Evaluation of a Novel Quantitative Multiparametric MR Sequence for Radiation Therapy Treatment Response Assessment.

ArXiv Pub Date : 2025-03-28
Yuhao Yan, R Adam Bayliss, Florian Wiesinger, Jose de Arcos Rodriguez, Adam R Burr, Andrew M Baschnagel, Brett A Morris, Carri K Glide-Hurst
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Abstract

Background: Multi-parametric MRI has shown great promise to rapidly derive multiple quantitative imaging biomarkers for treatment response assessment.

Purpose: To evaluate a novel Deep-Learning-enhanced MUlti-PArametric MR sequence (DL-MUPA) for treatment response assessment for brain metastases patients treated with stereotactic radiosurgery (SRS) and head-and-neck (HnN) cancer patients undergoing conventionally fractionation adaptive radiation therapy.

Methods: DL-MUPA derives quantitative T1 and T2 relaxation time maps from a single 4-6-minute scan denoised via DL method using least-squares dictionary fitting. Longitudinal phantom benchmarking was performed on a NIST-ISMRM phantom over one year. In patients, longitudinal DL-MUPA data were acquired on a 1.5T MR-simulator, including pre-treatment (PreTx) and every ~3 months after SRS (PostTx) in brain, and PreTx, mid-treatment and 3 months PostTx in HnN. Delta analysis was performed calculating changes of mean T1 and T2 values within gross tumor volumes (GTVs), residual disease (RD, HnN), parotids, and submandibular glands (HnN) for treatment response assessment. Uninvolved normal tissues (normal appearing white matter in brain, masseter in HnN) were evaluated to quantify within-subject repeatability.

Results: Phantom benchmarking revealed excellent inter-session repeatability (coefficient of variance <0.9% for T1, <6.6% for T2), suggesting reliability for longitudinal studies once systematic biases are adjusted. Uninvolved normal tissue suggested acceptable within-subject repeatability (brain |ΔT1mean|<36ms/5.0%, |ΔT2mean|<2ms/5.0%, HnN |ΔT1mean|<69ms/7.0%, |ΔT2mean|<4ms/17.8% due to low T2). In brain, remarkable changes were noted in resolved metastasis (4-month PostTx ΔT1mean=155ms/13.7%) and necrotic settings (ΔT1mean=214-502ms/17.6-39.7%, ΔT2mean=7-41ms/8.7-41.4%, 6-month to 3-month PostTx). In HnN, two base of tongue tumors exhibited T2 enhancement (PostTx GTV ΔT2mean>7ms/12.8%, RD ΔT2mean>10ms/18.1%). A case with nodal disease resolved PostTx (GTV ΔT1mean=-541ms/-39.5%, ΔT2mean=-24ms/-32.7%, RD ΔT1mean=-400ms/-29.2%, ΔT2mean=-25ms/-35.3%). Enhancement was found in involved parotids (PostTx ΔT1mean>82ms/12.4%, ΔT2mean>6ms/13.4%) and submandibular glands (PostTx ΔT1mean>135ms/14.6%, ΔT2mean>17ms/34.5%) while the uninvolved organs remained stable.

Conclusions: Preliminary results suggest promise of DL-MUPA for treatment response assessment and highlight potential endpoints for functional sparing.

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评价一种新的定量多参数磁共振序列用于放射治疗治疗反应评估。
目的:评估深度学习增强的多参数磁共振序列(DL-MUPA)在脑转移患者接受立体定向放射手术(SRS)和头颈部癌症(HnN)患者接受常规分步适应性放射治疗中的治疗反应评估。方法:DL- mupa从单次4-6分钟扫描中获得定量的T1和T2图,通过DL方法使用字典拟合进行降噪。幻影基准测试在NIST-ISMRM幻影上进行。在1.5T磁共振模拟器上获得纵向患者数据,包括治疗前(prex)和每3个月的脑SRS (PostTx),以及HnN的prex,治疗中期和3个月的PostTx。计算总肿瘤体积(GTVs)、残留病变(RD、HnN)、腮腺和下颌下腺(HnN)的平均T1和T2值的变化,以评估治疗反应。未受累的正常组织(脑白质外观正常,HnN咬肌)作为对照。结果:幻影基准测试显示出优异的会话间重复性(方差系数13%,RD $\Delta$T2>18%)。淋巴结疾病解决后tx (GTV $\Delta$T1=-40%, $\Delta$T2=-33%, RD $\Delta$T1=-29%, $\Delta$T2=-35%)。受累的腮腺(PostTx $\Delta$T1>12%, $\Delta$T2>13%)和下颌骨腺(PostTx $\Delta$T1>15%, $\Delta$T2>35%)增强,而未受累的器官保持稳定。结论:DL-MUPA有望用于治疗反应评估和识别功能保留的潜在终点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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