PRISM: Differentiable Analysis-by-Synthesis for Fixel Recovery in Diffusion MRI

• URL: http://arxiv.org/abs/2604.00250v1
• Date: Tue, 31 Mar 2026 21:22:49 GMT
• Title: PRISM: Differentiable Analysis-by-Synthesis for Fixel Recovery in Diffusion MRI
• Abstract summary: This work introduces, a differentiable analysis-by-synthesis framework that fits an explicit multi-compartment forward model end-to-end cerebrospinal model.<n> PRISM supports fast MSE objective and a Rician negative log-ze-likelihood (NLL) that jointly learns without information.<n> PRISM achieves 3.5 degrees best-match angular error with 95% recall is 1.9x lower than best baseline.
• Score: 1.3063136334195184
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion MRI microstructure fitting is nonconvex and often performed voxelwise, which limits fiber peak recovery in narrow crossings. This work introduces PRISM, a differentiable analysis-by-synthesis framework that fits an explicit multi-compartment forward model end-to-end over spatial patches. The model combines cerebrospinal fluid (CSF), gray matter, up to K white-matter fiber compartments (stick-and-zeppelin), and a restricted compartment, with explicit fiber directions and soft model selection via repulsion and sparsity priors. PRISM supports a fast MSE objective and a Rician negative log-likelihood (NLL) that jointly learns sigma without oracle information. A lightweight nuisance calibration module (smooth bias field and per-measurement scale/offset) is included for robustness and regularized to identity in clean-data tests. On synthetic crossing-fiber data (SNR=30; five methods, 16 crossing angles), PRISM achieves 3.5 degrees best-match angular error with 95% recall, which is 1.9x lower than the best baseline (MSMT-CSD, 6.8 degrees, 83% recall); in NLL mode with learned sigma, error drops to 2.3 degrees with 99% recall, resolving crossings down to 20 degrees. On the DiSCo1 phantom (NLL mode), PRISM improves connectivity correlation over CSD baselines at all four tracking angles (best r=.934 at 25 degrees vs. .920 for MSMT-CSD). Whole-brain HCP fitting (~741k voxels, MSE mode) completes in ~12 min on a single GPU with near-identical results across random seeds.

Related papers

• Beyond Argmax: A Mechanistic Study of Semantic Retention in Frozen Foundation-Model Composition for Generalized Few-Shot 3D Segmentation [0.0]
  We investigate how much useful semantic information is lost when heterogeneous sources are collapsed to a single class before they can interact.<n>We find that most information is recovered by retaining a compact set of plausible alternatives.<n>The contribution is a controlled diagnosis of premature semantic collapse as a repeatable information bottleneck in heterogeneous frozen-model composition.
  arXiv  Detail & Related papers  (2026-09-10T18:25:53Z)
• Solution for UCF UrbanTwin LUMPI Track: Sim-to-Real Urban LiDAR 3D Object Detection [54.03436243843709]
  We present our solution to the LUMPI track of the UCF UrbanTwin Sim2Real LiDAR Challenge at the 6th DriveX Workshop, ECCV 2026.<n>The detector must be trained only on synthetic data and is evaluated on 50 held-out real LiDAR frames.<n>A separate 50-frame synthetic submission is evaluated for point-cloud realism.
  arXiv  Detail & Related papers  (2026-09-07T15:03:36Z)
• Hardware-Efficient Exchange-Only QML: Singlet-Triplet Spin Chains via Inter-pair Coupling without Magnetic Gradients [0.0]
  Two-spin units offer higher density but rely on local magnetic field gradients for control, increasing integration complexity.<n>We propose a resource-efficient quantum machine learning architecture that achieves high expressibility using minimal two-spin units and Heisenberg exchange interactions alone.
  arXiv  Detail & Related papers  (2026-08-29T03:13:07Z)
• Modeling Normal Is All You Need: Joint Latent Clustering for Anomaly Detection in Multimodal Cyber-Physical Systems [47.027290803102666]
  Normal behaviour is the union of many imbalanced, curved, thin-fringed operating regimes.<n>We model the normal law with a jointly learned latent representation plus explicit Gaussian-mixture mode clustering.<n>We evaluate under a deliberately fair protocol: raw point-wise metrics with no point adjustment, a trivial-detector difficulty split, prevalence-matched F1, and train-normal-only calibration.
  arXiv  Detail & Related papers  (2026-07-07T10:10:00Z)
• Towards Robust Training in NNGPT AutoML Pipeline: A Loss-Optimizer Pairing Selection Study [48.83701310501069]
  This paper investigates whether a single recipe is sufficient for heterogeneous architecture pools, or whether the optimal pairing varies across structurally diverse models.<n>We conduct a systematic empirical study of all $3 times 6 = 18$ combinations of six classifiers paired with three loss functions: Cross-Entropy (CEL), Negative Log-Likelihood (NLL), and the recently introduced genetically evolved NGL loss across the base models presented in LEMUR heterogeneous architecture pool on six image classification datasets.<n>Our results confirm that no single pairing is universally optimal. Cross-Entropy with Adam or AdamW is the most robust choice across
  arXiv  Detail & Related papers  (2026-06-18T20:51:42Z)
• Entropy Across the Bridge: Conditional-Marginal Discretization for Flow and Schrödinger Samplers [68.9946633641494]
  Flow matching and Schrdinger bridges define probability, yet their inference grids are usually or inherited from one-endpoint diffusion.<n>We derive a conditional-marginal entropy-rate objective for bridge-aware discretization, separating endpoint-conditioned bridge geometry from marginal flow evolution.<n>On EDM/CIFAR-10, the entropic time-discretization gives the best tested five-step FID (186.3 pm 4.0 versus 200.5 pm 2.9 for linear and 238.0 pm 5.3 for cosine)
  arXiv  Detail & Related papers  (2026-05-15T16:11:10Z)
• Reframing preprocessing selection as model-internal calibration in near-infrared spectroscopy: A large-scale benchmark of operator-adaptive PLS and Ridge models [0.2609784101826761]
  This paper studies the case where that search can be collapsed into one calibration step.<n> Linear operator-adaptive calibration gives comparable prediction quality to exhaustive preprocessing screening.
  arXiv  Detail & Related papers  (2026-05-13T14:23:00Z)
• Less is More in Semantic Space: Intrinsic Decoupling via Clifford-M for Fundus Image Classification [13.23226865033351]
  We propose Clifford-M, a lightweight backbone that replaces feed-forward expansion and frequency-splitting modules with sparse geometric interaction.<n>Without pre-training, Clifford-M achieves a mean AUC-ROC of 0.8142 and a mean macro-F1 of 0.5481 on ODIR-5K using only 0.85M parameters.
  arXiv  Detail & Related papers  (2026-03-21T13:00:05Z)
• K-GMRF: Kinetic Gauss-Markov Random Field for First-Principles Covariance Tracking on Lie Groups [8.489406212619164]
  We propose K-GMRF, an online, training-free framework for covariance tracking.<n>Our method interprets observations as torques driving a latent angular velocity, propagated via a structure-preserving symplectic integrator.<n>We theoretically prove that this second-order dynamics achieves zero steady-state error under constant rotation.
  arXiv  Detail & Related papers  (2026-03-20T03:16:36Z)
• BadCLIP++: Stealthy and Persistent Backdoors in Multimodal Contrastive Learning [73.46118996284888]
  Research on backdoor attacks against multimodal contrastive learning models faces two key challenges: stealthiness and persistence.<n>We propose BadCLIP++, a unified framework that tackles both challenges.<n>For stealthiness, we introduce a semantic-fusion QR micro-trigger that embeds imperceptible patterns near task-relevant regions.<n>For persistence, we stabilize trigger embeddings via radius shrinkage and centroid alignment.
  arXiv  Detail & Related papers  (2026-02-19T08:31:16Z)
• Function-Space Decoupled Diffusion for Forward and Inverse Modeling in Carbon Capture and Storage [65.51149575007149]
  We present Fun-DDPS, a generative framework that combines function-space diffusion models with differentiable neural operator surrogates for both forward and inverse modeling.<n>Fun-DDPS produces physically consistent realizations free from the high-frequency artifacts observed in joint-state baselines.
  arXiv  Detail & Related papers  (2026-02-12T18:58:12Z)
• DS-CIM: Digital Stochastic Computing-In-Memory Featuring Accurate OR-Accumulation via Sample Region Remapping for Edge AI Models [8.92683306412944]
  This paper introduces a digital CIM (DS-CIM) architecture that achieves both high accuracy and efficiency.<n>We implement multiply-accumulation (MAC) in a compact, unsigned OR-based circuit by modifying the data representation.<n>Our core strategy, a shared Random Number Generator (PRNG) with 2D, enables single-cycle mutually exclusive activation to eliminate OR-gate collisions.
  arXiv  Detail & Related papers  (2026-01-10T23:56:33Z)
• Time-Series at the Edge: Tiny Separable CNNs for Wearable Gait Detection and Optimal Sensor Placement [3.7765281299298015]
  We study on-device time-series analysis for gait detection in Parkinson's disease (PD) from short windows of triaxial acceleration, targeting resource-latency wearables and edge nodes.<n>We compare magnitude thresholding to three 1D CNNs for time-series analysis: a literature baseline (separable convolutions) and two ultra-light models - one purely separable and one with residual connections.
  arXiv  Detail & Related papers  (2025-11-29T08:52:41Z)
• Environment-Aware Indoor LoRaWAN Path Loss: Parametric Regression Comparisons, Shadow Fading, and Calibrated Fade Margins [3.776919981139063]
  Indoor LoRaWAN propagation is shaped by structural and time-varying context factors.<n>We present an environment-aware, statistically disciplined path loss framework evaluated using leakage-safe cross-validation.
  arXiv  Detail & Related papers  (2025-10-05T20:14:48Z)
• Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch [72.97553348776425]
  We introduce DARE to set most delta parameters without affecting the abilities of Supervised Fine-Tuning (SFT) LMs. Then, we use DARE as a versatile plug-in to sparsify delta parameters of multiple SFT models and merge them into a single model. We also utilize DARE to create a merged LM that ranks first among models with 7 billion parameters on the Open Leaderboard.
  arXiv  Detail & Related papers  (2023-11-06T13:43:07Z)
• Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion [56.38386580040991]
  Consistency Trajectory Model (CTM) is a generalization of Consistency Models (CM) CTM enables the efficient combination of adversarial training and denoising score matching loss to enhance performance. Unlike CM, CTM's access to the score function can streamline the adoption of established controllable/conditional generation methods.
  arXiv  Detail & Related papers  (2023-10-01T05:07:17Z)

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.