ISSN 1004-4140
CN 11-3017/P

4DCT成像与重建方法综述

严振峣, 高河伟, 张丽

严振峣, 高河伟, 张丽. 4DCT成像与重建方法综述[J]. CT理论与应用研究(中英文), 2024, 33(2): 243-262. DOI: 10.15953/j.ctta.2023.102.
引用本文: 严振峣, 高河伟, 张丽. 4DCT成像与重建方法综述[J]. CT理论与应用研究(中英文), 2024, 33(2): 243-262. DOI: 10.15953/j.ctta.2023.102.
YAN Z Y, GAO H W, ZHANG L. A Review of 4DCT Imaging and Reconstruction Methods[J]. CT Theory and Applications, 2024, 33(2): 243-262. DOI: 10.15953/j.ctta.2023.102. (in Chinese).
Citation: YAN Z Y, GAO H W, ZHANG L. A Review of 4DCT Imaging and Reconstruction Methods[J]. CT Theory and Applications, 2024, 33(2): 243-262. DOI: 10.15953/j.ctta.2023.102. (in Chinese).

4DCT成像与重建方法综述

基金项目: 国家自然科学基金(融合动态能谱与个性化建模的冠状动脉容积成像方法与关键技术(62031020))。
详细信息
    作者简介:

    严振峣: 男,清华大学工程物理系粒子信息获取与处理研究室在读博士,主要从事4DCT和心脏动态成像领域的算法研究,E-mail:yan-zy20@mails.tsinghua.edu.cn

    通讯作者:

    张丽: 女,清华大学工程物理系粒子信息获取与处理研究室主任、首席研究员,E-mail:zli@mail.tsinghua.edu.cn

  • 中图分类号: O  242;TP  391.41

A Review of 4DCT Imaging and Reconstruction Methods

  • 摘要:

    本文概述过去20多年四维CT(4DCT)相关成像技术的发展,对4DCT的概念、扫描模式以及重建算法做了比较详细的介绍。文章归纳总结4DCT的5大类重建算法,评估各类算法的优势、劣势和关键技术问题,并对主要文献中的重建方法进行统计分析,拟展示目前4DCT重建算法的研究方向和未来的发展趋势。

    Abstract:

    In this paper, the main literature related to 4DCT imaging and reconstruction techniques over the past 20 years is reviewed, and the contents are summarized. This paper provides a systematic and comprehensive introduction to 4DCT research from five perspectives: the concept of 4DCT, scanning mode and imaging method, reconstruction algorithm, application, research status, and future development expectations. In this study, five types of reconstruction algorithms are summarized, and the advantages, disadvantages, and research difficulties of each algorithm are briefly evaluated. Finally, we conduct a brief statistical analysis on the cited works from the perspective of reconstruction methods, revealing the research progress and future research trends of 4DCT reconstruction algorithms.

  • 滤泡性淋巴瘤(follicular lymphoma,FL)是非霍奇金淋巴瘤(non-Hodgkin’s lymphoma,NHL)的常见类型之一,多发生于淋巴结,也可见于胃肠道、皮肤等结外器官或部位,罕见发生于骨骼肌。结外病灶的诊断对评估FL患者预后具有重要价值。能谱增强CT成像能够通过分析不同组织的能谱曲线确定其同质性,因此能帮助诊断继发性结外淋巴瘤。

    本文报告我院收治的1例以能谱增强CT扫描诊断骨骼肌继发性结外FL的病例。

    患者,男,52岁,因发现左侧腹股沟区一枚硬币大小的肿物2月余于2021年9月于外院就诊。2021年9月6日行腹股沟彩超检查示双侧腹股沟淋巴结肿大,行穿刺活检术,10月11日病理报告示倾向为低级别滤泡性淋巴瘤。同时患者自觉消瘦明显,1月内体重下降5 kg,伴夜间出汗明显,肢软乏力。

    于10月13日行正电子发射计算机断层显像(position emission tomography,PET)/计算机断层扫描(computed tomography,CT)检查发现:①全身多区域淋巴结肿大、脾肿大伴氟代脱氧葡萄糖(fluoro deoxy glucose,FDG)代谢增高,全身多发骨骼FDG代谢增高,均考虑淋巴瘤累及;②肝肿大,肝内稍低密度灶伴边缘FDG代谢轻度增高,淋巴瘤累及不除外。

    10月18日患者于外院住院治疗,并于全麻下行功能性颈部淋巴结清除术,术后予谷胱甘肽、兰索拉唑、盐酸左西替利嗪、氟比洛芬酯综合治疗。术后病理免疫组化:CD3(背景T细胞+),CD20(+),CD19(+),CD10(+),cD21(树突细胞+),CD23(树突细胞+),CD5(背景T细胞+),CyclinD1( − ),Bc12(生发中心41),Bc16(生发中心+),Ki67(生发中心15%),PD-1(散在+),结合苏木精-伊红染色法(hematoxylin-eosin stain, HE stain)可符合为滤泡性淋巴瘤I级。

    2021年11月9日,患者因肢麻乏力于我院住院治疗,行上、下腹部CT平扫示:右侧心膈角、肝门区、腹膜后、腹腔、盆壁多发肿大淋巴结,部分融合;脾脏肿大(图1)。行骨髓穿刺+活检示:骨髓增生极度活跃,骨小梁20%,纤维脂肪5%,造血主质75%,三系均见,以中晚幼成熟为主,B淋巴细胞相对明显增生,呈实性、滤泡状生长,表达BCL-2,全片见巨核细胞10个,网状纤维重度增生(MF-3),结合临床所供病史及实验室检查,倾向滤泡性淋巴瘤累及骨髓。

    图  1  上腹部CT平扫:双侧腰大肌形态、密度无明显差异;腹膜后多发肿大淋巴结;脾肿大
    Figure  1.  Computed tomography (CT) scan of the upper abdomen

    患者临床诊断为滤泡性淋巴瘤Ann Arbor IV期,FL国际预后指数(FL international prognosticindex,FLIPI)3分(高危)。目前IV期FL普遍认为不可治愈,且FL为惰性淋巴瘤,大部分患者病变进展缓慢,相当长时间不接受治疗仍可保持良好的生活质量,故该患者未行放、化疗治疗,在患者要求下于我院行中医保守治疗。

    2022年4月患者因腰痛进行性加重2月,妨碍行走就诊我院。血液检查结果:血红蛋白106 g/L,白细胞计数2.75×109/L(正常范围3.50~9.50×109/L),血小板178×109/L,乳酸脱氢酶205.8 U/L,尿β2-微球蛋白2.32 mg/L(正常范围 0.00~0.30 mg/L)。乙型和丙型肝炎病毒以及人类免疫缺陷病毒血清学检测呈阴性。

    2022年4月19日行腰椎磁共振(magnetic resonance imaging,MRI)平扫(图2),因幽闭恐惧症发作无法耐受长时间扫描,且患者拒绝行PET/CT检查及穿刺活检和手术。为明确诊断和病变范围遂于次日行腰椎能谱增强CT检查。

    图  2  腰椎MRI平扫
    (a)和(b)L1-L2水平右侧腰大肌见一肿块,呈T1WI低、T2WI稍高信号。
    Figure  2.  Lumbar spine MRI plain scan

    采用256排CT扫描仪(Revolution CT,GE healthcare)行平扫及增强扫描。采用宝石能谱成像(gemstone spectral imaging,GSI)扫描模式,扫描参数如表1所示。所用对比剂为非离子型碘对比剂(博莱科信宜,国药准字H20053385),浓度370 mg/mL,用量1 mL/kg,流率2 mL/s,生理盐水20 mL,流率3 mL/s。数据采集完成后,重建带能谱数据信息(GSI DATA)序列,层厚0.625 mm,并将所有序列传入工作站进行后处理分析。

    表  1  能谱增强CT扫描方式及参数
    Table  1.  Spectral computed tomography (CT) scan modes and parameters
    扫描方式及参数名具体扫描方式及参数值扫描方式及参数名具体扫描方式及参数值
      采集体位  仰卧位,头先进   层间距  0.625 mm
      扫描方向、范围  自胸椎下缘向下扫描至骶椎   管电压  GSI模式(瞬时切换80 kVp、140 kVp)
      探测器宽度  64×0.625 mm   管电流  自动毫安模式(200~650 mA,NI:9)
      扫描方法  经验法   矩阵  512×512
      层厚  0.625 mm   机架旋转时间  0.6 s/r
    下载: 导出CSV 
    | 显示表格

    将重建后的薄层图像传送到 GE AW4.7工作站,利用GSI Volume Viewer软件进行能谱的多参数分析。在动脉晚期,右侧腰大肌病变强化程度最适宜观察,且受到的血管影干扰最小,因此选择此期进行后处理分析。于右侧肿大腰大肌、对侧镜面位置正常腰大肌及腹膜后肿大淋巴结区域选取合适的感兴趣区(region of interest,ROI)。ROI面积应尽量一致,且均不大于50 mm2,应避开坏死、钙化、囊变、血管影及边缘区。

    利用能谱曲线工具获取ROI能谱曲线,分别计算动脉期3组能谱曲线斜率K。计算公式:$K=({\rm{CT}} $40 keV$-\,{\rm{CT}}$XkeV$)/(X-40)$,其中X分别为70、100及140 keV。

    腹膜后见多发肿大淋巴结,邻近的右侧L1-2水平腰大肌较对侧正常腰大肌增厚。GSI分析:右侧L1-2水平腰大肌能谱曲线走形与腹膜后肿大淋巴结能谱曲线走形接近,与对侧正常形态腰大肌能谱曲线走形差异较大(图3(a))。根据能谱曲线分别计算3条曲线的斜率K:右侧腰大肌KVP40-70=2.38、KVP40-100=1.5及KVP40-140=0.99;左侧腰大肌KVP40-70=1.41、KVP40-100=0.89及KVP40-140=0.6;腹膜后肿大淋巴结KVP40-70=2.76、KVP40-100=1.76及KVP40-140=1.135(表2)。右侧腰大肌的曲线斜率与腹膜后淋巴结斜率接近,与正常腰大肌斜率差异较大,提示右侧腰大肌被淋巴瘤浸润可能性大。

    图  3  腰椎能谱CT增强图像
    (a)动脉晚期 ROI对应能谱曲线(蓝线代表右侧肿大腰大肌,绿线代表左侧正常腰大肌,紫线代表腹膜后肿大淋巴结)。(b)腰椎 CT平扫:腹膜后多发肿大淋巴结;右侧腰大肌较对侧明显肿胀。(c)和(d)腰椎能谱增强 CT动脉晚期轴位图像:双侧腰大肌及腹膜后肿大淋巴结ROI对应的CT值;右侧肿大腰大肌增强后强化程度略高于左侧正常腰大肌,密度均匀。
    Figure  3.  Lumbar spine spectral computed tomography (CT) image
    表  2  双侧腰大肌及腹膜后肿大淋巴结的KVP40-70KVP40-100KVP40-140
    Table  2.  KVP40-70, KVP40-100 and KVP40-140 of the bilateral psoas major muscle and enlarged retroperitoneal lymph nodes
    项目KVP40-70KVP40-100KVP40-140
    右侧肿大腰大肌 2.381.500.99
    左侧正常腰大肌 1.410.890.60
    后腹膜肿大淋巴结2.761.761.14
    下载: 导出CSV 
    | 显示表格

    考虑到中医保守治疗效果不佳,为进一步治疗,患者目前于外院诊疗,病情稳定。

    FL是NHL中的一种常见类型,是淋巴滤泡生发中心B细胞来源的一种惰性B细胞淋巴增殖性疾病,以弥漫性淋巴结肿大、骨髓受累和脾肿大为特征,常见于中、老年人,女性稍多于男性。WHO统计FL约占NHL的22.1%,但在中国人群中发生率较低,只占NHL的10% 左右。

    FL主要累及淋巴结,结外受累较少见。肿瘤区域个数的增加可提高FL患者的FLIPI指数进而增加患者的危险度分级,因此结外淋巴瘤的诊断对评估FL患者的病情及预后具有重要价值。累及骨骼肌的淋巴瘤非常少见,仅占霍奇金淋巴瘤(Hodgkin lymphoma,HL)病例的 0.3%、NHL病例的1.5%[1],多数病理类型为弥漫大B细胞性淋巴瘤(diffuse large B cell lymphoma,DLBCL),而原发性骨骼肌FL的报道极为罕见,且没有关于其发病率的流行病学数据[2]

    骨骼肌淋巴瘤通常通过3种途径发生:①作为一种原发性结外疾病发生;②通过血源性或淋巴途径传播;③通过邻近器官浸润,如骨骼或淋巴结。原发性骨骼肌淋巴瘤发生在腿部损伤后、靠近针注射部位和同性恋男性直肠已有报道[3]

    由于下肢易受损伤,这可能提示原发性骨骼肌淋巴瘤可能和机械刺激有关。继发性骨骼肌淋巴瘤最常见的原因是从邻近淋巴结或其他原发病灶(如骨)扩散或转移而来[1],这与本病例的影像表现相符。

    FL临床表现多为受累肌肉出现进行性增大的软组织肿块、肿胀、疼痛、发热、出汗和体重减轻,最常见于下肢,占所有报告病例的50%[3]。本病例在确诊半年余后出现进行性加重的腰痛,严重影响生活质量,影像学检查提示右侧腰大肌新发的软组织肿块。

    能谱增强CT已广泛应用于淋巴瘤的诊断中,目前研究多集中于与鼻咽癌、纵膈胸腺瘤、结节病、肠道肿瘤和转移性淋巴结等疾病的鉴别[4-9]。鉴于骨骼肌淋巴瘤发生的罕见性,通过能谱增强CT诊断的经验较缺乏,只在超声、平扫CT、MRI及PET/CT的相关研究中有零星报道[2,10-15]

    CT可通过病灶的形态、大小、边界、密度及强化方式来诊断骨骼肌淋巴瘤,表现为受累肌肉肿胀,边界清晰或不清晰,与正常肌肉相比呈均质的等或高或低密度,增强后病变肌肉呈轻微至明显不同程度的强化。作为混合能量图像,普通CT的CT值不能准确反映病灶实际的CT值大小。能谱增强CT成像通过快速切换能量并进行数据采集,可获得不同组织在40~140 keV各单能量图像下的CT值[16]。通过对比能谱曲线的形态及斜率可确定病灶的来源、鉴别良恶性。

    本例患者患有幽闭恐惧症并拒绝再次穿刺、手术及PET/CT检查,遂行能谱增强CT示右侧腰大肌与腹膜后肿大淋巴结能谱曲线走行及形态相近,虽未取得腰大肌病理结果验证,结合既往病史、病理与影像学检查考虑为骨骼肌淋巴瘤浸润的可能性最大。

    迄今为止,大多数FL患者仍然不可治愈。FL的治疗应充分考虑患者耐受情况、肿瘤负荷及复发风险而采取个体化治疗。目前各指南推荐的一线治疗方案均为CD20单抗+化疗;对于晚期(Ⅲ 期、Ⅳ期)患者根据美国国立综合癌症网络(national comprehensive cancer nelwork,NCCN)指南推荐如果低肿瘤负荷且没有症状,可采取观察和等待策略;若存在肿瘤高负荷表现、肿瘤进展、受累器官功能受损等治疗指征时,建议开始治疗[17]

    本病例为 Ⅳ 期,FLIPI 3分(高危)患者,前期临床症状轻微,要求中医保守治疗;后期出现严重腰痛,考虑淋巴瘤浸润骨骼肌可能大,提示病变进展,遂于外院就诊进一步治疗。

    骨骼肌淋巴瘤的诊断需结合临床病史、实验室检查、影像学检查及病理组织学检查结果。当骨骼肌出现进行性增大的软组织肿块,伴疼痛、发热、短期内消瘦,应怀疑本病,尤其在淋巴瘤患者中,应怀疑继发性骨骼肌淋巴瘤。影像学特征具非特异性,需与其他疾病(如原发性软组织肉瘤、其他原发软组织恶性肿瘤、创伤或肌炎等)鉴别。

    (1)软组织肉瘤:包括平滑肌肉瘤、横纹肌肉瘤、脂肪肉瘤、滑膜肉瘤等,表现为受累肌肉的形态和轮廓改变伴不规则软组织肿块,肿块平扫及增强的密度和信号不均匀,实性部分大多强化明显,易出现坏死、液化,瘤周水肿较淋巴瘤明显。

    (2)外周型原始神经外胚瘤:与骨骼肌淋巴瘤同属小细胞肿瘤,好发部位相似,但外周型原始神经外胚瘤多见于10~20岁儿童或青少年,临床发病率低,相对罕见,恶性程度高,易复发转移,预后相对较差,对放化疗敏感。在影像学上肿瘤表现为大软组织肿块,浸润性、跨越性、快速生长,血供丰富,明显不均匀强化,其形态较淋巴瘤更不规则,边界更不清楚,囊变坏死更显著,强化更明显且不均匀。

    (3)神经纤维瘤病:是一类常染色体显性遗传性疾病,可分为3型:Ⅰ型神经纤维瘤病、Ⅱ型神经纤维瘤病和 Ⅲ 型施万细胞瘤病。此类疾病表型差异性大,以皮肤病变、周围神经系统病变和中枢神经系统肿瘤为主,引起多发的、渐进性的损害。神经纤维瘤多发生于肌间隙,与肌肉分界清,邻近肌肉受压变形但密度、信号大多正常;少部分发生于肌肉内,病灶形态不规则,受累肌肉多失去正常形态,多肌肉受累时,肌间隙模糊。

    (4)炎症:起病急,临床症状明显,表现为局部红肿热痛,部分患者全身症状明显,抗炎治疗有效。CT或MRI表现为受累肌肉明显肿胀,肌间隙模糊,筋膜及皮下水肿较明显。

    本文报道了1例以能谱增强CT成像帮助诊断继发性骨骼肌FL的病例,在MRI、PET/CT及穿刺活检无法进行时,能谱增强CT可作为辅助诊断的潜在新方法。通过重建及比较不同组织的能谱衰减曲线,可初步确定病变来源,为广大医师在临床诊疗过程中提供诊断新思路、新方法。

  • 图  1   心脏4DCT成像过程示意图

    Figure  1.   Cardiac 4DCT imaging process

    图  2   CT运动伪影特征可以通过3个简单的动态对象

    Figure  2.   CT motion artifact feature illustrated by three simple dynamic objects

  • [1]

    ROBB R A. The dynamic spatial reconstructor: An X-ray video fluoroscopic CT scanner for dynamic volume imaging of moving organs[J]. IEEE Transactions on Medical Imaging, 1982, 1(1): 22−33. DOI: 10.1109/TMI.1982.4307545.

    [2]

    ENDO M, TSUNOO T, KANDATSU S, et al. Four-dimensional computed tomography (4DCT): Concepts and preliminary development[J]. Radiation Medicine, 2003, 21(1): 17−22.

    [3]

    CHEN G H, THERIAULT-LAUZIER P, TANG J, et al. Time-resolved interventional cardiac C-arm cone-beam CT: An application of the PICCS algorithm[J]. IEEE Transactions on Medical Imaging, 2011, 31(4): 907−923.

    [4]

    ICHIKAWA T, KUMAZAKI T. 4D-CT: A new development in three-dimensional hepatic computed tomography[J]. Journal of Nippon Medical School, 2000, 67(1): 24−27. DOI: 10.1272/jnms.67.24.

    [5]

    SHEPP L A, HILAL S K, SCHULZ R A. The tuning fork artifact in computerized tomography[J]. Computer Graphics and Image Processing, 1979, 10(3): 246−255. DOI: 10.1016/0146-664X(79)90004-2.

    [6]

    MAYO J R, MÜLLER N L, HENKELMAN R M. The double-fissure sign: A motion artifact on thin-section CT scans[J]. Radiology, 1987, 165(2): 580−581. DOI: 10.1148/radiology.165.2.3659392.

    [7]

    RITCHIE C J, HSIEH J, GARD M F, et al. Predictive respiratory gating: A new method to reduce motion artifacts on CT scans[J]. Radiology, 1994, 190(3): 847−852. DOI: 10.1148/radiology.190.3.8115638.

    [8]

    CRAWFORD C R, KING K F, RITCHIE C J, et al. Respiratory compensation in projection imaging using magnification and displacement model[J]. IEEE Transactions on Medical Imaging, 1996, 15(3): 327−332. DOI: 10.1109/42.500141.

    [9]

    MOORREES J, BEZAK E. Four dimensional CT imaging: A review of current technologies and modalities[J]. Australasian Physical & Engineering Sciences in Medicine, 2012, 35(1): 9−23.

    [10]

    KEALL P. 4-dimensional computed tomography imaging and treatment planning[J]. Seminars in Radiation Oncology, 2003, 14(1): 81−90.

    [11]

    MAH D, HANLEY J, ROSENZWEIG K E, et al. Technical aspects of the deep inspiration breath-holding technique in the treatment of thoracic cancer[J]. International Journal of Radiation Oncology, Biology, Physics, 2000, 48(4): 1175−1185. DOI: 10.1016/S0360-3016(00)00747-1.

    [12]

    KEALL P J, CHEN G T Y, JOSHI S, et al. Time-the fourth dimension in radiotherapy (ASTRO Panel discussion)[J]. International Journal of Radiation Oncology, Biology, Physics, 2003, 57(S2): S8−S9.

    [13]

    FENSTER A, DOWNEY D B, CARDINAL H. Three-dimensional ultrasound imaging[J]. Physics in Medicine & Biology, 2001, 46(5): R67.

    [14]

    ENDO M, MORI S, TSUNOO T, et al. Development and performance evaluation of the first model of 4DCT-scanner[C]//IEEE. 2002 IEEE Nuclear Science Symposium Conference Record, Norfolk, VA, USA, 2002, 3: 1824-1828.

    [15]

    FORD E C, MAGERAS G S, YORKE E, et al. Respiration‐correlated spiral CT: A method of measuring respiratory‐induced anatomic motion for radiation treatment planning[J]. Medical Physics, 2003, 30(1): 88−97. DOI: 10.1118/1.1531177.

    [16]

    VEDAM S S, KEALL P J, KINI V R, et al. Acquiring a four-dimensional computed tomography dataset using an external respiratory signal[J]. Physics in Medicine & Biology, 2002, 48(1): 45.

    [17]

    KEALL P J, STARKSCHALL G, SHUKLA H E E, et al. Acquiring 4D thoracic CT scans using a multislice helical method[J]. Physics in Medicine & Biology, 2004, 49(10): 2053.

    [18]

    LOW D A, NYSTROM M, KALININ E, et al. A method for the reconstruction of four-dimensional synchronized CT scans acquired during free breathing[J]. Medical Physics, 2003, 30(6): 1254−1263. DOI: 10.1118/1.1576230.

    [19]

    PAN T, LEE T Y, RIETZEL E, et al. 4D-CT imaging of a volume influenced by respiratory motion on multislice CT[J]. Medical Physics, 2004, 31(2): 333−340. DOI: 10.1118/1.1639993.

    [20]

    KALENDER W A, SEISSLER W, KLOTZ E, et al. Spiral volumetric CT with single-breath-hold technique, continuous transport, and continuous scanner rotation[J]. Radiology, 1990, 176(1): 181−183. DOI:10.1148/ radiology.176.1.2353088.

    [21]

    PAN T. Helical 4DCT and comparison with cine 4DCT[J]. 4D Modeling and Estimation of Respiratory Motion for Radiation Therapy, 2013: 25-41.

    [22]

    WU X, XIAO S, ZHANG Y. Registration-based super-resolution reconstruction for lung 4D-CT[C]//IEEE. 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, USA, 2014: 2444-2447.

    [23]

    ZHANG Y, WU X, YANG W, et al. Super-resolution reconstruction for 4D computed tomography of the lung via the projections onto convex sets approach[J]. Medical Physics, 2014, 41(11): 111917. DOI:10.1118/ 1.4899185.

    [24]

    WANG T, CAO L, YANG W, et al. Adaptive patch-based POCS approach for super-resolution reconstruction of 4D-CT lung data[J]. Physics in Medicine & Biology, 2015, 60(15): 5939.

    [25]

    ZHANG Y, WU G, YAP P T, et al. Hierarchical patch-based sparse representation: A new approach for resolution enhancement of 4D-CT lung data[J]. IEEE Transactions on Medical Imaging, 2012, 31(11): 1993−2005. DOI: 10.1109/TMI.2012.2202245.

    [26]

    LIU H, LIN Y, IBRAGIMOV B, et al. Low-dose 4D-CT super-resolution reconstruction via inter-plane motion estimation based on optical flow[J]. Biomedical Signal Processing and Control, 2020, 62: 102085. DOI: 10.1016/j.bspc.2020.102085.

    [27]

    ZHANG Y, WU G, YAP P T, et al. Reconstruction of super-resolution lung 4D-CT using patch-based sparse representation[C]//IEEE. 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, 2012: 925-931.

    [28]

    PAN T. Comparison of helical and cine acquisitions for 4D-CT imaging with multi-slice CT[J]. Medical Physics, 2005, 32(2): 627−634. DOI: 10.1118/1.1855013.

    [29]

    ZENG R, FESSLER J A, BALTER J M, et al. Iterative sorting for 4DCT images based on internal anatomy motion[C]//IEEE. 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Arlington, VA, USA, 2007: 744-747.

    [30]

    WINK N M, PANKNIN C, SOLBERG T D. Phase versus amplitude sorting of 4D-CT data[J]. Journal of Applied Clinical Medical Physics, 2006, 7(1): 77−85.

    [31]

    GEORG M, SOUVENIR R, HOPE A, et al. Manifold learning for 4DCT reconstruction of the lung[C]//IEEE. 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Anchorage, AK, USA, 2008: 1-8.

    [32]

    LI G, CITRIN D, CAMPHAUSEN K, et al. Advances in 4D medical imaging and 4D radiation therapy[J]. Technology in Cancer Research & Treatment, 2008, 7(1): 67−81.

    [33]

    BERLINGER K, SAUER O, VENCES L, et al. A simple method for labeling CT images with respiratory states[J]. Medical Physics, 2006, 33(9): 3144−3148. DOI: 10.1118/1.2229420.

    [34]

    KACHELRIEß M, ULZHEIMER S, KALENDER W A. ECG-correlated image reconstruction from subsecond multi-slice spiral CT scans of the heart[J]. Medical Physics, 2000, 27(8): 1881−1902. DOI: 10.1118/1.1286552.

    [35]

    SCHIEVANO S, CAPELLI C, YOUNG C, et al. Four-dimensional computed tomography: A method of assessing right ventricular outflow tract and pulmonary artery deformations throughout the cardiac cycle[J]. European Radiology, 2011, 21: 36−45. DOI: 10.1007/s00330-010-1913-5.

    [36]

    CARNES G, GAEDE S, YU E, et al. A fully automated non-external marker 4D-CT sorting algorithm using a serial cine scanning protocol[J]. Physics in Medicine & Biology, 2009, 54(7): 2049.

    [37]

    LI R, LEWIS J H, CERVINO L I, et al. 4DCT sorting based on patient internal anatomy[J]. Physics in Medicine & Biology, 2009, 54(15): 4821.

    [38]

    LU J, GUERRERO T M, MUNRO P, et al. Four-dimensional cone-beam CT with adaptive gantry rotation and adaptive data sampling[J]. Medical Physics, 2007, 34(9): 3520−3529. DOI: 10.1118/1.2767145.

    [39]

    ZIJP L, SONKE J J, van HERK M. Extraction of the respiratory signal from sequential thorax cone-beam X-ray images[C]//International Conference on the Use of Computers in Radiation Therapy, 2004: 507-509.

    [40]

    RIT S, SARRUT D, GINESTET C. Respiratory signal extraction for 4DCT imaging of the thorax from cone-beam CT projections[C]//Medical Image Computing and Computer-assisted Intervention (MICCAI 2005): 8th International Conference, Palm Springs, CA, USA, October 26-29, 2005, Proceedings, Part I 8. Springer Berlin Heidelberg, 2005: 556-563.

    [41]

    ZHANG S X, ZHOU L H, YU H, et al. 4D-CT reconstruction based on body volume change[C]//World Congress on Medical Physics and Biomedical Engineering, 2009, Munich, Germany: Vol. 25/1 Radiation Oncology. Springer Berlin Heidelberg, 2009: 569-572.

    [42]

    ZHANG S, ZHOU L, LIN S, et al. 4D-CT reconstruction based on pulmonary average CT values[J]. Bio-Medical Materials and Engineering, 2014, 24(1): 85−94. DOI: 10.3233/BME-130787.

    [43]

    HUGO G D, ROSU M. Advances in 4D radiation therapy for managing respiration: Part I—4D imaging[J]. Zeitschrift für Medizinische Physik, 2012, 22(4): 258−271.

    [44]

    COOPER B J, O'BRIEN R T, BALIK S, et al. Respiratory triggered 4D cone-beam computed tomography: A novel method to reduce imaging dose[J]. Medical Physics, 2013, 40(4): 041901. DOI: 10.1118/1.4793724.

    [45]

    PAN T, MARTIN R M, LUO D. New prospective 4D-CT for mitigating the effects of irregular respiratory motion[J]. Physics in Medicine & Biology, 2017, 62(15): N350.

    [46]

    PAN T, SUN X, LUO D. Improvement of the cine-CT based 4D-CT imaging[J]. Medical Physics, 2007, 34(11): 4499−4503. DOI: 10.1118/1.2794225.

    [47]

    KEALL P J, VEDAM S S, GEORGE R, et al. Respiratory regularity gated 4DCT acquisition: Concepts and proof of principle[J]. Australasian Physics & Engineering Sciences in Medicine, 2007, 30: 211−220.

    [48]

    LU W, PARIKH P J, HUBENSCHMIDT J P, et al. A comparison between amplitude sorting and phase-angle sorting using external respiratory measurement for 4DCT[J]. Medical Physics, 2006, 33(8): 2964−2974. DOI: 10.1118/1.2219772.

    [49]

    DIETRICH L, JETTER S, TÜCKING T, et al. Linac-integrated 4D cone beam CT: First experimental results[J]. Physics in Medicine & Biology, 2006, 51(11): 2939.

    [50]

    LI H, NOEL C, GARCIA-RAMIREZ J, et al. Clinical evaluations of an amplitude-based binning algorithm for 4DCT reconstruction in radiation therapy[J]. Medical Physics, 2012, 39(2): 922−932. DOI: 10.1118/1.3679015.

    [51]

    RIETZEL E, PAN T, CHEN G T Y. Four-dimensional computed tomography: Image formation and clinical protocol[J]. Medical Physics, 2005, 32(4): 874−889. DOI: 10.1118/1.1869852.

    [52]

    JIA X, LOU Y, DONG B, et al. 4D computed tomography reconstruction from few-projection data via temporal non-local regularization[C]//Medical Image Computing and Computer-assisted Intervention (MICCAI 2010): 13th International Conference, Beijing, China, 2010, Proceedings, Part I 13. Springer Berlin Heidelberg, 2010: 143-150.

    [53]

    LENG S, ZAMBELLI J, TOLAKANAHALLI R, et al. Streaking artifacts reduction in four-dimensional cone-beam computed tomography[J]. Medical Physics, 2008, 35(10): 4649−4659. DOI: 10.1118/1.2977736.

    [54]

    WERNER R, SENTKER T, MADESTA F, et al. Intelligent 4DCT sequence scanning (i4DCT): Concept and performance evaluation[J]. Medical Physics, 2019, 46(8): 3462−3474. DOI: 10.1002/mp.13632.

    [55]

    AHMAD M, BALTER P, PAN T. Four-dimensional volume-of-interest reconstruction for cone-beam computed tomography: Guided radiation therapy[J]. Medical Physics, 2011, 38(10): 5646−5656. DOI: 10.1118/1.3634058.

    [56] 张书旭,周凌宏,徐海荣,等. 基于相邻图像最相似原理的 4D-CT 图像重建研究[J]. 中国医学物理学杂志,2009,26(5): 1409−1414.

    ZHANG S X, ZHOU L H, XU H R, et al. Four-dimensional computerized tomography (4D-CT) reconstruction based on the most similarity principle of spatial adjacent images[J]. Chinese Journal of Medical Physics, 2009, 26(5): 1409−1414. (in Chinese).

    [57]

    HE T, XUE Z, NITSCH P L, et al. Helical mode lung 4D-CT reconstruction using Bayesian model[C]//Medical Image Computing and Computer-Assisted Intervention (MICCAI 2013): 16th International Conference, Nagoya, Japan, 2013, Proceedings, Part III 16. Springer Berlin Heidelberg, 2013: 33-40.

    [58]

    WERNER R, SZKITSAK J, SENTKER T, et al. Comparison of intelligent 4DCT sequence scanning and conventional spiral 4DCT: A first comprehensive phantom study[J]. Physics in Medicine & Biology, 2021, 66(1): 015004.

    [59]

    WERNER R, SENTKER T, MADESTA F, et al. Intelligent 4DCT sequence scanning (i4DCT): First scanner prototype implementation and phantom measurements of automated breathing signal-guided 4DCT[J]. Medical Physics, 2020, 47(6): 2408−2412. DOI: 10.1002/mp.14106.

    [60]

    GAI N, AXEL L. Correction of motion artifacts in linogram and projection reconstruction MRI using geometry and consistency constraints[J]. Medical Physics, 1996, 23(2): 251−262. DOI: 10.1118/1.597713.

    [61]

    WATKINS W T, LI R, LEWIS J, et al. Patient-specific motion artifacts in 4DCT[J]. Medical Physics, 2010, 37(6 Part 1): 2855-2861.

    [62]

    PACK J D, MANOHAR A, RAMANI S, et al. Four-dimensional computed tomography of the left ventricle, Part I: Motion artifact reduction[J]. Medical Physics, 2022, 49(7): 4404−4418. DOI: 10.1002/mp.15709.

    [63]

    LU W, MACKIE T R. Tomographic motion detection and correction directly in sinogram space[J]. Physics in Medicine & Biology, 2002, 47(8): 1267.

    [64]

    LI S, PELIZZARI C A, CHEN G T Y. Unfolding patient motion with biplane radiographs[J]. Medical Physics, 1994, 21(9): 1427−1433. DOI: 10.1118/1.597188.

    [65]

    BALTER J M, LAM K L, SANDLER H M, et al. Automated localization of the prostate at the time of treatment using implanted radiopaque markers: Technical feasibility[J]. International Journal of Radiation Oncology, Biology and Physics, 1995, 33(5): 1281−1286. DOI: 10.1016/0360-3016(95)02083-7.

    [66]

    CROOK J M, RAYMOND Y, SALHANI D, et al. Prostate motion during standard radiotherapy as assessed by fiducial markers[J]. Radiotherapy and Oncology, 1995, 37(1): 35−42. DOI: 10.1016/0167-8140(95)01613-L.

    [67]

    EL NAQA I, LOW D A, DEASY J O, et al. Automated breathing motion tracking for 4D computed tomography[C]//2003 IEEE Nuclear Science Symposium. Conference Record (IEEE Cat. No. 03CH37515). IEEE, 2003, 5: 3219-3222.

    [68]

    McKINNON G C, BATES R H T. Towards imaging the beating heart usefully with a conventional CT scanner[J]. IEEE Transactions on Biomedical Engineering, 1981(2): 123−127.

    [69]

    ZHENG Z, SUN M, PAVKOVICH J, et al. Fast 4D cone-beam reconstruction using the McKinnon-Bates algorithm with truncation correction and nonlinear filtering[C]//Medical Imaging 2011: Physics of Medical Imaging. SPIE, 2011, 7961: 817-824.

    [70]

    BERGNER F, BERKUS T, OELHAFEN M, et al. An investigation of 4D cone-beam CT algorithms for slowly rotating scanners[J]. Medical Physics, 2010, 37(9): 5044−5053. DOI: 10.1118/1.3480986.

    [71]

    QI Z, CHEN G H. Performance studies of four-dimensional cone-beam computed tomography[J]. Physics in Medicine & Biology, 2011, 56(20): 6709.

    [72]

    STAR-LACK J, SUN M, OELHAFEN M, et al. A modified McKinnon-Bates (MKB) algorithm for improved 4D cone-beam computed tomography (CBCT) of the lung[J]. Medical Physics, 2018, 45(8): 3783−3799. DOI: 10.1002/mp.13034.

    [73]

    KIM S, CHANG Y, RA J B. Cardiac motion correction based on partial angle reconstructed images in X-ray CT[J]. Medical Physics, 2015, 42(5): 2560−2571. DOI: 10.1118/1.4918580.

    [74]

    LI T, SCHREIBMANN E, YANG Y, et al. Motion correction for improved target localization with on-board cone-beam computed tomography[J]. Physics in Medicine & Biology, 2005, 51(2): 253.

    [75]

    RIT S, WOLTHAUS J, van HERK M, et al. On-the-fly motion-compensated cone-beam CT using an a priori motion model[C]//Medical Image Computing and Computer-assisted Intervention (MICCAI 2008): 11th International Conference, New York, NY, USA, 2008, Proceedings, Part I 11. Springer Berlin Heidelberg, 2008: 729-736.

    [76]

    WU G, WANG Q, LIAN J, et al. Reconstruction of 4D-CT from a single free-breathing 3D-CT by spatial-temporal image registration[C]//Information Processing in Medical Imaging: Proceedings of the Conference. NIH Public Access 2011, 22: 686.

    [77]

    BREHM M, PAYSAN P, OELHAFEN M, et al. Artifact-resistant motion estimation with a patient-specific artifact model for motion-compensated cone-beam CT[J]. Medical Physics, 2013, 40(10): 101913. DOI: 10.1118/1.4820537.

    [78]

    BREHM M, BERKUS T, OEHLHAFEN M, et al. Motion-compensated 4D cone-beam computed tomography[C]//2011 IEEE Nuclear Science Symposium Conference Record. IEEE, 2011: 3986-3993.

    [79]

    WANG J, GU X. High-quality four-dimensional cone-beam CT by deforming prior images[J]. Physics in Medicine & Biology, 2012, 58(2): 231.

    [80]

    CHRISTOFFERSEN C P V, HANSEN D, POULSEN P, et al. Registration-based reconstruction of four-dimensional cone beam computed tomography[J]. IEEE Transactions on Medical Imaging, 2013, 32(11): 2064−2077. DOI: 10.1109/TMI.2013.2272882.

    [81]

    McCLELLAND J R, BLACKALL J M, TARTE S, et al. A continuous 4D motion model from multiple respiratory cycles for use in lung radiotherapy[J]. Medical Physics, 2006, 33(9): 3348−3358. DOI: 10.1118/1.2222079.

    [82]

    RIBLETT M J, CHRISTENSEN G E, WEISS E, et al. Data-driven respiratory motion compensation for four-dimensional cone-beam computed tomography (4D-CBCT) using groupwise deformable registration[J]. Medical Physics, 2018, 45(10): 4471−4482. DOI: 10.1002/mp.13133.

    [83]

    LI R, JIA X, LEWIS J H, et al. Real-time volumetric image reconstruction and 3D tumor localization based on a single X-ray projection image for lung cancer radiotherapy[J]. Medical Physics, 2010; 37(6 Part 1): 2822-2826.

    [84]

    LI T, KOONG A, XING L. Enhanced 4D cone-beam CT with inter-phase motion model[J]. Medical Physics, 2007, 34(9): 3688−3695. DOI: 10.1118/1.2767144.

    [85]

    YANG D, LU W, LOW D A, et al. 4D-CT motion estimation using deformable image registration and 5D respiratory motion modeling[J]. Medical Physics, 2008, 35(10): 4577−4590. DOI: 10.1118/1.2977828.

    [86]

    HUANG X, ZHANG Y, WANG J. A biomechanical modeling-guided simultaneous motion estimation and image reconstruction technique (SMEIR-Bio) for 4D-CBCT reconstruction[J]. Physics in Medicine & Biology, 2018, 63(4): 045002.

    [87]

    SCHÄFER D, BERTRAM M, CONRADS N, et al. Motion compensation for cone-beam CT based on 4D motion field of sinogram tracked markers[C]//International Congress Series. Elsevier, 2004, 1268: 189-194.

    [88]

    SARRUT D, BOLDEA V, MIGUET S, et al. Simulation of four-dimensional CT images from deformable registration between inhale and exhale breath-hold CT scans[J]. Medical Physics, 2006, 33(3): 605−617. DOI: 10.1118/1.2161409.

    [89]

    HAHN J, BRUDER H, ROHKOHL C, et al. Motion compensation in the region of the coronary arteries based on partial-angle reconstruction from short-scan CT data[J]. Medical Physics, 2017, 44(11): 5795−5813. DOI: 10.1002/mp.12514.

    [90]

    BLONDEL C, VAILLANT R, MALANDAIN G, et al. 3D tomographic reconstruction of coronary arteries using a precomputed 4D motion field[J]. Physics in Medicine & Biology, 2004, 49(11): 2197.

    [91]

    BLONDEL C, MALANDAIN G, VAILLANT R, et al. Reconstruction of coronary arteries from a single rotational X-ray projection sequence[J]. IEEE Transactions on Medical Imaging, 2006, 25(5): 653−663. DOI: 10.1109/TMI.2006.873224.

    [92]

    ZHANG Y, TEHRANI J N, WANG J. A biomechanical modeling guided CBCT estimation technique[J]. IEEE Transactions on Medical Imaging, 2016, 36(2): 641−652.

    [93]

    ZHONG Z, GU X, MAO W, et al. 4D cone-beam CT reconstruction using multiorgan meshes for sliding motion modeling[J]. Physics in Medicine & Biology, 2016, 61(3): 996.

    [94]

    RUECKERT D, SONODA L I, HAYES C, et al. Nonrigid registration using free-form deformations: Application to breast MR images[J]. IEEE Transactions on Medical Imaging, 1999, 18(8): 712−721. DOI:10. 1109/42.796284.

    [95]

    HORN B K P, SCHUNCK B G. Determining optical flow[J]. Artificial Intelligence, 1981, 17(1/3): 185−203. DOI: 10.1016/0004-3702(81)90024-2.

    [96]

    THIRION J P. Image matching as a diffusion process: An analogy with Maxwell's demons[J]. Medical Image Analysis, 1998, 2(3): 243−260. DOI: 10.1016/S1361-8415(98)80022-4.

    [97]

    CRUM W R, HARTKENS T, HILL D L G. Non-rigid image registration: Theory and practice[J]. The British Journal of Radiology, 2004, 77(S2): S140−S153. DOI: 10.1259/bjr/25329214.

    [98]

    BOOKSTEIN F L. Principal warps: Thin-plate splines and the decomposition of deformations[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, 11(6): 567−585. DOI: 10.1109/34.24792.

    [99]

    ISOLA A A, ZIEGLER A, SCHÄFER D, et al. Motion compensated iterative reconstruction of a region of interest in cardiac cone-beam CT[J]. Computerized Medical Imaging and Graphics, 2010, 34(2): 149−159. DOI: 10.1016/j.compmedimag.2009.08.004.

    [100]

    MÜLLER K, SCHWEMMER C, HORNEGGER J, et al. Evaluation of interpolation methods for surface-based motion compensated tomographic reconstruction for cardiac angiographic C-arm data[J]. Medical Physics, 2013, 40(3): 031107. DOI: 10.1118/1.4789593.

    [101]

    DAVIS M H, KHOTANZAD A, FLAMIG D P, et al. A physics-based coordinate transformation for 3-D image matching[J]. IEEE Transactions on Medical Imaging, 1997, 16(3): 317−328. DOI: 10.1109/42.585766.

    [102]

    STAUB D, DOCEF A, BROCK R S, et al. 4D Cone-beam CT reconstruction using a motion model based on principal component analysis[J]. Medical Physics, 2011, 38(12): 6697−6709. DOI: 10.1118/1.3662895.

    [103] 邵光普. 基于主成分分析减少 4DCT 图像伪影算法研究[D]. 济南: 山东师范大学,2020.
    [104]

    DESBAT L, ROUX S, GRANGEAT P, et al. Exact fan-beam compensated reconstruction formula for time-dependent affine deformations[C]//Fully Three Dimensional Reconstruction in Radiology and Nuclear Medicine, Saint Malo, France, 2003.

    [105]

    RITCHIE C J, CRAWFORD C R, GODWIN J D, et al. Correction of computed tomography motion artifacts using pixel-specific back-projection[J]. IEEE Transactions on Medical Imaging, 1996, 15(3): 333−342. DOI: 10.1109/42.500142.

    [106]

    ZHANG H, KRUIS M, SONKE J J. Directional sinogram interpolation for motion weighted 4D cone-beam CT reconstruction[J]. Physics in Medicine & Biology, 2017, 62(6): 2254.

    [107]

    MARTIN R, AHMAD M, HUGO G, et al. Iterative volume of interest based 4D cone beam CT[J]. Medical Physics, 2017, 44(12): 6515−6528. DOI: 10.1002/mp.12575.

    [108]

    PARK J C, ZHANG H, CHEN Y, et al. Common-mask-guided image reconstruction (c-MGIR) for enhanced 4D cone-beam computed tomography[J]. Physics in Medicine & Biology, 2015, 60(23): 9157.

    [109]

    MORY C, JANSSENS G, RIT S. Motion-aware temporal regularization for improved 4D cone-beam computed tomography[J]. Physics in Medicine & Biology, 2016, 61(18): 6856.

    [110]

    Den OTTER L A, CHEN K, JANSSENS G, et al. 4D cone-beam CT reconstruction from sparse: View CBCT data for daily motion assessment in pencil beam scanned proton therapy (PBS-PT)[J]. Medical Physics, 2020, 47(12): 6381−6387. DOI: 10.1002/mp.14521.

    [111]

    VANDEMEULEBROUCKE J, BERNARD O, RIT S, et al. Automated segmentation of a motion mask to preserve sliding motion in deformable registration of thoracic CT[J]. Medical Physics, 2012, 39(2): 1006−1015. DOI: 10.1118/1.3679009.

    [112]

    PACK J D, NOO F. Dynamic computed tomography with known motion field[C]//Medical imaging 2004: Image Processing. SPIE, 2004, 5370: 2097-2104.

    [113]

    RIT S, SARRUT D, DESBAT L. Comparison of analytic and algebraic methods for motion-compensated cone-beam CT reconstruction of the thorax[J]. IEEE Transactions on Medical Imaging, 2009, 28(10): 1513−1525. DOI: 10.1109/TMI.2008.2008962.

    [114]

    WANG J, GU X. Simultaneous motion estimation and image reconstruction (SMEIR) for 4D cone-beam CT[J]. Medical Physics, 2013, 40(10): 101912. DOI: 10.1118/1.4821099.

    [115]

    Van NIEUWENHOVE V, de BEENHOUWER J, VLASSENBROECK J, et al. MoVIT: A tomographic reconstruction framework for 4D-CT[J]. Optics Express, 2017, 25(16): 19236−19250. DOI: 10.1364/OE.25.019236.

    [116]

    de Schryver T, Dierick M, Heyndrickx M, et al. Motion-compensated micro-CT reconstruction for in-situ analysis of dynamic processes[J]. Scientific Reports, 2018, 8(1): 7655. DOI: 10.1038/s41598-018-25916-5.

    [117]

    HINKLE J, SZEGEDI M, WANG B, et al. 4DCT image reconstruction with diffeomorphic motion model[J]. Medical Image Analysis, 2012, 16(6): 1307−1316. DOI: 10.1016/j.media.2012.05.013.

    [118]

    YAN H, ZHEN X, FOLKERTS M, et al. A hybrid reconstruction algorithm for fast and accurate 4D cone-beam CT imaging[J]. Medical Physics, 2014, 41(7): 071903. DOI: 10.1118/1.4881326.

    [119]

    McCLELLAND J R, MODAT M, ARRIDGE S, et al. A generalized framework unifying image registration and respiratory motion models and incorporating image reconstruction, for partial image data or full images[J]. Physics in Medicine & Biology, 2017, 62(11): 4273.

    [120]

    DANG H, WANG A S, SUSSMAN M S, et al. dPIRPLE: A joint estimation framework for deformable registration and penalized-likelihood CT image reconstruction using prior images[J]. Physics in Medicine & Biology, 2014, 59(17): 4799.

    [121]

    WU G, LIAN J, SHEN D. Improving image: Guided radiation therapy of lung cancer by reconstructing 4D-CT from a single free-breathing 3D-CT on the treatment day[J]. Medical Physics, 2012, 39(12): 7694−7709. DOI: 10.1118/1.4768226.

    [122]

    REED A W, KIM H, ANIRUDH R, et al. Dynamic CT reconstruction from limited views with implicit neural representations and parametric motion fields[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021: 2258-2268.

    [123]

    ZHANG Y, YIN F F, SEGARS W P, et al. A technique for estimating 4D-CBCT using prior knowledge and limited-angle projections[J]. Medical Physics, 2013, 40(12): 121701. DOI: 10.1118/1.4825097.

    [124]

    ZHANG Y, YANG J, ZHANG L, et al. Digital reconstruction of high-quality daily 4D cone-beam CT images using prior knowledge of anatomy and respiratory motion[J]. Computerized Medical Imaging and Graphics, 2015, 40: 30−38. DOI: 10.1016/j.compmedimag.2014.10.007.

    [125]

    DHOU S, HUGO G D, DOCEF A. Motion-based projection generation for 4D-CT reconstruction[C]//2014 IEEE International Conference on Image Processing (ICIP). IEEE, 2014: 1698-1702.

    [126]

    BERGNER F, BERKUS T, OELHAFEN M, et al. Autoadaptive phase-correlated (AAPC) reconstruction for 4D CBCT[J]. Medical Physics, 2009, 36(12): 5695−5706. DOI: 10.1118/1.3260919.

    [127]

    GAO H, GUO M, LI R, et al. 4DCT and 4D cone-beam CT reconstruction using temporal regularizations[J]. Graphics Processing Unit-Based High Performance Computing in Radiation Therapy. Series: Series in Medical Physics and Biomedical Engineering, 2015: 63-82.

    [128]

    MASCOLO-FORTIN J, MATENINE D, ARCHAMBAULT L, et al. A fast 4D cone-beam CT reconstruction method based on the OSC-TV algorithm[J]. Journal of X-ray Science and Technology, 2018, 26(2): 189−208. DOI: 10.3233/XST-17289.

    [129]

    SIDKY E Y, PAN X. Image reconstruction in circular cone-beam computed tomography by constrained total-variation minimization[J]. Physics in Medicine & Biology, 2008, 53(17): 4777.

    [130]

    WU H, MAIER A, FAHRIG R, et al. Spatial-temporal total variation regularization (STTVR) for 4D-CT reconstruction[C]//Medical Imaging 2012: Physics of Medical Imaging. SPIE, 2012, 8313: 1018-1024.

    [131]

    BREDIES K, KUNISCH K, POCK T. Total generalized variation[J]. SIAM Journal on Imaging Sciences, 2010, 3(3): 492−526. DOI: 10.1137/090769521.

    [132]

    ZHANG Y, ZHANG W, LEI Y, et al. Few-view image reconstruction with fractional-order total variation[J]. Journal the Optical Society of America A, 2014, 31(5): 981−995. DOI: 10.1364/JOSAA.31.000981.

    [133]

    GONG C, ZENG L. Adaptive iterative reconstruction based on relative total variation for low-intensity computed tomography[J]. Signal Processing, 2019, 165: 149−162. DOI: 10.1016/j.sigpro.2019.06.031.

    [134]

    RITSCHL L, SAWALL S, KNAUP M, et al. Iterative 4D cardiac micro-CT image reconstruction using an adaptive spatio-temporal sparsity prior[J]. Physics in Medicine & Biology, 2012, 57(6): 1517.

    [135]

    GAO H, LI R, LIN Y, et al. 4D cone beam CT via spatiotemporal tensor framelet[J]. Medical Physics, 2012, 39(11): 6943−6946. DOI: 10.1118/1.4762288.

    [136]

    GAO H, QI X S, GAO Y, et al. Megavoltage CT imaging quality improvement on TomoTherapy via tensor framelets[J]. Medical Physics, 2013, 40(8): 081919. DOI: 10.1118/1.4816303.

    [137]

    HAN H, GAO H, XING L. Low-dose 4D cone-beam CT via joint spatiotemporal regularization of tensor framelet and nonlocal total variation[J]. Physics in Medicine & Biology, 2017, 62(16): 6408.

    [138]

    TIAN Z, JIA X, JIANG S B. GPU-based low-dose 4DCT reconstruction via temporal non-local means[J]. arXiv Preprint arXiv: 1009. 1351, 2010.

    [139]

    JIA X, TIAN Z, LOU Y, et al. Four-dimensional cone beam CT reconstruction and enhancement using a temporal nonlocal means method[J]. Medical Physics, 2012, 39(9): 5592−5602. DOI: 10.1118/1.4745559.

    [140]

    KIM H, CHEN J, WANG A, et al. Non-local total-variation (NLTV) minimization combined with reweighted L1-norm for compressed sensing CT reconstruction[J]. Physics in Medicine & Biology, 2016, 61(18): 6878.

    [141]

    KAZANTSEV D, GUO E, KAESTNER A, et al. Temporal sparsity exploiting nonlocal regularization for 4D computed tomography reconstruction[J]. Journal of X-ray Science and Technology, 2016, 24(2): 207−219. DOI: 10.3233/XST-160546.

    [142]

    GAO H, CAI J F, SHEN Z, et al. Robust principal component analysis-based four-dimensional computed tomography[J]. Physics in Medicine & Biology, 2011, 56(11): 3181.

    [143]

    GAO H, YU H, OSHER S, et al. Multi-energy CT based on a prior rank, intensity, and sparsity models (PRISM)[J]. Inverse Problems, 2011, 27(11): 115012. DOI: 10.1088/0266-5611/27/11/115012.

    [144]

    OTAZO R, CANDES E, SODICKSON D K. Low-rank plus sparse matrix decomposition for accelerated dynamic MRI with separation of background and dynamic components[J]. Magnetic Resonance in Medicine, 2015, 73(3): 1125−1136. DOI: 10.1002/mrm.25240.

    [145]

    LINGALA S G, HU Y, DIBELLA E, et al. Accelerated dynamic MRI exploiting sparsity and low-rank structure: k-t SLR[J]. IEEE Transactions on Medical Imaging, 2011, 30(5): 1042−1054. DOI: 10.1109/TMI.2010.2100850.

    [146]

    GAO H, LIN H, AHN C B, et al. PRISM: A divide-and-conquer low-rank and sparse decomposition model for dynamic MRI[J]. UCLA Computational and Applied Mathematics Reports, 2011: 11-26.

    [147]

    KIM K S, YE J C. Low-dose limited-view 4DCT reconstruction using patch-based low-rank regularization[C]//2013 IEEE Nuclear Science Symposium and Medical Imaging Conference (2013 NSS/MIC). IEEE, 2013: 1-4.

    [148]

    CHEN G H, TANG J, LENG S. Prior image-constrained compressed sensing (PICCS): A method to accurately reconstruct dynamic CT images from highly undersampled projection datasets[J]. Medical Physics, 2008, 35(2): 660−663. DOI: 10.1118/1.2836423.

    [149]

    NETT B, TANG J, LENG S, et al. Tomosynthesis via total variation minimization reconstruction and prior image-constrained compressed sensing (PICCS) on a C-arm system[C]//Medical Imaging 2008: Physics of Medical Imaging. SPIE, 2008, 6913: 800-809.

    [150]

    CHEN G H, TANG J, NETT B, et al. Prior image-constrained compressed sensing (PICCS) and applications in X-ray computed tomography[J]. Current Medical Imaging, 2010, 6(2): 119−134. DOI: 10.2174/157340510791268498.

    [151]

    LENG S, TANG J, ZAMBELLI J, et al. High temporal resolution and streak-free four-dimensional cone-beam computed tomography[J]. Physics in Medicine & Biology, 2008, 53(20): 5653.

    [152] 林嘉慧, 边兆英, 马建华, 等. 数据冗余信息引导的低剂量心肌灌注CT成像方法[J]. 南方医科大学学报, 2018, 38(01): 27−33. DOI: 10.3969/j.issn.1673-4254.2018.01.005.
    [153] 陶熙. 基于反投影数据分析与时序均值先验的低剂量CT成像新方法研究[D]. 广州:南方医科大学,2020.
    [154]

    CHEN G H, LI Y. Synchronized multiartifact reduction with tomographic reconstruction (SMART-RECON): A statistical model based iterative image reconstruction method to eliminate limited‐view artifacts and to mitigate the temporal‐average artifacts in time-resolved CT[J]. Medical Physics, 2015, 42(8): 4698−4707. DOI: 10.1118/1.4926430.

    [155]

    LI Y, GARRETT J W, LI K, et al. An enhanced SMART-RECON algorithm for time-resolved C-arm cone-beam CT imaging[J]. IEEE Transactions on Medical Imaging, 2019, 39(6): 1894−1905.

    [156]

    LI Y, CAO X, XING Z, et al. Image quality improvement in MDCT cardiac imaging via SMART-RECON method[C]//Medical Imaging 2017: Physics of Medical Imaging. SPIE, 2017, 10132: 771-777.

    [157]

    LI Y, SPEIDEL M A, FRANÇOIS C J, et al. Radiation-dose reduction in CT myocardial perfusion imaging using SMART-RECON[J]. IEEE Transactions on Medical Imaging, 2017, 36(12): 2557−2568. DOI: 10.1109/TMI.2017.2747521.

    [158]

    LI Y, GARRETT J W, LI K, et al. Time-resolved C-arm cone beam CT angiography (TR-CBCTA) imaging from a single short-scan C-arm cone-beam CT acquisition with intra-arterial contrast injection[J]. Physics in Medicine & Biology, 2018, 63(7): 075001.

    [159]

    GAO H, LI L, HU X. Compressive diffusion MRI, Part 3: Prior image constrained low-rank model (PCLR)[C]//Proc ISMRM. Proceedings of ISMRM 2013 Annual Meeting and Exhibition Activity, Salt Lake City, Utah, USA, 2013: 2605.

    [160]

    TUY H K. An inversion formula for cone-beam reconstruction[J]. SIAM Journal on Applied Mathematics, 1983, 43(3): 546−552. DOI: 10.1137/0143035.

    [161]

    HANSEN D C, SØRENSEN T S. Fast 4D cone-beam CT from 60s acquisitions[J]. Physics and Imaging in Radiation Oncology, 2018, 5: 69−75. DOI: 10.1016/j.phro.2018.02.004.

    [162]

    ELAD M, AHARON M. Image denoising via sparse and redundant representations over learned dictionaries[J]. IEEE Transactions on Image Processing, 2006, 15(12): 3736−3745. DOI: 10.1109/TIP.2006.881969.

    [163]

    XU Q, YU H, MOU X, et al. Low-dose X-ray CT reconstruction via dictionary learning[J]. IEEE Transactions on Medical Imaging, 2012, 31(9): 1682−1697. DOI: 10.1109/TMI.2012.2195669.

    [164]

    CANDES E J, WAKIN M B, BOYD S P. Enhancing sparsity by reweighted ℓ 1 minimization[J]. Journal of Fourier Analysis and Applications, 2008, 14: 877−905. DOI: 10.1007/s00041-008-9045-x.

    [165]

    SIDKY E Y, CHARTRAND R, PAN X. Image reconstruction from few views by non-convex optimization[C]//2007 IEEE Nuclear Science Symposium Conference Record. IEEE, 2007, 5: 3526-3530.

    [166]

    HUANG J, HUANG X, METAXAS D. Learning with dynamic group sparsity[C]//2009 IEEE 12th International Conference on Computer Vision. IEEE, 2009: 64-71.

    [167]

    KALMAN R E. A new approach to linear filtering and prediction problems[J]. Journal of Basic Engineering, 1960, 82(1): 35−45. DOI: 10.1115/1.3662552.

    [168]

    CLARK D P, BADEA C T. Convolutional regularization methods for 4D, X-ray CT reconstruction[C]// Medical Imaging 2019: Physics of Medical Imaging. SPIE, 2019, 10948: 574-585.

    [169]

    MADESTA F, SENTKER T, GAUER T, et al. Self-contained deep learning-based boosting of 4D cone-beam CT reconstruction[J]. Medical Physics, 2020, 47(11): 5619−5631. DOI: 10.1002/mp.14441.

    [170]

    HAN Y S, YOO J, YE J C. Deep residual learning for compressed sensing CT reconstruction via persistent homology analysis[J]. arXiv Preprint, 2016. DOI: 10.48550/arXiv.1611.06391.2016.

    [171]

    XIE S, ZHENG X, CHEN Y, et al. Artifact removal using improved GoogLeNet for sparse-view CT reconstruction[J]. Scientific Reports, 2018, 8(1): 6700. DOI: 10.1038/s41598-018-25153-w.

    [172]

    FU Y, LEI Y, WANG T, et al. Deep learning in medical image registration: A review[J]. Physics in Medicine & Biology, 2020, 65(20): 20TR01.

    [173]

    TENG X, CHEN Y, ZHANG Y, et al. Respiratory deformation registration in 4D-CT/cone beam CT using deep learning[J]. Quantitative Imaging in Medicine and Surgery, 2021, 11(2): 737. DOI: 10.21037/qims-19-1058.

    [174]

    HUANG X, ZHANG Y, CHEN L, et al. U-net-based deformation vector field estimation for motion-compensated 4D-CBCT reconstruction[J]. Medical Physics, 2020, 47(7): 3000−3012. DOI: 10.1002/mp.14150.

    [175]

    WOLTERINK J, ZWIENENBERG J, BRUNE C. Implicit neural representations for deformable image registration[C]//International Conference on Medical Imaging with Deep Learning. PMLR, 2022: 1349-1359.

    [176]

    HERRMANN J, FONSECA Da CRUZ A, GERARD S E, et al. Dynamic 5DCT Image reconstruction reveals interactions between cardiogenic and respiratory motion[M]//B28. See For Yourself: Evaluating Lung Function with CT and MRI. American Thoracic Society, 2022: A2552-A2552.

    [177]

    JIANG Z, ZHANG Z, CHANG Y, et al. Enhancement of 4-D cone-beam computed tomography (4D-CBCT) using a dual-encoder convolutional neural network (DeCNN)[J]. IEEE Transactions on Radiation and Plasma Medical Sciences, 2021, 6(2): 222−230.

    [178]

    YANG B, CHEN X, YUAN S, et al. Deep learning improves image quality and radiomics reproducibility for high-speed four-dimensional computed tomography reconstruction[J]. Radiotherapy and Oncology, 2022, 170: 184−189. DOI: 10.1016/j.radonc.2022.02.034.

    [179]

    JIANG Z, CHANG Y, ZHANG Z, et al. Fast four-dimensional cone-beam computed tomography reconstruction using deformable convolutional networks[J]. Medical Physics, 2022, 49(10): 6461−6476. DOI: 10.1002/mp.15806.

    [180]

    JUNG S, LEE S, JEON B, et al. Deep learning based coronary artery motion artifact compensation using style-transfer synthesis in CT images[C]//Simulation and Synthesis in Medical Imaging: Third International Workshop, SASHIMI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings 3. Springer International Publishing, 2018: 100-110.

    [181]

    ELSS T, NICKISCH H, WISSEL T, et al. Motion estimation in coronary CT angiography images using convolutional neural networks[C]//Medical Imaging with Deep Learning, 2018.

    [182]

    GUO Y, BI L, AHN E, et al. A spatiotemporal volumetric interpolation network for 4d dynamic medical image[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 4726-4735.

    [183]

    SAUPPE S, KUHM J, BREHM M, et al. Motion vector field phase-to-amplitude resampling for 4D motion-compensated cone-beam CT[J]. Physics in Medicine & Biology, 2018, 63(3): 035032.

    [184]

    SHIEH C C, GONZALEZ Y, LI B, et al. SPARE: Sparse-view reconstruction challenge for 4D cone-beam CT from a 1 min scan[J]. Medical Physics, 2019, 46(9): 3799−3811. DOI: 10.1002/mp.13687.

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  • 收稿日期:  2023-04-29
  • 录用日期:  2023-10-25
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