When Brains Disagree: Biological Ambiguity Underlies the Challenge of Amyloid PET Synthesis from Structural MRI

• URL: http://arxiv.org/abs/2605.11867v1
• Date: Tue, 12 May 2026 09:47:44 GMT
• Title: When Brains Disagree: Biological Ambiguity Underlies the Challenge of Amyloid PET Synthesis from Structural MRI
• Abstract summary: MRI captures neurodegeneration, while PET measures amyloid pathology.<n>As a result, similar MRI patterns may correspond to different amyloid states, creating ambiguous one-to-many mappings.<n>We show that ambiguity in the data distribution, rather than architectural capacity, constrains performance.
• Score: 2.3047461366838755
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Structural MRI-to-amyloid PET synthesis has been proposed as a non-invasive alternative for amyloid assessment in Alzheimer's disease (AD). However, reported performance of identical models varies widely across studies, and increasingly complex architectures have not led to consistent gains. This inconsistency is thought to be caused by a fundamental biological ambiguity: MRI captures neurodegeneration, while PET measures amyloid pathology - two processes that are often temporally decoupled in AD. As a result, similar MRI patterns may correspond to different amyloid states, creating ambiguous one-to-many mappings. MRI-to-amyloid PET synthesis may therefore be intrinsically ill-posed; however, this idea has yet to be tested scientifically. The aim of this work is to test this hypothesis through two controlled experiments. We first control the training distribution by stratifying paired MRI-PET data by amyloid and neurodegeneration status. Using two standard synthesis models under a controlled design, we show that biologically unambiguous mappings are learnable in isolation, but performance collapses when data ambiguity is introduced. This demonstrates that ambiguity in the data distribution, rather than architectural capacity, constrains performance. Second, we show that introducing orthogonal biological information in the form of plasma biomarkers resolves this ambiguity. When multimodal inputs are incorporated, performance improves and stability is restored. Together, these findings suggest that limited and inconsistent performance in MRI-to-amyloid PET synthesis is explained by intrinsic biological ambiguity, and that stable, meaningful progress requires multimodal integration rather than architectural complexity.

Related papers

• Predicting Immune Biomarkers with MultiModal Mixture-of-Expert Pathology Foundation Models Empowers Precision Oncology [47.79180201495836]
  MixTIME is a multimodal foundation model that integrates pathology foundation models trained across distinct modalities.<n>It predicts multiplex immunofluorescence (mIF) protein expression from hematoxylin and eosin whole-slide images.<n>MixTIME achieves state-of-the-art performance across 17 protein markers as measured by correlation metrics.
  arXiv  Detail & Related papers  (2026-06-16T16:22:42Z)
• RelA-Diffusion: Relativistic Adversarial Diffusion for Multi-Tracer PET Synthesis from Multi-Sequence MRI [6.650038888450141]
  Multi-tracer positron emission tomography (PET) provides critical insights into diverse neuropathological processes.<n>High costs, radiation exposure, and restricted tracer availability limit routine acquisition of multi-tracer PET.<n>Recent efforts have explored deep learning approaches for synthesizing PET images from structural MRI.
  arXiv  Detail & Related papers  (2026-02-24T20:30:50Z)
• Pattern-Aware Diffusion Synthesis of fMRI/dMRI with Tissue and Microstructural Refinement [34.55493442995441]
  We propose PDS, a pattern-aware dual-modal 3D diffusion framework for cross-modality learning.<n>We also introduce a tissue refinement network integrated with a efficient microstructure refinement to maintain structural fidelity and fine details.<n>PDS achieves state-of-the-art results, with PSNR/SSIM scores of 29.83 dB/90.84% for fMRI synthesis and 30.00 dB/77.55% for dMRI synthesis.
  arXiv  Detail & Related papers  (2025-11-07T03:51:00Z)
• SLaM-DiMM: Shared Latent Modeling for Diffusion Based Missing Modality Synthesis in MRI [0.0]
  Brain MRI scans are often found in four modalities, consisting of T1-weighted with and without contrast enhancement (T1ce and T1w), T2-weighted imaging (T2w) and Flair.<n>We propose SLaM-DiMM, a novel missing modality generation framework that harnesses the power of diffusion models to synthesize any of the four target MRI modalities from other available modalities.
  arXiv  Detail & Related papers  (2025-09-19T14:27:35Z)
• CoCoLIT: ControlNet-Conditioned Latent Image Translation for MRI to Amyloid PET Synthesis [2.333160549379721]
  High dimensionality and structural complexity of 3D neuroimaging data pose challenges for MRI-to-PET translation.<n>We present CoCoLIT, a diffusion-based latent generative framework that incorporates three main innovations.<n>We evaluate CoCoLIT's performance on publicly available datasets and find that our model significantly outperforms state-of-the-art methods on both image-based and amyloid-related metrics.
  arXiv  Detail & Related papers  (2025-08-02T09:58:30Z)
• An interpretable generative multimodal neuroimaging-genomics framework for decoding Alzheimer's disease [13.213387075528017]
  Alzheimer's disease (AD) is the most prevalent form of dementia worldwide, encompassing a prodromal stage known as Mild Cognitive Impairment (MCI)<n>The objective of the work was to capture structural and functional modulations of brain structure and function relying on multimodal MRI data and Single Nucleotide Polymorphisms.
  arXiv  Detail & Related papers  (2024-06-19T07:31:47Z)
• Three-Dimensional Amyloid-Beta PET Synthesis from Structural MRI with Conditional Generative Adversarial Networks [45.426889188365685]
  Alzheimer's Disease hallmarks include amyloid-beta deposits and brain atrophy. PET is expensive, invasive and exposes patients to ionizing radiation. MRI is cheaper, non-invasive, and free from ionizing radiation but limited to measuring brain atrophy.
  arXiv  Detail & Related papers  (2024-05-03T14:10:29Z)
• Amyloid-Beta Axial Plane PET Synthesis from Structural MRI: An Image Translation Approach for Screening Alzheimer's Disease [49.62561299282114]
  An image translation model is implemented to produce synthetic amyloid-beta PET images from structural MRI that are quantitatively accurate. We found that the synthetic PET images could be produced with a high degree of similarity to truth in terms of shape, contrast and overall high SSIM and PSNR.
  arXiv  Detail & Related papers  (2023-09-01T16:26:42Z)
• Cross-Attention is Not Enough: Incongruity-Aware Dynamic Hierarchical Fusion for Multimodal Affect Recognition [69.32305810128994]
  Incongruity between modalities poses a challenge for multimodal fusion, especially in affect recognition. We propose the Hierarchical Crossmodal Transformer with Dynamic Modality Gating (HCT-DMG), a lightweight incongruity-aware model. HCT-DMG: 1) outperforms previous multimodal models with a reduced size of approximately 0.8M parameters; 2) recognizes hard samples where incongruity makes affect recognition difficult; 3) mitigates the incongruity at the latent level in crossmodal attention.
  arXiv  Detail & Related papers  (2023-05-23T01:24:15Z)
• Confidence-guided Lesion Mask-based Simultaneous Synthesis of Anatomic and Molecular MR Images in Patients with Post-treatment Malignant Gliomas [65.64363834322333]
  Confidence Guided SAMR (CG-SAMR) synthesizes data from lesion information to multi-modal anatomic sequences. module guides the synthesis based on confidence measure about the intermediate results. experiments on real clinical data demonstrate that the proposed model can perform better than the state-of-theart synthesis methods.
  arXiv  Detail & Related papers  (2020-08-06T20:20:22Z)
• Lesion Mask-based Simultaneous Synthesis of Anatomic and MolecularMR Images using a GAN [59.60954255038335]
  The proposed framework consists of a stretch-out up-sampling module, a brain atlas encoder, a segmentation consistency module, and multi-scale label-wise discriminators. Experiments on real clinical data demonstrate that the proposed model can perform significantly better than the state-of-the-art synthesis methods.
  arXiv  Detail & Related papers  (2020-06-26T02:50:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.