!pip install imageio

Requirement already satisfied: imageio in /opt/conda/envs/fastai/lib/python3.8/site-packages (2.9.0)
Requirement already satisfied: numpy in /opt/conda/envs/fastai/lib/python3.8/site-packages (from imageio) (1.19.1)
Requirement already satisfied: pillow in /opt/conda/envs/fastai/lib/python3.8/site-packages (from imageio) (7.2.0)

import imageio
import torch

As an example, I've downloaded some dicom files from this site

!curl "https://www.visus.com/fileadmin/content/pictures/Downloads/JiveX_DICOME_Viewer/case1.zip" > "case1.zip"

!unzip -q case1.zip

  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100 8449k  100 8449k    0     0   149k      0  0:00:56  0:00:56 --:--:--  475k

and simply pass the folder to imageio like this:

np_arr = imageio.volread('case1')

Reading DICOM (examining files): 1/31 files (3.2%31/31 files (100.0%)
  Found 1 correct series.
Reading DICOM (loading data): 31/31  (100.0%)

turn into torch tensor

As I prefert to work with PyTorch tensors...

dicom_torch = torch.from_numpy(np_arr)

dicom_torch.shape

torch.Size([31, 512, 512])

... and this is how it looks like

import matplotlib.pyplot as plt
%matplotlib inline

plt.imshow(dicom_torch[10])

<matplotlib.image.AxesImage at 0x7f3ebe5effd0>