When the first transformer-based language models were published in the late 2010s, pretraining with general text and then fine-tuning the model on a task-specific dataset often achieved the state-of-the-art performance. However, more recent work suggests that for some tasks, directly prompting the pretrained model matches or surpasses fine-tuning in performance with few or no model parameter updates required. The use of prompts with language models for natural language processing (NLP) tasks is known as prompt learning. We investigated the viability of prompt learning on clinically meaningful decision tasks and directly compared this with more traditional fine-tuning methods. Results show that prompt learning methods were able to match or surpass the performance of traditional fine-tuning with up to 1000 times fewer trainable parameters, less training time, less training data, and lower computation resource requirements. We argue that these characteristics make prompt learning a very desirable alternative to traditional fine-tuning for clinical tasks, where the computational resources of public health providers are limited, and where data can often not be made available or not be used for fine-tuning due to patient privacy concerns. The complementary code to reproduce the experiments presented in this work can be found at https://github.com/NtaylorOX/Public_Clinical_Prompt .

Image registration is essential for medical image applications where alignment of voxels across multiple images is needed for qualitative or quantitative analysis. With recent advances in deep neural networks and parallel computing, deep learning-based medical image registration methods become competitive with their flexible modeling and fast inference capabilities. However, compared to traditional optimization-based registration methods, the speed advantage may come at the cost of registration performance at inference time. Besides, deep neural networks ideally demand large training datasets while optimization-based methods are training-free. To improve registration accuracy and data efficiency, we propose a novel image registration method, termed Recurrent Inference Image Registration (RIIR) network. RIIR is formulated as a meta-learning solver for the registration problem in an iterative manner. RIIR addresses the accuracy and data efficiency issues, by learning the update rule of optimization, with implicit regularization combined with explicit gradient input. We extensively evaluated RIIR on brain MRI, lung CT, and quantitative cardiac MRI datasets, in terms of both registration accuracy and training data efficiency. Our experiments showed that RIIR outperformed a range of deep learning-based methods, even with only 5% of the training data, demonstrating high data efficiency. Key findings from our ablation studies highlighted the important added value of the hidden states introduced in the recurrent inference framework for meta-learning. Our proposed RIIR offers a highly data-efficient framework for deep learning-based medical image registration.

Purpose: To develop and evaluate a robust cardiac mapping sequence at 3 T, using Bloch–Siegert shift (BSS)‐based preparations.Methods: A longitudinal magnetization preparation module was designed to encode . After magnetization tip‐down, off‐resonant Fermi pulses, placed symmetrically around two refocusing pulses, induced BSS, followed by tipping back of the magnetization. Bloch simulations were used to optimize refocusing pulse parameters and to assess the mapping sensitivity. Relaxation‐induced error was simulated for various / times. The effective mapping range was determined in phantom experiments, and maps were compared to the conventional BSS method and subadiabatic hyperbolic‐secant 8 (HS8) pulse‐sensitized method. Cardiac maps were acquired in healthy subjects, and evaluated for repeatability and imaging plane intersection consistency. The technique was modified for three‐dimensional (3D) acquisition of the whole heart in a single breath‐hold, and compared to two‐dimensional (2D) acquisition.Results: Simulations indicate that the proposed preparation can be tailored to achieve high mapping sensitivity across various ranges, with maximum sensitivity at the upper range. /‐induced bias did not exceed 5.2. Experimentally reproduced sensitization closely matched simulations for (mean difference 0.0310.022, compared to 0.0180.025 in the HS8‐sensitized method), and showed 20‐fold reduction in the standard deviation of repeated scans, compared with conventional BSS mapping, and an equivalent 2‐fold reduction compared with HS8‐sensitization. Robust cardiac map quality was obtained, with an average test‐retest variability of 0.0270.043 relative to normalized magnitude, and plane intersection bias of 0.0520.031. 3D acquisitions showed good agreement with 2D scans (mean absolute deviation 0.0550.061).Conclusion: BSS‐based preparations enable robust and tailorable 2D/3D cardiac mapping at 3 T in a single breath‐hold.

$T_{1\rho }$ mapping is emerging as a potential, contrast-free alternative to late gadolinium enhancement (LGE) for assessment of myocardial viability. However, strong signal contributions from the blood pool can impede quantitative evaluation at the (sub)endocardium. In this work, we study the effectiveness of dark-blood (DB) contrast in adiabatic $T_{1\rho }$ ($T_{1\rho ,adiab}$) mapping at 3T, using slice-selective and non-selective adiabatic spin-lock pulses. Adiabatic DB-$T_{1\rho ,adiab}$ preparations consisted of an odd number of slice-selective adiabatic full passage (AFP) pulses, followed by a final, non-selective AFP pulse. This preparation induces $T_{1\rho ,adiab}$ decay within the imaging slice while inverting the magnetization outside. A delay ($\delta$) between preparation and imaging allowed for relaxation and inflow of the inverted blood to achieve DB contrast. Bias and precision of DB- and bright-blood (BB)-$T_{1\rho ,adiab}$ were compared in phantom and in healthy subjects (n = 10). Blood suppression efficacy and apparent myocardial thickness in DB imaging were investigated in simulations, phantom, and in vivo. The clinical feasibility of DB-$T_{1\rho ,adiab}$ mapping was evaluated in a small cohort of patients (n = 7) with suspected cardiovascular diseases. DB-$T_{1\rho ,adiab}$ values were in agreement with reference BB-$T_{1\rho ,adiab}$ values in phantom (myocardium-like vial BB: 219.27 ± 4.80 ms, DB: 218.09 ± 8.22 ms) and in healthy subjects (BB: 182.32 ± 28.27 ms, DB: 183.49 ± 45.54 ms). A moderate increase in intra-($wC{V}_{i,r}$) and inter-scan variability ($\overline{wC{V}_i}$) was observed in the DB method, compared with conventional BB imaging, for phantom and healthy subjects (in vivo $wC{V}_{i,r}$ BB: 15.51 ± 2.65%, DB: 24.82 ± 4.18%; in vivo $\overline{wC{V}_i}$ BB: 3.38 ± 0.86%, DB: 7.24 ± 2.55%). Longer delay times improved blood suppression in vivo for DB-$T_{1\rho ,adiab}$, albeit at increased intra-scan variability in phantom and in vivo (DB $wC{V}_{i,r}$ for $\delta$ = 0 ms: 4.90 ± 0.83% in phantom, 13.44 ± 2.91% in vivo, for $\delta$ = 600 ms: 8.14 ± 1.88% in phantom, 26.25 ± 5.19% in vivo). Average apparent myocardial thickness was slightly higher when using DB-$T_{1\rho ,adiab}$ compared with BB-$T_{1\rho ,adiab}$ (BB: 7.33 ± 2.05 mm, DB: 7.99 ± 2.46 mm). DB-$T_{1\rho ,adiab}$ maps yielded comparable image quality to BB-$T_{1\rho ,adiab}$ maps in patients. DB-$T_{1\rho ,adiab}$ mapping represents an alternative to BB-$T_{1\rho ,adiab}$ for myocardial assessment with the potential for improved visualization of the (sub-)endocardium.