Library · Signals, sensing & imaging
What Image Reconstruction & Tomography got wrong, from 49 theses
Summary written by a model from the records below: The records describe practical challenges and algorithmic failures across medical, optical, and acoustic tomographic image reconstruction workflows. Investigators frequently encountered reconstruction artifacts, forward model simplifications, sensor hardware bottlenecks, and performance degradations under distribution shifts or severe undersampling. These records come from graduate theses, 2021 to 2026, each labeled with its degree where the repository states it, and each links to its thesis. They were extracted by language models reading the full text, so treat each as a lead to read, not a verdict.
Plus 9 problems the authors left open, listed at the end.
Failure modes, by theses reporting them
Select a mode to jump to its records.
Iterative and filtering reconstruction methods introduce artifacts or underperform simpler baselines
Iterative deconvolution, filtered backprojection, and optimization-based solvers often introduced edge distortions, attenuated necessary frequencies, or produced spurious noise artifacts. In multiple benchmarks, these complex iterative or regularized schemes underperformed standard analytical baselines, direct transforms, or simpler filtering pipelines.
Tried and failed
Iterative deconvolution algorithms applied to interferometric dirty map reconstruction. Reason: alters noise covariance properties and introduces spurious residual artifacts into the map
Tried and failed
filtered backprojection and SIRT tomography reconstruction applied to continuous slab-like geometries. Reason: attenuates low-frequency components, artificially introducing non-physical through-holes in continuous slabs
Quantitative multiscale methods for scanning transmission electron microscopy of low-dimensional materials in two and three dimensions · UT Austin · PhD
Tried and failed
increasing iterative motion correction steps applied to dynamic image reconstruction. Outcome: worse than baseline. Reason: accumulating residual errors in unsampled frequency domain regions degraded reconstruction metrics
Deep learning for accelerated magnetic resonance imaging · Imperial · PhD
Tried and failed
3D Fourier deconvolution with Tikhonov regularization applied to spatial scanning signal reconstruction. Reason: produced severe edge artifacts and poor agreement with measured signals
Rapid signal modeling via directed acyclic graphs and magnet tip nanofabrication for magnetic resonance force microscopy · Cornell · PhD
Lost to a baseline
dAUTOMAP reconstruction was blurrier and produced higher SSIM loss than Filtered Back Projection (FBP) on noiseless/high-resolution 256x256 and 512x512 CT images.
Design and applications of cold-cathode X-ray imaging systems · MIT · PhD
Lost to a baseline
Model-based minimization acoustic reconstruction yielded lower reconstruction accuracy and smeared Bragg peak sharpness compared to iterative time-reversal.
Development of an acoustic dose-profile measurement technique for short pulse proton and ion beams · Imperial · PhD
Lost to a baseline
Simultaneous Algebraic Reconstruction Technique (SART) after 10 iterations was outperformed by GMDL-2P (achieved lower PSNR, SSIM, and higher RMSE on low-dose CT reconstruction)
Deep Learning-based CBCT Projection Interpolation, Reconstruction, and Post-processing for Radiation Therapy · Duke · PhD
Lost to a baseline
Gegenbauer reconstruction (SSIM 0.85003) and Piecewise filtered Fourier (SSIM 0.84592) lost to unfiltered Fourier baseline (SSIM 0.91128) and filtered Fourier (SSIM 0.92145) on 2D MRI datasets with estimated edges.
On the Epistemology of Gibbs Ringing Reduction Algorithm Performance · University College Dublin · PhD
Lost to a baseline
Piecewise filtered Fourier reconstruction (mean SSIM 0.83302) lost to unfiltered Fourier baseline (mean SSIM 0.90462) and filtered Fourier (mean SSIM 0.90831) on the 2D synthetic dataset.
On the Epistemology of Gibbs Ringing Reduction Algorithm Performance · University College Dublin · PhD
Lost to a baseline
Model-based ADMM reconstruction lost to deep learning-based PaDI network (yielding far less spatially informative results with high edge artifacts)
Computational Bio-Optical Imaging with Novel Sensor Arrays · Duke · PhD
Lost to a baseline
In X-ray tomography with 1% noise and active subspace dimension r = 1, TSVD (84.58% relative error) outperformed Tikhonov (21.36% relative error), DI (21.36% relative error), and DIAS (21.35% relative error) under over-regularization (alpha = 10^8) where Tikhonov reached 80.77%, DI reached 78.08%, and DIAS reached 74.29%.
Accelerating inverse solutions with machine learning and randomization · UT Austin · PhD
Lost to a baseline
For Substantia Nigra and Dentate Nucleus ROIs, TFIR had higher susceptibility reconstruction error compared to ground truth than MEDI-SMV.
Deep learning models fail under domain shift, data scarcity, and loss of phase information
Neural network reconstruction models trained without physics adaptations, on synthetic noise, or on small datasets suffered severe degradation when exposed to out-of-distribution data or physical domain shifts. Discarding raw phase information or training generative architectures from scratch on sparse data led to blurred features, artifacts, and catastrophic reconstruction errors.
Tried and failed
synthetic procedural noise generation for training data applied to microscopy image denoising and artifact removal. Outcome: did not generalise. Reason: procedurally generated corruptions failed to accurately replicate experimental noise characteristics, degrading reconstruction quality
Accelerating microscopy for nanoscale fabrication via deep-learning image reconstruction · UT Austin · PhD
Tried and failed
deep learning based image reconstruction applied to snapshot Fourier ptychography. Outcome: did not generalise. Reason: training data lacked artistic images, leading to significant reconstruction errors on out-of-distribution inputs
Sampling in Computational Cameras · Duke · PhD
Tried and failed
deep learning reconstruction without domain adaptation applied to radio tomographic imaging across environments. Outcome: did not generalise. Reason: Severe distribution shift and model mismatch between source and target environments degraded reconstruction accuracy.
Radio Tomographic Imaging with Deep Learning · Georgia Tech · PhD
Tried and failed
supervised deep neural networks applied to out-of-distribution light-field functional microscopy reconstruction. Outcome: did not generalise. Reason: distribution shift from structural training data due to noise and scattering mismatches
Computational methods for 3D imaging of neural activity in light-field microscopy · Imperial · PhD
Tried and failed
pure deep learning estimators without physics adaptation applied to tomographic reconstruction under model mismatch. Outcome: did not generalise. Reason: models lacked robustness to mismatched forward projection models across varying noise levels
Radio Tomographic Imaging with Deep Learning · Georgia Tech · PhD
Tried and failed
unsupervised deep learning without dataset-specific tuning applied to cross-dataset dynamic MRI reconstruction. Outcome: did not generalise. Reason: lack of task-specific retraining caused severe domain shift, producing black images and massive reconstruction error
Generalizable low-latency accelerated dynamic MRI · Iowa State · PhD
Tried and failed
retrospectively undersampled training for accelerated imaging reconstruction applied to prospective magnetic resonance image reconstruction. Outcome: did not generalise. Reason: prospective acquisition contains spatial distortions and contrast shifts not simulated by retrospective undersampling
Tried and failed
training diffusion models from scratch on small datasets applied to image reconstruction. Outcome: overfit. Reason: training complex generative diffusion architectures from scratch with limited subject data led to severe overfitting and poor reconstruction
Multi-contrast magnetic resonance imaging with deep generative learning · UT Austin · PhD
Lost to a baseline
When inputting late-time overdensity directly into a CNN without standard reconstruction preprocessing, the CNN failed to improve upon standard reconstruction alone.
Quantum Black Holes and the Primordial Universe · Harvard · PhD
Considered and rejected
Considered and rejected: Rejected using real MRI phase grafting onto natural images for reconstruction generalization because it limits training diversity and fails to scale to large datasets.
Generalizable deep learning based medical image segmentation · Imperial · PhD
Tried and failed
training CNNs on envelope-detected data applied to ultrasound image reconstruction. Outcome: worse than baseline. Reason: envelope detection discards phase information, causing blurred speckle patterns compared to raw RF/IQ representations
Ultrasound Imaging: From Physical Modeling to Deep Learning · EPFL · PhD
Tried and failed
training radiance fields from scratch on sparse data applied to dynamic scene 3D reconstruction updating. Outcome: worse than baseline. Reason: sparse updated images provided insufficient coverage in vacated regions compared to fine-tuning existing representations
Addressing Challenges in Object-Based Robot Navigation and Mapping · MIT · PhD
Hardware constraints, sensor geometry, and calibration errors undermine reconstruction quality
Physical constraints such as short sensor baselines, positioning errors, missing low-frequency transducer components, and inadequate voxel resolution directly degraded reconstructed images. Attempts to calibrate or interpolate across mismatched coordinate systems or narrow depths of field resulted in severe phase offsets, focal blurring, and persistent artifacts.
Tried and failed
cone beam tomosynthesis acquisition applied to volumetric spatial frequency reconstruction. Reason: cone beam geometry induced severe artifacts causing phase inversion of high-frequency components at off-center positions
Development And Evaluation Of Next Generation Tomosynthesis · Penn · PhD
Tried and failed
alternating joint optimization and calibration applied to iterative image reconstruction. Reason: multiple alternating iterations yielded no noticeable quality improvement over a single round
On Improving the Acquisition and Reconstruction Of Spatio-Temporal Magnetic Resonance Imaging · MIT · PhD
Tried and failed
direct coherent summation of multi-view images applied to sparse multistatic 3D image reconstruction. Reason: sensor positioning errors caused significant phase offsets, resulting in severe image defocusing
Multistatic-polarimetric SAR for sparsely sampled 3D imaging · Cranfield · PhD
Tried and failed
multi-plane target calibration in narrow depth-of-field imaging applied to microscopic tomographic optical field reconstruction. Outcome: no signal. Reason: narrow depth of field caused severe blurring of out-of-focus calibration markers across offset planes
Inter-scale energy transfer in turbulent premixed combustion · Georgia Tech · PhD
Tried and failed
rectangular sensor array layout with short baseline applied to tomographic wave-based damage reconstruction. Reason: short inter-sensor distances produced tiny separation coefficients and local reconstruction artifacts around transmitters
Pipeline health monitoring using helical guided ultrasonic waves · UT Austin · PhD
Tried and failed
filtered back-projection with ramp filtering applied to plane-wave ultrasound image reconstruction. Outcome: worse than baseline. Reason: missing low-frequency components in transducer Gaussian pulses negated ramp filter benefits
An Angular Framework for Ultrasound Imaging · EPFL · PhD
Tried and failed
computed tomography and ultrasound imaging applied to micrometer-scale fibrous networks. Outcome: no signal. Reason: spatial resolution was insufficient to resolve fibers with diameters under four micrometers
Imaging, Mechanics, Construction, and Sonification of Three-Dimensional Spider Webs · MIT · PhD
Tried and failed
stainless steel dry spherical electrodes applied to low-frequency bioimpedance tomography. Outcome: worse than baseline. Reason: higher contact impedance and polarization effects at low AC frequencies compared to standard Ag/AgCl electrodes
From Systemic to Regional: Personal Health and Medical Monitoring Systems that Adapt to Individual Variance · MIT · PhD
Considered and rejected
Considered and rejected: Freehand 3D ultrasound without position sensors, rejected because lack of positional data yields inadequate volumetric reconstruction quality preventing quantitative measurements
Evaluation of tomographic 3D ultrasound for the assessment of vascular pathology · Imperial · PhD
Lost to a baseline
Uncorrected full HSS reconstruction and gridding NUFFT achieved lower SSIM (0.51–0.57) on a 1H resolution phantom than BART NUFFT with GIRF correction (0.64).
Advanced spectroscopic imaging techniques for X-nuclei cardiac magnetic resonance imaging · Oxford · PhD
Simplified forward models and ray approximations fail to capture wave physics and diffraction
Relying on simplified ray-optics or unelongated geometric basis functions led to severe resolution loss, divergence, and blurring when wave diffraction and tissue heterogeneity were present. Neglecting wave effects such as diffraction limits, speckle phase corruption, and ballistic photon contributions prevented accurate tomographic recovery.
Tried and failed
iterative bent-ray ultrasound waveform tomography applied to high-contrast cortical bone imaging. Outcome: did not converge. Reason: diverged and formed wave-like artefacts despite ground truth or segmented average background initialisation
Ultrasonic bone characterisation: towards in vivo proximal-femur imaging · Imperial · PhD
Tried and failed
electrical impedance tomography reconstruction applied to sub-surface lesion shape classification. Reason: Inverse problem ill-posedness and low spatial resolution blurred all target geometries into circular high-conductivity regions
Wearable, implantable health sensors based on graphene and 2D materials · UT Austin · PhD
Tried and failed
zero spatial frequency component inclusion in depth fitting applied to diffuse optical tomography depth reconstruction. Outcome: worse than baseline. Reason: unmodeled ballistic photons at zero frequency skewed single-exponent decay constants and increased depth error
Optical Imaging Of Tissue Physiology With Exogenous Contrast Agents · Penn · PhD
Tried and failed
forward-modeling with unelongated geometric basis functions applied to 3D plasma radiation tomography. Reason: the basis set lacked elongated structures needed to match anisotropic spatial radiation profiles
Tried and failed
ray-optics backprojection reconstruction applied to light-field microscopic volume imaging. Reason: geometric ray-optics approximations fail to model wave diffraction limits required for sub-cellular resolution
Fourier Light-Field Microscopy: Design, Optimization, and Applications · Georgia Tech · PhD
Tried and failed
aperture-based multiplexing in miniaturized optical probes applied to endoscopic optical coherence tomography. Reason: optical element thickness relative to probe diameter caused severe diffractive degradation
Volumetric Optical Imaging of Tissue Microstructure for Grading of Dysplasia In Vivo · MIT · PhD
Considered and rejected
Considered and rejected: Rejected standard ray-theory tomography (bent ray tomography alone) due to severe resolution loss from neglecting diffraction
Shear-horizontal guided wave tomography · Imperial · Doctorate
Considered and rejected
Considered and rejected: Rejected pure Diffraction Tomography (Born/Rytov approximations alone) due to contrast/size limitations and phase-unwrapping instability
Shear-horizontal guided wave tomography · Imperial · Doctorate
Considered and rejected
Considered and rejected: Rejected direct ray-based travel-time tomography due to low spatial resolution (Fresnel zone ~2.2 cm vs ~1 mm for FWI).
Machine learning for ultrasound brain imaging · Imperial · Doctorate
Tried and failed
spatial-domain microlens array light-field imaging applied to 3D volumetric micro-structure reconstruction. Reason: Severe reconstruction artifacts and loss of resolution near the native focal plane due to phase singularities.
Fourier Light-Field Microscopy: Design, Optimization, and Applications · Georgia Tech · PhD
Aggressive undersampling and rigid sparsity priors degrade image features and create aliasing
Applying compressed sensing or tomographic reconstruction under aggressive undersampling ratios caused severe streak artifacts, structural distortion, and spatial aliasing. Handcrafted sparsity priors such as L1 regularization or wavelet thresholding frequently altered natural speckle patterns and corrupted continuous intensity gradients.
Tried and failed
iterative proportional fitting applied to tomographic reconstruction from sparse 1D projections. Outcome: unstable. Reason: vulnerability to noise and inability to handle zero counts in sparse data led to reconstruction artifacts
Computational approaches for spatialomics data: construction, integration and analysis of biomolecular atlases · EPFL · PhD
Tried and failed
gridrec Fourier tomography reconstruction applied to sparse-view computed tomography. Outcome: worse than baseline. Reason: sparse projection sampling introduced severe noise and streak artifacts into reconstructed volumes
Mechanical Design of Selected Natural Ceramic Cellular Solids · Virginia Tech · Doctorate
Tried and failed
L1 sparsity regularization in inverse problem reconstruction applied to computational 3D optical tomography. Reason: Introduced severe visual noise, background artifacts, and blurred cross-sectional edges.
Tried and failed
temporally static undersampling patterns applied to dynamic image reconstruction. Outcome: worse than baseline. Reason: fixed spatiotemporal sampling creates redundant aliasing compared to temporally shifted sampling trajectories
MRI techniques for quantitative and microstructure imaging · MIT · PhD
Lost to a baseline
Reconstruction and segmentation metrics on the CINE dataset were consistently lower than on SKM-TEA due to stronger aliasing artifacts induced by the VISTA undersampling pattern
Leveraging Uncertainties in Medical Prediction Systems · Tübingen · PhD
Tried and failed
compressed sensing error compensation without noise applied to full field modal dynamic reconstruction. Outcome: worse than baseline. Reason: compensating for non-existent errors in clean data introduced artificial artifacts and degraded accuracy
Full Field Reconstruction Enhanced With Operational Modal Analysis and Compressed Sensing for General Dynamic Loading · Virginia Tech · Doctorate
Tried and failed
extreme sub-Nyquist compressed sensing reconstruction applied to ultrasound image reconstruction. Outcome: worse than baseline. Reason: severe undersampling (5% rate) caused structural distortion and drastically elevated reconstruction error
Compact modular open platform for low-cost ultrasound imaging · Imperial · PhD
Tried and failed
sparse wavelet regularization using iterative shrinkage thresholding applied to ultrasound image reconstruction. Reason: Altered natural speckle patterns and improperly thresholded continuous linear intensity gradients.
Ultrasound Imaging: From Physical Modeling to Deep Learning · EPFL · PhD
Tried and failed
regular grid spatial sampling for sparse reconstruction applied to enclosed wavefield reconstruction. Outcome: worse than baseline. Reason: equidistant sampling causes spatial aliasing and coherence issues, underperforming randomized sensor placement
Sound Field Reconstruction in a room through Sparse Recovery and its application in Room Modal Equalization · EPFL · PhD
Tried and failed
sparse tomographic reconstruction using basis function parameterization applied to atmospheric density mapping. Outcome: data insufficient. Reason: sparse source events caused severe reconstruction errors and unrealistically high density estimates
Unifying VLF Transmitter and Lightning-driven D-Region Ionosphere Remote Sensing · Georgia Tech · PhD
Left open by the authors
Problems the authors named and did not get to.
Left open
Develop iterative reconstruction methods for second-harmonic optical diffraction tomography using nonlinear beam propagation forward models to reduce missing-cone artifacts. Blocker: None
Exploring Novel Modalities for Optical Diffraction Tomography · EPFL · PhD
Left open
Develop iterative or deep learning reconstruction algorithms to correct missing-cone axial elongation artifacts in tilt-angle stimulated Raman projection tomography. Blocker: None
Volumetric stimulated Raman scattering microscopy · Boston University · PhD
Left open
Integrate deep learning-based projection filtering into the reconstruction pipeline and extend the method to limited-angle CBCT reconstruction. Blocker: None
Deep Learning-based CBCT Projection Interpolation, Reconstruction, and Post-processing for Radiation Therapy · Duke · PhD
Left open
Investigate Deep Image Prior for mitigating multiple scattering artifacts and experimental uncertainties in large-scale optical coherence refraction tomography. Blocker: Requires multi-gigavoxel experimental OCRT dataset or specialized hardware apparatus to capture multi-angle scans
Computational 3D Optical Imaging Using Wavevector Diversity · Duke · PhD
Left open
Adapt the Z-reconstruction algorithm to handle experimental artifacts, defective detector channels, and container edge motion effects. Blocker: Requires raw dual-energy cargo X-ray radiography system data with experimental artifacts and defective detector channels
Reconstructing the Atomic Number of Cargo X-ray Images using Dual Energy Radiography · MIT · PhD
Left open
Apply cone-beam refractive-index reconstruction algorithms to Optical Projection Tomography data of extended biological specimens. Blocker: Requires optical projection tomography physical experimental setup or specialized raw biological sample projection data.
Physics-based modeling and inverse problems in optical imaging · EPFL · PhD
Left open
Implement SADIR iterative reconstruction using projection domain CT data and evaluate its impact on COVID-19 classification accuracy. Blocker: Raw CT projection-domain data (sinograms) for clinical COVID-19 cases is rarely publicly accessible.
Real-Time Computed Tomography-based Medical Diagnosis Using Deep Learning · Virginia Tech · Master's
Left open
Apply the coronary biomechanics pipeline to Photon Counting Computed Tomography (PCCT) images to evaluate soft lipid detection and reduce blooming artifacts. Blocker: Access to clinical Photon Counting CT (PCCT) coronary angiography datasets.
Noninvasive assessment of coronary artery plaque vulnerability using computational solid mechanics · UT Austin · PhD
Left open
Apply synthetic data augmentation methods developed for MRI reconstruction to medical image classification tasks. Blocker: The unfinished work is a broad direction without specific architectures, target classification datasets, or validation protocols defined.
DEEP LEARNING-BASED METHODS FOR IMPROVING ACCELERATED MAGNETIC RESONANCE IMAGE RECONSTRUCTION · Johns Hopkins · PhD
Who has worked on this
Authors of the public theses cited on this page, most-cited first.
- Wenhao Liu · Georgia Tech · PhD · 2024
Fourier Light-Field Microscopy: Design, Optimization, and Applications - Yue Wang · University College Dublin · PhD · 2024
On the Epistemology of Gibbs Ringing Reduction Algorithm Performance - Ziyan He · Georgia Tech · PhD · 2023
Radio Tomographic Imaging with Deep Learning - Ke Lu · Duke · PhD · 2022
Deep Learning-based CBCT Projection Interpolation, Reconstruction, and Post-processing for Radiation Therapy - Andreas Armin Ernst Zimmermann · Imperial · Doctorate · 2021
Shear-horizontal guided wave tomography - Dimitris Perdios · EPFL · PhD · 2021
Ultrasound Imaging: From Physical Modeling to Deep Learning - Paul Fischer · Tübingen · PhD · 2026
Leveraging Uncertainties in Medical Prediction Systems - Yan Liu · EPFL · PhD · 2026
Physics-based modeling and inverse problems in optical imaging - Benjamin Stein-Lubrano · MIT · PhD · 2025
Forward Modeling for Bolometry and Disruption Mitigation in Tokamaks or How to Kill Your Plasma With Confidence, Style, and Pizzazz - Maria Maxouti · Imperial · PhD · 2025
Development of an acoustic dose-profile measurement technique for short pulse proton and ion beams - Ziqi Lu · MIT · PhD · 2025
Addressing Challenges in Object-Based Robot Navigation and Mapping - Christopher James Shallue · Harvard · PhD · 2025
Quantum Black Holes and the Primordial Universe
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