DiffDenoise: Self-Supervised Medical Image Denoising with Conditional Diffusion Models

Many self-supervised denoising approaches have been proposed in recent years. However, these methods tend to overly smooth images, resulting in the loss of fine structures that are essential for medical applications. In this paper, we propose DiffDenoise, a powerful self-supervised denoising approach tailored for medical images, designed to preserve high-frequency details. Our approach comprises three stages. First, we train a diffusion model on noisy images, using the outputs of a pretrained Blind-Spot Network as conditioning inputs. Next, we introduce a novel stabilized reverse sampling technique, which generates clean images by averaging diffusion sampling outputs initialized with a pair of symmetric noises. Finally, we train a supervised denoising network using noisy images paired with the denoised outputs generated by the diffusion model. Our results demonstrate that DiffDenoise outperforms existing state-of-the-art methods in both synthetic and real-world medical image denoising tasks. We provide both a theoretical foundation and practical insights, demonstrating the method's effectiveness across various medical imaging modalities and anatomical structures.

Tuning-free multi-coil compressed sensing MRI with Parallel Variable Density Approximate Message Passing

Purpose: To develop a tuning-free method for multi-coil compressed sensing MRI that performs competitively with algorithms with an optimally tuned sparse parameter. Theory: The Parallel Variable Density Approximate Message Passing (P-VDAMP) algorithm is proposed. For Bernoulli random variable density sampling, P-VDAMP obeys a "state evolution", where the intermediate per-iteration image estimate is distributed according to the ground truth corrupted by a Gaussian vector with approximately known covariance. State evolution is leveraged to automatically tune sparse parameters on-the-fly with Stein's Unbiased Risk Estimate (SURE). Methods: P-VDAMP is evaluated on brain, knee and angiogram datasets at acceleration factors 5 and 10 and compared with four variants of the Fast Iterative Shrinkage-Thresholding algorithm (FISTA), including two tuning-free variants from the literature. Results: The proposed method is found to have a similar reconstruction quality and time to convergence as FISTA with an optimally tuned sparse weighting. Conclusions: P-VDAMP is an efficient, robust and principled method for on-the-fly parameter tuning that is competitive with optimally tuned FISTA and offers substantial robustness and reconstruction quality improvements over competing tuning-free methods.

Automated detection and quantification of COVID-19 airspace disease on chest radiographs: A novel approach achieving radiologist-level performance using a CNN trained on digital reconstructed radiographs (DRRs) from CT-based ground-truth

Purpose: To leverage volumetric quantification of airspace disease (AD) derived from a superior modality (CT) serving as ground truth, projected onto digitally reconstructed radiographs (DRRs) to: 1) train a convolutional neural network to quantify airspace disease on paired CXRs; and 2) compare the DRR-trained CNN to expert human readers in the CXR evaluation of patients with confirmed COVID-19. Materials and Methods: We retrospectively selected a cohort of 86 COVID-19 patients (with positive RT-PCR), from March-May 2020 at a tertiary hospital in the northeastern USA, who underwent chest CT and CXR within 48 hrs. The ground truth volumetric percentage of COVID-19 related AD (POv) was established by manual AD segmentation on CT. The resulting 3D masks were projected into 2D anterior-posterior digitally reconstructed radiographs (DRR) to compute area-based AD percentage (POa). A convolutional neural network (CNN) was trained with DRR images generated from a larger-scale CT dataset of COVID-19 and non-COVID-19 patients, automatically segmenting lungs, AD and quantifying POa on CXR. CNN POa results were compared to POa quantified on CXR by two expert readers and to the POv ground-truth, by computing correlations and mean absolute errors. Results: Bootstrap mean absolute error (MAE) and correlations between POa and POv were 11.98% [11.05%-12.47%] and 0.77 [0.70-0.82] for average of expert readers, and 9.56%-9.78% [8.83%-10.22%] and 0.78-0.81 [0.73-0.85] for the CNN, respectively. Conclusion: Our CNN trained with DRR using CT-derived airspace quantification achieved expert radiologist level of accuracy in the quantification of airspace disease on CXR, in patients with positive RT-PCR for COVID-19.