-9968 nnns volume:127 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.36 Technische Akustik AR 127
allfieldsGer	10.1016/j.ultras.2022.106833 doi (DE-627)ELV008580758 (ELSEVIER)S0041-624X(22)00139-1 DE-627 ger DE-627 rda eng 530 DE-600 50.36 bkl Gao, Junling verfasserin aut A progressively dual reconstruction network for plane wave beamforming with both paired and unpaired training data 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier High-frame-rate plane wave (PW) imaging suffers from unsatisfactory image quality due to the absence of focus in transmission. Although coherent compounding from tens of PWs can improve PW image quality, it in turn results in a decreased frame rate, which is limited for tracking fast moving tissues. To overcome the trade-off between frame rate and image quality, we propose a progressively dual reconstruction network (PDRN) to achieve adaptive beamforming and enhance the image quality via both supervised and transfer learning in the condition of single or a few PWs transmission. Specifically, the proposed model contains a progressive network and a dual network to form a close loop and provide collaborative supervision for model optimization. The progressive network takes the channel delay of each spatial point as input and progressively learns coherent compounding beamformed data with increased numbers of steered PWs step by step. The dual network learns the downsampling process and reconstructs the beamformed data with decreased numbers of steered PWs, which reduces the space of the possible learning functions and improves the model’s discriminative ability. In addition, the dual network is leveraged to perform transfer learning for the training data without sufficient steered PWs. The simulated, in vivo, vocal cords (VCs), and public available CUBDL dataset are collected for model evaluation. Ultrasound plane wave imaging Beamforming Coherent compounding Progressively dual reconstruction network Transfer learning Xu, Lei verfasserin aut Zou, Qin verfasserin aut Zhang, Bo verfasserin aut Wang, Diya verfasserin aut Wan, Mingxi verfasserin aut Enthalten in Ultrasonics Amsterdam [u.a.] : Elsevier, 1963 127 Online-Ressource (DE-627)320499251 (DE-600)2012001-1 (DE-576)093886101 1874-9968 nnns volume:127 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.36 Technische Akustik AR 127
allfieldsSound	10.1016/j.ultras.2022.106833 doi (DE-627)ELV008580758 (ELSEVIER)S0041-624X(22)00139-1 DE-627 ger DE-627 rda eng 530 DE-600 50.36 bkl Gao, Junling verfasserin aut A progressively dual reconstruction network for plane wave beamforming with both paired and unpaired training data 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier High-frame-rate plane wave (PW) imaging suffers from unsatisfactory image quality due to the absence of focus in transmission. Although coherent compounding from tens of PWs can improve PW image quality, it in turn results in a decreased frame rate, which is limited for tracking fast moving tissues. To overcome the trade-off between frame rate and image quality, we propose a progressively dual reconstruction network (PDRN) to achieve adaptive beamforming and enhance the image quality via both supervised and transfer learning in the condition of single or a few PWs transmission. Specifically, the proposed model contains a progressive network and a dual network to form a close loop and provide collaborative supervision for model optimization. The progressive network takes the channel delay of each spatial point as input and progressively learns coherent compounding beamformed data with increased numbers of steered PWs step by step. The dual network learns the downsampling process and reconstructs the beamformed data with decreased numbers of steered PWs, which reduces the space of the possible learning functions and improves the model’s discriminative ability. In addition, the dual network is leveraged to perform transfer learning for the training data without sufficient steered PWs. The simulated, in vivo, vocal cords (VCs), and public available CUBDL dataset are collected for model evaluation. Ultrasound plane wave imaging Beamforming Coherent compounding Progressively dual reconstruction network Transfer learning Xu, Lei verfasserin aut Zou, Qin verfasserin aut Zhang, Bo verfasserin aut Wang, Diya verfasserin aut Wan, Mingxi verfasserin aut Enthalten in Ultrasonics Amsterdam [u.a.] : Elsevier, 1963 127 Online-Ressource (DE-627)320499251 (DE-600)2012001-1 (DE-576)093886101 1874-9968 nnns volume:127 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.36 Technische Akustik AR 127
language	English
source	Enthalten in Ultrasonics 127 volume:127
sourceStr	Enthalten in Ultrasonics 127 volume:127
format_phy_str_mv	Article
bklname	Technische Akustik
institution	findex.gbv.de
topic_facet	Ultrasound plane wave imaging Beamforming Coherent compounding Progressively dual reconstruction network Transfer learning
dewey-raw	530
isfreeaccess_bool	false
container_title	Ultrasonics
authorswithroles_txt_mv	Gao, Junling @@aut@@ Xu, Lei @@aut@@ Zou, Qin @@aut@@ Zhang, Bo @@aut@@ Wang, Diya @@aut@@ Wan, Mingxi @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	320499251
dewey-sort	3530
id	ELV008580758
language_de	englisch
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author	Gao, Junling
spellingShingle	Gao, Junling ddc 530 bkl 50.36 misc Ultrasound plane wave imaging misc Beamforming misc Coherent compounding misc Progressively dual reconstruction network misc Transfer learning A progressively dual reconstruction network for plane wave beamforming with both paired and unpaired training data
authorStr	Gao, Junling
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topic_title	530 DE-600 50.36 bkl A progressively dual reconstruction network for plane wave beamforming with both paired and unpaired training data Ultrasound plane wave imaging Beamforming Coherent compounding Progressively dual reconstruction network Transfer learning
topic	ddc 530 bkl 50.36 misc Ultrasound plane wave imaging misc Beamforming misc Coherent compounding misc Progressively dual reconstruction network misc Transfer learning
topic_unstemmed	ddc 530 bkl 50.36 misc Ultrasound plane wave imaging misc Beamforming misc Coherent compounding misc Progressively dual reconstruction network misc Transfer learning
topic_browse	ddc 530 bkl 50.36 misc Ultrasound plane wave imaging misc Beamforming misc Coherent compounding misc Progressively dual reconstruction network misc Transfer learning
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title	A progressively dual reconstruction network for plane wave beamforming with both paired and unpaired training data
ctrlnum	(DE-627)ELV008580758 (ELSEVIER)S0041-624X(22)00139-1
title_full	A progressively dual reconstruction network for plane wave beamforming with both paired and unpaired training data
author_sort	Gao, Junling
journal	Ultrasonics
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author_browse	Gao, Junling Xu, Lei Zou, Qin Zhang, Bo Wang, Diya Wan, Mingxi
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format_se	Elektronische Aufsätze
author-letter	Gao, Junling
doi_str_mv	10.1016/j.ultras.2022.106833
dewey-full	530
author2-role	verfasserin
title_sort	a progressively dual reconstruction network for plane wave beamforming with both paired and unpaired training data
title_auth	A progressively dual reconstruction network for plane wave beamforming with both paired and unpaired training data
abstract	High-frame-rate plane wave (PW) imaging suffers from unsatisfactory image quality due to the absence of focus in transmission. Although coherent compounding from tens of PWs can improve PW image quality, it in turn results in a decreased frame rate, which is limited for tracking fast moving tissues. To overcome the trade-off between frame rate and image quality, we propose a progressively dual reconstruction network (PDRN) to achieve adaptive beamforming and enhance the image quality via both supervised and transfer learning in the condition of single or a few PWs transmission. Specifically, the proposed model contains a progressive network and a dual network to form a close loop and provide collaborative supervision for model optimization. The progressive network takes the channel delay of each spatial point as input and progressively learns coherent compounding beamformed data with increased numbers of steered PWs step by step. The dual network learns the downsampling process and reconstructs the beamformed data with decreased numbers of steered PWs, which reduces the space of the possible learning functions and improves the model’s discriminative ability. In addition, the dual network is leveraged to perform transfer learning for the training data without sufficient steered PWs. The simulated, in vivo, vocal cords (VCs), and public available CUBDL dataset are collected for model evaluation.
abstractGer	High-frame-rate plane wave (PW) imaging suffers from unsatisfactory image quality due to the absence of focus in transmission. Although coherent compounding from tens of PWs can improve PW image quality, it in turn results in a decreased frame rate, which is limited for tracking fast moving tissues. To overcome the trade-off between frame rate and image quality, we propose a progressively dual reconstruction network (PDRN) to achieve adaptive beamforming and enhance the image quality via both supervised and transfer learning in the condition of single or a few PWs transmission. Specifically, the proposed model contains a progressive network and a dual network to form a close loop and provide collaborative supervision for model optimization. The progressive network takes the channel delay of each spatial point as input and progressively learns coherent compounding beamformed data with increased numbers of steered PWs step by step. The dual network learns the downsampling process and reconstructs the beamformed data with decreased numbers of steered PWs, which reduces the space of the possible learning functions and improves the model’s discriminative ability. In addition, the dual network is leveraged to perform transfer learning for the training data without sufficient steered PWs. The simulated, in vivo, vocal cords (VCs), and public available CUBDL dataset are collected for model evaluation.
abstract_unstemmed	High-frame-rate plane wave (PW) imaging suffers from unsatisfactory image quality due to the absence of focus in transmission. Although coherent compounding from tens of PWs can improve PW image quality, it in turn results in a decreased frame rate, which is limited for tracking fast moving tissues. To overcome the trade-off between frame rate and image quality, we propose a progressively dual reconstruction network (PDRN) to achieve adaptive beamforming and enhance the image quality via both supervised and transfer learning in the condition of single or a few PWs transmission. Specifically, the proposed model contains a progressive network and a dual network to form a close loop and provide collaborative supervision for model optimization. The progressive network takes the channel delay of each spatial point as input and progressively learns coherent compounding beamformed data with increased numbers of steered PWs step by step. The dual network learns the downsampling process and reconstructs the beamformed data with decreased numbers of steered PWs, which reduces the space of the possible learning functions and improves the model’s discriminative ability. In addition, the dual network is leveraged to perform transfer learning for the training data without sufficient steered PWs. The simulated, in vivo, vocal cords (VCs), and public available CUBDL dataset are collected for model evaluation.
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title_short	A progressively dual reconstruction network for plane wave beamforming with both paired and unpaired training data
remote_bool	true
author2	Xu, Lei Zou, Qin Zhang, Bo Wang, Diya Wan, Mingxi
author2Str	Xu, Lei Zou, Qin Zhang, Bo Wang, Diya Wan, Mingxi
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doi_str	10.1016/j.ultras.2022.106833
up_date	2024-07-06T20:11:31.499Z
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A progressively dual reconstruction network for plane wave beamforming with both paired and unpaired training data

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