Diffusion Models in Medical Imaging: A Comprehensive Survey
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摘要:
以扩散模型为代表的生成式人工智能近年来在医学成像领域取得了迅猛的进展。为了帮助更多学者全面的了解扩散模型这一先进技术，本文旨在提供扩散模型在医学成像领域的详细概述。首先以扩散模型的起源演变为主线介绍扩散建模框架的基础理论和基本概念；其次根据扩散模型的特点提供其在医学成像领域的系统分类，并涵盖不同成像模态如磁共振成像、计算机断层扫描、正电子发射计算机断层显像和光声成像等的广泛应用；最后讨论目前扩散模型的局限性并展望未来研究的潜在发展方向，为研究者后续的探索提供了一个直观的起始点。部分代码开源在网址：https://github.com/yqx7150/Diffusion-Models-for-Medical-Imaging。
Abstract:Generative artificial intelligence represented by diffusion models has significantly contributed to medical imaging reconstruction. To help researchers comprehensively understand the rich content of diffusion models, this review provides a detailed overview of diffusion models used in medical imaging reconstruction. The theoretical foundation and fundamental concepts underlying the diffusion modeling framework were first introduced, describing their origin and evolution. Second, a systematic characteristic-based taxonomy of diffusion models used in medical imaging reconstruction is provided, broadly covering their application to imaging modalities, including magnetic resonance imaging (MRI), computed tomography (CT), positron emission computed tomography (PET), and photoacoustic imaging (PAI). Finally, we discuss the limitations of current diffusion models and anticipate potential directions of future research, providing an intuitive starting point for subsequent exploratory research. Related codes are available at GitHub: https://github.com/yqx7150/Diffusion-Models-for-Medical-Imaging.
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腺泡细胞癌（acinic cell carcinoma，ACC）是一种涎腺上皮源性的恶性肿瘤，最常见的发病部位是腮腺[1]。除发生在腮腺和头颈部的其他涎腺外，腺泡细胞癌还可原发于肺（支气管）、乳腺、鼻腔等[2]，其中肺（支气管）内原发的腺泡细胞癌极少见。1972年，Fechner等[3]首次报道了1例肺原发腺泡细胞癌，至今为止个案文献报道不足35例。肺腺泡细胞癌被认为是一种生长缓慢的惰性肿瘤，临床症状缺乏特异性，常容易与其他肺内肿瘤混淆。
综合以上因素，临床医生不易诊断该类疾病，故本文报道了1例经手术病理证实的原发性肺腺泡细胞癌。本例患者体重减轻，无其他症状，外院CT提示肺结核球可能，但查血清肿瘤标志物-癌胚抗原升高，欲行手术治疗至我院心胸外科就诊。本文主要报道该病例的MSCT影像表现、MSCT在其诊疗中的价值及其鉴别诊断，以提高对该类疾病的认识和影像诊断水平。
1. 病历资料
1.1 临床资料与实验室检查
患者男性，15岁。因“入学体检发现左肺上叶占位3周”，3周前体检胸部平片提示左肺上叶占位，遂就诊至当地医院感染性疾病科，再次胸部CT见左肺上叶结节状影及少许条状密度增高影，结节边界清楚，其内可见较多钙化，考虑结核球可能。查肿瘤标志物癌胚抗原10.88 ng/mL升高；并行左肺上叶结节CT引导下穿刺活检提示肿瘤细胞绕血管生长，胞浆较透亮，见少量红染胶质样管型及红染无结构坏死，无法分类，抗酸染色未检出抗酸杆菌，建议完整切除肿物病检。
患者为求手术治疗，于2022年8月22至恩施土家族苗族自治州中心医院就诊。患者诉发病以来体重下降3 kg，余无其他不适。查体：体温36.4 ℃，脉搏68 r/min，呼吸18 r/min（规则），血压118/64 mmHg。双肺呼吸音清，未闻及干湿啰音。心率68 r/min，律齐，无病理性杂音。血气分析：二氧化碳分压（PCO2）43.8 mmHg，氧分压（PO2）85.9 mmHg。生化检验：复查癌胚抗原11.12 ng/mL，仍升高。
1.2 其他检查
纤维支气管镜刷片查见较多纤毛柱状上皮细胞，少量淋巴细胞、中性粒细胞，未发现恶性肿瘤细胞。
肺功能：1 s用力呼气量（forced expiratory volume in one second，FEV1）3.86 L；每分钟最大通气量（maximun ventilatory volumn，MVV）162.4％。颈部、甲状腺彩超：甲状腺左右叶囊性结节并胶质结晶；双侧颈部淋巴结增大。颅脑、颌面部及上腹部CT平扫未见异常。
1.3 胸部MSCT表现
该患者的胸部CT表现（图1）。左肺上叶舌段单发结节影，边界清楚，大小约3.0 cm×3.0 cm×2.5 cm。边缘欠光整，可见浅分叶改变，结节边缘可见弧形空气密度影，呈“空气新月征”改变，结节近、远侧可见支气管稍扩张，管壁稍增厚，远侧扩张支气管腔内少许异常密度充填。结节周围亦见少许条絮状影。
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图 1 胸部MSCT表现（a）～（d）胸部CT平扫肺窗，左肺上叶舌段结节，结节边缘可见“空气新月征”（粗箭），结节近侧、远侧支气管轻度扩张（细箭）；（e）矢状位CT重建：远侧支气管稍扩张，腔内少许异常密度充填。（f）～（h）纵隔窗，结节呈浅分叶改变，结节内密度不均匀，伴有多发不规则条、片状钙化；（g）和（h）胸部CT增强，动脉期结节显著强化，CT值约133 HU；静脉期病灶强化稍减低，CT值约94 HU。（i）MPR重建：病灶供血动脉显示；（j）MIP：病灶由左肺上叶舌段动脉分支供血。Figure 1. Manifestations seen on CT纵隔窗显示结节密度不均匀，其内见不规则条、片状高密度影，非钙化区CT值约45 HU左右。增强扫描动脉期结节明显强化，CT值约133 HU；静脉期CT值约93 HU，强化欠均匀。纵隔内未见肿大淋巴结。
1.4 病理检查
CT引导下左肺上叶结节穿刺活检。送检穿刺组织内可见肿瘤细胞绕血管生长，胞浆较透亮，并见少量红染胶质样管型及红染无结构坏死，免疫组化细胞角蛋白（cytokeratin，CK，+），细胞角蛋白7（cytokeratin 7，CK7，+）及波形蛋白（vimentin，VIM，+），呈双相表达，其余均为阴性，无法分类，建议完整切除肿物病检。抗酸染色未检出抗酸杆菌。
左肺上叶切除+系统性淋巴结清扫术后病理检查（图2）。左肺上叶：唾液腺型癌（符合腺泡细胞癌），肿物大小3.0 cm×3.0 cm×2.6 cm，伴广泛钙化，未见脉管内癌栓及神经侵犯，未见气腔内播散。支气管断端未见癌浸润。左肺上叶周围肺组织充血淤血。淋巴结：肺门0/1；第5组0/2；第7组0/2；第10组0/3；第11组0/1均未见癌转移。免疫组化：细胞角蛋白CK（+），CK20（-），CK7（+），甲状腺转录因子1（thyriod transcription factor-1，TTF-1，-），胃酶样天冬氨酸蛋白酶A（NapsinA，-），S-100（-），P63（-），P40（-），CD56（-），绒毛蛋白（Villin，-），Ki-67阳性细胞数3％，癌胚抗原（carcinoembryonic antigen，CEA，部分+），波形蛋白（Vimentin，+）。
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图 2 病理图像（a）（HE×10）瘤组织与周围肺组织边界较清楚，瘤组织呈片巢状分布，部分区域呈腺样排列，腺腔内见红染无结构分泌样物，瘤细胞呈类圆形、多边形、柱状，部分胞浆空亮，间质较多浆细胞浸润。（b）（HE×20）瘤组织呈片巢状分布，瘤细胞轻度异型，部分胞浆空亮，间质见浆细胞浸润。（c）（HE×20）瘤组织呈片巢状分布，部分区域呈腺样排列，腺腔内见红染无结构分泌样物，瘤细胞呈类圆形、多边形、柱状，部分胞浆空亮。（d）（HE×20）瘤组织浸润支气管粘膜，间质见钙化。（e）免疫组化：CK（＋）。（f）免疫组化：CK7（＋）。Figure 2. Pathological images of the lesion1.5 治疗
经完善术前相关检查，于2022年8月25日在全麻下行胸腔镜左肺上叶癌根治性切除术（左肺上叶切除术+系统性淋巴结清扫术）。术中见肿瘤位于左肺上叶上舌段，直径约3.0 cm，质地硬，肿瘤表面胸膜凹陷皱褶。切除左肺上叶舌段送快速病检：左肺上叶恶性肿瘤，浸润性癌，印片查见癌细胞。遂切除左肺上叶，同时清扫肺门、隆突下、叶间、主肺动脉窗淋巴结。
术后第14 d患者生命体征平稳，切口Ⅱ/甲级愈合，痊愈出院。
1.6 随访
肿瘤科会诊意见：肺腺泡细胞癌低度恶性，T1N0M0 IA3，建议患者定期随访。患者于2023年2月21日至我院随诊，胸部CT平扫未见复发。
2. 讨论
原发性肺涎腺型肿瘤属于肺内低度恶性肿瘤，它起源于气管、支气管黏膜下腺体，故通常表现为紧邻支气管的孤立性肿物，并少有淋巴结转移[4]。其组织学特征及生物学行为与涎腺对应的肿瘤类似。
2021年版WHO肺肿瘤分类[5]中列出7类唾液腺型肿瘤，包括腺样囊性癌、黏液表皮样癌、上皮-肌上皮癌、多形性腺瘤、玻璃样透明细胞癌、肌上皮瘤和肌上皮癌，其中以黏液表皮样癌和腺样囊性癌为最常见的病理类型，而未将腺泡细胞癌列入其中，可能与其病例太少、认识有限有关。原发性肺腺泡细胞癌的病因及发病机制均尚未明确，有研究者调查发现男女发病比例1.6∶1，表明该病可能与性激素有关[6]。本病临床表现缺乏特异性，最常见的症状是咯血和咳嗽[7]。
2.1 MSCT表现及诊疗价值
气管黏膜下腺体主要分布于段支气管以上，故肺内腺泡细胞癌多为中央型，周围型少见，CT表现肿瘤多靠近肺门。肿瘤边界光整，与其恶性程度低有关。本例病灶为左肺上叶舌段支气管腔内外肿块，病灶部分阻塞支气管管腔形成“空气新月征”[8]，另外一部分紧邻支气管，病灶近端、远端支气管扩张、管壁增厚，部分支气管内见异常密度影，这些表现可能与肿瘤阻塞支气管引起炎症反复刺激所致或粘液栓形成等。病灶内多发钙化，相关文献中未见明确报道，在原发于肺内的其他唾液腺肿瘤中，有报道表示肿瘤内钙化与黏液吸收不全钙盐沉积有关[9]，考虑到肺内原发腺泡细胞癌组织学特征及生物学行为与原发于腮腺者相似，但有关文献显示腮腺腺泡细胞癌钙化少见，且常表现为沙粒样钙化[10]，与本病例并不相符，还需搜集更多病例资料并结合病理综合分析。增强病灶呈显著性强化，可能与肿瘤血供丰富有关。
MSCT是诊断原发性肺腺泡细胞癌的重要检查方法，特别是多平面重建，不仅可明确病变部位、形态及其与气管的关系，还能评价肿瘤向外侵犯程度及纵隔淋巴结转移，有利于指导肿瘤分期、临床治疗方案制订，具有较高的术前诊断价值[11]。多期增强扫描，不仅可以了解病变本身密度变化，还能观察其血供情况，有利于与常见的支气管肺癌呈轻、中度强化进行鉴别。此外患者长期随访行CT检查可了解有无复发、预后情况。CT检查贯穿于整个诊疗过程中，对于临床诊断、治疗非常重要。通过CT表现早期发现、诊断疾病，可极大提高患者的治疗效果，改善预后。
2.2 诊断与鉴别诊断
原发性肺腺泡细胞癌的确诊依赖于病理检查（细胞形态学特征和免疫组化），需与以下几类疾病鉴别。
原发于唾液腺的腺泡细胞癌转移至肺：唾液腺肿瘤的既往史或影像学检查发现唾液腺原发灶，基本可明确为肺内转移，此外肺内转移瘤常常表现为肺内多发结节灶，且肺内结节与气管、支气管无明确关系。
肺内原发孤立结节或肿瘤。肺内常见恶性肿瘤：肺鳞癌、肺腺癌，肺鳞癌老年男性多发，与吸烟有关，更多以中央型肺癌为主要表现，早期即可引起支气管狭窄或阻塞性肺炎，易出现纵隔及肺门淋巴结肿大；肺腺癌易发生于女性患者，多数腺癌起源于较小的支气管，多为周围型肺癌，肿瘤的特点有空泡征、空气支气管征，边缘有毛刺、分叶征、血管集束征、胸膜凹陷等，病灶大片状钙化少见，且增强多呈轻、中度强化，较少显著性强化。此外，肺腺癌细胞异型性较大，肺泡上皮标志物TTF1、Napsin A阳性[12]。
肺内其他类型涎腺型肿瘤。如黏液表皮样癌和腺样囊性癌，腺样囊性癌CT多表现为气管壁的弥漫性不规则增厚，与周围组织分界不清，很少钙化，增强多呈轻、中度强化[11]，并且通常肿瘤细胞表达CD117[1]，而腺泡细胞癌CD117阴性；黏液表皮样癌多表现为气管内肿物，可伴钙化，增强多明显强化[13]，其CT表现可与该病例相似，但病灶囊变较腺泡细胞癌更常见，最终可通过细胞学形态以及免疫组化加以鉴别。
肺内良性结节与肿瘤。结核球和错构瘤，结核球周围发现卫星灶较多见，可有钙化，增强不强化或边缘环形强化，本病例病灶周围少许斑点状影可能是误诊为结核的主要征象。错构瘤是支气管内最常见的良性肿瘤，周围型错构瘤较多见，肿瘤内脂肪成分是诊断错构瘤的重要依据，瘤体内可见斑点状或爆米花状钙化是错构瘤特征性表现，但增强绝大多数病灶无明显强化。此外其他肿瘤如支气管类癌，临床上合并有神经内分泌症状。
2.3 治疗与预后
手术根治性切除肿瘤是实现患者长期生存的主要治疗方法[14]，而对手术无法切除（如肿瘤侵犯邻近器官、已有远处转移等）或手术风险高的患者，可以选择辅助放疗、化疗，但放疗、化疗的效果还有待进一步研究[6]。
有研究表明，Ki67抗原的表达与涎腺型肿瘤患者的生存期显著相关，ki67指数≥10％时，肿瘤易复发，淋巴结转移率也较高[15]，本例患者Ki-67阳性细胞数3％，故预后相对较好。但有文献报道，手术治疗后数10年仍有可能复发或转移，因此还需对患者长期随访[16]。
本文报道了1例青少年肺内原发腺泡细胞癌的病例，在实际工作中，医生极易将其误诊为结核或肺内良性肿瘤，此病例报道旨在提高医务人员对该病的认识，减少误诊，避免延误治疗。
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图 1 （a）扩散模型起源演变的时间轴；（b）按照关键词（diffusion model | medical imaging）、（score-based model | medical imaging）、（diffusion | medical | probabilistic model）在Google Scholar和Arxiv Sanity Preserver进行近5年相关论文检索，筛选并删除相同的结果最终统计扩散模型应用于不同成像模态分类的论文比例
Figure 1. (a) Timeline of the origin and evolution of the diffusion model; (b) Relevant papers published within the last five years found via Google Scholar and Arxiv Sanity Preserver searches using the keywords (diffusion model | medical imaging), (score-based model | medical imaging), and (diffusion | medical | probabilistic model). Duplicate results were identified and deleted before calculating the proportion of papers applying a diffusion model to different imaging modality classifications
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图 2 扩散模型应用于不同成像模态的论文分类
Figure 2. Classification of papers describing the application of diffusion models to different imaging modalities
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图 3 扩散模型分别在图像域和K空间域的基本框架。其中前向过程从初始数据开始逐步添加噪声，使其最终接近纯噪声分布；逆向过程通过学习逐步去噪，从随机噪声中还原为高质量的目标数据
Figure 3. Basic framework of the diffusion model in the image and K-space domain. The forward process gradually adds noise from the initial data to approach a pure noise distribution; the reverse process gradually removes noise through learning and recovers high-quality target data from random noise
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图 4 单线圈脑部数据集在伪径向采样模式下加速因子为5时不同方法的重建结果比较。绿色框和红色框分别表示感兴趣区域及其残差图
Figure 4. Comparison of reconstruction results using different methods on a single-coil brain dataset in pseudo-radial sampling mode with an acceleration factor of 5. The green and red boxes indicate the region of interest and its residual map, respectively
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图 5 不同方法生成的CT图像的数值比较
Figure 5. Numerical comparison of CT images generated by different methods
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图 6 不同算法在泊松采样模式下加速因子为4的重建结果比较
Figure 6. Comparison of reconstruction results using different algorithms with an acceleration factor of 4 in Poisson sampling mode
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图 7 随机采样模式下加速因子为5和均匀采样模式下加速因子为4的fastMRI膝关节和SIAT脑部重建结果
Figure 7. Reconstruction results of fastMRI knee joint and SIAT brain imaging with an acceleration factor of 5 in random sampling mode and an acceleration factor of 4 in uniform sampling mode
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图 8 不同算法下人体CT扫描有限角度（60°和90°）重建结果（右下角数值为PSNR值）
Figure 8. Reconstruction results of limited angles (60° and 90°) of human CT scan data using different algorithms (the value in each lower right corner is the PSNR value)
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图 9 5 e3剂量下CT的重建结果及残差图
Figure 9. Reconstruction results and residual CT images at a dose of 5 e3
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图 10 稀疏角度为32°时重建血管的过程（数字分别代表PSNR值和SSIM值）
Figure 10. Process of blood vessel reconstruction when the sparse angle is 32° (The numbers represent PSNR and SSIM values, respectively)
表 1 五大生成模型全方位对比
Table 1 Comprehensive comparison of five generative models
特点/模型 VAE GAN EBM Flow Diffusion Model 基本原理 由编码器将数据映射到潜在空间概率分布，再由解码器生成数据 生成器和判别器对抗训练，生成器生成数据，判别器区分真假 定义一个可微的能量函数，将数据点的概率分布与其能量值联系 通过可逆变换将简单分布映射到复杂数据分布。 从噪声分布起，逐步去噪恢复目标数据 优点 1.生成质量高
2.训练稳定
3.潜在空间连续1.生成质量高
2.多样性好
3.应用广泛1.灵活性高
2.隐式生成
3.表示能力强1.高效样本生成和密度估计
2.可解释性强1.生成质量高
2.强大的建模能力
3.广泛的应用场景缺点 1.生成样本模糊
2.计算复杂度高
3.难捕捉复杂分布1.训练困难
2.对数据敏感
3.计算资源消耗大1.配分函数难计算
2.训练不稳定
3.采样效率低1.设计合适的变换
2.模块具有挑战性
3.计算资源需求高1.训练过程复杂
2.对噪声模型依赖性
3.生成速度较慢训练稳定性 稳定 不稳定 稳定 稳定 稳定 生成质量 较高 高 较高 高 高 计算资源需求 中等 高 中等 高 高 模型复杂度 中等 高 中等 高 高 可解释性 较好 较差 较好 好 较差 灵活性 较低 高 高 高 高 对数据分布假设 较强 较弱 较强 较强 较弱 数据质量敏感性 较低 高 较低 较低 较低 生成速度 中等 中等 中等 中等 较慢 
下载: 导出CSV
表 2 不同重建算法结果定量比较
Table 2 Quantitative comparison between the results of different reconstruction algorithms
ESPIRiT LINDBERG EBMRec SAKE WKGM SVD-WKGM T1 GE brain 2D random R=4 39.08/0.933 38.98/0.961 40.17/0.968 41.54/0.952 40.67/0.969 43.85/0.970 2D random R=6 36.01/0.921 35.16/0.958 36.55/0.952 38.09/0.932 37.14/0.957 39.94/0.960 T2 transverse brain 2D Poisson R=4 31.74/0.819 32.87/0.901 33.19/0.915 33.91/0.896 33.35/0.907 34.58/0.917 2D Poisson R=10 28.95/0.798 26.17/0.822 29.59/0.839 29.75/0.823 29.17/0.823 31.69/0.841
下载: 导出CSV
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[1] BAO J M, CHEN D, WEN F, et al. CVAE-GAN: Fine-grained image generation through asymmetric training[C]//Proceedings of the IEEE international conference on computer vision, Venice, Italy, 2017: 2745-2754. DOI: 10.1109/iccv.2017.299.
[2] RAZAVI A, Van den OORD A, VINYALS O. Generating diverse high-fidelity images with VQ-VAE-2[J]. Advances in neural information processing systems, 2019, 32. DOI: 10.48550/arXiv.1906.00446.
[3] KONG Z F, PING W, HUANG J J, et al. Diffwave: A versatile diffusion model for audio synthesis[J]. arXiv Preprint arXiv: 2009.09761, 2020.
[4] OORD A, DIELEMAN S, ZEN H, et al. Wavenet: A generative model for raw audio[J]. arXiv Preprint arXiv: 1609.03499, 2016.
[5] LI X, THICKSTUN J, GULRAJANI I, et al. Diffusion-lm improves controllable text generation[J]. Advances in Neural Information Processing Systems, 2022, 35: 4328-4343. DOI: 10.48550/arXiv.2205.14217.
[6] YANG G D, HUANG X, HAO Z K, et al. Pointflow: 3D point cloud generation with continuous normalizing flows[C]//Proceedings of the IEEE/CVF international conference on computer vision, Seoul, Korea (South), 2019: 4541-4550. DOI: 10.48550/arXiv.1906.12320.
[7] BOND-TAYLOR S, LEACH A, LONG Y, et al. Deep generative modelling: A comparative review of VAEs, GANs, normalizing flows, energy-based and autoregressive models[J]. IEEE transactions on pattern analysis and machine intelligence, 2021. DOI: 10.48550/arXiv.2103.04922.
[8] GOODFELLOW I, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial nets[J]. Advances in neural information processing systems, 2014, 27. DOI: 10.48550/arXiv.1406.2661.
[9] JIMENEZ REZENDE D, MOHAMED S, WIERSTRA D. Stochastic backpropagation and approximate inference in deep generative models[J]. arXiv Preprint arXiv: 1401.4082, 2014.
[10] DINH L, SOHL-DICKSTEIN J, BENGIO S. Density estimation using real nvp[J]. arXiv Preprint arXiv: 1605.08803, 2016.
[11] SONG Y, SOHL-DICKSTEIN J, KINGMA D P, et al. Score-based generative modeling through stochastic differential equations[J]. arXiv Preprint arXiv: 2011.13456, 2020.
[12] KARRAS T, AITTALA M, AILA T, et al. Elucidating the design space of diffusion-based generative models[J]. Advances in Neural Information Processing Systems, 2022, 35: 26565-26577. DOI: 10.48550/arXiv.2206.00364.
[13] SOHL-DICKSTEIN J, WEISS E, MAHESWARANATHAN N, et al. Deep unsupervised learning using nonequilibrium thermodynamics[C]//International Conference on Machine Learning, Lille, France, 2015: 2256-2265. DOI: 10.48550/arXiv.1503.03585.
[14] SONG Y, DURKAN C, MURRAY I, et al. Maximum likelihood training of score-based diffusion models[J]. Advances in Neural Information Processing Systems, 2021, 34: 1415-1428. DOI: 10.48550/arXiv.2101.09258.
[15] SINHA A, SONG J M, MENG C L, et al. D2c: Diffusion-decoding models for few-shot conditional generation[J]. Advances in Neural Information Processing Systems, 2021, 34: 12533-12548. DOI: 10.48550/arXiv.2106.06819.
[16] VAHDAT A, KREIS K, KAUTZ J. Score-based generative modeling in latent space[J]. Advances in Neural Information Processing Systems, 2021, 34: 11287-11302. DOI: 10.48550/arXiv.2106.05931.
[17] SAHARIA C, HO J, CHAN W, et al. Image super-resolution via iterative refinement[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 45(4): 4713-4726. DOI: 10.48550/arXiv.2104.07636.
[18] PANDEY K, MUKHERJEE A, RAI P, et al. Diffusevae: Efficient, controllable and high-fidelity generation from low-dimensional latents[J]. arXiv Preprint arXiv: 2201.00308, 2022.
[19] BAO F, LI C X, ZHU J, et al. Analytic-dpm: An analytic estimate of the optimal reverse variance in diffusion probabilistic models[J]. arXiv preprint arXiv: 2201.06503, 2022.
[20] DOCKHORN T, VAHDAT A, KREIS K. Score-based generative modeling with critically-damped Langevin diffusion[J]. arXiv preprint arXiv: 2112.07068, 2021.
[21] LIU N, LI S, DU Y L, et al. Compositional visual generation with composable diffusion models[C]//European Conference on Computer Vision, Tel-Aviv, Israel, 2022: 423-439. DOI: 10.48550/arXiv.2206.01714.
[22] JIANG Y M, YANG S, QIU H N, et al. Text2human: Text-driven controllable human image generation[J]. ACM Transactions on Graphics (TOG), 2022, 41(4): 1-11. DOI: 10.48550/arXiv.2205.15996.
[23] BATZOLIS G, STANCZUK J, SCHONLIEB C B, et al. Conditional image generation with score-based diffusion models[J]. arXiv preprint arXiv: 2111.13606, 2021.
[24] DANIELS M, MAUNU T, HAND P. Score-based generative neural networks for large-scale optimal transport[J]. Advances in neural information processing systems, 2021, 34: 12955-12965. DOI: 10.48550/arXiv.2110.03237.
[25] CHUNG H, SIM B, YE J C. Come-closer-diffuse-faster: Accelerating conditional diffusion models for inverse problems through stochastic contraction[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, USA, 2022: 12413-12422. DOI: 10.48550/arXiv.2112.05146.
[26] KAWAR B, ELAD M, ERMON S, et al. Denoising diffusion restoration models[J]. Advances in Neural Information Processing Systems, 2022, 35: 23593-23606. DOI: 10.48550/arXiv.2201.11793.
[27] ESSER P, ROMBACH R, BLATTMANN A, et al. Imagebart: Bidirectional context with multinomial diffusion for autoregressive image synthesis[J]. Advances in Neural Information Processing Systems, 2021, 34: 3518-3532. DOI: 10.48550/arXiv.2108.08827.
[28] LUGMAYR A, DANELLJAN M, ROMERO A, et al. Repaint: Inpainting using denoising diffusion probabilistic models[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, USA, 2022: 11461-11471. DOI: 10.48550/arXiv.2201.09865.
[29] JING B, CORSO G, BERLINGHIERI R, et al. Subspace diffusion generative models[C]//European Conference on Computer Vision, Tel-Aviv, Israel, 2022: 274-289. DOI: 10.1007/978-3-031-20050-2_17.
[30] BARANCHUK D, RUBACHEV I, VOYNOV A, et al. Label-efficient semantic segmentation with diffusion models[J]. arXiv preprint arXiv: 2112.03126, 2021.
[31] GRAIKOS A, MALKIN N, JOJIC N, et al. Diffusion models as plug-and-play priors[J]. Advances in Neural Information Processing Systems, 2022, 35: 14715-14728. DOI: 10.48550/arXiv.2206.09012.
[32] WOLLEB J, SANDKÜHLER R, BIEDER F, et al. Diffusion models for implicit image segmentation ensembles[C]//International Conference on Medical Imaging with Deep Learning, New Orleans, USA, 2022: 1336-1348. DOI: 10.48550/arXiv.2112.03145.
[33] AMIT T, SHAHARBANY T, NACHMANI E, et al. Segdiff: Image segmentation with diffusion probabilistic models[J]. arXiv preprint arXiv: 2112.00390, 2021.
[34] ZIMMERMANN R S, SCHOTT L, SONG Y, et al. Score-based generative classifiers[J]. arXiv preprint arXiv: 2110.00473, 2021.
[35] PINAYA W H L, GRAHAM M S, GRAY R, et al. Fast unsupervised brain anomaly detection and segmentation with diffusion models[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention, New Orleans, USA, 2022: 705-714. DOI: 10.48550/arXiv.2206.03461.
[36] WOLLEB J, BIEDER F, SANDKUHLER R, et al. Diffusion models for medical anomaly detection[C]//International Conference on Medical image computing and computer-assisted intervention, New Orleans, USA, 2022: 35-45. DOI: 10.48550/arXiv.2203.04306.
[37] WYATT J, LEACH A, SCHMON S M, et al. Anoddpm: Anomaly detection with denoising diffusion probabilistic models using simplex noise[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, USA, 2022: 650-656. DOI: 10.1109/CVPRW56347.2022.00080.
[38] JALAL A, ARVINTE M, DARAS G, et al. Robust compressed sensing MRI with deep generative priors[J]. Advances in Neural Information Processing Systems, 2021, 34: 14938-14954. DOI: 10.48550/arXiv.2108.01368.
[39] CHUNG H, YE J C. Score-based diffusion models for accelerated MRI[J]. Medical image analysis, 2022, 80: 102479. DOI: 10.48550/arXiv.2110.05243.
[40] CAO H Q, TAN C, GAO Z Y, et al. A survey on generative diffusion model[J]. arXiv preprint arXiv: 2209.02646, 2022.
[41] YANG L, ZHANG Z L, SONG Y, et al. Diffusion models: A comprehensive survey of methods and applications[J]. ACM Computing Surveys, 2023, 56(4): 1-39. DOI: 10.48550/arXiv.2209.00796.
[42] KAZEROUNI A, AGHDAM E K, HEIDARI M, et al. Diffusion models in medical imaging: A comprehensive survey[J]. Medical Image Analysis, 2023: 102846. DOI: 10.1016/jmedia.2023.102846.
[43] VINCENT P, LAROCHELLE H, BENGIO Y, et al. Extracting and composing robust features with denoising autoencoders[C]//Proceedings of the 25th international conference on Machine learning, Helsinki, Finland, 2008: 1096-1103. DOI: 10.1145/1390156.1390294.
[44] VINCENT P, LAROCHELLE H, LAJOIE I, et al. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion[J]. Journal of Machine Learning Research, 2010, 11(12). DOI: 10.5555/1756006.1953039.
[45] VINCENT P. A connection between score matching and denoising autoencoders[J]. Neural computation, 2011, 23(7): 1661-1674. DOI: 10.1162/NECO_a_00142.
[46] SONG Y, ERMON S. Generative modeling by estimating gradients of the data distribution[J]. Advances in Neural Information Processing Systems, 2019, 32. DOI: 10.48550/arXiv.1907.05600.
[47] SONG Y, GARG S, SHI J, et al. Sliced score matching: A scalable approach to density and score estimation[C]//Proceedings of The 35th Uncertainty in Artificial Intelligence Conference, Tel Aviv, Israel, 2020: 574-584. DOI: 10.48550/arXiv.1905.07088.
[48] HO J, JAIN A, ABBEEL P. Denoising diffusion probabilistic models[J]. Advances in Neural Information Processing Systems, 2020, 33: 6840-6851. DOI: 10.48550/arXiv.2006.11239.
[49] ERHAN D, COURVILLE A, BENGIO Y, et al. Why does unsupervised pre-training help deep learning?[C]//Proceedings of the 13th International Conference on Artificial Intelligence and Statistics, Pittsburgh, USA, 2010: 201-208.
[50] Le CUN Y, FOGELMAN-SOULIE F. Modeles connexionnistes de l’apprentissage[J]. Intellectica, 1987, 2(1): 114-143. DOI: 10.3406/intel.1987.1804.
[51] GALLINARI P, LECUN Y, THIRIA S, et al. Distributed associative memories: A comparison[M]. La Villette, Paris: Proceedings of Cognitiva 87, 1987.
[52] SEUNG H S. Learning continuous attractors in recurrent networks[J]. Advances in Neural Information Processing Systems, 1997, 10. DOI: 10.5555/3008904.3008997.
[53] TEZCAN K C, BAUMGARTNER C F, LUECHINGER R, et al. MR image reconstruction using deep density priors[J]. IEEE Transactions on Medical Imaging, 2018, 38(7): 1633-1642. DOI: 10.1109/TMI.2018.2887072.
[54] LIU Q G, YANG Q X, CHENG H T, et al. Highly under-sampled magnetic resonance imaging reconstruction using autoencoding priors[J]. Magnetic Resonance in Medicine, 2020, 83(1): 322-336. DOI: 10.1002/mrm.27921.
[55] WANG S Y, LV J J, HE Z N, et al. Denoising auto-encoding priors in undecimated wavelet domain for MR image reconstruction[J]. Neurocomputing, 2021, 437: 325-338. DOI: 10.1016/j.neucom.2020.09.086.
[56] LIU X S, ZHANG M H, LIU Q G, et al. Multi-contrast MR reconstruction with enhanced denoising autoencoder prior learning[C]//2020 IEEE 17th International Symposium on Biomedical Imaging, Iowa City, USA, 2020: 1-5. DOI: 10.1109/ISBI45749.2020.9098334.
[57] ZHANG M H, LI M T, ZHOU J J, et al. High-dimensional embedding network derived prior for compressive sensing MRI reconstruction[J]. Medical Image Analysis, 2020, 64: 101717. DOI: 10.1016/j.media.2020.101717.
[58] BLOCK A, MROUEH Y, RAKHLIN A. Generative modeling with denoising auto-encoders and Langevin sampling[J]. arXiv Preprint arXiv: 2002.00107, 2020.
[59] PARISI G. Correlation functions and computer simulations[J]. Nuclear Physics B, 1981, 180(3): 378-384. DOI: 10.1016/0550-3213(81)90056-0.
[60] GRENANDER U, MILLER M I. Representations of knowledge in complex systems[J]. Journal of the Royal Statistical Society: Series B (Methodological), 1994, 56(4): 549-581. DOI: 10.1111/j.2517-6161.1994.tb02000.x.
[61] QUAN C, ZHOU J J, ZHU Y Z, et al. Homotopic gradients of generative density priors for MR image reconstruction[J]. IEEE Transactions on Medical Imaging, 2021, 40(12): 3265-3278. DOI: 10.48550/arXiv.2008.06284.
[62] ZHU W Q, GUAN B, WANG S S, et al. Universal generative modeling for calibration-free parallel MR imaging[C]//2022 IEEE 19th International Symposium on Biomedical Imaging, Kolkata, India, 2022: 1-5. DOI: 10.1109/ISBI52829.2022.9761446.
[63] HE Z N, ZHANG Y K, GUAN Y, et al. Iterative reconstruction for low-dose CT using deep gradient priors of generative model[J]. IEEE Transactions on Radiation and Plasma Medical Sciences, 2022, 6(7): 741-754. DOI: 10.48550/arXiv.2009.12760.
[64] LYU Q, WANG G. Conversion between CT and MRI images using diffusion and score-matching models[J]. arXiv Preprint arXiv: 2209.12104, 2022.
[65] CHUNG H, LEE E S, YE J C. MR image denoising and super-resolution using regularized reverse diffusion[J]. IEEE Transactions on Medical Imaging, 2022, 42(4): 922-934. DOI: 10.48550/arXiv.2203.12621.
[66] MENG X X, GU Y N, PAN Y S, et al. A novel unified conditional score-based generative framework for multi-modal medical image completion[J]. arXiv Preprint arXiv: 2207.03430, 2022.
[67] LUO G, BLUMENTHAL M, HEIDE M, et al. Bayesian MRI reconstruction with joint uncertainty estimation using diffusion models[J]. Magnetic Resonance in Medicine, 2023, 90(1): 295-311. DOI: 10.1002/mrm.29624.
[68] PENG C, GUO P F, ZHOU S K, et al. Towards performant and reliable undersampled MR reconstruction via diffusion model sampling[C]// International Conference on Medical Image Computing and Computer-Assisted Intervention, Singapore, Singapore, 2022: 623-633. DOI: 10.1007/978-3-031-16446-0_59.
[69] GUNGOR A, DAR S U H, OZTURK S, et al. Adaptive diffusion priors for accelerated MRI reconstruction[J]. Medical Image Analysis, 2023: 102872. DOI: 10.1016/j.media.2023.102872.
[70] XIE Y T, LI Q Z. Measurement-conditioned denoising diffusion probabilistic model for under-sampled medical image reconstruction[J]. arXiv Preprint arXiv: 2203.03623, 2022.
[71] OZTURKLER B, LIU C, ECKART B, et al. SMRD: Sure-based robust MRI reconstruction with diffusion models[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention, Vancouver, Canada, 2023: 199-209. DOI: 10.1007/978-3-031-43898-1_20.
[72] MIRZA M U, DALMAZ O, BEDEL H A, et al. Learning fourier-constrained diffusion bridges for MRI reconstruction[J]. arXiv Preprint arXiv: 2308.01096, 2023.
[73] BIAN W Y, JANG A, LIU F. Diffusion modeling with domain-conditioned prior guidance for accelerated MRI and qMRI reconstruction[J]. arXiv Preprint arXiv: 2309.00783, 2023.
[74] KORKMAZ Y, CUKUR T, PATEL V M. Self-supervised MRI reconstruction with unrolled diffusion models[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention, Vancouver, Canada, 2023: 491-501. DOI: 10.1007/978-3-031-43999-5_47.
[75] CHEN L X, TIAN X Y, WU J J, et al. JSMoCo: Joint coil sensitivity and motion correction in parallel MRI with a self-calibrating score-based diffusion model[J]. arXiv Preprint arXiv: 2310.09625, 2023.
[76] RAVULA S, LEVAC B, JALAL A, et al. Optimizing sampling patterns for compressed sensing MRI with diffusion generative models[J]. arXiv Preprint arXiv: 2306.03284, 2023.
[77] SONG Y, SHEN L Y, XING L, et al. Solving inverse problems in medical imaging with score-based generative models[J]. arXiv Preprint arXiv: 2111.08005, 2021.
[78] CHUNG H, SIM B, RYU D, et al. Improving diffusion models for inverse problems using manifold constraints[J]. arXiv Preprint arXiv: 2206.00941, 2022.
[79] TU Z J, JIANG C, GUAN Y, et al. K-space and image domain collaborative energy-based model for parallel MRI reconstruction[J]. Magnetic Resonance Imaging, 2023, 99: 110-122. DOI: 10.1016/j.mri.2023.02.004.
[80] PENG H, JIANG C, CHENG J, et al. One-shot generative prior in hankel-k-space for parallel imaging reconstruction[J]. IEEE Transactions on Medical Imaging, 2023. DOI: 10.1109/TMI.2023.3288219.
[81] YU C M, GUAN Y, KE Z W, et al. Universal generative modeling in dual domains for dynamic MRI[J]. NMR in Biomedicine, 2023, 36(12): e5011. DOI: 10.1002/nbm.5011.
[82] GUAN Y, YU C M, LU S Y, et al. Correlated and multi-frequency diffusion modeling for highly under-sampled MRI reconstruction[J]. arXiv Preprint arXiv: 2309.00853, 2023.
[83] ZHANG W, XIAO Z W, TAO H, et al. Low-rank tensor assisted K-space generative model for parallel imaging reconstruction[J]. Magnetic Resonance Imaging, 2023, 103: 198-207. DOI: 10.1016/j.mri.2023.07.004.
[84] CUI Z X, CAO CT, LIU S N, et al. Self-score: Self-supervised learning on score-based models for MRI reconstruction[J]. arXiv Preprint arXiv: 2209.00835, 2022.
[85] CUI Z X, LIU C C, FAN X H, et al. Physics-informed deep MRI: K-space interpolation meets heat diffusion[J]. IEEE Transactions on Medical Imaging, 2024, 43(10): 3503-3520. DOI: 10.1109/TMI.2024.3462988.
[86] CAO C T, CUI Z X, LIU S N, et al. High-frequency space diffusion models for accelerated MRI[J]. arXiv Preprint arXiv: 2208.05481, 2022.
[87] GUAN Y, YU C M, CUI Z X, et al. Correlated and multi-frequency diffusion modeling for highly under-sampled MRI reconstruction[J]. IEEE Transactions on Medical Imaging, 2024, 43(10): 3490-3502. DOI: 10.1109/TMI.2024.3381610.
[88] HUANG B, LU S Y, LIU Q G, et al. One-sample diffusion modeling in projection domain for low-dose CT imaging[J]. IEEE Transactions on Radiation and Plasma Medical Sciences, 2024. DOI: 10.1109/TRPMS.2024.3392248.
[89] WU W W, WANG Y Y. Data-iterative optimization score model for stable ultra-sparse-view CT reconstruction[J]. arXiv Preprint arXiv: 2308.14437, 2023.
[90] ZHANG W H, HUANG B, LIU Q G, et al. Low-rank angular prior guided multi-diffusion model for few-shot low-dose CT reconstruction[J]. IEEE Transactions on Radiation and Plasma Medical Sciences, 2024. DOI: 10.1109/TCI.2024.3503366.
[91] LIU J M, ANIRUDH R, THIAGARAJAN J J, et al. DOLCE: A model-based probabilistic diffusion framework for limited-angle CT reconstruction[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, Paris, France, 2023: 10498-10508. DOI: 10.48550/arXiv.2211.12340.
[92] XIA W J, SHI Y Y, NIU C, et al. Diffusion prior regularized iterative reconstruction for low-dose CT[J]. arXiv Preprint arXiv: 2310.06949, 2023.
[93] XIA W J, CONG W X, WANG G. Patch-based denoising diffusion probabilistic model for sparse-view CT reconstruction[J]. arXiv Preprint arXiv: 2211.10388, 2022.
[94] XIA W J, NIU C, CONG W X, et al. Sub-volume-based denoising diffusion probabilistic model for cone-beam CT reconstruction from incomplete data[J]. arXiv e-Prints, 2023: arXiv: 2303.12861.
[95] GAO Q, LI Z L, ZHANG J P, et al. CoreDiff: Contextual error-modulated generalized diffusion model for low-dose CT denoising and generalization[J]. arXiv Preprint arXiv: 2304.01814, 2023.
[96] WU W W, WANG Y Y, LIU Q G, et al. Wavelet-improved score-based generative model for medical imaging[J]. IEEE Transactions on Medical Imaging, 2023. DOI: 10.1109/TMI.2023.3325824.
[97] XU K, LU S Y, HUANG B, et al. Stage-by-stage wavelet optimization refinement diffusion model for sparse-view CT reconstruction[J]. arXiv Preprint arXiv: 2308.15942, 2023.
[98] ZHANG J J, MAO H Y, WANG X R, et al. Wavelet-inspired multi-channel score-based model for limited-angle CT reconstruction[J]. IEEE Transactions on Medical Imaging, 2024, 43(10): 3436-3448. DOI: 10.1109/TMI.2024.3367167.
[99] GUAN B, YANG C L, ZHANG L, et al. Generative modeling in sinogram domain for sparse-view CT reconstruction[J]. IEEE Transactions on Radiation and Plasma Medical Sciences, 2023. DOI: 10.1109/TRPMS.2023.3309474.
[100] LIU X, XIE Y Q, DIAO S H, et al. Unsupervised CT metal artifact reduction by plugging diffusion priors in dual domains[J]. IEEE Transactions on Medical Imaging, 2024, 43(10): 3533-3545. DOI: 10.1109/TMI.2024.3351201.
[101] WANG Y Y, LI Z R, WU W W. Time-reversion fast-sampling score-based model for limited-angle CT reconstruction[J]. IEEE Transactions on Medical Imaging, 2024, 43(10): 3449-3460. DOI: 10.1109/TMI.2024.3418838.
[102] WU W W, PAN J Y, WANG Y Y, et al. Multi-channel optimization generative model for stable ultra-sparse-view CT reconstruction[J]. IEEE Transactions on Medical Imaging, 2024, 43(10): 3416-3475. DOI: 10.1109/TMI.2024.3376414.
[103] HAN Z Y, WANG Y H, ZHOU L P, et al. Contrastive diffusion model with auxiliary guidance for coarse-to-fine PET reconstruction[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention, Vancouver, Canada, 2023: 239-249. DOI: 10.1007/978-3-031-43999-5_23.
[104] JIANG C W, PAN Y S, LIU M X, et al. PET-diffusion: Unsupervised PET enhancement based on the latent diffusion model[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention, Vancouver, Canada, 2023: 3-12. DOI: 10.1007/978-3-031-43907-0_1.
[105] XIE T F, CAO C T, CUI Z X, et al. Brain pet synthesis from MRI using joint probability distribution of diffusion model at ultrahigh fields[J]. arXiv Preprint arXiv: 2211.08901, 2022.
[106] XIE T F, CAO C R, CUI Z X, et al. Synthesizing PET images from high-field and ultra-high-field MR images using joint diffusion attention model[J]. arXiv Preprint arXiv: 2305.03901, 2023.
[107] XIE T F, CUI Z X, LUO C, et al. Joint diffusion: Mutual consistency-driven diffusion model for PET-MRI co-reconstruction[J]. arXiv Preprint arXiv: 2311.14473, 2023.
[108] PAN S Y, ABOUEI E, PENG J B, et al. Full-dose PET synthesis from low-dose PET using high-efficiency diffusion denoising probabilistic model[J]. arXiv Preprint arXiv: 2308.13072, 2023.
[109] SONG X L, WANG G J, ZHONG W H, et al. Sparse-view reconstruction for photoacoustic tomography com-bining diffusion model with model-based iteration[J]. Photoacoustics, 2023, 33: 100558. DOI: 10.1016/j.pacs.2023.100558.
[110] TONG S Q, LAN H R, NIE L M, et al. Score-based generative models for photoacoustic image reconstruction with rotation consistency constraints[J]. arXiv Preprint arXiv: 2306.13843, 2023.
[111] DOSOVITSKIY A, BEYER L, KOLESNIKOV A, et al. An image is worth 16x16 words: Transformers for image recognition at scale[J]. arXiv Preprint arXiv: 2010.11929, 2020.
[112] WANG Z H, YANG Y Y, CHEN Y Z, et al. Mutual information guided diffusion for zero-shot cross-modality medical image translation[J]. IEEE Transactions on Medical Imaging, 2024, 43(8): 2825-2838. DOI: 10.1109/TMI.2024.3382043.
[113] CHU J Y, DU C H, LIN X Y, et al. Highly accelerated MRI via implicit neural representation guided posterior sampling of diffusion models[J]. Medical Image Analysis, 2025, 100.
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